@article {68102,
	title = {Perceptual decisions exhibit hallmarks of dynamic Bayesian inference},
	journal = {bioRxiv},
	year = {2022},
	month = {2022 May 24},
	abstract = {<p>To interpret the sensory environment, the brain combines ambiguous sensory measurements with 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 the statistical structure of the task to maximize decision accuracy and show that its decisions are biased by task context. The magnitude of this decision bias is not a fixed property of the sensory measurement but depends on the observer{\textquoteright}s belief about the current context. The model therefore predicts that decision bias will grow with the reliability of the context cue, the stability of the environment, and with the number of trials since the last context switch. Analysis of human choice data validates all three predictions, providing evidence that the brain continuously updates probabilistic representations of the environment to best interpret an uncertain, ever-changing world.</p>
},
	doi = {10.1101/2022.05.23.493109},
	url = {https://doi.org/10.1101/2022.05.23.493109},
	author = {Julie A. Charlton and Wiktor F. M{\l}ynarski and Yoon H. Bai and Ann M. Hermundstad and Robbe L. T. Goris}
}
