Visual recency bias is explained by a mixture model of internal representations

Human bias towards more recent events is a common and well-studied phenomenon. Recent studies in visual perception have shown that this recency bias persists even when past events contain no information about the future. Reasons for this suboptimal behaviour are not well understood and the internal model that leads people to exhibit recency bias is unknown. Here we use a well-known orientation estimation task to frame the human recency bias in terms of incremental Bayesian inference. We show that the only Bayesian model capable of explaining the recency bias relies on a weighted mixture of past states. Furthermore, we suggest that this mixture model is a consequence of participants’ failure to infer a model for data in visual short term memory, and reflects the nature of the internal representations used in the task.

[1]  Zoubin Ghahramani,et al.  Probabilistic machine learning and artificial intelligence , 2015, Nature.

[2]  Richard F Murray,et al.  Cue combination on the circle and the sphere. , 2010, Journal of vision.

[3]  Rajesh P. N. Rao,et al.  An optimal estimation approach to visual perception and learning , 1999, Vision Research.

[4]  Ignacio Serrano-Pedraza,et al.  Moderate acute alcohol intoxication has minimal effect on surround suppression measured with a motion direction discrimination task. , 2015, Journal of vision.

[5]  Peter Dayan,et al.  A Probabilistic Palimpsest Model of Visual Short-term Memory , 2015, PLoS Comput. Biol..

[6]  Kristjan Kalm.  Recency-weighted Markovian inference , 2017, ArXiv.

[7]  F. D. Lange,et al.  Opposite Effects of Recent History on Perception and Decision , 2017, Current Biology.

[8]  P. Berkes,et al.  Statistically Optimal Perception and Learning: from Behavior to Neural Representations , 2022 .

[9]  W. Wagenaar,et al.  The perception of randomness , 1991 .

[10]  D. Whitney,et al.  Serial dependence in visual perception , 2011 .

[11]  Ivan Markovic,et al.  Speaker localization and tracking with a microphone array on a mobile robot using von Mises distribution and particle filtering , 2010, Robotics Auton. Syst..

[12]  Gerhard Kurz,et al.  Recursive nonlinear filtering for angular data based on circular distributions , 2013, 2013 American Control Conference.

[13]  John R. Anderson,et al.  Reflections of the Environment in Memory Form of the Memory Functions , 2022 .

[14]  Eero P. Simoncelli,et al.  Cardinal rules: Visual orientation perception reflects knowledge of environmental statistics , 2011, Nature Neuroscience.

[15]  D. Whitney,et al.  Serial Dependence in the Perception of Faces , 2014, Current Biology.

[16]  Bjorn Hubert-Wallander,et al.  Not all summary statistics are made equal: Evidence from extracting summaries across time. , 2015, Journal of vision.

[17]  R. E. Kalman,et al.  New Results in Linear Filtering and Prediction Theory , 1961 .

[18]  John R. Anderson,et al.  Human memory: An adaptive perspective. , 1989 .

[19]  Ivan Petrović,et al.  Speaker Localization and Tracking in Mobile Robot Environment Using a Microphone Array ? , 2010 .

[20]  A. Raftery.  A model for high-order Markov chains , 1985 .

[21]  D. Burr,et al.  Compressive mapping of number to space reflects dynamic encoding mechanisms, not static logarithmic transform , 2014, Proceedings of the National Academy of Sciences.

[22]  N. Cowan.  The magical number 4 in short-term memory: A reconsideration of mental storage capacity , 2001, Behavioral and Brain Sciences.

[23]  Gerhard Kurz,et al.  Recursive Bayesian filtering in circular state spaces , 2015, IEEE Aerospace and Electronic Systems Magazine.

[24]  David Whitney,et al.  The perceived stability of scenes: serial dependence in ensemble representations , 2017, Scientific Reports.

[25]  D. Burr,et al.  Vision: Efficient Adaptive Coding , 2014, Current Biology.

[26]  R. Sekuler,et al.  Similarity-based distortion of visual short-term memory is due to perceptual averaging , 2014, Vision Research.

[27]  Zoubin Ghahramani,et al.  Computational principles of movement neuroscience , 2000, Nature Neuroscience.

[28]  Simo Särkkä.  Bayesian Filtering and Smoothing: Bayesian filtering equations and exact solutions , 2013 .

[29]  Timothy F. Brady,et al.  A review of visual memory capacity: Beyond individual items and toward structured representations. , 2011, Journal of vision.

[30]  J. Tenenbaum,et al.  Probabilistic models of cognition: exploring representations and inductive biases , 2010, Trends in Cognitive Sciences.

[31]  A. Raftery,et al.  The Mixture Transition Distribution Model for High-Order Markov Chains and Non-Gaussian Time Series , 2002 .

[32]  A. Pouget,et al.  Probabilistic brains: knowns and unknowns , 2013, Nature Neuroscience.

[33]  Daniel P. Bliss,et al.  Serial Dependence across Perception, Attention, and Memory , 2017, Trends in Cognitive Sciences.