@article {69589, title = {A theory of rapid behavioral inferences under the pressure of time}, journal = {bioRxiv}, year = {2024}, month = {2024 Aug 27}, abstract = {

To survive, animals must be able quickly infer the state of their surroundings. For example, to successfully escape an approaching predator, prey must quickly estimate the direction of approach from incoming sensory stimuli. Such rapid inferences are particularly challenging because the animal has only a brief window of time to gather sensory stimuli, and yet the accuracy of inference is critical for survival. Due to evolutionary pressures, nervous systems have likely evolved effective computational strategies that enable accurate inferences under strong time limitations. Traditionally, the relationship between the speed and accuracy of inference has been described by the "speed-accuracy tradeoff" (SAT), which quantifies how the average performance of an ideal observer improves as the observer has more time to collect incoming stimuli. While this trial-averaged description can reasonably account for individual inferences made over long timescales, it does not capture individual inferences on short timescales, when trial-to-trial variability gives rise to diverse patterns of error dynamics. We show that an ideal observer can exploit this single-trial structure by adaptively tracking the dynamics of its belief about the state of the environment, which enables it make more rapid inferences and more reliably track its own error but also causes it to violate the SAT. We show that these features can be used to improve overall performance during rapid escape. The resulting behavior qualitatively reproduces features of escape behavior in the fruit fly Drosophila melanogaster, whose escapes have presumably been highly optimized by natural selection.

}, doi = {10.1101/2024.08.26.609738}, url = {https://www.doi.org/10.1101/2024.08.26.609738}, author = {Hermundstad, Ann M and M{\l}ynarski, Wiktor F} } @article {68806, title = {Environmental dynamics shape perceptual decision bias.}, journal = {PLoS Computational Biology}, volume = {19}, year = {2023}, month = {2023 Jun 08}, pages = {e1011104}, abstract = {

To interpret the sensory environment, the brain combines ambiguous sensory measurements with knowledge that reflects context-specific prior experience. But environmental contexts can change abruptly and unpredictably, resulting in uncertainty about the current context. Here we address two questions: how should context-specific prior knowledge optimally guide the interpretation of sensory stimuli in changing environments, and do human decision-making strategies resemble this optimum? We probe these questions with a task in which subjects report the orientation of ambiguous visual stimuli that were drawn from three dynamically switching distributions, representing different environmental contexts. We derive predictions for an ideal Bayesian observer that leverages knowledge about the statistical structure of the task to maximize decision accuracy, including knowledge about the dynamics of the environment. We show that its decisions are biased by the dynamically changing task context. The magnitude of this decision bias depends on the observer{\textquoteright}s continually evolving belief about the current context. The model therefore not only predicts that decision bias will grow as the context is indicated more reliably, but also as the stability of the environment increases, and as the number of trials since the last context switch grows. Analysis of human choice data validates all three predictions, suggesting that the brain leverages knowledge of the statistical structure of environmental change when interpreting ambiguous sensory signals.

}, keywords = {Bayes Theorem, Bias, Brain, Decision Making, Humans, Uncertainty}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1011104}, author = {Charlton, Julie A and M{\l}ynarski, Wiktor F and Bai, Yoon H and Hermundstad, Ann M and Goris, Robbe L T} } @article {49311, title = {Adaptive coding for dynamic sensory inference.}, journal = {eLife}, volume = {7}, year = {2018}, month = {2018 Jul 10}, abstract = {

Behavior relies on the ability of sensory systems to infer properties of the environment from incoming stimuli. The accuracy of inference depends on the fidelity with which behaviorally relevant properties of stimuli are encoded in neural responses. High-fidelity encodings can be metabolically costly, but low-fidelity encodings can cause errors in inference. Here, we discuss general principles that underlie the tradeoff between encoding cost and inference error. We then derive adaptive encoding schemes that dynamically navigate this tradeoff. These optimal encodings tend to increase the fidelity of the neural representation following a change in the stimulus distribution, and reduce fidelity for stimuli that originate from a known distribution. We predict dynamical signatures of such encoding schemes and demonstrate how known phenomena, such as burst coding and firing rate adaptation, can be understood as hallmarks of optimal coding for accurate inference.

}, issn = {2050-084X}, doi = {10.7554/eLife.32055}, author = {M{\l}ynarski, Wiktor F and Hermundstad, Ann M} }