scholarly journals A confirmation bias in perceptual decision-making due to hierarchical approximate inference

2018 ◽  
Author(s):  
Richard D. Lange ◽  
Ankani Chattoraj ◽  
Jeffrey M. Beck ◽  
Jacob L. Yates ◽  
Ralf M. Haefner
Keyword(s):  
Model Comparison ◽  
Empirical Studies ◽  
Temporal Integration ◽  
Sensory Information ◽  
Quantitative Model ◽  
Perceptual Decision Making ◽  
Sensory Evidence ◽  
Hierarchical Nature ◽  
Integration Strategies ◽  
The Brain

AbstractHuman decisions are known to be systematically biased. A prominent example of such a bias occurs when integrating a sequence of sensory evidence over time. Previous empirical studies differ in the nature of the bias they observe, ranging from favoring early evidence (primacy), to favoring late evidence (recency). Here, we present a unifying framework that explains these biases and makes novel psychophysical and neurophysiological predictions. By explicitly modeling both the approximate and the hierarchical nature of inference in the brain, we show that temporal biases depend on the balance between “sensory information” and “category information” in the stimulus. Finally, we present new data from a human psychophysics task that confirms a critical prediction of our framework showing that effective temporal integration strategies can be robustly changed within each subject, and that allows us to exclude alternate explanations through quantitative model comparison.

scholarly journals Peak-at-end rule: adaptive mechanism predicts time-dependent decision weighting

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Ryuto Yashiro ◽  
Isamu Motoyoshi
Keyword(s):  
Decision Process ◽  
Temporal Dynamics ◽  
Temporal Integration ◽  
Time Dependent ◽  
Adaptive Mechanism ◽  
Perceptual Decision Making ◽  
Estimation Task ◽  
Leaky Integrator ◽  
Sensory Evidence ◽  
The Brain

Abstract Humans make decisions under various natural circumstances, integrating multiple pieces of information that are distributed over space and time. Although psychophysical and physiological studies have investigated temporal dynamics underlying perceptual decision making, weighting profiles for inliers and outliers during temporal integration have yet to be fully investigated in most studies. Here, we examined the temporal weighting profile of a computational model characterized by a leaky integrator of sensory evidence. As a corollary of its leaky nature, the model predicts the recency effect and overweights outlying elements around the end of the stream. Moreover, we found that the model underweights outlying values occurring earlier in the stream (i.e., robust averaging). We also show that human observers exhibit exactly the same weighting profile in an average estimation task. These findings suggest that the adaptive decision process in the brain results in the time-dependent decision weighting, the “peak-at-end” rule, rather than the peak-end rule in behavioral economics.

scholarly journals Cortical circuit-based lossless neural integrator for perceptual decision-making

2017 ◽  
Author(s):  
Jung H. Lee ◽  
Joji Tsunada ◽  
Sujith Vijayan ◽  
Yale E. Cohen
Keyword(s):  
Decision Making ◽  
Computational Models ◽  
Empirical Studies ◽  
Activity Patterns ◽  
Sensory Information ◽  
Cortical Circuits ◽  
Perceptual Decision Making ◽  
Perceptual Decision ◽  
Sensory Evidence ◽  
Cortical Circuit

AbstractThe intrinsic uncertainty of sensory information (i.e., evidence) does not necessarily deter an observer from making a reliable decision. Indeed, uncertainty can be reduced by integrating (accumulating) incoming sensory evidence. It is widely thought that this accumulation is instantiated via recurrent rate-code neural networks. Yet, these networks do not fully explain important aspects of perceptual decision-making, such as a subject’s ability to retain accumulated evidence during temporal gaps in the sensory evidence. Here, we utilized computational models to show that cortical circuits can switch flexibly between ‘retention’ and ‘integration’ modes during perceptual decision-making. Further, we found that, depending on how the sensory evidence was readout, we could simulate ‘stepping’ and ‘ramping’ activity patterns, which may be analogous to those seen in different studies of decision-making in the primate parietal cortex. This finding may reconcile these previous empirical studies because it suggests these two activity patterns emerge from the same mechanism.

scholarly journals Transient neuronal suppression for exploitation of new sensory evidence

2022 ◽  
Vol 13 (1) ◽  
Author(s):  
Maxwell Shinn ◽  
Daeyeol Lee ◽  
John D. Murray ◽  
Hyojung Seo
Keyword(s):  
Decision Making ◽  
Neural Activity ◽  
Temporal Integration ◽  
Motor Preparation ◽  
Stimulus Onset ◽  
Frontal Eye Field ◽  
Perceptual Decision Making ◽  
Behavioral Studies ◽  
The Face ◽  
Sensory Evidence

AbstractIn noisy but stationary environments, decisions should be based on the temporal integration of sequentially sampled evidence. This strategy has been supported by many behavioral studies and is qualitatively consistent with neural activity in multiple brain areas. By contrast, decision-making in the face of non-stationary sensory evidence remains poorly understood. Here, we trained monkeys to identify and respond via saccade to the dominant color of a dynamically refreshed bicolor patch that becomes informative after a variable delay. Animals’ behavioral responses were briefly suppressed after evidence changes, and many neurons in the frontal eye field displayed a corresponding dip in activity at this time, similar to that frequently observed after stimulus onset but sensitive to stimulus strength. Generalized drift-diffusion models revealed consistency of behavior and neural activity with brief suppression of motor output, but not with pausing or resetting of evidence accumulation. These results suggest that momentary arrest of motor preparation is important for dynamic perceptual decision making.

scholarly journals United we sense, divided we fail: context-driven perception of ambiguous visual stimuli

2012 ◽  
Vol 367 (1591) ◽  
pp. 932-941 ◽  
Author(s):  
P. C. Klink ◽  
R. J. A. van Wezel ◽  
R. van Ee
Keyword(s):  
Contextual Information ◽  
Visual Stimuli ◽  
Sensory Information ◽  
Contextual Influences ◽  
Perceptual Decision Making ◽  
Response Characteristics ◽  
Conflicting Evidence ◽  
Neuronal Mechanisms ◽  
Neuronal Representation ◽  
The Brain

Ambiguous visual stimuli provide the brain with sensory information that contains conflicting evidence for multiple mutually exclusive interpretations. Two distinct aspects of the phenomenological experience associated with viewing ambiguous visual stimuli are the apparent stability of perception whenever one perceptual interpretation is dominant, and the instability of perception that causes perceptual dominance to alternate between perceptual interpretations upon extended viewing. This review summarizes several ways in which contextual information can help the brain resolve visual ambiguities and construct temporarily stable perceptual experiences. Temporal context through prior stimulation or internal brain states brought about by feedback from higher cortical processing levels may alter the response characteristics of specific neurons involved in rivalry resolution. Furthermore, spatial or crossmodal context may strengthen the neuronal representation of one of the possible perceptual interpretations and consequently bias the rivalry process towards it. We suggest that contextual influences on perceptual choices with ambiguous visual stimuli can be highly informative about the neuronal mechanisms of context-driven inference in the general processes of perceptual decision-making.

scholarly journals Evidence against perfect integration of sensory information during perceptual decision making

2016 ◽  
Vol 115 (2) ◽  
pp. 915-930 ◽  
Author(s):  
Matthew A. Carland ◽  
Encarni Marcos ◽  
David Thura ◽  
Paul Cisek
Keyword(s):  
Decision Making ◽  
Neural Activity ◽  
Temporal Integration ◽  
Sensory Information ◽  
Reaction Times ◽  
Perceptual Decision Making ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Perceptual Decision ◽  
Low Pass

Perceptual decision making is often modeled as perfect integration of sequential sensory samples until the accumulated total reaches a fixed decision bound. In that view, the buildup of neural activity during perceptual decision making is attributed to temporal integration. However, an alternative explanation is that sensory estimates are computed quickly with a low-pass filter and combined with a growing signal reflecting the urgency to respond and it is the latter that is primarily responsible for neural activity buildup. These models are difficult to distinguish empirically because they make similar predictions for tasks in which sensory information is constant within a trial, as in most previous studies. Here we presented subjects with a variant of the classic constant-coherence motion discrimination (CMD) task in which we inserted brief motion pulses. We examined the effect of these pulses on reaction times (RTs) in two conditions: 1) when the CMD trials were blocked and subjects responded quickly and 2) when the same CMD trials were interleaved among trials of a variable-motion coherence task that motivated slower decisions. In the blocked condition, early pulses had a strong effect on RTs but late pulses did not, consistent with both models. However, when subjects slowed their decision policy in the interleaved condition, later pulses now became effective while early pulses lost their efficacy. This last result contradicts models based on perfect integration of sensory evidence and implies that motion signals are processed with a strong leak, equivalent to a low-pass filter with a short time constant.

scholarly journals Neural Correlates of Evidence and Urgency During Human Perceptual Decision-Making in Dynamically Changing Conditions

2020 ◽  
Vol 30 (10) ◽  
pp. 5471-5483
Author(s):  
Y Yau ◽  
M Dadar ◽  
M Taylor ◽  
Y Zeighami ◽  
L K Fellows ◽  
...  
Keyword(s):  
Decision Making ◽  
Computational Models ◽  
Sensory Information ◽  
Neural Correlates ◽  
Primate Research ◽  
Perceptual Decision Making ◽  
Emotional Information ◽  
Perceptual Decision ◽  
The Face ◽  
The Brain

Abstract Current models of decision-making assume that the brain gradually accumulates evidence and drifts toward a threshold that, once crossed, results in a choice selection. These models have been especially successful in primate research; however, transposing them to human fMRI paradigms has proved it to be challenging. Here, we exploit the face-selective visual system and test whether decoded emotional facial features from multivariate fMRI signals during a dynamic perceptual decision-making task are related to the parameters of computational models of decision-making. We show that trial-by-trial variations in the pattern of neural activity in the fusiform gyrus reflect facial emotional information and modulate drift rates during deliberation. We also observed an inverse-urgency signal based in the caudate nucleus that was independent of sensory information but appeared to slow decisions, particularly when information in the task was ambiguous. Taken together, our results characterize how decision parameters from a computational model (i.e., drift rate and urgency signal) are involved in perceptual decision-making and reflected in the activity of the human brain.

scholarly journals Illusory sound texture reveals multi-second statistical completion in auditory scene analysis

2019 ◽  
Author(s):  
Richard McWalter ◽  
Josh H. McDermott
Keyword(s):  
Sensory Information ◽  
Auditory Scene Analysis ◽  
Scene Analysis ◽  
Sound Sources ◽  
Stochastic Nature ◽  
Perceptual Completion ◽  
Auditory Scene ◽  
Stable Representation ◽  
Sensory Evidence ◽  
The Brain

AbstractSound sources in the world are experienced as stable even when intermittently obscured, implying perceptual completion mechanisms that “fill in” missing sensory information. We demonstrate a filling-in phenomenon in which the brain extrapolates the statistics of background sounds (textures) over periods of several seconds when they are interrupted by another sound, producing vivid percepts of illusory texture. The effect differs from previously described completion effects in that 1) the extrapolated sound must be defined statistically given the stochastic nature of texture, and 2) in lasting much longer, enabling introspection and facilitating assessment of the underlying representation. Illusory texture appeared to be integrated into texture statistic estimates indistinguishably from actual texture, suggesting that it is represented similarly to actual texture. The illusion appears to represent an inference about whether the background is likely to continue during concurrent sounds, providing a stable representation of the environment despite unstable sensory evidence.

Decision making by urgency gating: theory and experimental support

2012 ◽  
Vol 108 (11) ◽  
pp. 2912-2930 ◽  
Author(s):  
David Thura ◽  
Julie Beauregard-Racine ◽  
Charles-William Fradet ◽  
Paul Cisek
Keyword(s):  
Decision Making ◽  
Neural Activity ◽  
Time Window ◽  
Sensory Information ◽  
Natural Conditions ◽  
Accuracy Criterion ◽  
Sensory Evidence ◽  
Dot Motion ◽  
The Brain ◽  
Random Dot Motion

It is often suggested that decisions are made when accumulated sensory information reaches a fixed accuracy criterion. This is supported by many studies showing a gradual build up of neural activity to a threshold. However, the proposal that this build up is caused by sensory accumulation is challenged by findings that decisions are based on information from a time window much shorter than the build-up process. Here, we propose that in natural conditions where the environment can suddenly change, the policy that maximizes reward rate is to estimate evidence by accumulating only novel information and then compare the result to a decreasing accuracy criterion. We suggest that the brain approximates this policy by multiplying an estimate of sensory evidence with a motor-related urgency signal and that the latter is primarily responsible for neural activity build up. We support this hypothesis using human behavioral data from a modified random-dot motion task in which motion coherence changes during each trial.

scholarly journals Illusory sound texture reveals multi-second statistical completion in auditory scene analysis

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Richard McWalter ◽  
Josh H. McDermott
Keyword(s):  
Sensory Information ◽  
Auditory Scene Analysis ◽  
Scene Analysis ◽  
Sound Sources ◽  
Stochastic Nature ◽  
Statistical Representation ◽  
Perceptual Completion ◽  
Auditory Scene ◽  
Sensory Evidence ◽  
The Brain

Abstract Sound sources in the world are experienced as stable even when intermittently obscured, implying perceptual completion mechanisms that “fill in” missing sensory information. We demonstrate a filling-in phenomenon in which the brain extrapolates the statistics of background sounds (textures) over periods of several seconds when they are interrupted by another sound, producing vivid percepts of illusory texture. The effect differs from previously described completion effects in that 1) the extrapolated sound must be defined statistically given the stochastic nature of texture, and 2) the effect lasts much longer, enabling introspection and facilitating assessment of the underlying representation. Illusory texture biases subsequent texture statistic estimates indistinguishably from actual texture, suggesting that it is represented similarly to actual texture. The illusion appears to represent an inference about whether the background is likely to continue during concurrent sounds, providing a stable statistical representation of the ongoing environment despite unstable sensory evidence.

scholarly journals Differentiating between integration and non-integration strategies in perceptual decision making

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Gabriel M Stine ◽  
Ariel Zylberberg ◽  
Jochen Ditterich ◽  
Michael N Shadlen
Keyword(s):  
Decision Making ◽  
Human Subjects ◽  
Perceptual Decision Making ◽  
Integration Model ◽  
Integration Strategy ◽  
Behavioral Observations ◽  
Integration Strategies ◽  
Multiple Samples ◽  
The Brain ◽  
Evidence Integration

Many tasks used to study decision-making encourage subjects to integrate evidence over time. Such tasks are useful to understand how the brain operates on multiple samples of information over prolonged timescales, but only if subjects actually integrate evidence to form their decisions. We explored the behavioral observations that corroborate evidence-integration in a number of task-designs. Several commonly accepted signs of integration were also predicted by non-integration strategies. Furthermore, an integration model could fit data generated by non-integration models. We identified the features of non-integration models that allowed them to mimic integration and used these insights to design a motion discrimination task that disentangled the models. In human subjects performing the task, we falsified a non-integration strategy in each and confirmed prolonged integration in all but one subject. The findings illustrate the difficulty of identifying a decision-maker’s strategy and support solutions to achieve this goal.

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