Pasturel_etal2020.bib

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@article{Maus2015,
	Abstract = {When repeatedly exposed to moving stimuli, the oculomotor system elicits anticipatory smooth pursuit (ASP) eye movements, even before the stimulus moves. ASP is affected oppositely to perceptual speed judgments of repetitive moving stimuli: After a sequence of fast stimuli, ASP velocity increases, whereas perceived speed decreases. These two effects---perceptual adaptation and oculomotor priming---could result from adapting a single common internal speed representation that is used for perceptual comparisons and for generating ASP. Here we test this hypothesis by assessing the temporal dependence of both effects on stimulus history. Observers performed speed discriminations on moving random dot stimuli, either while pursuing the movement or maintaining steady fixation. In both cases, responses showed perceptual adaptation: Stimuli preceded by fast speeds were perceived as slower, and vice versa. To evaluate oculomotor priming, we analyzed ASP velocity as a function of average stimulus speed in preceding trials and found strong positive dependencies. Interestingly, maximal priming occurred over short stimulus histories (∼two trials), whereas adaptation was maximal over longer histories (∼15 trials). The temporal dissociation of adaptation and priming suggests different underlying mechanisms. It may be that perceptual adaptation integrates over a relatively long period to robustly calibrate the operating range of the motion system, thereby avoiding interference from transient changes in stimulus speed. On the other hand, the oculomotor system may rapidly prime anticipatory velocity to efficiently match it to that of the pursuit target.},
	Author = {Maus, Gerrit W. and Potapchuk, Elena and Watamaniuk, Scott N. J. and Heinen, Stephen J.},
	Date = {2015-02-12},
	Date-Added = {2019-09-19 21:42:39 +0200},
	Date-Modified = {2019-09-19 21:43:06 +0200},
	Doi = {10.1167/15.2.16},
	Eprint = {25761334},
	Eprinttype = {pmid},
	File = {/Users/lolo/Zotero/storage/5YMDKHVL/Maus et al. - 2015 - Different time scales of motion integration for an.pdf},
	Issn = {1534-7362},
	Journal = {Journal of Vision},
	Journaltitle = {Journal of Vision},
	Number = {2},
	Pmcid = {PMC4528670},
	Shortjournal = {J Vis},
	Title = {Different Time Scales of Motion Integration for Anticipatory Smooth Pursuit and Perceptual Adaptation},
	Url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4528670/},
	Urldate = {2019-09-19},
	Volume = {15},
	Year = {2015},
	Bdsk-Url-1 = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4528670/},
	Bdsk-Url-2 = {https://doi.org/10.1167/15.2.16}}

@article{Barack16,
	Abstract = {Psychophysical techniques typically assume straightforward relationships between manipulations of real-world events, their effects on the brain, and behavioral reports of those effects. However, these relationships can be influenced by many complex, strategic factors that contribute to task performance. Here we discuss several of these factors that share two key features. First, they involve subjects making flexible use of time to process information. Second, this flexibility can reflect the rational regulation of information-processing trade-offs that can play prominent roles in particular temporal epochs: sensitivity to stability versus change for past information, speed versus accuracy for current information, and exploitation versus exploration for future goals. Understanding how subjects manage these trade-offs can be used to help design and interpret psychophysical studies.},
	Author = {Barack, David L and Gold, Joshua I},
	Date = {2016-04},
	Date-Modified = {2019-09-19 21:27:37 +0200},
	Doi = {10.1016/j.conb.2016.01.015},
	Eprint = {26921829},
	Eprinttype = {pmid},
	Issn = {1873-6882},
	Journal = {Current opinion in neurobiology},
	Journaltitle = {Current opinion in neurobiology},
	Keywords = {\#nosource},
	Pages = {121--125},
	Title = {Temporal Trade-Offs in Psychophysics.},
	Url = {http://www.ncbi.nlm.nih.gov/pubmed/26921829},
	Volume = {37},
	Year = {2016},
	Bdsk-Url-1 = {http://www.ncbi.nlm.nih.gov/pubmed/26921829%20http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4860089},
	Bdsk-Url-2 = {https://doi.org/10.1016/j.conb.2016.01.015}}

@article{Glaze15,
	Abstract = {In our dynamic world, decisions about noisy stimuli can require temporal accumulation of evidence to identify steady signals, differentiation to detect unpredictable changes in those signals, or both. Normative models can account for learning in these environments but have not yet been applied to faster decision processes. We present a novel, normative formulation of adaptive learning models that forms decisions by acting as a leaky accumulator with non-absorbing bounds. These dynamics, derived for both discrete and continuous cases, depend on the expected rate of change of the statistics of the evidence and balance signal identification and change detection. We found that, for two different tasks, human subjects learned these expectations, albeit imperfectly, then used them to make decisions in accordance with the normative model. The results represent a unified, empirically supported account of decision-making in unpredictable environments that provides new insights into the expectation-driven dynamics of the underlying neural signals.},
	Author = {Glaze, Christopher M and Kable, Joseph W and Gold, Joshua I},
	Date = {2015-08-31},
	Date-Modified = {2019-09-19 21:48:26 +0200},
	Doi = {10/gf7rb6},
	Editor = {Behrens, Timothy},
	File = {/Users/lolo/Zotero/storage/FR68SLKQ/Glaze et al. - 2015 - Normative evidence accumulation in unpredictable e.pdf;/Users/lolo/Zotero/storage/RI53JHH7/Glaze et al. - 2015 - Normative evidence accumulation in unpredictable e.pdf},
	Ids = {Glaze15a},
	Issn = {2050-084X},
	Journal = {eLife},
	Journaltitle = {eLife},
	Keywords = {decision-making,neuroscience,change detection,drift-diffusion model},
	Note = {00046},
	Pages = {e08825},
	Title = {Normative Evidence Accumulation in Unpredictable Environments},
	Url = {https://doi.org/10.7554/eLife.08825},
	Urldate = {2019-09-09},
	Volume = {4},
	Year = {2015},
	Bdsk-Url-1 = {https://doi.org/10.7554/eLife.08825},
	Bdsk-Url-2 = {https://doi.org/10/gf7rb6}}

@article{Peirce19,
	Abstract = {PsychoPy is an application for the creation of experiments in behavioral science (psychology, neuroscience, linguistics, etc.) with precise spatial control and timing of stimuli. It now provides a choice of interface; users can write scripts in Python if they choose, while those who prefer to construct experiments graphically can use the new Builder interface. Here we describe the features that have been added over the last 10 years of its development. The most notable addition has been that Builder interface, allowing users to create studies with minimal or no programming, while also allowing the insertion of Python code for maximal flexibility. We also present some of the other new features, including further stimulus options, asynchronous time-stamped hardware polling, and better support for open science and reproducibility. Tens of thousands of users now launch PsychoPy every month, and more than 90 people have contributed to the code. We discuss the current state of the project, as well as plans for the future.},
	Author = {Peirce, Jonathan and Gray, Jeremy R. and Simpson, Sol and MacAskill, Michael and H{\"o}chenberger, Richard and Sogo, Hiroyuki and Kastman, Erik and Lindel{\o}v, Jonas Kristoffer},
	Date = {2019-02-01},
	Date-Added = {2019-07-24 12:51:08 +0200},
	Date-Modified = {2019-09-19 21:28:19 +0200},
	Doi = {10/gft89w},
	File = {/Users/laurentperrinet/Zotero/storage/PMW8GGYU/Peirce et al. - 2019 - PsychoPy2 Experiments in behavior made easy.pdf},
	Issn = {1554-3528},
	Journal = {Behavior Research Methods},
	Journaltitle = {Behavior Research Methods},
	Keywords = {Experiment,Open science,Open-source,Psychology,Reaction time,Software,Timing},
	Langid = {english},
	Note = {00016},
	Number = {1},
	Pages = {195-203},
	Shortjournal = {Behav Res},
	Shorttitle = {{{PsychoPy2}}},
	Title = {{{PsychoPy2}}: {{Experiments}} in Behavior Made Easy},
	Url = {https://doi.org/10.3758/s13428-018-01193-y},
	Urldate = {2019-07-24},
	Volume = {51},
	Year = {2019},
	Bdsk-Url-1 = {https://doi.org/10.3758/s13428-018-01193-y},
	Bdsk-Url-2 = {https://doi.org/10/gft89w}}

@article{Schutz14,
	Author = {Sch{\"u}tz, Alexander C. and Kerzel, Dirk and Souto, David},
	Date = {2014-05-01},
	Date-Added = {2019-07-24 10:49:48 +0200},
	Date-Modified = {2019-09-19 21:27:56 +0200},
	Doi = {10/f56tvm},
	File = {/Users/laurentperrinet/Zotero/storage/RELAIJD7/Sch{\"u}tz et al. - 2014 - Saccadic adaptation induced by a perceptual task.pdf;/Users/laurentperrinet/Zotero/storage/AA9NXL5J/article.html},
	Issn = {1534-7362},
	Journal = {Journal of Vision},
	Journaltitle = {Journal of Vision},
	Langid = {english},
	Note = {00013},
	Number = {5},
	Pages = {4-4},
	Shortjournal = {Journal of Vision},
	Title = {Saccadic Adaptation Induced by a Perceptual Task},
	Url = {https://jov.arvojournals.org/article.aspx?articleid=2121659},
	Urldate = {2019-07-24},
	Volume = {14},
	Year = {2014},
	Bdsk-Url-1 = {https://jov.arvojournals.org/article.aspx?articleid=2121659},
	Bdsk-Url-2 = {https://doi.org/10/f56tvm}}


@article{Barnes2008,
abstract = {Ocular pursuit movements allow moving objects to be tracked with a combination of smooth movements and saccades. The principal objective is to maintain smooth eye velocity close to object velocity, thus minimising retinal image motion and maintaining acuity. Saccadic movements serve to realign the image if it falls outside the fovea, the area of highest acuity. Pursuit movements are often portrayed as voluntary but their basis lies in processes that sense retinal motion and can induce eye movements without active participation. The factor distinguishing pursuit from such reflexive movements is the ability to select and track a single object when presented with multiple stimuli. The selective process requires attention, which appears to raise the gain for the selected object and/or suppress that associated with other stimuli, the resulting competition often reducing pursuit velocity. Although pursuit is essentially a feedback process, delays in motion processing create problems of stability and speed of response. This is countered by predictive processes, probably operating through internal efference copy (extra-retinal) mechanisms using short-term memory to store velocity and timing information from prior stimulation. In response to constant velocity motion, the initial response is visually driven, but extra-retinal mechanisms rapidly take over and sustain pursuit. The same extra-retinal mechanisms may also be responsible for generating anticipatory smooth pursuit movements when past experience creates expectancy of impending object motion. Similar, but more complex, processes appear to operate during periodic pursuit, where partial trajectory information is stored and released in anticipation of expected future motion, thus minimising phase errors associated with motion processing delays. {\textcopyright} 2008 Elsevier Inc. All rights reserved.},
author = {Barnes, G. R.},
doi = {10.1016/j.bandc.2008.08.020},
issn = {02782626},
journal = {Brain and Cognition},
keywords = {Anticipation,Attention,Expectation,Eye movement,Prediction,Pursuit,Working memory},
month = {dec},
number = {3},
pages = {309--326},
title = {{Cognitive processes involved in smooth pursuit eye movements}},
volume = {68},
year = {2008}
}

@article{Batterink_etal_2015,
abstract = {Statistical learning allows learners to detect regularities in the environment and appears to emerge automatically as a consequence of experience. Statistical learning paradigms bear many similarities to those of artificial grammar learning and other types of implicit learning. However, whether learning effects in statistical learning tasks are driven by implicit knowledge has not been thoroughly examined. The present study addressed this gap by examining the role of implicit and explicit knowledge within the context of a typical auditory statistical learning paradigm. Learners were exposed to a continuous stream of repeating nonsense words. Learning was tested (a) directly via a forced-choice recognition test combined with a remember/know procedure and (b) indirectly through a novel reaction time (RT) test. Behavior and brain potentials revealed statistical learning effects with both tests. On the recognition test, accurate responses were associated with subjective feelings of stronger recollection, and learned nonsense words relative to nonword foils elicited an enhanced late positive potential indicative of explicit knowledge. On the RT test, both RTs and P300 amplitudes differed as a function of syllable position, reflecting facilitation attributable to statistical learning. Explicit stimulus recognition did not correlate with RT or P300 effects on the RT test. These results provide evidence that explicit knowledge is accrued during statistical learning, while bringing out the possibility that dissociable implicit representations are acquired in parallel. The commonly used recognition measure primarily reflects explicit knowledge, and thus may underestimate the total amount of knowledge produced by statistical learning. Indirect measures may be more sensitive indices of learning, capturing knowledge above and beyond what is reflected by recognition accuracy.},
author = {Batterink, Laura J. and Reber, Paul J. and Neville, Helen J. and Paller, Ken A.},
doi = {10.1016/j.jml.2015.04.004},
issn = {0749596X},
journal = {Journal of Memory and Language},
keywords = {Event-related potentials,Explicit memory,Implicit learning,Implicit memory,Statistical learning},
month = {aug},
pages = {62--78},
pmid = {26034344},
publisher = {Academic Press Inc.},
title = {{Implicit and explicit contributions to statistical learning}},
volume = {83},
year = {2015}
}
@article{SaffranAslinNewport1996,
abstract = {Learners rely on a combination of experience independent and experience- dependent mechanisms to extract information from the environment. Language acquisition involves both types of mechanisms, but most theorists emphasize the relative importance of experience-independent mechanisms. The present study shows that a fundamental task of language acquisition, segmentation of words from fluent speech, can be accomplished by 8-month-old infants based solely on the statistical relationships between neighboring speech sounds. Moreover, this word segmentation was based on statistical learning from only 2 minutes of exposure, suggesting that infants have access to a powerful mechanism for the computation of statistical properties of the language input.},
author = {Saffran, Jenny R. and Aslin, Richard N. and Newport, Elissa L.},
doi = {10.1126/science.274.5294.1926},
issn = {00368075},
journal = {Science},
month = {dec},
number = {5294},
pages = {1926--1928},
pmid = {8943209},
title = {{Statistical learning by 8-month-old infants}},
volume = {274},
year = {1996}
}

@article{Kowler_AnnRev2019,
abstract = {Smooth pursuit eye movements maintain the line of sight on smoothly moving targets. Although often studied as a response to sensory motion, pursuit anticipates changes in motion trajectories, thus reducing harmful consequences due to sensorimotor processing delays. Evidence for predictive pursuit includes ( a) anticipatory smooth eye movements (ASEM) in the direction of expected future target motion that can be evoked by perceptual cues or by memory for recent motion, ( b) pursuit during periods of target occlusion, and ( c) improved accuracy of pursuit with self-generated or biologically realistic target motions. Predictive pursuit has been linked to neural activity in the frontal cortex and in sensory motion areas. As behavioral and neural evidence for predictive pursuit grows and statistically based models augment or replace linear systems approaches, pursuit is being regarded less as a reaction to immediate sensory motion and more as a predictive response, with retinal motion serving as one of a number of contributing cues.},
author = {Kowler, Eileen and Rubinstein, Jason F. and Santos, Elio M. and Wang, Jie},
doi = {10.1146/annurev-vision-091718-014901},
issn = {2374-4642},
journal = {Annual Review of Vision Science},
month = {sep},
number = {1},
pages = {223--246},
publisher = {Annual Reviews},
title = {{Predictive Smooth Pursuit Eye Movements}},
volume = {5},
year = {2019}
}


@article{DallosJones1963,
	  title={Learning behavior of the eye fixation control system},
	  author={Dallos, Peter and Jones, R},
	  journal={IEEE Transactions on automatic control},
	  volume={8},
	  number={3},
	  pages={218--227},
	  year={1963},
	  publisher={IEEE}
	}

@article{Bogadhi2013,
	Author = {Bogadhi, Amarender R and Montagnini, Anna and Masson, Guillaume S},
	Date-Modified = {2018-03-07 14:10:51 +0000},
	Doi = {10.1167/13.13.5},
	Issn = {1534-7362},
	Journal = {Journal of Vision},
	Month = {nov},
	Number = {13},
	Pages = {5--5},
	Title = {{Dynamic interaction between retinal and extraretinal signals in motion integration for smooth pursuit}},
	Url = {http://jov.arvojournals.org/Article.aspx?doi=10.1167/13.13.5},
	Volume = {13},
	Year = {2013},
	Bdsk-Url-1 = {http://jov.arvojournals.org/Article.aspx?doi=10.1167/13.13.5},
	Bdsk-Url-2 = {https://dx.doi.org/10.1167/13.13.5}}

@article{Orban2013,
	Abstract = {The brain makes use of noisy sensory inputs to produce eye, head, or arm motion. In most instances, the brain combines this sensory information with predictions about future events. Here, we propose that Kalman filtering can account for the dynamics of both visually guided and predictive motor behaviors within one simple unifying mechanism. Our model relies on two Kalman filters: (1) one processing visual information about retinal input; and (2) one maintaining a dynamic internal memory of target motion. The outputs of both Kalman filters are then combined in a statistically optimal manner, i.e., weighted with respect to their reliability. The model was tested on data from several smooth pursuit experiments and reproduced all major characteristics of visually guided and predictive smooth pursuit. This contrasts with the common belief that anticipatory pursuit, pursuit maintenance during target blanking, and zero-lag pursuit of sinusoidally moving targets all result from different control systems. This is the first instance of a model integrating all aspects of pursuit dynamics within one coherent and simple model and without switching between different parallel mechanisms. Our model suggests that the brain circuitry generating a pursuit command might be simpler than previously believed and only implement the functional equivalents of two Kalman filters whose outputs are optimally combined. It provides a general framework of how the brain can combine continuous sensory information with a dynamic internal memory and transform it into motor commands.},
	Author = {Orban de Xivry, Jean-Jacques and Coppe, S{\'{e}}bastien and Blohm, Gunnar and Lef{\`{e}}vre, Philippe},
	Doi = {10.1523/JNEUROSCI.2321-13.2013},
	Journal = {Journal of Neuroscience},
	Month = {oct},
	Number = {44},
	Pages = {17301--17313},
	Pmid = {24174663},
	Title = {{Kalman filtering naturally accounts for visually guided and predictive smooth pursuit dynamics}},
	Volume = {33},
	Year = {2013},
	Bdsk-Url-1 = {https://dx.doi.org/10.1523/JNEUROSCI.2321-13.2013}}


@article{CollinsBarnes2009,
abstract = {In motor control, prediction of future events is vital for overcoming sensory-motor processing delays and facilitating rapid and accurate responses in a dynamic environment. In human ocular pursuit this is so pervasive that prediction of future target motion cannot easily be eliminated by randomizing stimulus parameters. We investigated the prediction of temporally randomized events during pursuit of alternating constant-velocity (ramp) stimuli in which the timing of direction changes varied unpredictably over a given range. Responses were not reactive; instead, smooth eye velocity began to decelerate in anticipation of each target reversal. In the first experiment, using a continuous-motion stimulus, we found that the time at which this occurred was relatively constant regardless of ramp duration, but increased as mean ramp duration of the range increased. Regression analysis revealed a quantitative association between deceleration timing and the previous two or three ramp durations in a trial, suggesting that recent stimulus history was used to create a running average of anticipatory timing. In the second experiment, we used discrete motion stimuli, with intervening periods of fixation, which allowed both target velocity and reversal timing to be varied, thereby decoupling ramp duration and displacement. This enabled us to confirm that the timing of anticipatory deceleration was based on the history of timing, rather than displacement, within the stimulus. We conclude that this strategy is used to minimize error amid temporal uncertainty, while simultaneously overcoming inherent delays in visuomotor processing.},
author = {Collins, C J S and Barnes, Graham R},
doi = {10.1523/JNEUROSCI.1636-09.2009},
issn = {1529-2401},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
month = {oct},
number = {42},
pages = {13302--14},
pmid = {19846718},
title = {{Predicting the unpredictable: weighted averaging of past stimulus timing facilitates ocular pursuit of randomly timed stimuli.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/19846718 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC6665182},
volume = {29},
year = {2009}
}


@article{Deravet_JOV2018,
	Author = {Deravet, Nicolas and Blohm, Gunnar and de Xivry, Jean-Jacques Orban and Lef{\`{e}}vre, Philippe},
	Doi = {10.1167/18.5.16},
	Issn = {1534-7362},
	Journal = {Journal of Vision},
	Month = {may},
	Number = {5},
	Pages = {16},
	Title = {{Weighted integration of short-term memory and sensory signals in the oculomotor system}},
	Url = {http://jov.arvojournals.org/article.aspx?doi=10.1167/18.5.16},
	Volume = {18},
	Year = {2018},
	Bdsk-Url-1 = {http://jov.arvojournals.org/article.aspx?doi=10.1167/18.5.16},
	Bdsk-Url-2 = {https://doi.org/10.1167/18.5.16}}

@article{OrbandeXivryMissalLefevre_JOV2012,
	Author = {{Orban de Xivry}, J. J. and Missal, M. and Lefevre, P.},
	Doi = {10.1167/8.15.6},
	Issn = {1534-7362},
	Journal = {Journal of Vision},
	Keywords = {eye,motion,pursuit,saccades,smooth,velocity},
	Month = {nov},
	Number = {15},
	Pages = {6--6},
	Publisher = {The Association for Research in Vision and Ophthalmology},
	Title = {{A dynamic representation of target motion drives predictive smooth pursuit during target blanking}},
	Url = {http://jov.arvojournals.org/Article.aspx?doi=10.1167/8.15.6},
	Volume = {8},
	Year = {2008},
	Bdsk-Url-1 = {http://jov.arvojournals.org/Article.aspx?doi=10.1167/8.15.6},
	Bdsk-Url-2 = {https://doi.org/10.1167/8.15.6}}

@article{Bastos12,
	Abstract = {This Perspective considers the influential notion of a canonical (cortical) microcircuit in light of recent theories about neuronal processing. Specifically, we conciliate quantitative studies of microcircuitry and the functional logic of neuronal computations. We revisit the established idea that message passing among hierarchical cortical areas implements a form of Bayesian inference---paying careful attention to the implications for intrinsic connections among neuronal populations. By deriving canonical forms for these computations, one can associate specific neuronal populations with specific computational roles. This analysis discloses a remarkable correspondence between the microcircuitry of the cortical column and the connectivity implied by predictive coding. Furthermore, it provides some intuitive insights into the functional asymmetries between feedforward and feedback connections and the characteristic frequencies over which they operate.},
	Author = {Bastos, Andre M and Usrey, W Martin and Adams, Rick A and Mangun, George R and Fries, Pascal and Friston, Karl J},
	Doi = {10.1016/j.neuron.2012.10.038},
	Issn = {0896-6273},
	Journal = {Neuron},
	Keywords = {perrinetadamsfriston14},
	Month = {nov},
	Number = {4},
	Pages = {695--711},
	Title = {{Canonical microcircuits for predictive coding}},
	Url = {http://dx.doi.org/10.1016/j.neuron.2012.10.038},
	Volume = {76},
	Year = {2012},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.neuron.2012.10.038}}

@article{Norton18,
	Abstract = {{$<$}h3{$>$}Abstract{$<$}/h3{$>$} {$<$}p{$>$}Optimal sensory decision-making requires the combination of uncertain sensory signals with prior expectations. The effect of prior probability is often described as a shift in the decision criterion. Can observers track sudden changes in probability? To answer this question, we used a change-point detection paradigm that is frequently used to examine behavior in changing environments. In a pair of orientation-categorization tasks, we investigated the effects of changing probabilities on decision-making. In both tasks, category probability was updated using a sample-and-hold procedure. We developed an ideal Bayesian change-point detection model in which the observer marginalizes over both the current run length (i.e., time since last change) and the current category probability. We compared this model to various alternative models that correspond to different strategies -- from approximately Bayesian to simple heuristics -- that the observers may have adopted to update their beliefs about probabilities. We find that probability is estimated following an exponential averaging model with a bias towards equal priors, consistent with a conservative bias. The mechanism underlying change of decision criterion is a combination of on-line estimation of prior probability and a stable, long-term equal-probability prior, thus operating at two very different timescales.{$<$}/p{$><$}h3{$>$}Author summary{$<$}/h3{$>$} {$<$}p{$>$}We demonstrate how people learn and adapt to changes to the probability of occurrence of one of two categories on decision-making under uncertainty. The study combined psychophysical behavioral tasks with computational modeling. We used two behavioral tasks: a typical forced-choice categorization task as well as one in which the observer specified the decision criterion to use on each trial before the stimulus was displayed. We formulated an ideal Bayesian change-point detection model and compared it to several alternative models. We found that the data are best fit by a model that estimates category probability based on recently observed exemplars with a bias towards equal probability. Our results suggest that the brain takes multiple relevant time scales into account when setting category expectations.{$<$}/p{$>$}},
	Author = {Norton, Elyse H. and Acerbi, Luigi and Ma, Wei Ji and Landy, Michael S.},
	Date = {2018-11-30},
	Date-Modified = {2019-09-19 21:40:55 +0200},
	Doi = {10/gfrhgk},
	File = {/Users/laurentperrinet/Zotero/storage/K9A42IPB/Norton et al. - 2018 - Human online adaptation to changes in prior probab.pdf;/Users/laurentperrinet/Zotero/storage/RFI3X96F/483842v1.html},
	Journal = {PLOS Computational Biology doi: 10.1371/journal.pcbi.1006681},
	Journaltitle = {bioRxiv},
	Langid = {english},
	Note = {00000},
	Pages = {483842},
	Title = {Human Online Adaptation to Changes in Prior Probability},
	Url = {https://www.biorxiv.org/content/10.1101/483842v1},
	Urldate = {2019-06-04},
	Year = {2018},
	Bdsk-Url-1 = {https://www.biorxiv.org/content/10.1101/483842v1},
	Bdsk-Url-2 = {https://doi.org/10/gfrhgk}}

@article{Kahlon1996,
	Abstract = {Learning was induced in smooth pursuit eye movements by repeated presentation of targets that moved at one speed for 100 msec and then changed to a second, higher or lower, speed. The learned changes, measured as eye acceleration for the first 100 msec of pursuit, were largest in a "late" interval from 50 to 80 msec after the onset of pursuit and were smaller and less consistent in the earliest 30 msec of pursuit. In each experiment, target motion in one direction consisted of learning trials, whereas target motion in the opposite (control) direction consisted of trials in which targets moved at a constant speed for the entire duration of the trial. Under these conditions, the learning did not generalize to the control direction. For target motion in the learning direction, the changes in pursuit generalized to responses evoked by targets moving at speeds ranging from 15 to 45 degrees/sec as well as to targets of different colors and sizes. Although learning was induced at the initiation of pursuit, it generalized to the response to image motion in the learning direction when it was presented during pursuit in the learning direction. However, learning did not generalize to the response to image motion in the learning direction when it was presented during pursuit in the control direction. The results suggest that the learning does not occur in purely sensory or motor coordinates but in an intermediate reference frame at least partly defined by the direction of eye movement. The selectivity of learning provides new evidence for a previously hypothesized neural "switch" that gates visual information on the basis of movement direction. This selectivity also suggests that the locus of pursuit learning is in pathways related to the operation of the switch.},
	Author = {Kahlon, M and Lisberger, S G},
	Isbn = {0270-6474 (Print) 0270-6474 (Linking)},
	Issn = {0270-6474},
	Journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
	Keywords = {visual motion,smooth pursuit eye movements,monkey,motor learning,coordi-,sensory-motor transformation,\#nosource},
	Mendeley-Groups = {biblio thesis},
	Note = {00065},
	Number = {22},
	Pages = {7270-7283},
	Pmid = {8929434},
	Title = {Coordinate System for Learning in the Smooth Pursuit Eye Movements of Monkeys.},
	Volume = {16},
	Year = {1996}}

@article{Fukushima1996,
	Abstract = {Adaptive changes in initial eye velocity of pursuit eye movement were examined in nine normal subjects using a target that moved in a multiple ramp fashion. Significant changes in initial eye velocity occurred rapidly after training in six of the subjects. The magnitude and direction of the induced changes were a function of the training conditions. Adaptive changes started 100-200 ms after onset of pursuit eye movement (usually 140 ms), suggesting that the late (but not early) component of initial eye velocity was under adaptive control by our training paradigms.},
	Author = {Fukushima, K and Tanaka, M and Suzuki, Y and Fukushima, J and Yoshida, T},
	Doi = {10/ff6s3h},
	Issn = {0168-0102},
	Journal = {Neuroscience research},
	Keywords = {smooth pursuit,human,adaptation,1981 for review,adaptive changes are required,for these subsystems in,latency,open-loop condition,peak velocity,see robinson,subsystems for accurate con-,the brain uses several,trol of eye movements,velocity step,\#nosource},
	Mendeley-Groups = {biblio thesis},
	Note = {00048},
	Number = {4},
	Pages = {391-398},
	Pmid = {8866520},
	Title = {Adaptive Changes in Human Smooth Pursuit Eye Movement.},
	Url = {https://dx.doi.org/10.1016/0168-0102(96)01068-1},
	Volume = {25},
	Year = {1996},
	Bdsk-Url-1 = {https://dx.doi.org/10.1016/0168-0102(96)01068-1},
	Bdsk-Url-2 = {https://dx.doi.org/10/ff6s3h}}

@article{Carpenter1995,
	Abstract = {The latency between the appearance of a visual target and the start of the saccadic eye movement made to look at it varies from trial to trial to an extent that is inexplicable in terms of ordinary 'physiological' processes such as synaptic delays and conduction velocities. An alternative interpretation is that it represents the time needed to decide whether a target is in fact present: decision processes are necessarily stochastic, because they depend on extracting information from noisy sensory signals. In one such model, the presence of a target causes a signal in a decision unit to rise linearly at a rate r from its initial value s0 until it reaches a fixed threshold theta, when a saccade is initiated. One can regard this decision signal as a neural estimate of the log likelihood of the hypothesis that the target is present, the threshold being the significance criterion or likelihood level at which the target is presumed to be present. Experiments manipulating the prior probability of the target's appearing confirm this notion: the latency distribution then changes in the way expected if s0 simply reflects the prior log likelihood of the stimulus.},
	Author = {Carpenter, R H and Williams, M L},
	Doi = {10/bjktb8},
	Isbn = {0028-0836},
	Issn = {0028-0836},
	Journal = {Nature},
	Keywords = {Sensory Thresholds,Humans,Reaction Time,Saccades,Models,Neurological,Saccades: physiology,Sensory Thresholds: physiology,\#nosource},
	Mendeley-Groups = {PhD/modeling},
	Note = {00821},
	Number = {6544},
	Pages = {59-62},
	Pmid = {7659161},
	Title = {Neural Computation of Log Likelihood in Control of Saccadic Eye Movements.},
	Url = {http://www.ncbi.nlm.nih.gov/pubmed/7659161},
	Volume = {377},
	Year = {1995},
	Bdsk-Url-1 = {http://www.ncbi.nlm.nih.gov/pubmed/7659161},
	Bdsk-Url-2 = {https://doi.org/10/bjktb8}}

@article{Sotiropoulos2011,
	Abstract = {Our perceptions are fundamentally altered by our knowledge of the world. When cloud-gazing, for example, we tend spontaneously to recognize known objects in the random configurations of evaporated moisture. How our brains acquire such knowledge and how it impacts our perceptions is a matter of heated discussion. A topic of recent debate has concerned the hypothesis that our visual system 'assumes' that objects are static or move slowly [1] rather than more quickly [1] , [2] and [3] . This hypothesis, or 'prior on slow speeds', was postulated because it could elegantly explain a number of perceptual biases observed in situations of uncertainty [2]. Interestingly, those biases affect not only the perception of speed, but also the direction of motion. For example, the direction of a line whose endpoints are hidden (as in the 'aperture problem') or poorly visible (for example, at low contrast or for short presentations) is more often perceived as being perpendicular to the line than it really is \textemdash{} an illusion consistent with expecting that the line moves more slowly than it really does. How this 'prior on slow speeds' is shaped by experience and whether it remains malleable in adults is unclear. Here, we show that systematic exposure to high-speed stimuli can lead to a reversal of this direction illusion. This suggests that the shaping of the brain's prior expectations of even the most basic properties of the environment is a continuous process.},
	Author = {Sotiropoulos, Grigorios and Seitz, Aaron R. and Seri\`es, Peggy},
	Doi = {10.1016/j.cub.2011.09.013},
	Issn = {09609822},
	Journal = {Current Biology},
	Keywords = {Visual Perception,Humans,Reproducibility of Results,Motion Perception,Adult,Bayes Theorem,Models,khoei12jpp,Biological,Optical Illusions,Psychological,prior_probability,Signal Detection,\#nosource},
	Month = nov,
	Number = {21},
	Pages = {R883----R884},
	Pmid = {22075425},
	Title = {Changing Expectations about Speed Alters Perceived Motion Direction},
	Url = {http://dx.doi.org/10.1016/j.cub.2011.09.013 http://www.ncbi.nlm.nih.gov/pubmed/22075425},
	Volume = {21},
	Year = {2011},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.cub.2011.09.013%20http://www.ncbi.nlm.nih.gov/pubmed/22075425},
	Bdsk-Url-2 = {http://dx.doi.org/10.1016/j.cub.2011.09.013}}

@article{RadilloBrady2017,
	Author = {Radillo, Adrian E and Veliz-Cuba, Alan and Josi{\'c}, Kre{\v{s}}imir and Kilpatrick, Zachary P},
	Journal = {Neural computation},
	Number = {6},
	Pages = {1561--1610},
	Publisher = {MIT Press},
	Title = {Evidence accumulation and change rate inference in dynamic environments},
	Volume = {29},
	Year = {2017}}

@article{Damasse18,
	Author = {Damasse, Jean-Bernard and Perrinet, Laurent U and Madelain, Laurent and Montagnini, Anna},
	Doi = {10.1167/18.11.14},
	Journal = {Journal of Vision},
	Title = {Reinforcement effects in anticipatory smooth eye movements},
	Url = {https://jov.arvojournals.org/article.aspx?articleid=2707670},
	Year = {2018},
	Bdsk-Url-1 = {https://jov.arvojournals.org/article.aspx?articleid=2707670},
	Bdsk-Url-2 = {https://doi.org/10.1167/18.11.14}}

@article{Cohen2007,
	Author = {Cohen, Jonathan D and McClure, Samuel M and Yu, Angela J},
	Journal = {Philosophical Transactions of the Royal Society of London B: Biological Sciences},
	Number = {1481},
	Pages = {933--942},
	Publisher = {The Royal Society},
	Title = {Should I stay or should I go? How the human brain manages the trade-off between exploitation and exploration},
	Volume = {362},
	Year = {2007}}

@article{Adams12,
	Abstract = {This paper introduces a model of oculomotor control during the smooth pursuit of occluded visual targets. This model is based upon active inference, in which subjects try to minimise their (proprioceptive) prediction error based upon posterior beliefs about the hidden causes of their (exteroceptive) sensory input. Our model appeals to a single principle--the minimisation of variational free energy--to provide Bayes optimal solutions to the smooth pursuit problem. However, it tries to accommodate the cardinal features of smooth pursuit of partially occluded targets that have been observed empirically in normal subjects and schizophrenia. Specifically, we account for the ability of normal subjects to anticipate periodic target trajectories and emit pre-emptive smooth pursuit eye movements--prior to the emergence of a target from behind an occluder. Furthermore, we show that a single deficit in the postsynaptic gain of prediction error units (encoding the precision of posterior beliefs) can account for several features of smooth pursuit in schizophrenia: namely, a reduction in motor gain and anticipatory eye movements during visual occlusion, a paradoxical improvement in tracking unpredicted deviations from target trajectories and a failure to recognise and exploit regularities in the periodic motion of visual targets. This model will form the basis of subsequent (dynamic causal) models of empirical eye tracking measurements, which we hope to validate, using psychopharmacology and studies of schizophrenia.},
	Author = {Adams, Rick A and Perrinet, Laurent U and Friston, Karl},
	Date = {2012-10},
	Date-Added = {2018-07-27 15:04:00 +0200},
	Date-Modified = {2018-07-27 15:04:17 +0200},
	Doi = {10.1371/journal.pone.0047502},
	Editor = {Zhang, Xiang Yang},
	Issn = {1932-6203},
	Journal = {{PLoS ONE}},
	Journaltitle = {{PloS} one},
	Keywords = {Bayes Theorem, Eye Movements, Eye Movements: physiology, Humans, Models, Motion Perception, Motion Perception: physiology, Pursuit, Schizophrenia, Schizophrenia: physiopathology, Smooth, Smooth: physiology, Theoretical, occlusion, schizophrenia, spem, spem free-energy Schizophrenia},
	Number = {10},
	Pages = {e47502},
	Pmid = {23110076},
	Rights = {All rights reserved},
	Title = {Smooth pursuit and visual occlusion: active inference and oculomotor control in schizophrenia.},
	Url = {http://dx.plos.org/10.1371/journal.pone.0047502 http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0047502#pone.0047502-Laruelle1},
	Version = {452},
	Volume = {7},
	Year = {2012},
	Bdsk-Url-1 = {http://dx.plos.org/10.1371/journal.pone.0047502%20http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0047502#pone.0047502-Laruelle1},
	Bdsk-Url-2 = {https://doi.org/10.1371/journal.pone.0047502}}

@article{Nassar10,
	Abstract = {Maintaining appropriate beliefs about variables needed for effective decision making can be difficult in a dynamic environment. One key issue is the amount of influence that unexpected outcomes should have on existing beliefs. In general, outcomes that are unexpected because of a fundamental change in the environment should carry more influence than outcomes that are unexpected because of persistent environmental stochasticity. Here we use a novel task to characterize how well human subjects follow these principles under a range of conditions. We show that the influence of an outcome depends on both the error made in predicting that outcome and the number of similar outcomes experienced previously. We also show that the exact nature of these tendencies varies considerably across subjects. Finally, we show that these patterns of behavior are consistent with a computationally simple reduction of an ideal-observer model. The model adjusts the influence of newly experienced outcomes according to ongoing estimates of uncertainty and the probability of a fundamental change in the process by which outcomes are generated. A prior that quantifies the expected frequency of such environmental changes accounts for individual variability, including a positive relationship between subjective certainty and the degree to which new information influences existing beliefs. The results suggest that the brain adaptively regulates the influence of decision outcomes on existing beliefs using straightforward updating rules that take into account both recent outcomes and prior expectations about higher-order environmental structure.},
	Author = {Nassar, Matthew R. and Wilson, Robert C. and Heasly, Benjamin and Gold, Joshua I.},
	Date = {2010-09-15},
	Date-Modified = {2019-09-19 21:47:59 +0200},
	Doi = {10.1523/JNEUROSCI.0822-10.2010},
	Eprint = {20844132},
	Eprinttype = {pmid},
	File = {/Users/lolo/Zotero/storage/3R6MNMAK/Nassar et al. - 2010 - An Approximately Bayesian Delta-Rule Model Explain.pdf},
	Issn = {0270-6474, 1529-2401},
	Journal = {Journal of Neuroscience},
	Journaltitle = {Journal of Neuroscience},
	Langid = {english},
	Number = {37},
	Pages = {12366-12378},
	Shortjournal = {J. Neurosci.},
	Title = {An {{Approximately Bayesian Delta}}-{{Rule Model Explains}} the {{Dynamics}} of {{Belief Updating}} in a {{Changing Environment}}},
	Url = {https://www.jneurosci.org/content/30/37/12366},
	Urldate = {2019-09-19},
	Volume = {30},
	Year = {2010},
	Bdsk-Url-1 = {https://www.jneurosci.org/content/30/37/12366},
	Bdsk-Url-2 = {https://doi.org/10.1523/JNEUROSCI.0822-10.2010}}

@article{Wilson13,
	Abstract = {Error-driven learning rules have received considerable attention because of their close relationships to both optimal theory and neurobiological mechanisms. However, basic forms of these rules are effective under only a restricted set of conditions in which the environment is stable. Recent studies have defined optimal solutions to learning problems in more general, potentially unstable, environments, but the relevance of these complex mathematical solutions to how the brain solves these problems remains unclear. Here, we show that one such Bayesian solution can be approximated by a computationally straightforward mixture of simple error-driven 'Delta' rules. This simpler model can make effective inferences in a dynamic environment and matches human performance on a predictive-inference task using a mixture of a small number of Delta rules. This model represents an important conceptual advance in our understanding of how the brain can use relatively simple computations to make nearly optimal inferences in a dynamic world.},
	Author = {Wilson, Robert C and Nassar, Matthew R and Gold, Joshua I and Behrens, Tim},
	Date = {2013},
	Date-Added = {2018-07-27 15:01:45 +0200},
	Date-Modified = {2018-07-27 15:02:13 +0200},
	Doi = {10.1371/journal.pcbi.1003150},
	Journal = {{PLoS} Comput Biol},
	Journaltitle = {{PLoS} Comput Biol},
	Number = {7},
	Title = {A Mixture of Delta-Rules Approximation to Bayesian Inference in Change-Point Problems},
	Url = {http://www.princeton.edu/$\sim$rcw2/papers/WilsonEtAl_PLOSCompBiol2013.pdf},
	Version = {269},
	Volume = {9},
	Year = {2013},
	Bdsk-Url-1 = {http://www.princeton.edu/$%5Csim$rcw2/papers/WilsonEtAl_PLOSCompBiol2013.pdf},
	Bdsk-Url-2 = {https://doi.org/10.1371/journal.pcbi.1003150}}

@article{Wilson18,
	Author = {Wilson, Robert C. and Nassar, Matthew R. and Tavoni, Gaia and Gold, Joshua I.},
	Date = {2018-06-26},
	Date-Added = {2018-07-27 15:01:45 +0200},
	Date-Modified = {2018-07-27 15:01:59 +0200},
	Doi = {10/gdvqg4},
	Issn = {1553-7358},
	Journaltitle = {{PLOS} Computational Biology},
	Keywords = {Algorithms, Approximation methods, Behavior, Simulation and modeling},
	Langid = {english},
	Note = {00000},
	Number = {6},
	Pages = {e1006210},
	Shortjournal = {{PLOS} Computational Biology},
	Shorttitle = {Correction},
	Title = {Correction: A Mixture of Delta-Rules Approximation to Bayesian Inference in Change-Point Problems},
	Url = {http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006210},
	Urldate = {2018-07-27},
	Version = {1179},
	Volume = {14},
	Year = {2018},
	Bdsk-Url-1 = {http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006210},
	Bdsk-Url-2 = {https://doi.org/10/gdvqg4}}

@incollection{Kahneman13,
	Author = {Kahneman, Daniel and Tversky, Amos},
	Booktitle = {Handbook of the fundamentals of financial decision making: Part I},
	Date-Added = {2018-07-27 14:58:25 +0200},
	Date-Modified = {2018-07-27 14:58:30 +0200},
	Pages = {99--127},
	Publisher = {World Scientific},
	Title = {Prospect theory: An analysis of decision under risk},
	Year = {2013}}

@article{Vilares2011,
	Abstract = {Experiments on humans and other animals have shown that uncertainty due to unreliable or incomplete information affects behavior. Recent studies have formalized uncertainty and asked which behaviors would minimize its effect. This formalization results in a wide range of Bayesian models that derive from assumptions about the world, and it often seems unclear how these models relate to one another. In this review, we use the concept of graphical models to analyze differences and commonalities across Bayesian approaches to the modeling of behavioral and neural data. We review behavioral and neural data associated with each type of Bayesian model and explain how these models can be related. We finish with an overview of different theories that propose possible ways in which the brain can represent uncertainty.},
	Author = {Vilares, Iris and Kording, Konrad},
	Date-Added = {2018-07-27 14:57:21 +0200},
	Date-Modified = {2018-07-27 14:57:21 +0200},
	Doi = {10.1111/j.1749-6632.2011.05965.x},
	File = {:Users/damasse.j-b/Documents/Mendeley Desktop/vilares2011.pdf:pdf},
	Isbn = {1749-6632},
	Issn = {00778923},
	Journal = {Annals of the New York Academy of Sciences},
	Keywords = {Bayesian models,Graphical models,Neural representations,Psychophysics,Uncertainty},
	Mendeley-Groups = {biblio thesis},
	Number = {1},
	Pages = {22--39},
	Pmid = {21486294},
	Title = {{Bayesian models: The structure of the world, uncertainty, behavior, and the brain}},
	Volume = {1224},
	Year = {2011},
	Bdsk-Url-1 = {https://doi.org/10.1111/j.1749-6632.2011.05965.x}}

@article{Meyniel15,
	Abstract = {Author Summary Learning is often accompanied by a ``feeling of knowing'', a growing sense of confidence in having acquired the relevant information. Here, we formalize this introspective ability, and we evaluate its accuracy and its flexibility in the face of environmental changes that impose a revision of one's mental model. We evaluate the hypothesis that the brain acts as a statistician that accurately tracks not only the most likely state of the environment, but also the uncertainty associated with its own inferences. We show that subjective confidence ratings varied across successive observations in tight parallel with a mathematical model of an ideal observer performing the optimal inference. Our results suggest that, during learning, the brain constantly keeps track of its own uncertainty, and that subjective confidence may derive from the learning process itself. Our results therefore suggest that subjective confidence, although currently under-explored, could provide key data to better understand learning.},
	Author = {Meyniel, Florent and Schlunegger, Daniel and Dehaene, Stanislas},
	Date-Added = {2018-07-27 14:55:09 +0200},
	Date-Modified = {2018-07-27 14:55:37 +0200},
	Doi = {10.1371/journal.pcbi.1004305},
	Journal = {PLOS Computational Biology},
	Number = {6},
	Pages = {1--25},
	Publisher = {Public Library of Science},
	Title = {{The Sense of Confidence during Probabilistic Learning: A Normative Account}},
	Url = {https://doi.org/10.1371/journal.pcbi.1004305},
	Volume = {11},
	Year = {2015},
	Bdsk-Url-1 = {https://doi.org/10.1371/journal.pcbi.1004305}}

@article{Beck12,
	Author = {Beck, Jeffrey M and Ma, Wei Ji and Pitkow, Xaq and Latham, Peter E and Pouget, Alexandre},
	Date-Added = {2018-07-27 14:54:25 +0200},
	Date-Modified = {2018-07-27 14:54:30 +0200},
	Journal = {Neuron},
	Number = {1},
	Pages = {30--39},
	Publisher = {Elsevier},
	Title = {Not noisy, just wrong: the role of suboptimal inference in behavioral variability},
	Volume = {74},
	Year = {2012}}

@article{Souto13,
	Author = {Souto, D. and Kerzel, D.},
	Date = {2013},
	Date-Added = {2018-07-27 14:51:05 +0200},
	Date-Modified = {2019-09-19 21:44:54 +0200},
	Doi = {10.1167/13.2.9},
	Issn = {1534-7362},
	Journal = {Journal of Vision},
	Journaltitle = {Journal of Vision},
	Number = {2},
	Pages = {9--9},
	Title = {Like a rolling stone: Naturalistic visual kinematics facilitate tracking eye movements},
	Url = {http://jov.arvojournals.org/Article.aspx?doi=10.1167/13.2.9},
	Version = {599},
	Volume = {13},
	Year = {2013},
	Bdsk-Url-1 = {http://jov.arvojournals.org/Article.aspx?doi=10.1167/13.2.9},
	Bdsk-Url-2 = {https://doi.org/10.1167/13.2.9}}

@article{Harris98,
	Abstract = {When we make saccadic eye movements or goal-directed arm movements, there is an infinite number of possible trajectories that the eye or arm could take to reach the target. However, humans show highly stereotyped trajectories in which velocity profiles of both the eye and hand are smooth and symmetric for brief movements. Here we present a unifying theory of eye and arm movements based on the single physiological assumption that the neural control signals are corrupted by noise whose variance increases with the size of the control signal. We propose that in the presence of such signal-dependent noise, the shape of a trajectory is selected to minimize the variance of the final eye or arm position. This minimum-variance theory accurately predicts the trajectories of both saccades and arm movements and the speed-accuracy trade-off described by Fitt's law. These profiles are robust to changes in the dynamics of the eye or arm, as found empirically. Moreover, the relation between path curvature and hand velocity during drawing movements reproduces the empirical 'two-thirds power law. This theory provides a simple and powerful unifying perspective for both eye and arm movement control.},
	Author = {Harris, Christopher M and Wolpert, Daniel M},
	Date = {1998},
	Date-Added = {2018-07-27 14:49:17 +0200},
	Date-Modified = {2018-07-27 14:49:29 +0200},
	Doi = {10.1038/29528},
	Issn = {0028-0836},
	Journaltitle = {Nature},
	Keywords = {Animals, Arm, Arm: physiology, Haplorhini, Humans, Models, Motor Activity, Motor Activity: physiology, Motor Neurons, Motor Neurons: physiology, Neurological, Saccades, Saccades: physiology},
	Number = {6695},
	Pages = {780--4},
	Pmid = {9723616},
	Title = {Signal-dependent noise determines motor planning.},
	Url = {http://www.ncbi.nlm.nih.gov/pubmed/9723616 http://www.nature.com/nature/journal/v394/n6695/abs/394780a0.html http://www.nature.com/doifinder/10.1038/29528},
	Version = {601},
	Volume = {394},
	Year = {1998},
	Bdsk-Url-1 = {http://www.ncbi.nlm.nih.gov/pubmed/9723616%20http://www.nature.com/nature/journal/v394/n6695/abs/394780a0.html%20http://www.nature.com/doifinder/10.1038/29528},
	Bdsk-Url-2 = {https://doi.org/10.1038/29528}}

@article{Daunizeau10a,
	Author = {Daunizeau, Jean and den Ouden, Hanneke E. M. and Pessiglione, Matthias and Kiebel, Stefan J. and Stephan, Klaas E. and Friston, Karl J.},
	Date = {2010},
	Date-Added = {2018-07-27 14:47:27 +0200},
	Date-Modified = {2019-09-19 21:43:52 +0200},
	Doi = {10.1371/journal.pone.0015554},
	Editor = {Sporns, Olaf},
	Issn = {1932-6203},
	Journal = {{PLoS} {ONE}},
	Journaltitle = {{PLoS} {ONE}},
	Number = {12},
	Pages = {e15554},
	Title = {Observing the Observer (I): Meta-Bayesian Models of Learning and Decision-Making},
	Url = {http://dx.plos.org/10.1371/journal.pone.0015554},
	Version = {599},
	Volume = {5},
	Year = {2010},
	Bdsk-Url-1 = {http://dx.plos.org/10.1371/journal.pone.0015554},
	Bdsk-Url-2 = {https://doi.org/10.1371/journal.pone.0015554}}

@article{Daunizeau10b,
	Author = {Daunizeau, Jean and den Ouden, Hanneke E. M. and Pessiglione, Matthias and Kiebel, Stefan J. and Friston, Karl J. and Stephan, Klaas E.},
	Date = {2010},
	Date-Added = {2018-07-27 14:47:27 +0200},
	Date-Modified = {2018-07-27 14:47:59 +0200},
	Doi = {10.1371/journal.pone.0015555},
	Editor = {Sporns, Olaf},
	Issn = {1932-6203},
	Journaltitle = {{PLoS} {ONE}},
	Keywords = {decision\_making, free energy},
	Number = {12},
	Pages = {e15555},
	Title = {Observing the Observer ({II}): Deciding When to Decide},
	Url = {http://dx.plos.org/10.1371/journal.pone.0015555},
	Version = {531},
	Volume = {5},
	Year = {2010},
	Bdsk-Url-1 = {http://dx.plos.org/10.1371/journal.pone.0015555},
	Bdsk-Url-2 = {https://doi.org/10.1371/journal.pone.0015555}}

@article{Krauzlis89,
	Author = {Krauzlis, {RJ} J. and Lisberger, S. G. {SG}},
	Date = {1989-03},
	Date-Added = {2018-07-27 14:46:28 +0200},
	Date-Modified = {2018-07-27 14:47:04 +0200},
	Doi = {10.1162/neco.1989.1.1.116},
	Issn = {0899-7667},
	Journal = {Neural Computation},
	Journaltitle = {Neural Computation},
	Keywords = {khoei12jpp},
	Number = {1},
	Pages = {116--122},
	Title = {A control systems model of smooth pursuit eye movements with realistic emergent properties},
	Url = {http://www.mitpressjournals.org/doi/10.1162/neco.1989.1.1.116},
	Version = {268},
	Volume = {1},
	Year = {1989},
	Bdsk-Url-1 = {http://www.mitpressjournals.org/doi/10.1162/neco.1989.1.1.116%20http://www.mitpressjournals.org/doi/abs/10.1162/neco.1989.1.1.116},
	Bdsk-Url-2 = {https://doi.org/10.1162/neco.1989.1.1.116}}

@inbook{Krauzlis2008,
	Address = {Amsterdam, NL},
	Author = {Krauzlis, R.J.},
	Booktitle = {Fundamental Neuroscience},
	Chapter = {Eye Movements},
	Date-Modified = {2019-09-19 21:44:35 +0200},
	Edition = {3rd edition},
	Editor = {Squires, L.R. and Berg, D},
	Publisher = {Elsevier},
	Pages = {775--795},
	Title = {Fundamental Neuroscience},
	Year = {2008}}

@article{Anderson2006,
	Author = {Anderson, Andrew J and Carpenter, R H S},
	Date-Added = {2018-07-27 14:32:06 +0200},
	Date-Modified = {2019-09-19 21:47:36 +0200},
	Doi = {10.1167/6.8.5},
	Journal = { Journal of Vision 2006},
	Keywords = {expectation,learning,model,probability,reaction time,saccade},
	Month = {July},
	Pages = {822--835},
	Title = {Changes in expectation consequent on experience , modeled by a simple , forgetful neural circuit},
	Volume = {6},
	Year = {2006}}

@article{Behrens07,
	Abstract = {Our decisions are guided by outcomes that are associated with decisions made in the past. However, the amount of influence each past outcome has on our next decision remains unclear. To ensure optimal decision-making, the weight given to decision outcomes should reflect their salience in predicting future outcomes, and this salience should be modulated by the volatility of the reward environment. We show that human subjects assess volatility in an optimal manner and adjust decision-making accordingly. This optimal estimate of volatility is reflected in the fMRI signal in the anterior cingulate cortex (ACC) when each trial outcome is observed. When a new piece of information is witnessed, activity levels reflect its salience for predicting future outcomes. Furthermore, variations in this ACC signal across the population predict variations in subject learning rates. Our results provide a formal account of how we weigh our different experiences in guiding our future actions.},
	Author = {Behrens, Timothy E. J. and Woolrich, Mark W. and Walton, Mark E. and Rushworth, Matthew F. S.},
	Date-Added = {2018-07-27 14:23:42 +0200},
	Date-Modified = {2018-07-27 14:23:42 +0200},
	Doi = {10.1038/nn1954},
	File = {Full Text PDF:/Users/laurentperrinet/Zotero/storage/GU3MXMCI/Behrens et al. - 2007 - Learning the value of information in an uncertain .pdf:application/pdf;Snapshot:/Users/laurentperrinet/Zotero/storage/C7QFU2RD/nn1954.html:text/html},
	Issn = {1097-6256},
	Journal = {Nature Neuroscience},
	Keywords = {Adolescent, Adult, Bayes Theorem, Brain Mapping, Decision Making, Female, Gyrus Cinguli, Humans, Image Processing, Computer-Assisted, Learning, Magnetic Resonance Imaging, Male, Oxygen, Pattern Recognition, Visual, Photic Stimulation, Probability, Reaction Time, Reinforcement (Psychology)},
	Language = {eng},
	Month = sep,
	Number = {9},
	Pages = {1214--1221},
	Pmid = {17676057},
	Title = {Learning the value of information in an uncertain world},
	Volume = {10},
	Year = {2007},
	Bdsk-Url-1 = {https://doi.org/10.1038/nn1954}}

@article{AdamsMackay2007,
	Address = {Cambridge, UK},
	Adsurl = {http://adsabs.harvard.edu/abs/2007arXiv0710.3742P},
	Archiveprefix = {arXiv},
	Author = {Adams, Ryan Prescott and MacKay, David~J.~C.},
	Date-Modified = {2019-07-18 10:55:19 +0200},
	Eprint = {0710.3742},
	Institution = {University of Cambridge},
	Journal = {ArXiv e-prints},
	Keywords = {Statistics - Machine Learning},
	Month = oct,
	Volume = {arXiv:0710.3742v1 [stat.ML]},
	Primaryclass = {stat.ML},
	Title = {Bayesian Online Changepoint Detection},
	Url = {http://arxiv.org/abs/0710.3742},
	Year = 2007,
	Url = {http://arxiv.org/abs/0710.3742}}

@article{Badler2006,
	Author = {Badler, J. B. and Heinen, S.J.},
	Doi = {10.1523/JNEUROSCI.3739-05.2006},
	Issn = {0270-6474},
	Journal = {Journal of Neuroscience},
	Keywords = {expectation,eye movements,gap,interaction,primate,smooth pursuit},
	Number = {17},
	Pages = {4519--4525},
	Title = {{Anticipatory Movement Timing Using Prediction and External Cues}},
	Url = {http://www.jneurosci.org/cgi/doi/10.1523/JNEUROSCI.3739-05.2006},
	Volume = {26},
	Year = {2006},
	Bdsk-Url-1 = {http://www.jneurosci.org/cgi/doi/10.1523/JNEUROSCI.3739-05.2006},
	Bdsk-Url-2 = {https://doi.org/10.1523/JNEUROSCI.3739-05.2006}}

@article{BeckerFuchs1985,
	Abstract = {Eye movements were recorded in human subjects who tracked a target spot which moved horizontally at constant speeds. At random times during its trajectory, the target disappeared for variable periods of time and the subjects attempted to continue tracking the invisible target. The smooth pursuit component of their eye movements was isolated and averaged. About 190 ms after the target disappeared, the smooth pursuit velocity began to decelerate rapidly. The time course of this deceleration was similar to that in response to a visible target whose velocity decreased suddenly. After a deceleration lasting about 280 ms, the velocity stabilized at a new, reduced level which we call the residual velocity. The residual velocity remained more or less constant or declined only slowly even when the target remained invisible for 4 s. When the same target velocity was used in all trials of an experiment, the subjects' residual velocity amounted to 60{\%} of their normal pursuit velocity. When the velocity was varied randomly from trial to trial, the residual velocity was smaller; for target velocities of 5, 10, and 20 deg/s it reached 55, 47, and 39{\%} respectively. The subjects needed to see targets of unforeseeable velocity for no more than 300 ms in order to develop a residual velocity that was characteristic of the given target velocity. When a target of unknown velocity disappeared at the very moment the subject expected it to start, a smooth movement developed nonetheless and reached within 300 ms a peak velocity of 5 deg/s which was independent of the actual target velocity and reflected a "default" value for the pursuit system. Thereafter the eyes decelerated briefly and then continued with a constant or slightly decreasing velocity of 2-4 deg/s until the target reappeared. Even when the subjects saw no moving target during an experiment, they could produce a smooth movement in the dark and could grade its velocity as a function of that of an imagined target. We suggest that the residual velocity reflects a first order prediction of target movement which is attenuated by a variable gain element. When subjects are pursuing a visible target, the gain of this element is close to unity. When the target disappears but continued tracking is attempted, the gain is reduced to a value between 0.4 and 0.6.},
	Author = {Becker, W. and Fuchs, a. F.},
	Doi = {10.1007/BF00237843},
	Journal = {Experimental Brain Research},
	Keywords = {Oculomotor system,Prediction,Residual smooth velocity,Smooth pursuit eye movements},
	Pages = {562--575},
	Pmid = {3979498},
	Title = {{Prediction in the oculomotor system: smooth pursuit during transient disappearance of a visual target}},
	Volume = {57},
	Year = {1985},
	Bdsk-Url-1 = {https://doi.org/10.1007/BF00237843}}

@article{Cicchini_PRSB_2018,
	Abstract = {The world tends to be stable from moment to moment, leading to strong serial correlations in natural scenes. As similar stimuli usually require similar behavioural responses, it is highly likely that the brain has developed strategies to leverage these regularities. A good deal of recent psychophysical evidence is beginning to show that the brain is sensitive to serial correlations, causing strong drifts in observer responses towards previously seen stimuli. However, it is still not clear that this tendency leads to a functional advantage. Here, we test a formal model of optimal serial dependence and show that as predicted, serial dependence in an orientation reproduction task is dependent on current stimulus reliability, with less precise stimuli, such as low spatial frequency oblique Gabors, exhibiting the strongest effects. We also show that serial dependence depends on the similarity between two successive stimuli, again consistent with the behaviour of an ideal observer aiming at minimizing reproduction errors. Lastly, we show that serial dependence leads to faster response times, indicating that the benefits of serial integration go beyond reproduction error. Overall our data show that serial dependence has a beneficial role at various levels of perception, consistent with the idea that the brain exploits the temporal redundancy of the visual scene as an optimization strategy.},
	Author = {Cicchini, Guido Marco and Mikellidou, Kyriaki and Burr, David C.},
	Doi = {10.1098/rspb.2018.1722},
	Issn = {0962-8452},
	Journal = {Proceedings of the Royal Society B: Biological Sciences},
	Keywords = {Bayesian,Kalman filter,optimal behaviour,orientation,serial dependence},
	Month = {nov},
	Number = {1890},
	Pages = {20181722},
	Pmid = {30381379},
	Title = {{The functional role of serial dependence}},
	Url = {http://www.ncbi.nlm.nih.gov/pubmed/30381379 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC6235035 http://www.royalsocietypublishing.org/doi/10.1098/rspb.2018.1722},
	Volume = {285},
	Year = {2018},
	Bdsk-Url-1 = {http://www.ncbi.nlm.nih.gov/pubmed/30381379%20http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC6235035%20http://www.royalsocietypublishing.org/doi/10.1098/rspb.2018.1722},
	Bdsk-Url-2 = {https://doi.org/10.1098/rspb.2018.1722}}

@article{ChopinMamassian2012,
	Abstract = {What humans perceive depends in part on what they have previously experienced. After repeated exposure to one stimulus, adaptation takes place in the form of a negative correlation between the current percept and the last displayed stimuli. Previous work has shown that this negative dependence can extend to a few minutes in the past, but the precise extent and nature of the dependence in vision is still unknown. In two experiments based on orientation judgments, we reveal a positive dependence of a visual percept with stimuli presented remotely in the past, unexpectedly and in contrast to what is known for the recent past. Previous theories of adaptation have postulated that the visual system attempts to calibrate itself relative to an ideal norm or to the recent past. We propose instead that the remote past is used to estimate the world's statistics and that this estimate becomes the reference. According to this new framework, adaptation is predictive: the most likely forthcoming percept is the one that helps the statistics of the most recent percepts match that of the remote past.},
	Author = {Chopin, Adrien and Mamassian, Pascal},
	Doi = {10.1016/j.cub.2012.02.021},
	Issn = {09609822},
	Journal = {Current Biology},
	Month = {apr},
	Number = {7},
	Pages = {622--626},
	Pmid = {22386314},
	Title = {{Predictive Properties of Visual Adaptation}},
	Url = {http://www.ncbi.nlm.nih.gov/pubmed/22386314 https://linkinghub.elsevier.com/retrieve/pii/S0960982212001704},
	Volume = {22},
	Year = {2012},
	Bdsk-Url-1 = {http://www.ncbi.nlm.nih.gov/pubmed/22386314%20https://linkinghub.elsevier.com/retrieve/pii/S0960982212001704},
	Bdsk-Url-2 = {https://doi.org/10.1016/j.cub.2012.02.021}}

@article{Cho2002,
	Author = {Cho, R and Nystrom, L and Jones, a and Braver, T and Holmes, P and Cohen, J},
	Journal = {Cog Aff Behav Neurosci.},
	Number = {412},
	Pages = {283--299},
	Title = {{Mechanisms underlying performance dependencies on stimulus history in a two-alternative forced choice task}},
	Volume = {2},
	Year = {2002}}

@article{Clifford2007,
	Abstract = {The term visual adaptation describes the processes by which the visual system alters its operating properties in response to changes in the environment. These continual adjustments in sensory processing are diagnostic as to the computational principles underlying the neural coding of information and can have profound consequences for our perceptual experience. New physiological and psychophysical data, along with emerging statistical and computational models, make this an opportune time to bring together experimental and theoretical perspectives. Here, we discuss functional ideas about adaptation in the light of recent data and identify exciting directions for future research. {\textcopyright} 2007 Elsevier Ltd. All rights reserved.},
	Author = {Clifford, Colin W G and Webster, Michael A. and Stanley, Garrett B. and Stocker, Alan A. and Kohn, Adam and Sharpee, Tatyana O. and Schwartz, Odelia},
	Doi = {10.1016/j.visres.2007.08.023},
	Journal = {Vision Research},
	Keywords = {Information processing,Perception,Physiology,Psychophysics,Sensory coding,Theoretical neuroscience},
	Number = {25},
	Pages = {3125--3131},
	Pmid = {17936871},
	Title = {{Visual adaptation: Neural, psychological and computational aspects}},
	Volume = {47},
	Year = {2007},
	Bdsk-Url-1 = {https://doi.org/10.1016/j.visres.2007.08.023}}

@article{Darlington_NatNeu2018,
	Abstract = {Actions are guided by a Bayesian-like interaction between priors based on experience and current sensory evidence. Here we unveil a complete neural implementation of Bayesian-like behavior, including adaptation of a prior. We recorded the spiking of single neurons in the smooth eye-movement region of the frontal eye fields (FEFSEM), a region that is causally involved in smooth-pursuit eye movements. Monkeys tracked moving targets in contexts that set different priors for target speed. Before the onset of target motion, preparatory activity encodes and adapts in parallel with the behavioral adaptation of the prior. During the initiation of pursuit, FEFSEM output encodes a maximum a posteriori estimate of target speed based on a reliability-weighted combination of the prior and sensory evidence. FEFSEM responses during pursuit are sufficient both to adapt a prior that may be stored in FEFSEM and, through known downstream pathways, to cause Bayesian-like behavior in pursuit.},
	Author = {Darlington, Timothy R and Beck, Jeffrey M and Lisberger, Stephen G},
	Doi = {10.1038/s41593-018-0233-y},
	Issn = {1546-1726},
	Journal = {Nature neuroscience},
	Month = {oct},
	Number = {10},
	Pages = {1442--1451},
	Pmid = {30224803},
	Title = {{Neural implementation of Bayesian inference in a sensorimotor behavior.}},
	Url = {http://www.nature.com/articles/s41593-018-0233-y http://www.ncbi.nlm.nih.gov/pubmed/30224803 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC6312195},
	Volume = {21},
	Year = {2018},
	Bdsk-Url-1 = {http://www.nature.com/articles/s41593-018-0233-y%20http://www.ncbi.nlm.nih.gov/pubmed/30224803%20http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC6312195},
	Bdsk-Url-2 = {https://doi.org/10.1038/s41593-018-0233-y}}

@article{Deneve1999,
	Abstract = {Many sensory and motor variables are encoded in the nervous system by the activities of large populations of neurons with bell-shaped tuning curves. Extracting information from these population codes is difficult because of the noise inherent in neuronal responses. In most cases of interest, maximum likelihood (ML) is the best read-out method and would be used by an ideal observer. Using simulations and analysis, we show that a close approximation to ML can be implemented in a biologically plausible model of cortical circuitry. Our results apply to a wide range of nonlinear activation functions, suggesting that cortical areas may, in general, function as ideal observers of activity in preceding areas.},
	Author = {Deneve, Sophie and Latham, Peter E and Pouget, Alexandre},
	Doi = {10.1038/11205},
	Issn = {1097-6256},
	Journal = {Nature neuroscience},
	Keywords = {Brain Mapping,Computer Simulation,Likelihood Functions,Nerve Net,Nerve Net: physiology,Neurons,Neurons: physiology,Normal Distribution,Poisson Distribution,Visual Cortex,Visual Cortex: cytology,Visual Cortex: physiology,coding,decoding,neural-code},
	Month = {aug},
	Number = {8},
	Pages = {740--5},
	Pmid = {10412064},
	Title = {{Reading population codes: a neural implementation of ideal observers.}},
	Url = {http://www.ncbi.nlm.nih.gov/pubmed/10412064 http://www.nature.com/doifinder/10.1038/11205},
	Volume = {2},
	Year = {1999},
	Bdsk-Url-1 = {http://www.ncbi.nlm.nih.gov/pubmed/10412064%20http://www.nature.com/doifinder/10.1038/11205},
	Bdsk-Url-2 = {https://doi.org/10.1038/11205}}

@article{Diaconescu2014,
	Abstract = {Inferring on others' (potentially time-varying) intentions is a fundamental problem during many social transactions. To investigate the underlying mechanisms, we applied computational modeling to behavioral data from an economic game in which 16 pairs of volunteers (randomly assigned to "player" or "adviser" roles) interacted. The player performed a probabilistic reinforcement learning task, receiving information about a binary lottery from a visual pie chart. The adviser, who received more predictive information, issued an additional recommendation. Critically, the game was structured such that the adviser's incentives to provide helpful or misleading information varied in time. Using a meta-Bayesian modeling framework, we found that the players' behavior was best explained by the deployment of hierarchical learning: they inferred upon the volatility of the advisers' intentions in order to optimize their predictions about the validity of their advice. Beyond learning, volatility estimates also affected the trial-by-trial variability of decisions: participants were more likely to rely on their estimates of advice accuracy for making choices when they believed that the adviser's intentions were presently stable. Finally, our model of the players' inference predicted the players' interpersonal reactivity index (IRI) scores, explicit ratings of the advisers' helpfulness and the advisers' self-reports on their chosen strategy. Overall, our results suggest that humans (i) employ hierarchical generative models to infer on the changing intentions of others, (ii) use volatility estimates to inform decision-making in social interactions, and (iii) integrate estimates of advice accuracy with non-social sources of information. The Bayesian framework presented here can quantify individual differences in these mechanisms from simple behavioral readouts and may prove useful in future clinical studies of maladaptive social cognition.},
	Author = {Diaconescu, Andreea O. and Mathys, Christoph and Weber, Lilian A.E. and Daunizeau, Jean and Kasper, Lars and Lomakina, Ekaterina I. and Fehr, Ernst and Stephan, Klaas E.},
	Doi = {10.1371/journal.pcbi.1003810},
	Issn = {15537358},
	Journal = {PLoS Computational Biology},
	Number = {9},
	Pmid = {25187943},
	Title = {{Inferring on the Intentions of Others by Hierarchical Bayesian Learning}},
	Volume = {10},
	Year = {2014},
	Bdsk-Url-1 = {https://doi.org/10.1371/journal.pcbi.1003810}}

@article{Falk1997,
	Abstract = {People attempting to generate random sequences usually produce more alternations than expected by chance. They also judge overalternating sequences as maximally random. In this article, the authors review findings, implications, and explanatory mechanisms concerning subjective randomness. The authors next present the general approach of the mathematical theory of complexity, which identifies the length of the shortest program for reproducing a sequence with its degree of randomness. They describe 3 experiments, based on mean group responses, indicating that the perceived randomness of a sequence is better predicted by various measures of its encoding difficulty than by its objective randomness. These results seem to imply that in accordance with the complexity view, judging the extent of a sequence's randomness is based on an attempt to mentally encode it. The experience of randomness may result when this attempt fails. (PsycINFO Database Record (c) 2012 APA, all rights reserved) (journal abstract)},
	Author = {Falk, Ruma and Konold, Clifford},
	Doi = {10.1037/0033-295X.104.2.301},
	Journal = {Psychological Review},
	Number = {2},
	Pages = {301--318},
	Title = {{Making Sense of Randomness: Implicit Encoding as a Basis for Judgment}},
	Volume = {104},
	Year = {1997},
	Bdsk-Url-1 = {https://doi.org/10.1037/0033-295X.104.2.301}}

@article{Fetsch2012,
	Abstract = {Integration of multiple sensory cues is essential for precise and accurate perception and behavioral performance, yet the reliability of sensory signals can vary across modalities and viewing conditions. Human observers typically employ the optimal strategy of weighting each cue in proportion to its reliability, but the neural basis of this computation remains poorly understood. We trained monkeys to perform a heading discrimination task from visual and vestibular cues, varying cue reliability randomly. The monkeys appropriately placed greater weight on the more reliable cue, and population decoding of neural responses in the dorsal medial superior temporal area closely predicted behavioral cue weighting, including modest deviations from optimality. We found that the mathematical combination of visual and vestibular inputs by single neurons is generally consistent with recent theories of optimal probabilistic computation in neural circuits. These results provide direct evidence for a neural mechanism mediating a simple and widespread form of statistical inference.},
	Author = {Fetsch, Christopher R and Pouget, Alexandre and DeAngelis, Gregory C and Angelaki, Dora E},
	Doi = {10.1038/nn.2983},
	Issn = {1097-6256},
	Journal = {Nature Neuroscience},
	Month = {jan},
	Number = {1},
	Pages = {146--154},
	Pmid = {22101645},
	Title = {{Neural correlates of reliability-based cue weighting during multisensory integration}},
	Url = {http://www.ncbi.nlm.nih.gov/pubmed/22101645 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC3398428 http://www.nature.com/articles/nn.2983},
	Volume = {15},
	Year = {2012},
	Bdsk-Url-1 = {http://www.ncbi.nlm.nih.gov/pubmed/22101645%20http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC3398428%20http://www.nature.com/articles/nn.2983},
	Bdsk-Url-2 = {https://doi.org/10.1038/nn.2983}}

@article{FischerWhitney2014,
	Abstract = {Visual input is often noisy and discontinuous, even though the physical environment is generally stable. The authors show that the visual system trades off change sensitivity to capitalize on physical continuity via serial dependence: present perception is biased toward past visual input. This bias is modulated by attention and governed by a spatiotemporally-tuned operator, a continuity field.},
	Author = {Fischer, Jason and Whitney, David},
	Doi = {10.1038/nn.3689},
	Issn = {1097-6256},
	Journal = {Nature Neuroscience},
	Keywords = {Object vision,Pattern vision},
	Month = {may},
	Number = {5},
	Pages = {738--743},
	Publisher = {Nature Publishing Group},
	Title = {{Serial dependence in visual perception}},
	Url = {http://www.nature.com/articles/nn.3689},
	Volume = {17},
	Year = {2014},
	Bdsk-Url-1 = {http://www.nature.com/articles/nn.3689},
	Bdsk-Url-2 = {https://doi.org/10.1038/nn.3689}}

@article{Friston2003,
	Abstract = {This article is about how the brain data mines its sensory inputs. There are several architectural principles of functional brain anatomy that have emerged from careful anatomic and physiologic studies over the past century. These principles are considered in the light of representational learning to see if they could have been predicted a priori on the basis of purely theoretical considerations. We first review the organisation of hierarchical sensory cortices, paying special attention to the distinction between forward and backward connections. We then review various approaches to representational learning as special cases of generative models, starting with supervised learning and ending with learning based upon empirical Bayes. The latter predicts many features, such as a hierarchical cortical system, prevalent top-down backward influences and functional asymmetries between forward and backward connections that are seen in the real brain. The key points made in this article are: (i) hierarchical generative models enable the learning of empirical priors and eschew prior assumptions about the causes of sensory input that are inherent in non-hierarchical models. These assumptions are necessary for learning schemes based on information theory and efficient or sparse coding, but are not necessary in a hierarchical context. Critically, the anatomical infrastructure that may implement generative models in the brain is hierarchical. Furthermore, learning based on empirical Bayes can proceed in a biologically plausible way. (ii) The second point is that backward connections are essential if the processes generating inputs cannot be inverted, or the inversion cannot be parameterised. Because these processes involve many-to-one mappings, are non-linear and dynamic in nature, they are generally non-invertible. This enforces an explicit parameterisation of generative models (i.e. backward connections) to afford recognition and suggests that forward architectures, on their own, are not sufficient for perception. (iii) Finally, non-linearities in generative models, mediated by backward connections, require these connections to be modulatory, so that representations in higher cortical levels can interact to predict responses in lower levels. This is important in relation to functional asymmetries in forward and backward connections that have been demonstrated empirically. {\textcopyright} 2003 Elsevier Ltd. All rights reserved.},
	Author = {Friston, Karl},
	Doi = {10.1016/j.neunet.2003.06.005},
	Issn = {08936080},
	Journal = {Neural Networks},
	Keywords = {Bayesian,Generative models,Inference,Information theory,Predictive coding},
	Number = {9},
	Pages = {1325--1352},
	Pmid = {14622888},
	Title = {{Learning and inference in the brain}},
	Volume = {16},
	Year = {2003},
	Bdsk-Url-1 = {https://doi.org/10.1016/j.neunet.2003.06.005}}

@article{Friston2010,
	Abstract = {A free-energy principle has been proposed recently that accounts for action, perception and learning. This Review looks at some key brain theories in the biological (for example, neural Darwinism) and physical (for example, information theory and optimal control theory) sciences from the free-energy perspective. Crucially, one key theme runs through each of these theories - optimization. Furthermore, if we look closely at what is optimized, the same quantity keeps emerging, namely value (expected reward, expected utility) or its complement, surprise (prediction error, expected cost). This is the quantity that is optimized under the free-energy principle, which suggests that several global brain theories might be unified within a free-energy framework.},
	Archiveprefix = {arXiv},
	Arxivid = {arXiv:1507.02142v2},
	Author = {Friston, Karl.},
	Booktitle = {Nature Reviews Neuroscience},
	Date-Modified = {2019-09-19 21:48:58 +0200},
	Doi = {10.1038/nrn2787},
	Eprint = {arXiv:1507.02142v2},
	Journal = {Nature Reviews Neuroscience},
	Month = {feb},
	Number = {2},
	Pages = {127--138},
	Pmid = {20068583},
	Title = {The free-energy principle: A unified brain theory?},
	Url = {http://www.nature.com/doifinder/10.1038/nrn2787},
	Volume = {11},
	Year = {2010},
	Bdsk-Url-1 = {http://www.nature.com/doifinder/10.1038/nrn2787},
	Bdsk-Url-2 = {https://doi.org/10.1038/nrn2787}}

@article{Heinen2005,
	Abstract = {Smooth pursuit eye movements are guided largely by retinal-image motion. To compensate for neural conduction delays, the brain employs a predictive mechanism to generate anticipatory pursuit that precedes target motion (E. Kowler, 1990). A critical question for interpreting neural signals recorded during pursuit concerns how this mechanism is interfaced with sensorimotor processing. It has been shown that the predictor is not simply turned-off during randomization because anticipatory eye velocity remains when target velocity is randomized (E. Kowler {\&} S. McKee, 1987; G. W. Kao {\&} M. J. Morrow, 1994). This study was completed to compare pursuit behavior during randomized motion-onset timing with that occurring during direction or speed randomization. We found that anticipatory eye velocity persisted despite motion-onset randomization, and that anticipation onset time was between that observed in the different constant-timing conditions. This centering strategy was similar to the bias of eye velocity magnitude away from extremes observed when direction or speed was randomized. Such a strategy is comparable to least-squares error minimization, and could be used to facilitate acquisition of a target when it begins to move. Centering was in some observers accounted for by a shift of eye velocity toward that generated in the preceding trial. The results make unlikely a model in which the predictor is disengaged by randomizing stimulus timing, and suggest that predictive signals always interact with those used in sensorimotor processing during smooth pursuit.},
	Author = {Heinen, S. J. and Badler, J. B. and Ting, W.},
	Doi = {10.1167/5.6.1},
	Journal = {Journal of Vision},
	Keywords = {anticipation,human,prediction,smooth pursuit,timing,visual motion},
	Number = {6},
	Pages = {1--1},
	Pmid = {16097862},
	Title = {{Timing and velocity randomization similarly affect anticipatory pursuit}},
	Url = {http://jov.arvojournals.org/Article.aspx?doi=10.1167/5.6.1},
	Volume = {5},
	Year = {2005},
	Bdsk-Url-1 = {http://jov.arvojournals.org/Article.aspx?doi=10.1167/5.6.1},
	Bdsk-Url-2 = {https://doi.org/10.1167/5.6.1}}

@inproceedings{Hoyer2003,
	Abstract = {The responses of cortical sensory neurons are notoriously variable, with the number of spikes evoked by identical stimuli varying significantly from trial to trial. This variability is most often interpreted as 'noise', purely detrimental to the sensory system. In this paper, we propose an al-ternative view in which the variability is related to the uncertainty, about world parameters, which is inherent in the sensory stimulus. Specifi-cally, the responses of a population of neurons are interpreted as stochas-tic samples from the posterior distribution in a latent variable model. In addition to giving theoretical arguments supporting such a representa-tional scheme, we provide simulations suggesting how some aspects of response variability might be understood in this framework.},
	Author = {Hoyer, Patrik O and Hyvarinen, Aapo},
  	booktitle={Advances in neural information processing systems},
  	volume = {15},
  	Pages = {293--300},
	Title = {{Interpreting neural response variability as Monte Carlo sampling of the posterior}},
	Url = {http://books.google.com/books?hl=en{\&}lr={\&}id=AAVSDw4Rw9UC{\&}oi=fnd{\&}pg=PA293{\&}dq=Interpreting+Neural+Response+Variability+as+Monte+Carlo+Sampling+of+the+Posterior{\&}ots=U5tjvCjwAR{\&}sig=8EU3--mLxGZtqKQmDaaQSkNVuMA{\%}5Cnpapers3://publication/uuid/CFA8AACE-D8A0-4D64-9F},
	Year = {2003}}

@article{Huettel2002,
	Abstract = {We demonstrate that regions within human prefrontal cortex develop moment-to-moment models for patterns of events occurring in the sensory environment. Subjects viewed a random binary sequence of images, each presented singly and each requiring a different button press response. Patterns occurred by chance within the presented series of images. Using functional magnetic resonance imaging (fMRI), we identified activity evoked by viewing a stimulus that interrupted a pattern. Prefrontal activation was evoked by violations of both repeating and alternating patterns, and the amplitude of this activation increased with increasing pattern length. Violations of repeating patterns, but not of alternating patterns, activated the basal ganglia.},
	Author = {Huettel, Scott A. and Mack, Peter B. and McCarthy, Gregory},
	Doi = {10.1038/nn841},
	Journal = {Nature Neuroscience},
	Number = {5},
	Pages = {485--490},
	Pmid = {11941373},
	Title = {{Perceiving patterns in random series: Dynamic processing of sequence in prefrontal cortex}},
	Volume = {5},
	Year = {2002},
	Bdsk-Url-1 = {https://doi.org/10.1038/nn841}}

@article{Hyman1953,
	Abstract = {The information conveyed by a stimulus was varied in 3 ways: "(a) the number of equally probable alternatives from which it could be chosen, (b) the proportion of times it could occur relative to the other possible alternatives, and (c) the probability of its occurrence as a function of the immediately preceding stimulus presentation. The reaction time to the amount of information in the stimulus produced a linear regression for each of the three ways{\ldots} ."},
	Author = {Hyman, Ray},
	Doi = {10.1037/h0056940},
	Journal = {Journal of Experimental Psychology},
	Keywords = {RESPONSE PROCESSES},
	Number = {3},
	Pages = {188--196},
	Pmid = {13052851},
	Title = {{Stimulus information as a determinant of reaction time}},
	Volume = {45},
	Year = {1953},
	Bdsk-Url-1 = {https://doi.org/10.1037/h0056940}}

@book{Jaynes2014,
	ISBN = {0-521-59271-2},
	Author = {Jaynes, E.T.},
	Booktitle = {Igarss 2014},
	Date-Modified = {2019-09-19 21:51:42 +0200},
	Publisher = {Cambridge university press},
	Title = {Probability Theory: The Logic of Science},
	Year = {2014},
	}

@article{Jardri2017,
	Abstract = {Schizophrenia is a mental disorder characterized by hallucinations and delusions. Here the authors report a novel probabilistic inference task in which compared to healthy subjects, schizophrenia patients show greater degree of circular inference that matches the severity of their clinical symptoms.},
	Author = {Jardri, Renaud and Duverne, Sandrine and Litvinova, Alexandra S and Den{\`{e}}ve, Sophie},
	Doi = {10.1038/ncomms14218},
	File = {:Users/montagnini.a/Library/Application Support/Mendeley Desktop/Downloaded/Jardri et al. - 2017 - Experimental evidence for circular inference in schizophrenia.pdf:pdf},
	Issn = {2041-1723},
	Journal = {Nature Communications},
	Keywords = {Computational neuroscience,Medical research,Schizophrenia},
	Month = {apr},
	Number = {1},
	Pages = {14218},
	Publisher = {Nature Publishing Group},
	Title = {{Experimental evidence for circular inference in schizophrenia}},
	Url = {http://www.nature.com/articles/ncomms14218},
	Volume = {8},
	Year = {2017},
	Bdsk-Url-1 = {http://www.nature.com/articles/ncomms14218},
	Bdsk-Url-2 = {https://doi.org/10.1038/ncomms14218}}

@article{KanaiVerstraten2005,
	Abstract = {Visual neurons show fast adaptive behavior in response to brief visual input. However, the perceptual consequences of this rapid neural adaptation are less known. Here, we show that brief exposure to a moving adaptation stimulus---ranging from tens to hundreds of milliseconds---influences the perception of a subsequently presented ambiguous motion test stimulus. Whether the ambiguous motion is perceived to move in the same direction (priming), or in the opposite direction (rapid motion aftereffect) varies systematically with the duration of the adaptation stimulus and the adaptation-test blank interval. These biases appear and decay rapidly. Moreover, when the adapting stimulus is itself ambiguous, these effects are not produced. Instead, the percept for the subsequent test stimulus is biased to the perceived direction of the adaptation stimulus. This effect (perceptual sensitization) builds gradually over the time between the adaptation and test stimuli. Our results indicate that rapid adaptation plays a role mainly within early motion processing, whereas a slow potentiation controls the sensitivity at a later stage.},
	Author = {Kanai, Ryota and Verstraten, Frans A.J.},
	Doi = {10.1016/J.VISRES.2005.05.014},
	File = {:Users/montagnini.a/Library/Application Support/Mendeley Desktop/Downloaded/Kanai, Verstraten - 2005 - Perceptual manifestations of fast neural plasticity Motion priming, rapid motion aftereffect and perceptual s.pdf:pdf},
	Issn = {0042-6989},
	Journal = {Vision Research},
	Month = {nov},
	Number = {25-26},
	Pages = {3109--3116},
	Publisher = {Pergamon},
	Title = {{Perceptual manifestations of fast neural plasticity: Motion priming, rapid motion aftereffect and perceptual sensitization}},
	Url = {https://www.sciencedirect.com/science/article/pii/S0042698905002634?via{\%}3Dihub},
	Volume = {45},
	Year = {2005},
	Bdsk-Url-1 = {https://doi.org/10.1016/J.VISRES.2005.05.014}}

@article{Karvelis2018,
	Abstract = {Recent theories propose that schizophrenia/schizotypy and autistic spectrum disorder are related to impairments in Bayesian inference that is, how the brain integrates sensory information (likelihoods) with prior knowledge. However existing accounts fail to clarify: (i) how proposed theories differ in accounts of ASD vs. schizophrenia and (ii) whether the impairments result from weaker priors or enhanced likelihoods. Here, we directly address these issues by characterizing how 91 healthy participants, scored for autistic and schizotypal traits, implicitly learned and combined priors with sensory information. This was accomplished through a visual statistical learning paradigm designed to quantitatively assess variations in individuals' likelihoods and priors. The acquisition of the priors was found to be intact along both traits spectra. However, autistic traits were associated with more veridical perception and weaker influence of expectations. Bayesian modeling revealed that this was due, not to weaker prior expectations, but to more precise sensory representations.},
	Author = {Karvelis, Povilas and Seitz, Aaron R and Lawrie, Stephen M and Seri{\`{e}}s, Peggy},
	Doi = {10.7554/eLife.34115},
	Issn = {2050-084X},
	Journal = {eLife},
	Month = {may},
	Title = {{Autistic traits, but not schizotypy, predict increased weighting of sensory information in Bayesian visual integration}},
	Url = {https://elifesciences.org/articles/34115},
	Volume = {7},
	Year = {2018},
	Bdsk-Url-1 = {https://elifesciences.org/articles/34115},
	Bdsk-Url-2 = {https://doi.org/10.7554/eLife.34115}}

@article{KnillPouget2004,
	Abstract = {To use sensory information efficiently to make judgments and guide action in the world, the brain must represent and use information about uncertainty in its computations for perception and action. Bayesian methods have proven successful in building computational theories for perception and sensorimotor control, and psychophysics is providing a growing body of evidence that human perceptual computations are "Bayes' optimal". This leads to the "Bayesian coding hypothesis": that the brain represents sensory information probabilistically, in the form of probability distributions. Several computational schemes have recently been proposed for how this might be achieved in populations of neurons. Neurophysiological data on the hypothesis, however, is almost non-existent. A major challenge for neuroscientists is to test these ideas experimentally, and so determine whether and how neurons code information about sensory uncertainty.},
	Author = {Knill, David C. and Pouget, Alexandre},
	Doi = {10.1016/j.tins.2004.10.007},
	Issn = {01662236},
	Journal = {Trends in Neurosciences},
	Month = {dec},
	Number = {12},
	Pages = {712--719},
	Pmid = {15541511},
	Title = {{The Bayesian brain: the role of uncertainty in neural coding and computation}},
	Url = {http://www.ncbi.nlm.nih.gov/pubmed/15541511 https://linkinghub.elsevier.com/retrieve/pii/S0166223604003352},
	Volume = {27},
	Year = {2004},
	Bdsk-Url-1 = {http://www.ncbi.nlm.nih.gov/pubmed/15541511%20https://linkinghub.elsevier.com/retrieve/pii/S0166223604003352},
	Bdsk-Url-2 = {https://doi.org/10.1016/j.tins.2004.10.007}}

@article{Kohn2007,
	Abstract = {Recent sensory experience affects both perception and the response properties of visual neurons. Here I review a rapid from of experience-dependent plasticity that follows adaptation, the presentation of a particular stimulus or ensemble of stimuli for periods ranging from tens of milliseconds to minutes. Adaptation has a rich history in psychophysics, where it is often used as a tool for dissecting the perceptual mechanisms of vision. Although we know comparatively little about the neurophysiological effects of adaptation, work in the last decade has revealed a rich repertoire of effects. This review focuses on this recent physiological work, the cellular and biophysical mechanisms that may underlie the observed effects, and the functional benefit that they may afford. I conclude with a brief discussion of some important open questions in the field.},
	Author = {Kohn, Adam and Kohn, Adam},
	Doi = {10.1152/jn.00086.2007.},
	Journal = {Journal of Neurophysiology},
	Pages = {3155--3164},
	Pmid = {17344377},
	Title = {{Visual Adaptation: Physiology, Mechanisms, and Functional Bene ts}},
	Volume = {10461},
	Year = {2007},
	Bdsk-Url-1 = {https://doi.org/10.1152/jn.00086.2007.}}

@article{Kolossa2013,
	Abstract = {It has long been recognized that the amplitude of the P300 component of event--related brain potentials is sensitive to the degree to which eliciting stimuli are surprising to the observers (Donchin, 1981). While Squires et al. (1976) showed and modeled dependencies of P300 amplitudes from observed stimuli on various time scales, Mars et al. (2008) proposed a computational model keeping track of stimulus probabilities on a long--term time scale. We suggest here a computational model which integrates prior information with short--term, long--term, and alternation--based experiential influences on P300 amplitude fluctuations. To evaluate the new model, we measured trial--by--trial P300 amplitude fluctuations in a simple two--choice response time task, and tested the computational models of trial--by--trial P300 amplitudes using Bayesian model evaluation. The results reveal that the new digital filtering (DIF) model provides a superior account of the trial--by--trial P300 amplitudes when compared to both, Squires et al.'s (1976) model, and Mars et al.'s (2008) model. We show that the P300--generating system can be described as two parallel first--order infinite impulse response (IIR) low--pass filters and an additional fourth--order finite impulse response (FIR) high--pass filter. Implications of the acquired data are discussed with regard to the neurobiological distinction between short--term, long--term, and working memory as well as from the point of view of predictive coding models and Bayesian learning theories of cortical function.},
	Author = {Kolossa, Antonio and Fingscheidt, Tim and Wessel, Karl and Kopp, Bruno},
	Doi = {10.3389/fnhum.2012.00359},
	Journal = {Frontiers in Human Neuroscience},
	Keywords = {bayesian surprise,digital,event-related brain potentials,p300,predictive surprise,predictive surprise, Bayesian surprise, event-related brain potentials, P300, single trial EEG, digital filtering,single trial eeg},
	Number = {February},
	Pages = {1--18},
	Pmid = {23404628},
	Title = {{A Model-Based Approach to Trial-By-Trial P300 Amplitude Fluctuations}},
	Url = {http://journal.frontiersin.org/article/10.3389/fnhum.2012.00359/abstract},
	Volume = {6},
	Year = {2013},
	Bdsk-Url-1 = {http://journal.frontiersin.org/article/10.3389/fnhum.2012.00359/abstract},
	Bdsk-Url-2 = {https://doi.org/10.3389/fnhum.2012.00359}}

@article{Kowler1979a,
	Author = {Kowler, E and Steinman, RM},
	Doi = {10.1016/0042-6989(79)90238-4},
	Issn = {0042-6989},
	Journal = {Vision Research},
	Number = {6},
	Pages = {619 - 632},
	Title = {The effect of expectations on slow oculomotor control-I. Periodic target steps},
	Url = {http://www.sciencedirect.com/science/article/pii/0042698979902384},
	Volume = {19},
	Year = {1979},
	Bdsk-Url-1 = {http://www.sciencedirect.com/science/article/pii/0042698979902384},
	Bdsk-Url-2 = {https://doi.org/10.1016/0042-6989(79)90238-4}}

@article{Kowler1979b,
	Author = {Kowler, Eileen and Steinman, Robert M.},
	Doi = {10.1016/0042-6989(79)90239-6},
	Issn = {0042-6989},
	Journal = {Vision Research},
	Number = {6},
	Pages = {633 - 646},
	Title = {The effect of expectations on slow oculomotor control---II. Single target displacements},
	Url = {http://www.sciencedirect.com/science/article/pii/0042698979902396},
	Volume = {19},
	Year = {1979},
	Bdsk-Url-1 = {http://www.sciencedirect.com/science/article/pii/0042698979902396},
	Bdsk-Url-2 = {https://doi.org/10.1016/0042-6989(79)90239-6}}

@article{Kowler1984,
	Abstract = {Prior work had shown that smooth eye movements depend both on the motion of the target on the retina and on the subject's expectations about future target motion (Kowler and Steinman. 1979a,b). Effects of expectation cannot be eliminated by making target motions unpredictable (Kowler and Steinman. 1981). The experiment reported here shows that effects of expectations on smooth eye movement depend in a lawful way on the history of prior target motions. Anticipatory smooth eye movements (involuntary drifts in the direction of future target motion) were measured while subjects fixated a stationary target that was expected to step in an unpredictable direction (right or left). Anticipatory smooth eye movement velocity depended on the sequence of steps in prior trials e.g. velocity was faster to the right when the prior steps were to the right. The influence of prior steps diminished the further back into the past the step occurred. Sequential dependencies were also observed for the saccades used to track the target steps. Anticipatory smooth eye movement velocity was predicted by a two-state Markov model developed by Falmagne et al. (1975) for similar sequential dependencies observed in a manual reaction-time task (button-pressing). The model uses the prior sequence of target motions to predict the subject's expectation and assumes that the expectation determines anticipatory smooth eye movement velocity. The fit of the model to the data was good which shows that taking expectations into account is both necessary and feasible. Taking expectations into account, quantitatively, allows accurate predictions about smooth eye movement velocity when target motions are unpredictable. {\textcopyright} 1984.},
	Author = {Kowler, Eileen and Martins, Albert J. and Pavel, M.},
	Doi = {10.1016/0042-6989(84)90122-6},
	Journal = {Vision Research},
	Number = {3},
	Pages = {197--210},
	Pmid = {6719834},
	Title = {{The effect of expectations on slow oculomotor control-IV. Anticipatory smooth eye movements depend on prior target motions}},
	Volume = {24},
	Year = {1984},
	Bdsk-Url-1 = {https://doi.org/10.1016/0042-6989(84)90122-6}}

@article{Kowler1989,
	Author = {Kowler, Eileen},
	Doi = {10.1016/0042-6989(89)90052-7},
	Issn = {00426989},
	Journal = {Vision Research},
	Month = {jan},
	Number = {9},
	Pages = {1049--1057},
	Title = {{Cognitive expectations, not habits, control anticipatory smooth oculomotor pursuit}},
	Url = {http://linkinghub.elsevier.com/retrieve/pii/0042698989900527},
	Volume = {29},
	Year = {1989},
	Bdsk-Url-1 = {http://linkinghub.elsevier.com/retrieve/pii/0042698989900527},
	Bdsk-Url-2 = {https://doi.org/10.1016/0042-6989(89)90052-7}}

@article{Kowler2014,
	Author = {Kowler, Eileen and Aitkin, Cd and Ross, Nm},
	Doi = {10.1167/14.5.10.doi},
	Issn = {1534-7362},
	Journal = {Journal of Vision},
	Pages = {1--16},
	Title = {{Davida Teller Award Lecture 2013: The importance of prediction and anticipation in the control of smooth pursuit eye movements}},
	Url = {http://www.journalofvision.org/content/14/5/10.short},
	Volume = {14},
	Year = {2014},
	Bdsk-Url-1 = {http://www.journalofvision.org/content/14/5/10.short},
	Bdsk-Url-2 = {https://doi.org/10.1167/14.5.10.doi}}

@article{Lu2009,
	Author = {Lu, Zhong Lin and Yu, Cong and Watanabe, Takeo and Sagi, Dov and Levi, Dennis},
	Doi = {10.1016/j.visres.2009.09.023},
	Issn = {00426989},
	Journal = {Vision Research},
	Number = {21},
	Pages = {2531--2534},
	Publisher = {Elsevier Ltd},
	Title = {{Perceptual learning: Functions, mechanisms, and applications}},
	Url = {http://dx.doi.org/10.1016/j.visres.2009.09.023},
	Volume = {49},
	Year = {2009},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.visres.2009.09.023}}

@article{Ma2006,
	Abstract = {Recent psychophysical experiments indicate that humans perform near-optimal Bayesian inference in a wide variety of tasks, ranging from cue integration to decision making to motor control. This implies that neurons both represent probability distributions and combine those distributions according to a close approximation to Bayes' rule. At first sight, it would seem that the high variability in the responses of cortical neurons would make it difficult to implement such optimal statistical inference in cortical circuits. We argue that, in fact, this variability implies that populations of neurons automatically represent probability distributions over the stimulus, a type of code we call probabilistic population codes. Moreover, we demonstrate that the Poisson-like variability observed in cortex reduces a broad class of Bayesian inference to simple linear combinations of populations of neural activity. These results hold for arbitrary probability distributions over the stimulus, for tuning curves of arbitrary shape and for realistic neuronal variability.},
	Author = {Ma, Wei Ji and Beck, Jeffrey M and Latham, Peter E and Pouget, Alexandre},
	Doi = {10.1038/nn1790},
	Issn = {1097-6256},
	Journal = {Nature Neuroscience},
	Month = {nov},
	Number = {11},
	Pages = {1432--1438},
	Pmid = {17057707},
	Title = {{Bayesian inference with probabilistic population codes}},
	Url = {http://www.ncbi.nlm.nih.gov/pubmed/17057707 http://www.nature.com/articles/nn1790},
	Volume = {9},
	Year = {2006},
	Bdsk-Url-1 = {http://www.ncbi.nlm.nih.gov/pubmed/17057707%20http://www.nature.com/articles/nn1790},
	Bdsk-Url-2 = {https://doi.org/10.1038/nn1790}}

@article{Ma2014,
	Abstract = {Organisms must act in the face of sensory, motor, and reward uncertainty stemming from a pandemonium of stochasticity and missing information. In many tasks, organisms can make better decisions if they have at their disposal a representation of the uncertainty associated with task-relevant variables. We formalize this problem using Bayesian decision theory and review recent behavioral and neural evidence that the brain may use knowledge of uncertainty, confidence, and probability.},
	Author = {Ma, Wei Ji and Jazayeri, Mehrdad},
	Doi = {10.1146/annurev-neuro-071013-014017},
	Journal = {Annual Review of Neuroscience},
	Keywords = {bayesian inference,decision making,perception,population encoding},
	Number = {1},
	Pages = {205--220},
	Pmid = {25032495},
	Title = {{Neural Coding of Uncertainty and Probability}},
	Url = {http://www.annualreviews.org/doi/10.1146/annurev-neuro-071013-014017},
	Volume = {37},
	Year = {2014},
	Bdsk-Url-1 = {http://www.annualreviews.org/doi/10.1146/annurev-neuro-071013-014017},
	Bdsk-Url-2 = {https://doi.org/10.1146/annurev-neuro-071013-014017}}

@article{Mathys11,
	Abstract = {Computational learning models are critical for understanding mechanisms of adaptive behavior. However, the two major current frameworks, reinforcement learning (RL) and Bayesian learning, both have certain limitations. For example, many Bayesian models are agnostic of inter-individual variability and involve complicated integrals, making online learning difficult. Here, we introduce a generic hierarchical Bayesian framework for individual learning under multiple forms of uncertainty (e.g., environmental volatility and perceptual uncertainty). The model assumes Gaussian random walks of states at all but the first level, with the step size determined by the next highest level. The coupling between levels is controlled by parameters that shape the influence of uncertainty on learning in a subject-specific fashion. Using variational Bayes under a mean-field approximation and a novel approximation to the posterior energy function, we derive trial-by-trial update equations which (i) are analytical and extremely efficient, enabling real-time learning, (ii) have a natural interpretation in terms of RL, and (iii) contain parameters representing processes which play a key role in current theories of learning, e.g., precision-weighting of prediction error. These parameters allow for the expression of individual differences in learning and may relate to specific neuromodulatory mechanisms in the brain. Our model is very general: it can deal with both discrete and continuous states and equally accounts for deterministic and probabilistic relations between environmental events and perceptual states (i.e., situations with and without perceptual uncertainty). These properties are illustrated by simulations and analyses of empirical time series. Overall, our framework provides a novel foundation for understanding normal and pathological learning that contextualizes RL within a generic Bayesian scheme and thus connects it to principles of optimality from probability theory.},
	Author = {Mathys, Christoph and Daunizeau, Jean and Friston, Karl J and Stephan, Klaas E},
	Date-Modified = {2018-07-27 12:17:30 +0000},
	Doi = {10.3389/fnhum.2011.00039},
	Issn = {1662-5161},
	Journal = {Frontiers in human neuroscience},
	Keywords = {volatility, acetylcholine, decision-, decisionmaking, dopamine, hierarchical models, neuromodul, neuromodulation, serotonin, variational bayes, variational Bayes},
	Month = jan,
	Number = {May},
	Pages = {39},
	Pmid = {21629826},
	Title = {A bayesian foundation for individual learning under uncertainty.},
	Url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3096853&tool=pmcentrez&rendertype=abstract},
	Volume = {5},
	Year = {2011},
	Bdsk-Url-1 = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3096853&tool=pmcentrez&rendertype=abstract},
	Bdsk-Url-2 = {https://doi.org/10.3389/fnhum.2011.00039}}

@article{Meyniel16,
	Abstract = {The brain constantly infers the causes of the inputs it receives and uses these inferences to generate statistical expectations about future observations. Experimental evidence for these expectations and their violations include explicit reports, sequential effects on reaction times, and mismatch or surprise signals recorded in electrophysiology and functional MRI. Here, we explore the hypothesis that the brain acts as a near-optimal inference device that constantly attempts to infer the time-varying matrix of transition probabilities between the stimuli it receives, even when those stimuli are in fact fully unpredictable. This parsimonious Bayesian model, with a single free parameter, accounts for a broad range of findings on surprise signals, sequential effects and the perception of randomness. Notably, it explains the pervasive asymmetry between repetitions and alternations encountered in those studies. Our analysis suggests that a neural machinery for inferring transition probabilities lies at the core of human sequence knowledge.},
	Author = {Meyniel, Florent and Maheu, Maxime and Dehaene, Stanislas},
	Date-Modified = {2018-07-27 14:55:58 +0200},
	Doi = {10.1371/journal.pcbi.1005260},
	Issn = {15537358},
	Journal = {PLoS Computational Biology},
	Number = {12},
	Pages = {1--26},
	Pmid = {28030543},
	Title = {{Human Inferences about Sequences: A Minimal Transition Probability Model}},
	Volume = {12},
	Year = {2016},
	Bdsk-Url-1 = {https://doi.org/10.1371/journal.pcbi.1005260}}

@article{Meyniel13,
	Abstract = {
No pain, no gain: cost-benefit trade-off has been formalized in classical decision theory to account for how we choose whether to engage effort. However, how the brain decides when to have breaks in the course of effort production remains poorly understood. We propose that decisions to cease and resume work are triggered by a cost evidence accumulation signal reaching upper and lower bounds, respectively. We developed a task in which participants are free to exert a physical effort knowing that their payoff would be proportional to their effort duration. Functional {\{}MRI{\}} and magnetoencephalography recordings conjointly revealed that the theoretical cost evidence accumulation signal was expressed in proprioceptive regions (bilateral posterior insula). Furthermore, the slopes and bounds of the accumulation process were adapted to the difficulty of the task and the money at stake. Cost evidence accumulation might therefore provide a dynamical mechanistic account of how the human brain maximizes benefits while preventing exhaustion.
},
	Author = {Meyniel, Florent and Sergent, Claire and Rigoux, Lionel and Daunizeau, Jean and Pessiglione, Mathias},
	Date-Modified = {2018-07-27 14:55:37 +0200},
	Doi = {10.1073/pnas.1211925110},
	Issn = {1091-6490},
	Journal = {Proceedings of the National Academy of Sciences of the United States of America},
	Keywords = {decision-making},
	Number = {7},
	Pages = {2641--2646},
	Pmid = {23341598},
	Title = {{Neurocomputational account of how the human brain decides when to have a break.}},
	Url = {http://dx.doi.org/10.1073/pnas.1211925110},
	Volume = {110},
	Year = {2013},
	Bdsk-Url-1 = {http://dx.doi.org/10.1073/pnas.1211925110}}

@article{Montagnini2007,
	Abstract = {The quality of the representation of an object's motion is limited by the noise in the sensory input as well as by an intrinsic ambiguity due to the spatial limitation of the visual motion analyzers (aperture problem). Perceptual and oculomotor data demonstrate that motion processing of extended objects is initially dominated by the local 1D motion cues, related to the object's edges and orthogonal to them, whereas 2D information, related to terminators (or edge-endings), takes progressively over and leads to the final correct representation of global motion. A Bayesian framework accounting for the sensory noise and general expectancies for object velocities has proven successful in explaining several experimental findings concerning early motion processing [Weiss, Y., Adelson, E., 1998. Slow and smooth: a Bayesian theory for the combination of local motion signals in human vision. MIT Technical report, A.I. Memo 1624]. In particular, these models provide a qualitative account for the initial bias induced by the 1D motion cue. However, a complete functional model, encompassing the dynamical evolution of object motion perception, including the integration of different motion cues, is still lacking. Here we outline several experimental observations concerning human smooth pursuit of moving objects and more particularly the time course of its initiation phase, which reflects the ongoing motion integration process. In addition, we propose a recursive extension of the Bayesian model, motivated and constrained by our oculomotor data, to describe the dynamical integration of 1D and 2D motion information. We compare the model predictions for object motion tracking with human oculomotor recordings.},
	Author = {Montagnini, Anna and Mamassian, Pascal and Perrinet, Laurent U and Castet, Eric and Masson, Guillaume S},
	Doi = {10.1016/j.jphysparis.2007.10.013},
	Issn = {0928-4257},
	Journal = {Journal of physiology, Paris},
	Keywords = {Bayes Theorem,Biological,Eye Movements,Eye Movements: physiology,Humans,Models,Motion,Motion Perception,Motion Perception: physiology,Oculomotor Nerve,Oculomotor Nerve: physiology},
	Number = {1-3},
	Pages = {64--77},
	Pmid = {18036790},
	Title = {{Bayesian modeling of dynamic motion integration.}},
	Url = {http://www.ncbi.nlm.nih.gov/pubmed/18036790},
	Volume = {101},
	Year = {2007},
	Bdsk-Url-1 = {http://www.ncbi.nlm.nih.gov/pubmed/18036790},
	Bdsk-Url-2 = {https://doi.org/10.1016/j.jphysparis.2007.10.013}}

@article{Montagnini2010,
	Author = {Montagnini, Anna and Souto, David and Masson, Guillaume S},
	Journal = {Journal of Vision},
	Keywords = {aSPEM-motion},
	Number = {7},
	Pages = {554a},
	Title = {Anticipatory eye-movements under uncertainty: a window onto the internal representation of a visuomotor prior},
	Volume = {10},
	Year = {2010}}

@article{Ossmy2013,
	Abstract = {A key computation underlying perceptual decisions is the temporal integration of "evidence" in favor of different states of the world. Studies from psychology and neuroscience have shown that observers integrate multiple samples of noisy perceptual evidence over time toward a decision. An influential model posits perfect evidence integration (i.e., without forgetting), enabling optimal decisions based on stationary evidence. However, in real-life environments, the perceptual evidence typically changes continuously. We used a computational model to show that, under such conditions, performance can be improved by means of leaky (forgetful) integration, if the integration timescale is adapted toward the predominant signal duration. We then tested whether human observers employ such an adaptive integration process. Observers had to detect visual luminance "signals" of variable strength, duration, and onset latency, embedded within longer streams of noise. Different sessions entailed predominantly short or long signals. The rate of performance improvement as a function of signal duration indicated that observers indeed changed their integration timescale with the predominant signal duration, in accordance with the adaptive integration account. Our findings establish that leaky integration of perceptual evidence is flexible and that cognitive control mechanisms can exploit this flexibility for optimizing the decision process.},
	Author = {Ossmy, Ori and Moran, Rani and Pfeffer, Thomas and Tsetsos, Konstantinos and Usher, Marius and Donner, Tobias H.},
	Doi = {10.1016/j.cub.2013.04.039},
	Issn = {09609822},
	Journal = {Current Biology},
	Month = {jun},
	Number = {11},
	Pages = {981--986},
	Pmid = {23684972},
	Title = {{The Timescale of Perceptual Evidence Integration Can Be Adapted to the Environment}},
	Url = {http://www.ncbi.nlm.nih.gov/pubmed/23684972 https://linkinghub.elsevier.com/retrieve/pii/S0960982213004806},
	Volume = {23},
	Year = {2013},
	Bdsk-Url-1 = {http://www.ncbi.nlm.nih.gov/pubmed/23684972%20https://linkinghub.elsevier.com/retrieve/pii/S0960982213004806},
	Bdsk-Url-2 = {https://doi.org/10.1016/j.cub.2013.04.039}}

@article{PerrinetAdamasFriston2014,
	Abstract = {This paper considers the problem of sensorimotor delays in the optimal control of (smooth) eye movements under uncertainty. Specifically, we consider delays in the visuo-oculomotor loop and their implications for active inference. Active inference uses a generalisation of Kalman filtering to provide Bayes optimal estimates of hidden states and action in generalised coordinates of motion. Representing hidden states in generalised coordinates provides a simple way of compensating for both sensory and oculomotor delays. The efficacy of this scheme is illustrated using neuronal simulations of pursuit initiation responses, with and without compensation. We then consider an extension of the generative model to simulate smooth pursuit eye movements---in which the visuo-oculomotor system believes both the target and its centre of gaze are attracted to a (hidden) point moving in the visual field. Finally, the generative model is equipped with a hierarchical structure, so that it can recognise and remember unseen (occluded) trajectories and emit anticipatory responses. These simulations speak to a straightforward and neurobiologically plausible solution to the generic problem of integrating information from different sources with different temporal delays and the particular difficulties encountered when a system---like the oculomotor system---tries to control its environment with delayed signals.},
	Archiveprefix = {arXiv},
	Arxivid = {1610.05564},
	Author = {Perrinet, Laurent U and Adams, Rick A and Friston, Karl J},
	Doi = {10.1007/s00422-014-0620-8},
	Eprint = {1610.05564},
	Journal = {Biological Cybernetics},
	Keywords = {Active inference,Generalised coordinates,Oculomotor delays,Smooth pursuit eye movements,Tracking eye movements,Variational free energy},
	Month = {dec},
	Number = {6},
	Pages = {777--801},
	Pmid = {25128318},
	Title = {{Active inference, eye movements and oculomotor delays}},
	Url = {http://link.springer.com/10.1007/s00422-014-0620-8 http://www.ncbi.nlm.nih.gov/pubmed/25128318 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4250571},
	Volume = {108},
	Year = {2014},
	Bdsk-Url-1 = {http://link.springer.com/10.1007/s00422-014-0620-8%20http://www.ncbi.nlm.nih.gov/pubmed/25128318%20http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4250571},
	Bdsk-Url-2 = {https://doi.org/10.1007/s00422-014-0620-8}}

@article{Rashbass1961,
	Author = {Rashbass, C.},
	Doi = {10.1113/jphysiol.1961.sp006811},
	Issn = {00223751},
	Journal = {The Journal of Physiology},
	Keywords = {EYE/physiology},
	Month = {dec},
	Number = {2},
	Pages = {326--338},
	Pmid = {14490422},
	Publisher = {Wiley-Blackwell},
	Title = {{The relationship between saccadic and smooth tracking eye movements}},
	Url = {http://www.ncbi.nlm.nih.gov/pubmed/14490422 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC1359508 http://doi.wiley.com/10.1113/jphysiol.1961.sp006811},
	Volume = {159},
	Year = {1961},
	Bdsk-Url-1 = {http://www.ncbi.nlm.nih.gov/pubmed/14490422%20http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC1359508%20http://doi.wiley.com/10.1113/jphysiol.1961.sp006811},
	Bdsk-Url-2 = {https://doi.org/10.1113/jphysiol.1961.sp006811}}

@article{SantosKowler2017,
	Abstract = {Anticipatory smooth eye movements (ASEM) (smooth eye movements in the direction of 22 anticipated target motion) are elicited by cues that signal the direction of future target motion 23 with high levels of certainty. Natural cues, however, rarely convey information with perfect 24 certainty, and responses to uncertainty provide insights about how predictive behaviors are 25 generated. Subjects smoothly pursued targets that moved to the right or left with varying cued 26 probabilities. ASEM strength in a given direction increased with the probability level. The type 27 of cue also played a role. ASEM elicited by symbolic visual cues tended to underweight low 28 probabilities and overweight high probabilities. Cues based on memory (varying the proportion 29 of trials with left or right motion) produced the opposite pattern, overweighting low probabilities 30 and underweighting high probabilities. Finally, cues whose perceptual structure depicted the 31 motion path produced a bias in ASEM in the depicted direction that was maintained across levels 32 of cue congruency. The results show that the smooth pursuit system relies on a combination of 33 signals, including memory for recent target motions, interpretation of cues, and prior beliefs 34 about the relationship between the perceptual configuration and the motion path to determine the 35 anticipatory response in the presence of uncertainty.},
	Author = {Santos, Elio M. and Kowler, Eileen},
	Doi = {10.1167/17.13.13.doi},
	Issn = {15347362},
	Journal = {Journal of Vision},
	Number = {2017},
	Pages = {1--16},
	Title = {{Anticipatory smooth pursuit eye movements evoked by probabilistic cues}},
	Volume = {17},
	Year = {2017},
	Bdsk-Url-1 = {https://doi.org/10.1167/17.13.13.doi}}

@article{SoutoGegenfurtnerSchuetz2016,
abstract = {Visual uncertainty may affect saccade adaptation in two complementary ways. First, an ideal adaptor should take into account the reliability of visual information for determining the amount of correction, predicting that increasing visual uncertainty should decrease adaptation rates. We tested this by comparing observers' direction discrimination and adaptation rates in an intra-saccadic-step paradigm. Second, clearly visible target steps may generate a slower adaptation rate since the error can be attributed to an external cause, instead of an internal change in the visuo-motor mapping that needs to be compensated. We tested this prediction by measuring saccade adaptation to different step sizes. Most remarkably, we found little correlation between estimates of visual uncertainty and adaptation rates and no slower adaptation rates with more visible step sizes. Additionally, we show that for low contrast targets backward steps are perceived as stationary after the saccade, but that adaptation rates are independent of contrast. We suggest that the saccadic system uses different position signals for adapting dysmetric saccades and for generating a trans-saccadic stable visual percept, explaining that saccade adaptation is found to be independent of visual uncertainty.},
author = {Souto, David and Gegenfurtner, Karl R. and Sch{\"{u}}tz, Alexander C.},
doi = {10.3389/fnhum.2016.00227},
file = {:Users/montagnini.a/Library/Application Support/Mendeley Desktop/Downloaded/Souto, Gegenfurtner, Sch{\"{u}}tz - 2016 - Saccade adaptation and visual uncertainty.pdf:pdf},
issn = {16625161},
journal = {Frontiers in Human Neuroscience},
keywords = {Eye movements,Saccade adaptation,Saccadic suppression of displacement,Sensorimotor adaptation,Visual perception},
month = {may},
number = {MAY2016},
publisher = {Frontiers Media S. A},
title = {{Saccade adaptation and visual uncertainty}},
volume = {10},
year = {2016}
}


@article{Squires1976,
	Author = {Squires, K.C. and Wickens, C. and Squires, K. and Donchin, Emanuel},
	Journal = {Science},
	Number = {6},
	Pages = {1142--1146},
	Title = {{The Effect of Stimulus Sequence}},
	Volume = {193},
	Year = {1976}}

@article{Tiest2009,
	Abstract = {The thermal diffusivity of an object is a parameter that controls the rate at which heat is extracted from the hand when it touches that object. It is an important feature for distinguishing materials by means of touch. In order to quantitatively describe the ability of human observers to discriminate between materials on the basis of heat extraction rate, we conducted an experiment in which this heat extraction was performed in a controlled way. In different conditions, subjects were repeatedly asked to select from two stimuli the one that cooled faster. The discrimination threshold was around 43{\%} of the extraction rate. A rate that was twice as slow also yielded twice the absolute threshold. When we halved the temperature difference between the beginning and end of the stimulus, the threshold did not change as much. In separate experiments, we investigated the different cues that were available in the stimulus: initial cooling rate and end temperature. Both cues were used for discrimination, but cooling rate seemed to be the most important.},
	Archiveprefix = {arXiv},
	Arxivid = {NIHMS150003},
	Author = {Tiest, Wouter M Bergmann and Kappers, Astrid M L},
	Doi = {10.3758/APP},
	Eprint = {NIHMS150003},
	Journal = {Attention, perception {\&} psychophysics},
	Number = {3},
	Pages = {481--489},
	Pmid = {19304639},
	Title = {{Tactile perception of thermal diffusivity.}},
	Volume = {71},
	Year = {2009},
	Bdsk-Url-1 = {https://doi.org/10.3758/APP}}

@article{Verstraten1994,
	Abstract = {Following a period of adaptation to a pattern moving in a particular direction, a subsequently viewed stationary pattern appears to move in the opposite direction for some time: the movement after effect (MAE). The MAE lasts longer when the test pattern is not immediately or not continuously presented after adaptation. This phenomenon is called storage. So far research indicates that storage only occurs when textured visual stimulation is absent during part of the test phase or if the processing of a stationary test stimulus is prevented (e.g. by binocular rivalry). We present evidence that storage-like phenomena can occur even while a textured and moving visual stimulus is phenomenally present. We adapted binocularly to uni-directional motion of a random-pixel array M1 for 60 sec. This stimulus was immediately followed by another moving pattern M2. Its motion direction was orthogonal to that of M1. The presentation time of M2 was the independent variable. A stationary pattern was presented immediately after presentation of M2. The direction of the resulting integrated uni-directional MAE was measured. For short presentation times of M2 there is an integrated uni-directional MAE, which shows an interaction of the output of units stimulated by both moving patterns. However, it appeared that the effect of M1 on the direction of this combined uni-directional MAE is much longer present than would be expected from the MAE duration of M1, when tested in isolation. {\textcopyright} 1994.},
	Author = {Verstraten, F. A.J. and Fredericksen, R. E. and Gr{\"{u}}sser, O. J. and {Van De Grind}, W. A.},
	Doi = {10.1016/0042-6989(94)90297-6},
	Issn = {00426989},
	Journal = {Vision Research},
	Keywords = {Storage Visual aftereffects Adaptation Motion Ego motion},
	Number = {9},
	Pages = {1149--1155},
	Pmid = {8184559},
	Title = {{Recovery from motion adaptation is delayed by successively presented orthogonal motion}},
	Volume = {34},
	Year = {1994},
	Bdsk-Url-1 = {https://doi.org/10.1016/0042-6989(94)90297-6}}

@article{Vossel14,
	Abstract = {Inferring the environment's statistical structure and adapting behavior accordingly is a fundamental modus operandi of the brain. A simple form of this faculty based on spatial attentional orienting can be studied with Posner's location-cueing paradigm in which a cue indicates the target location with a known probability. The present study focuses on a more complex version of this task, where probabilistic context (percentage of cue validity) changes unpredictably over time, thereby creating a volatile environment. Saccadic response speed (RS) was recorded in 15 subjects and used to estimate subject-specific parameters of a Bayesian learning scheme modeling the subjects' trial-by-trial updates of beliefs. Different response models---specifying how computational states translate into observable behavior---were compared using Bayesian model selection. Saccadic RS was most plausibly explained as a function of the precision of the belief about the causes of sensory input. This finding is in accordance with current Bayesian theories of brain function, and specifically with the proposal that spatial attention is mediated by a precision-dependent gain modulation of sensory input. Our results provide empirical support for precision-dependent changes in beliefs about saccade target locations and motivate future neuroimaging and neuropharmacological studies of how Bayesian inference may determine spatial attention.},
	Author = {Vossel, S. and Mathys, Christoph and Daunizeau, Jean and Bauer, M. and Driver, J. and Friston, Karl J. and Stephan, K. E.},
	Date-Modified = {2018-07-27 12:17:30 +0000},
	Doi = {10.1093/cercor/bhs418},
	Issn = {1047-3211},
	Journal = {Cerebral Cortex},
	Keywords = {free energy, saccades},
	Number = {6},
	Pages = {1436--1450},
	Pmid = {23322402},
	Title = {Spatial {Attention}, {Precision}, and {Bayesian} {Inference}: {A} {Study} of {Saccadic} {Response} {Speed}},
	Url = {http://www.cercor.oxfordjournals.org/cgi/doi/10.1093/cercor/bhs418},
	Volume = {24},
	Year = {2014},
	Bdsk-Url-1 = {http://www.cercor.oxfordjournals.org/cgi/doi/10.1093/cercor/bhs418},
	Bdsk-Url-2 = {https://doi.org/10.1093/cercor/bhs418}}

@article{Webster2011,
	Author = {Webster, M. A.},
	Doi = {10.1167/11.5.3},
	Issn = {1534-7362},
	Journal = {Journal of Vision},
	Month = {may},
	Number = {5},
	Pages = {3--3},
	Title = {{Adaptation and visual coding}},
	Url = {http://jov.arvojournals.org/Article.aspx?doi=10.1167/11.5.3},
	Volume = {11},
	Year = {2011},
	Bdsk-Url-1 = {http://jov.arvojournals.org/Article.aspx?doi=10.1167/11.5.3},
	Bdsk-Url-2 = {https://doi.org/10.1167/11.5.3}}

@article{Westheimer1954,
	Abstract = {Eye movement responses to event the simples visual stimulus moving in only one dimension in an otherwise structureless visual field are the result of activities at several levels. One may distinguish factors operating at three somewhat arbitrarily delineated levels: (a) factors involving the mechanical aspects of the orbit and the other levels intervening between the initiation of the nerve impulses designed to move the eye and the actual eye movement; (b) factors involving the operation of what are often called the psycho-optical reflexes, i.e., the mechanisms which make the eyes move so as to place and retain the image of a light stimulus constantly and accurately on the fovea; (c) factors operating at higher levels of complexity.},
	Author = {Westheimer, Gerald},
	Doi = {10.1001/archopht.1954.00920050938013},
	Issn = {2374765X},
	Journal = {A.M.A. Archives of Ophthalmology},
	Number = {6},
	Pages = {932--941},
	Pmid = {13217540},
	Title = {{Eye movement responses to a horizontally moving visual stimulus}},
	Volume = {52},
	Year = {1954},
	Bdsk-Url-1 = {https://doi.org/10.1001/archopht.1954.00920050938013}}

@article{WuDelgadoMaloney2012,
	Abstract = {There is considerable evidence that human economic decision-making deviates from the predictions of expected utility theory (EUT) and that human performance conforms to EUT in many perceptual and motor decision tasks. It is possible that these results reflect a real difference in decision-making in the 2 domains but it is also possible that the observed discrepancy simply reflects typical differences in experimental design. We developed a motor task that is mathematically equivalent to choosing between lotteries and used it to compare how the same subject chose between classical economic lotteries and the same lotteries presented in equivalent motor form. In experiment 1, we found that subjects are more risk seeking in deciding between motor lotteries. In experiment 2, we used cumulative prospect theory to model choice and separately estimated the probability weighting functions and the value functions for each subject carrying out each task. We found no patterned differences in how subjects represented outcome value in the motor and the classical tasks. However, the probability weighting functions for motor and classical tasks were markedly and significantly different. Those for the classical task showed a typical tendency to overweight small probabilities and underweight large probabilities, and those for the motor task showed the opposite pattern of probability distortion. This outcome also accounts for the increased risk-seeking observed in the motor tasks of experiment 1. We conclude that the same subject distorts probability, but not value, differently in making identical decisions in motor and classical form.},
	Author = {Wu, Shih-Wei and Delgado, Mauricio R. and Maloney, Laurence T.},
	Doi = {10.1073/PNAS.0900102106},
	File = {:Users/montagnini.a/Library/Application Support/Mendeley Desktop/Downloaded/Wu, Delgado, Maloney - 2009 - Economic decision-making compared with an equivalent motor task.pdf:pdf},
	Issn = {0027-8424},
	Journal = {Proceedings of the National Academy of Sciences},
	Month = {apr},
	Number = {15},
	Pages = {6088--6093},
	Pmid = {19332799},
	Publisher = {National Academy of Sciences},
	Title = {{Economic decision-making compared with an equivalent motor task}},
	Url = {https://www.pnas.org/content/106/15/6088},
	Volume = {106},
	Year = {2009},
	Bdsk-Url-1 = {https://www.pnas.org/content/106/15/6088},
	Bdsk-Url-2 = {https://doi.org/10.1073/PNAS.0900102106}}

@article{Yu2009,
	Abstract = {In a variety of behavioral tasks, subjects exhibit an automatic and apparently sub- optimal sequential effect: they respond more rapidly and accurately to a stimulus if it reinforces a local pattern in stimulus history, such as a string of repetitions or alternations, compared to when it violates such a pattern. This is often the case even if the local trends arise by chance in the context of a randomized design, such that stimulus history has no real predictive power. In thiswork,we use a normative Bayesian framework to examine the hypothesis that such idiosyncrasies may re- flect the inadvertent engagement ofmechanisms critical for adapting to a changing environment. We show that prior belief in non-stationarity can induce experimen- tally observed sequential effects in an otherwise Bayes-optimal algorithm. The Bayesian algorithm is shown to be well approximated by linear-exponential filter- ing of past observations, a feature also apparent in the behavioral data. We derive an explicit relationship between the parameters and computations of the exact Bayesian algorithm and those of the approximate linear-exponential filter. Since the latter is equivalent to a leaky-integration process, a commonly used model of neuronal dynamics underlying perceptual decision-making and trial-to-trial de- pendencies, our model provides a principled account of why such dynamics are useful. We also show that parameter-tuning of the leaky-integration process is possible, using stochastic gradient descent based only on the noisy binary inputs. This is a proof of concept that not only can neurons implement near-optimal pre- diction based on standard neuronal dynamics, but that they can also learn to tune the processing parameters without explicitly representing probabilities.},
	Author = {Yu, Angela J and Cohen, Jonathan D},
	Journal = {Advances in Neural Information Processing Systems},
	Pages = {1873--80},
	Pmid = {26412953},
	Title = {{Sequential effects: Superstition or rational behavior?}},
	Url = {http://www.pni.princeton.edu/ncc/publications/2009/YuNIPS09.pdf},
	Volume = {21},
	Year = {2009},
	Bdsk-Url-1 = {http://www.pni.princeton.edu/ncc/publications/2009/YuNIPS09.pdf}}

@article{Wacongne2012,
	Abstract = {The mismatch negativity (MMN) is thought to index the activation of specialized neural networks for active prediction and deviance detection. However, a detailed neuronal model of the neurobiological mechanisms underlying the MMN is still lacking, and its computational foundations remain debated. We propose here a detailed neuronal model of auditory cortex, based on predictive coding, that accounts for the critical features of MMN. The model is entirely composed of spiking excitatory and inhibitory neurons interconnected in a layered cortical architecture with distinct input, predictive, and prediction error units. A spike-timing dependent learning rule, relying upon NMDA receptor synaptic transmission, allows the network to adjust its internal predictions and use a memory of the recent past inputs to anticipate on future stimuli based on transition statistics. We demonstrate that this simple architecture can account for the major empirical properties of the MMN. These include a frequency-dependent response to rare deviants, a response to unexpected repeats in alternating sequences (ABABAA{\ldots}), a lack of consideration of the global sequence context, a response to sound omission, and a sensitivity of the MMN to NMDA receptor antagonists. Novel predictions are presented, and a new magnetoencephalography experiment in healthy human subjects is presented that validates our key hypothesis: the MMN results from active cortical prediction rather than passive synaptic habituation.},
	Author = {Wacongne, C. and Changeux, J.-P. and Dehaene, S.},
	Doi = {10.1523/JNEUROSCI.5003-11.2012},
	Issn = {0270-6474},
	Journal = {Journal of Neuroscience},
	Month = {mar},
	Number = {11},
	Pages = {3665--3678},
	Pmid = {22423089},
	Title = {{A Neuronal Model of Predictive Coding Accounting for the Mismatch Negativity}},
	Url = {http://www.ncbi.nlm.nih.gov/pubmed/22423089 http://www.jneurosci.org/cgi/doi/10.1523/JNEUROSCI.5003-11.2012},
	Volume = {32},
	Year = {2012},
	Bdsk-Url-1 = {http://www.ncbi.nlm.nih.gov/pubmed/22423089%20http://www.jneurosci.org/cgi/doi/10.1523/JNEUROSCI.5003-11.2012},
	Bdsk-Url-2 = {https://doi.org/10.1523/JNEUROSCI.5003-11.2012}}

@article{WolpertGhahramani2000,
	Abstract = {Unifying principles of movement have emerged from the computational study of motor control. We review several of these principles and show how they apply to processes such as motor planning, control, estimation, prediction and learning. Our goal is to demonstrate how specific models emerging from the computational approach provide a theoretical framework for movement neuroscience.},
	Author = {Wolpert, Daniel M. and Ghahramani, Zoubin},
	Doi = {10.1038/81497},
	Issn = {1097-6256},
	Journal = {Nature Neuroscience},
	Month = {nov},
	Number = {S11},
	Pages = {1212--1217},
	Pmid = {11127840},
	Title = {{Computational principles of movement neuroscience}},
	Url = {http://www.ncbi.nlm.nih.gov/pubmed/11127840 http://www.nature.com/articles/nn1100{\_}1212},
	Volume = {3},
	Year = {2000},
	Bdsk-Url-1 = {http://www.ncbi.nlm.nih.gov/pubmed/11127840%20http://www.nature.com/articles/nn1100%7B%5C_%7D1212},
	Bdsk-Url-2 = {https://doi.org/10.1038/81497}}

@article{WallmanFuchs1998,
	Abstract = {The brain maintains the accuracy of saccadic eye movements by adjusting saccadic amplitude relative to the target distance (i.e., saccade gain) on the basis of the performance of recent saccades. If an experimenter surreptitiously moves the target backward during each saccade, thereby causing the eyes to land beyond their targets, saccades undergo a gradual gain reduction. The error signal driving this conventional saccadic gain adaptation could be either visual (the postsaccadic distance of the target from the fovea) or motoric (the direction and size of the corrective saccade that brings the eye onto the back-stepped target). Similarly, the adaptation itself might be a motor adjustment (change in the size of saccade for a given perceived target distance) or a visual remapping (change in the perceived target distance). We studied these possibilities in experiments both with rhesus macaques and with humans. To test whether the error signal is motoric, we used a paradigm devised by Heiner Deubel. The Deubel paradigm differed from the conventional adaptation paradigm in that the backward step that occurred during the saccade was brief, and the target then returned to its original displaced location. This ploy replaced most of the usual backward corrective saccades with forward ones. Nevertheless, saccadic gain gradually decreased over hundreds of trials. Therefore, we conclude that the direction of saccadic gain adaptation is not determined by the direction of corrective saccades. To test whether gain adaptation is a manifestation of a static visual remapping, we decreased the gain of 10 degrees horizontal saccades by conventional adaptation and then tested the gain to targets appearing at retinal locations unused during adaptation. To make the target appear in such "virgin territory," we had it jump first vertically and then 10 degrees horizontally; both jumps were completed and the target spot extinguished before saccades were made sequentially to the remembered target locations. Conventional adaptation decreased the gain of the second, horizontal saccade even though the target was in a nonadapted retinal location. In contrast, the horizontal component of oblique saccades made directly to the same virgin location showed much less gain decrease, suggesting that the adaptation is specific to saccade direction rather than to target location. Thus visual remapping cannot account for the entire reduction of saccadic gain. We conclude that saccadic gain adaptation involves an error signal that is primarily visual, not motor, but that the adaptation itself is primarily motor, not visual.},
	Author = {Wallman, Josh and Fuchs, Albert F.},
	Doi = {10.1152/jn.1998.80.5.2405},
	Issn = {0022-3077},
	Journal = {Journal of Neurophysiology},
	Month = {nov},
	Number = {5},
	Pages = {2405--2416},
	Pmid = {9819252},
	Title = {{Saccadic Gain Modification: Visual Error Drives Motor Adaptation}},
	Url = {http://www.ncbi.nlm.nih.gov/pubmed/9819252 http://www.physiology.org/doi/10.1152/jn.1998.80.5.2405},
	Volume = {80},
	Year = {1998},
	Bdsk-Url-1 = {http://www.ncbi.nlm.nih.gov/pubmed/9819252%20http://www.physiology.org/doi/10.1152/jn.1998.80.5.2405},
	Bdsk-Url-2 = {https://doi.org/10.1152/jn.1998.80.5.2405}}