scholarly journals Attention as a Bayesian inference process

2011 ◽  
Author(s):  
Sharat Chikkerur ◽  
Thomas Serre ◽  
Cheston Tan ◽  
Tomaso Poggio
Keyword(s):  
Bayesian Inference ◽  
Inference Process

scholarly journals The self as a prior for the other: social learning under paranoia

2021 ◽  
Author(s):  
Joseph M Barnby ◽  
Nichola Raihani ◽  
Peter Dayan
Keyword(s):  
Bayesian Inference ◽  
Social Interactions ◽  
Social Preferences ◽  
Value Orientation ◽  
Social Value ◽  
The Self ◽  
The Other ◽  
Social Value Orientation ◽  
Inference Process ◽  
Social Partners

To benefit from social interactions, people need to predict how their social partners will behave. Such predictions arise through integrating prior expectations with evidence from observations, but where the priors come from and whether they influence the integration is not clear. Furthermore, this process can be affected by factors such as paranoia, in which the tendency to form biased impressions of others is common. Using a modified social value orientation (SVO) task in a large online sample (n=697), we showed that participants used a Bayesian inference process to learn about partners, with priors that were based on their own preferences. Paranoia was associated with preferences for earning more than a partner and less flexible beliefs regarding a partner’s social preferences. Alignment between the preferences of participants and their partners was associated with better predictions and with reduced attributions of harmful intent to partners.

scholarly journals Tactile length contraction as Bayesian inference

2016 ◽  
Vol 116 (2) ◽  
pp. 369-379 ◽  
Author(s):  
Jonathan Tong ◽  
Vy Ngo ◽  
Daniel Goldreich
Keyword(s):  
Bayesian Inference ◽  
Inference Process ◽  
Low Velocity ◽  
Temporal Separation ◽  
Length Contraction ◽  
Significant Length ◽  
Reference Distance ◽  
Fixed Reference ◽  
Observer Model ◽  
The Brain

To perceive, the brain must interpret stimulus-evoked neural activity. This is challenging: The stochastic nature of the neural response renders its interpretation inherently uncertain. Perception would be optimized if the brain used Bayesian inference to interpret inputs in light of expectations derived from experience. Bayesian inference would improve perception on average but cause illusions when stimuli violate expectation. Intriguingly, tactile, auditory, and visual perception are all prone to length contraction illusions, characterized by the dramatic underestimation of the distance between punctate stimuli delivered in rapid succession; the origin of these illusions has been mysterious. We previously proposed that length contraction illusions occur because the brain interprets punctate stimulus sequences using Bayesian inference with a low-velocity expectation. A novel prediction of our Bayesian observer model is that length contraction should intensify if stimuli are made more difficult to localize. Here we report a tactile psychophysical study that tested this prediction. Twenty humans compared two distances on the forearm: a fixed reference distance defined by two taps with 1-s temporal separation and an adjustable comparison distance defined by two taps with temporal separation t ≤ 1 s. We observed significant length contraction: As t was decreased, participants perceived the two distances as equal only when the comparison distance was made progressively greater than the reference distance. Furthermore, the use of weaker taps significantly enhanced participants' length contraction. These findings confirm the model's predictions, supporting the view that the spatiotemporal percept is a best estimate resulting from a Bayesian inference process.

scholarly journals The material-weight illusion is a Bayes-optimal percept under competing density priors

2018 ◽  
Author(s):  
Megan A K Peters ◽  
Ling-Qi Zhang ◽  
Ladan Shams
Keyword(s):  
Bayesian Inference ◽  
Explanatory Power ◽  
Weight Perception ◽  
Human Perception ◽  
Hierarchical Bayesian ◽  
Inference Process ◽  
Haptic Information ◽  
Principled Explanation ◽  
Hierarchical Bayesian Inference ◽  
Weight Illusion

The material-weight illusion (MWI) is one example in a class of weight perception illusions that seem to defy principled explanation. In this illusion, when an observer lifts two objects of the same size and mass, but that appear to be made of different materials, the denser-looking (e.g., metal-look) object is perceived as lighter than the less-dense-looking (e.g., polystyrene-look) object. Like the size-weight illusion (SWI), this perceptual illusion occurs in the opposite direction of predictions from an optimal Bayesian inference process, which predicts that the denser-looking object should be perceived as heavier, not lighter. The presence of this class of illusions challenges the often-tacit assumption that Bayesian inference holds universal explanatory power to describe human perception across (nearly) all domains: If an entire class of perceptual illusions cannot be captured by the Bayesian framework, how could it be argued that human perception truly follows optimal inference? However, we recently showed that the SWI can be explained by an optimal hierarchical Bayesian causal inference process (Peters, Ma & Shams, 2016) in which the observer uses haptic information to arbitrate among competing hypotheses about objects’ possible density relationship. Here we extend the model to demonstrate that it can readily explain the MWI as well. That hierarchical Bayesian inference can explain both illusions strongly suggests that even puzzling percepts arise from optimal inference processes.

Automation reliability and trust: A Bayesian inference approach

2018 ◽  
Vol 62 (1) ◽  
pp. 202-206 ◽  
Author(s):  
Chenlan Wang ◽  
Chongjie Zhang ◽  
X. Jessie Yang
Keyword(s):  
Bayesian Inference ◽  
System Reliability ◽  
Probabilistic Inference ◽  
Inference Process ◽  
Bayesian Estimates ◽  
Trust In Automation ◽  
Human Operators

Research shows that over repeated interactions with automation, human operators are able to learn how reliable the automation is and update their trust in automation. The goal of the present study is to investigate if this learning and inference process approximately follow the principle of Bayesian probabilistic inference. First, we applied Bayesian inference to estimate human operators’ perceived system reliability and found high correlations between the Bayesian estimates and the perceived reliability for the majority of the participants. We then correlated the Bayesian estimates with human operators’ reported trust and found moderate correlations for a large portion of the participants. Our results suggest that human operators’ learning and inference process for automation reliability can be approximated by Bayesian inference.

scholarly journals Moving in time: Bayesian causal inference explains movement coordination to auditory beats

2014 ◽  
Vol 281 (1786) ◽  
pp. 20140751 ◽  
Author(s):  
Mark T. Elliott ◽  
Alan M. Wing ◽  
Andrew E. Welchman
Keyword(s):  
Bayesian Inference ◽  
Causal Inference ◽  
The Other ◽  
Motor Timing ◽  
Single Event ◽  
Inference Process ◽  
Movement Coordination ◽  
Movement Timing ◽  
Timing Errors ◽  
Auditory Signals

Many everyday skilled actions depend on moving in time with signals that are embedded in complex auditory streams (e.g. musical performance, dancing or simply holding a conversation). Such behaviour is apparently effortless; however, it is not known how humans combine auditory signals to support movement production and coordination. Here, we test how participants synchronize their movements when there are potentially conflicting auditory targets to guide their actions. Participants tapped their fingers in time with two simultaneously presented metronomes of equal tempo, but differing in phase and temporal regularity. Synchronization therefore depended on integrating the two timing cues into a single-event estimate or treating the cues as independent and thereby selecting one signal over the other. We show that a Bayesian inference process explains the situations in which participants choose to integrate or separate signals, and predicts motor timing errors. Simulations of this causal inference process demonstrate that this model provides a better description of the data than other plausible models. Our findings suggest that humans exploit a Bayesian inference process to control movement timing in situations where the origin of auditory signals needs to be resolved.

scholarly journals The material-weight illusion is a Bayes-optimal percept under competing density priors

2018 ◽  
Author(s):  
Megan A K Peters ◽  
Ling-Qi Zhang ◽  
Ladan Shams
Keyword(s):  
Bayesian Inference ◽  
Explanatory Power ◽  
Weight Perception ◽  
Human Perception ◽  
Hierarchical Bayesian ◽  
Inference Process ◽  
Haptic Information ◽  
Principled Explanation ◽  
Hierarchical Bayesian Inference ◽  
Weight Illusion

The material-weight illusion (MWI) is one example in a class of weight perception illusions that seem to defy principled explanation. In this illusion, when an observer lifts two objects of the same size and mass, but that appear to be made of different materials, the denser-looking (e.g., metal-look) object is perceived as lighter than the less-dense-looking (e.g., polystyrene-look) object. Like the size-weight illusion (SWI), this perceptual illusion occurs in the opposite direction of predictions from an optimal Bayesian inference process, which predicts that the denser-looking object should be perceived as heavier, not lighter. The presence of this class of illusions challenges the often-tacit assumption that Bayesian inference holds universal explanatory power to describe human perception across (nearly) all domains: If an entire class of perceptual illusions cannot be captured by the Bayesian framework, how could it be argued that human perception truly follows optimal inference? However, we recently showed that the SWI can be explained by an optimal hierarchical Bayesian causal inference process (Peters, Ma & Shams, 2016) in which the observer uses haptic information to arbitrate among competing hypotheses about objects’ possible density relationship. Here we extend the model to demonstrate that it can readily explain the MWI as well. That hierarchical Bayesian inference can explain both illusions strongly suggests that even puzzling percepts arise from optimal inference processes.

scholarly journals The material-weight illusion is a Bayes-optimal percept under competing density priors

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5760 ◽  
Author(s):  
Megan A.K. Peters ◽  
Ling-Qi Zhang ◽  
Ladan Shams
Keyword(s):  
Bayesian Inference ◽  
Explanatory Power ◽  
Weight Perception ◽  
Human Perception ◽  
Hierarchical Bayesian ◽  
Inference Process ◽  
Haptic Information ◽  
Principled Explanation ◽  
Hierarchical Bayesian Inference ◽  
Weight Illusion

The material-weight illusion (MWI) is one example in a class of weight perception illusions that seem to defy principled explanation. In this illusion, when an observer lifts two objects of the same size and mass, but that appear to be made of different materials, the denser-looking (e.g., metal-look) object is perceived as lighter than the less-dense-looking (e.g., polystyrene-look) object. Like the size-weight illusion (SWI), this perceptual illusion occurs in the opposite direction of predictions from an optimal Bayesian inference process, which predicts that the denser-looking object should be perceived as heavier, not lighter. The presence of this class of illusions challenges the often-tacit assumption that Bayesian inference holds universal explanatory power to describe human perception across (nearly) all domains: If an entire class of perceptual illusions cannot be captured by the Bayesian framework, how could it be argued that human perception truly follows optimal inference? However, we recently showed that the SWI can be explained by an optimal hierarchical Bayesian causal inference process (Peters, Ma & Shams, 2016) in which the observer uses haptic information to arbitrate among competing hypotheses about objects’ possible density relationship. Here we extend the model to demonstrate that it can readily explain the MWI as well. That hierarchical Bayesian inference can explain both illusions strongly suggests that even puzzling percepts arise from optimal inference processes.

scholarly journals The Size-Weight Illusion is not anti-Bayesian after all: a unifying Bayesian account

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2124 ◽  
Author(s):  
Megan A.K. Peters ◽  
Wei Ji Ma ◽  
Ladan Shams
Keyword(s):  
Bayesian Inference ◽  
Bayesian Model ◽  
Human Perception ◽  
Inference Process ◽  
Bayesian Approaches ◽  
Density Relationship ◽  
Final Estimate ◽  
Weight Illusion

When we lift two differently-sized but equally-weighted objects, we expect the larger to be heavier, but the smallerfeelsheavier. However, traditional Bayesian approaches with “larger is heavier” priors predict the smaller object should feellighter; this Size-Weight Illusion (SWI) has thus been labeled “anti-Bayesian” and has stymied psychologists for generations. We propose that previous Bayesian approaches neglect the brain’s inference process about density. In our Bayesian model, objects’ perceived heaviness relationship is based on both their size and inferred density relationship: observers evaluate competing, categorical hypotheses about objects’ relative densities, the inference about which is then used to produce the final estimate of weight. The model can qualitatively and quantitatively reproduce the SWI and explain other researchers’ findings, and also makes a novel prediction, which we confirmed. This same computational mechanism accounts for other multisensory phenomena and illusions; that the SWI follows the same process suggests that competitive-prior Bayesian inference can explain human perception across many domains.

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