Bayesian Inference with Incomplete Knowledge Explains Perceptual Confidence and its Deviations from Accuracy

Khalvati, Koosha; Kiani, Roozbeh; Rao, Rajesh P. N.

doi:10.1101/2020.09.18.304220

Cited by 14 publications

(27 citation statements)

References 69 publications

(133 reference statements)

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“…3D), making it unlikely to be reactive recruitment of engagement mechanisms due to perceived stimulus difficulty. We remind that stimulus difficulty is not predictable prior to the stimulus onset and inferring the level of difficulty from a stimulus is time-consuming (Khalvati et al 2020). Second, and more critically, higher engagement should lead to better behavioral performance, but the activity along the difficulty encoding axis was uncorrelated with the monkey’s accuracy (Fig.…”

Section: Discussionmentioning

confidence: 99%

The geometry of the representation of decision variable and stimulus difficulty in the parietal cortex

Okazawa

Hatch

Mancoo

et al. 2021

Preprint

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SummaryLateral intraparietal (LIP) neurons represent formation of perceptual decisions involving eye movements. In circuit models for these decisions, neural ensembles that encode actions compete to form decisions. Consequently, decision variables (DVs) are represented as partially potentiated action plans, where ensembles increase their average responses for stronger evidence supporting their preferred actions. As another consequence, DV representation and readout are implemented similarly for decisions with identical competing actions, irrespective of input and task context differences. Here, we challenge those core principles using a novel face-discrimination task, where LIP firing rates decrease with supporting evidence, contrary to conventional motion-discrimination tasks. These opposite response patterns arise from similar mechanisms in which decisions form along curved population-response manifolds misaligned with action representations. These manifolds rotate in state space based on task context, necessitating distinct readouts. We show similar manifolds in lateral and medial prefrontal cortices, suggesting a ubiquitous representational geometry across decision-making circuits.

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Section: Discussionmentioning

confidence: 99%

The geometry of the representation of decision variable and stimulus difficulty in the parietal cortex

Okazawa

Hatch

Mancoo

et al. 2021

Preprint

Self Cite

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“…All code supporting the findings of this study are available at: https://github.com/ koosha66/POMDP-Confidence 73 .…”

Section: Data Availabilitymentioning

confidence: 96%

Bayesian inference with incomplete knowledge explains perceptual confidence and its deviations from accuracy

2021

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In perceptual decisions, subjects infer hidden states of the environment based on noisy sensory information. Here we show that both choice and its associated confidence are explained by a Bayesian framework based on partially observable Markov decision processes (POMDPs). We test our model on monkeys performing a direction-discrimination task with post-decision wagering, demonstrating that the model explains objective accuracy and predicts subjective confidence. Further, we show that the model replicates well-known discrepancies of confidence and accuracy, including the hard-easy effect, opposing effects of stimulus variability on confidence and accuracy, dependence of confidence ratings on simultaneous or sequential reports of choice and confidence, apparent difference between choice and confidence sensitivity, and seemingly disproportionate influence of choice-congruent evidence on confidence. These effects may not be signatures of sub-optimal inference or discrepant computational processes for choice and confidence. Rather, they arise in Bayesian inference with incomplete knowledge of the environment.

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“…Finally, our model suggests that the uncertainty estimation can be resolved at a perceptual level. A recent study introduced a value-based Bayesian framework based on partially observable Markov decision processes to explain the results in the earlier confidence study (Khalvati et al, 2021). In the framework, both the perceptual decisions and the opt-out decisions were based on the same hidden belief updated with the sensory inputs.…”

Section: Discussionmentioning

confidence: 99%

An Optimal Policy Model for Concurrent Uncertainty Estimation During Decision Making

Chen

et al. 2021

Preprint

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We often postpone or even avoid making decisions when we feel uncertain. Uncertainty estimation is not an afterthought of decision making but a dynamic process that accompanies decision making in parallel and affects decision making. To study concurrent uncertainty estimation during decision making, we adapted the classic random-dots motion direction discrimination task to allow a reaction-time measure of uncertainty responses. Subjects were asked to judge whether a patch of random dots was moving left or right. In addition, they could seek assistance by choosing to look at a second stimulus that had the same direction but high coherence any time during the task. The task allows us to measure the reaction time of both the perceptual decisions and the uncertainty responses. The subjects were more likely to choose the uncertainty response when the motion coherence was low, while their reaction times of the uncertainty responses showed individual variations. To account for the subjects' behavior, we created an optimal policy decision model in which decisions are based on the value functions computed from the accumulated evidence using a drift-diffusion process. Model simulations captured key features of the subjects' choices, reaction times, and proportions of uncertainty responses. Varying model parameters explained individual variations in the subjects and the correlations between decision accuracy, proportions of uncertainty responses, and reaction times at the individual level. Our model links perceptual decisions and value-based decisions and indicates that concurrent uncertainty estimation may be based on comparisons between values of uncertainty responses and perceptual decisions, both of which may be derived from the same evidence accumulation process during decision making. It provides a theoretical framework for future investigations, including the ones that aim at the underlying neural mechanism.

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Bayesian Inference with Incomplete Knowledge Explains Perceptual Confidence and its Deviations from Accuracy

Cited by 14 publications

References 69 publications

The geometry of the representation of decision variable and stimulus difficulty in the parietal cortex

The geometry of the representation of decision variable and stimulus difficulty in the parietal cortex

Bayesian inference with incomplete knowledge explains perceptual confidence and its deviations from accuracy

An Optimal Policy Model for Concurrent Uncertainty Estimation During Decision Making

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