Biology subjects

Stocker, A. A.

Publications and source records attributed to Stocker, A. A..

11 recordsLinked to original sources

bioRxiv2026

The resource-rational dynamics of evidence accumulation

Evidence accumulation is a fundamental aspect of human decision-making. However, how the precise temporal structure of evidence shapes the accumulation process has not been systematically studied. As a result, current understanding of evidence accumulation remains largely limited to its time-averaged behavior. We tested human subjects in a visual estimation task in which they inferred the angular position of an unknown source from a noisy stimulus sequence. Introducing systematic temporal perturbations, i.e., breaks of different durations and at different positions in the otherwise regular evidence sequence, revealed that subjects actively compensated for the memory loss endured during the break by dynamically enhancing evidence integration and memory maintenance immediately after the break. We derived a new time-continuous Bayesian updating model that is dynamically constrained by optimal performance-effort trade-offs. With two free parameters determining the overall resource-efficiencies of encoding and memory maintenance, the model accurately predicts the rich dependencies of subjects accumulation behavior on the evidence schedule, including subjects individual tendencies to emphasize either early (primacy) or late (recency) samples in the evidence sequence. Our results suggest that evidence accumulation is a non-stationary, dynamically controlled process that optimally balances the information gained from incoming evidence against the cognitive effort required to acquire and maintain it. The proposed model is general and should apply broadly across many task domains.

animal behavior and cognition↗

bioRxiv2024

Dynamics of efficient ensemble coding

Ensemble coding creates compressed representations of a stimulus array. When discriminating the ensemble average against a reference, however, items in the ensemble with feature values closer to the reference are typically weighed stronger. We have recently shown that this "robust averaging" behavior can be explained as a form of efficient coding, where the sensory encoding precision is dynamically adapted to efficiently represent ensemble stimuli according to their overall distribution relative to a trial-by-trial varying reference. However, the specific mechanisms underlying such dynamic, efficient ensemble coding remain unknown. Here, we demonstrate that the relative timing between the presentation of the reference and the stimulus ensemble strongly affects efficient ensemble coding. We systematically probed participants decision behavior for varying time intervals between the presentation of the ensemble and the reference. We found that efficient ensemble coding was only clearly established when reference and ensemble were simultaneously presented. It was much weaker when the ensemble preceded the reference, and largely absent when the ensemble followed the reference. As captured by our model, reduced efficient ensemble coding thereby coincided with decreased decision accuracy in those asynchronous conditions. Our results indicate that any temporal offset between the ensemble and reference stimuli substantially disrupts the dynamic and efficient allocation of coding resource. This suggests that efficient ensemble coding is not the result of a preparatory attentional process nor due to evidence selection at the decision stage. Rather, it arises from a fast interaction between the simultaneously evoked, sensory representations of reference and ensemble stimuli.

neuroscience↗

bioRxiv2024

The tilt illusion arises from an efficient reallocation of neural coding resources at the contextual boundary

The tilt illusion -- a bias in the perceived orientation of a center stimulus induced by an oriented surround -- illustrates how context shapes visual perception. While the tilt illusion has been the subject of quantitative study for over 85 years, we still lack a comprehensive account of the phenomenon that connects its neural and behavioral characteristics. Here, we demonstrate that the tilt illusion originates from a dynamic change in neural coding precision induced by the surround context. We simultaneously obtained psychophysical and fMRI responses from human subjects while they viewed gratings in the absence and presence of an oriented surround, and extracted sensory encoding precision from their behavioral and neural data. Both measures show that in the absence of a surround, encoding reflects the natural scene statistics of orientation. However, in the presence of an oriented surround, encoding precision is significantly increased for stimuli similar to the surround orientation. This local change in encoding is sufficient to accurately predict the behavioral characteristics of the tilt illusion using a Bayesian observer model. The effect of surround modulation increases along the ventral stream, and is localized to the portion of the visual cortex with receptive fields at the center-surround boundary. The pattern of change in coding accuracy reflects the surround-conditioned orientation statistics in natural scenes, but cannot be explained by local stimulus configuration. Our results suggest that the tilt illusion naturally emerges from a dynamic coding strategy that efficiently reallocates neural coding resources based on the current stimulus context.

neuroscience↗

bioRxiv2024

Confirmation Bias through Selective Use of Evidence in Human Cortex

Decision-makers often process new evidence selectively, depending on their current beliefs about the world. We asked whether such confirmation biases result from biases in the encoding of sensory evidence in the brain, or alternatively in the utilization of encoded evidence for behavior. Human participants estimated the source of a sequence of visual-spatial evidence samples while we measured cortical population activity with magnetoencephalography. Halfway through the sequence, participants were prompted to judge the more likely source category. We find that processing of subsequent evidence depends on its consistency with the previously chosen category. Evidence encoded in parietal cortex contributes more to the estimation report when that evidence is consistent with the previous choice compared to when it contradicts that choice. Our results indicate that information contradicting pre-existing beliefs has little impact on subsequent behavior, despite being precisely encoded in the brain. This provides room for deliberative control to counteract confirmation biases.

neuroscience↗

bioRxiv2024

A linear sensorimotor transformation accounts for response range-dependent biases in human heading estimation

Accurate estimation of heading direction from optic flow is a crucial aspect of human spatial perception. Previous psychophysical studies have shown that humans are typically biased in their estimates of heading directions, but the reported results are inconsistent. While some studies found that humans generally underestimate heading direction (central bias), others find the opposite, an overestimation of heading direction (peripheral bias). We conducted three psychophysical experiments showing that these conflicting findings do not reflect inherent differences in heading perception but are caused by the different sizes of the response range that participants were allowed to utilize when reporting their estimates. Notably, we show that participants heading estimates monotonically scale with the size of the response range, leading to underestimation for small and overestimation for large response ranges. Additionally, neither the speed profile of the optic flow pattern nor the response method (mouse vs. keyboard) significantly affected participants estimates. Furthermore, we introduce a Bayesian heading estimation model that can quantitatively account for participants heading reports. The model assumes an efficient sensory encoding of heading direction according to a prior inferred from human heading discrimination data. In addition, the model assumes a response mapping that linearly scales the perceptual estimate with a scaling factor that monotonically depends on the size of the response range. This simple perception-action model accurately predicts participants estimates both in terms of mean and variance across all experimental conditions. Our findings underscore that human heading perception follows efficient Bayesian inference; differences in participants reported estimates can be parsimoniously explained as differences in mapping percept to probe response. Author summaryHumans can estimate the direction of their self-motion (heading) from the associated visual motion pattern (optic flow) on their retinae. While these heading estimates are typically biased, previous studies have found quite conflicting bias patterns despite using very similar optic flow stimuli. Our findings demonstrate that these differences in participants reported estimates can be attributed to differences in the response range within which participants were able to move their cursor to report their estimates. We introduce an efficient Bayesian observer model to quantitatively analyze these conflicting bias patterns. The model assumes that perceived heading directions are identical for identical optical flow patterns, yet the reported heading directions are the result of an additional response mapping that linearly maps the percept to the reported estimate. This model fits the data well and demonstrate that participants reported perceptual estimates can be substantially modulated by post-perceptual response transformations, in particular under conditions where they do not receive feedback. Our study is a reminder that psychophysical measurements necessarily provide only an indirect account of perception. Full explanations of such data require the inclusion of appropriate post-perceptual transformations that describe the mapping from perception to action.

neuroscience↗

bioRxiv2024

Adaptation optimizes sensory encoding of future stimuli

Sensory neurons continually adapt their response characteristics according to recent sensory input. However, it is unclear how such a reactive process shaped by sensory history can benefit the organism going forward. Here, we test the hypothesis that adaptation indeed acts proactively in the sense that it optimally adjusts sensory encoding for the future, i.e. for the next expected sensory input. We first quantified adaptation induced changes in sensory encoding by psychophysically measuring discrimination thresholds for visual orientation under different adaptation conditions. Using an information theoretic analysis, we found that adaptation consistently reallocates coding resources such that encoding accuracy peaks at the adaptor orientation while total coding capacity remains constant. We then asked whether this characteristic change in encoding accuracy is predicted by the temporal statistics of natural visual input. By analyzing the retinal input of freely behaving human subjects in natural environments, we found that the distribution of local visual orientations in the retinal input stream at any moment in time is also peaked at the mean orientation computed over a short input history leading up to that moment. We further tested our hypothesis with a recurrent neural network trained to predict the next frame of natural scene videos (PredNet). We simulated our human adaptation experiment with PredNet while analyzing its internal sensory representation. We found that the network exhibited the same change in encoding accuracy as observed in human subjects, and as predicted by the natural input statistics. Taken together, our results suggest that adaptation induced changes in encoding accuracy are an attempt of the visual systems to be best possibly prepared for future sensory input.

neuroscience↗

bioRxiv2022

Holistic inference explains human perception of stimulus orientation

Perception of stimulus features such as orientation is widely considered a Bayesian inference process. In contrast to previous Bayesian observer models, we propose that perception is a holistic inference process that operates across all levels of the representational hierarchy. We test this hypothesis in the context of a typical psychophysical matching task in which subjects are asked to estimate the perceived orientation of a test stimulus by adjusting a probe stimulus (method-of-adjustment). We present a holistic matching model that assumes that subjects responses reflect an optimal match between the test and the probe stimulus, both in terms of their inferred feature (orientation) but also their higher-level (category) representation. Validation against multiple existing psychophysical datasets demonstrates that the model provides a quantitatively detailed and accurate description of subjects response behavior including data that previous models fail to even qualitatively account for.

animal behavior and cognition↗

bioRxiv2021

Prior expectations in visual speed perception predict encoding characteristics of neurons in area MT

Bayesian inference provides an elegant theoretical framework for understanding the characteristic biases and discrimination thresholds in visual speed perception. However, the framework is difficult to validate due to its flexibility and the fact that suitable constraints on the structure of the sensory uncertainty have been missing. Here, we demonstrate that a Bayesian observer model constrained by efficient coding not only well explains human visual speed perception but also provides an accurate quantitative account of the tuning characteristics of neurons known for rep-resenting visual speed. Specifically, we found that the population coding accuracy for visual speed in area MT ("neural prior") is precisely predicted by the power-law, slow-speed prior extracted from fitting the Bayesian model to psychophysical data ("behavioral prior") to the point that the two priors are indistinguishable in a cross-validation model comparison. Our results demonstrate a quantitative validation of the Bayesian observer model constrained by efficient coding at both the behavioral and neural levels. Significance StatementStatistical regularities of the environment play an important role in shaping both neural representations and perceptual behavior. Most previous works addressed these two aspects independently. Here we present a quantitative validation of a theoretical framework that makes joint predictions for neural coding and behavior, based on the assumption that neural representations of sensory information are efficient but also optimally used in generating a percept. Specifically, we demonstrate that the neural tuning characteristics for visual speed in brain area MT are precisely predicted by the statistical prior expectations extracted from psychophysical data. As such, our results provide a normative link between perceptual behavior and the neural representation of sensory information in the brain.

neuroscience↗

bioRxiv2020

Categorical judgments do not modify sensory information in working memory

Categorical judgments can systematically bias the perceptual interpretation of stimulus features. However, it remained unclear whether categorical judgments directly modify working memory representations or, alternatively, generate these biases via an inference process down-stream from working memory. To address this question we ran two novel psychophysical experiments in which human subjects had to revert their categorical judgments about a stimulus feature, if incorrect based on feedback, before providing an estimate of the feature. If categorical judgments indeed directly altered sensory representations in working memory, subjects’ estimates should reflect some aspects of their initial (incorrect) categorical judgment in those trials.We found no traces of the initial categorical judgment. Rather, subjects seem to be able to flexibly switch their categorical judgment if needed and use the correct corresponding categorical prior to properly perform feature inference. A cross-validated model comparison also revealed that feedback may lead to selective memory recall such that only memory samples that are consistent with the categorical judgment are accepted for the inference process. Our results suggest that categorical judgments do not modify sensory information in working memory but rather act as top-down expectation in the subsequent sensory recall and inference process down-stream from working memory.Competing Interest StatementThe authors have declared no competing interest.View Full Text

animal behavior and cognition↗

bioRxiv2019

Benefits of commitment in hierarchical inference

Humans have the tendency to commit to a single interpretation of what has caused some observed evidence rather than considering all possible alternatives. This tendency can explain various forms of confirmation and reference biases. However, committing to a single high-level interpretation seems short-sighted and irrational, and thus it is unclear why humans seem motivated to pursue such strategy.\n\nIn a first step toward answering this question, we systematically quantified how this strategy affects estimation accuracy at the feature level in the context of two universal hierarchical inference tasks, categorical perception and causal cue combination. Using model simulations, we demonstrate that although estimation is generally impaired when conditioned on only a single high-level inter-pretation, the impairment is not uniform across the entire feature range. On the contrary, compared to a full inference strategy that considers all high-level interpretations, accuracy is actually better for feature values for which the probability of an incorrect categorical/structural commitment is relatively low. That is to say, if an observer is reasonably certain about the high-level interpretation of the feature, it is advantageous to condition subsequent feature inference only on that particular interpretation. We also show that this benefit of commitment is substantially amplified if late noise corrupts information processing (e.g., during retention in working memory). Our results suggest that top-down inference strategies that solely rely on the most likely high-level interpretation can be favorable and at least locally outperform a full inference strategy.

animal behavior and cognition↗

bioRxiv2016

A new law of human perception

Perception is a subjective experience that depends on the expectations and beliefs of an observer1. Psychophysical measures provide an objective yet indirect characterization of this experience by describing the dependency between the physical properties of a stimulus and the corresponding perceptually guided behavior2. Two fundamental psychophysical measures characterize an observers perception of a stimulus: how well the observer can discriminate the stimulus from similar ones (discrimination threshold) and how strongly the observers perceived stimulus value deviates from the true stimulus value (perceptual bias). It has long been thought that these two perceptual characteristics are independent3. Here we demonstrate that discrimination threshold and perceptual bias show a surprisingly simple mathematical relation. The relation, which we derived from assumptions of optimal sensory encoding and decoding4, is well supported by a wide range of reported psychophysical data5-16 including perceptual changes induced by spatial17,18 and temporal19-23 context, and attention24. The large empirical support suggests that the proposed relation represents a new law of human perception. Our results imply that universal rules govern the computational processes underlying human perception.

animal behavior and cognition↗