Probabilistic models of perception posit that subjective uncertainty related to any perceptual decision is represented in the cortex via probability distributions that encode features in a task-relevant, distributed manner (e.g. Probabilistic Sampling PS, Fiser et. al 2010). According to PS, to achieve any decision, this posterior distribution needs to be sampled through time. However, traditional evidence integration (EI) models also assume sequential integration of external sensory information over time, and they can predict the accuracy in such a task. Which process shapes the trial-by-trial time course of human behavior? In a series of human behavioral experiments, we found that both processes are present in the time-course of a perceptual judgment and that their mutual influence on behavior is flexible. We used an estimation-based variant of the classical random dot motion (RDM) task, where in each trial, participants (N=14) reported their best estimate of stimulus direction and their subjective uncertainty about their decision simultaneously. The objective uncertainty for each trial was chosen locally from two coherence values (30% vs. 55%), and globally across trials by either blocking or randomly interleaving the trial-by-trial coherence values. Stimuli were presented for varying length of time the RDM for durations between 100ms and 1.5sec. Confirming our analytical derivations, we found a significant and positive overall correlation between error and subjective uncertainty beyond 300-500 msec as a function of time in all participants. As such positive correlations are the hallmark of PS and cannot be caused by EI, these results indicate that, indeed, probabilistic inference processes dominate the latter part of decision making. Specifically, by splitting up trials in RDM duration, we found a marked decrease in both error-uncertainty correlation and absolute error within the first 300-500ms, indicating EI, followed by a significant increase throughout the remaining time, indicating PS. Importantly, the transition between these segments shifted as a function of both local and global objective uncertainty. Thus, we propose that in perceptual decision making based on dynamic stimuli with limited information perceptual process is not simply noisy evidence integration, but rather a probabilistic inference process. Moreover, this process in perceptual judgments follow a pattern that is related to that found during learning in an uncertain environment: When the global uncertainty is high, PS begins dominating earlier in time if local uncertainty is low compared to when local uncertainty is high. In contrast, when the global uncertainty is low – PS takes over at the same time regardless of the level of local signal uncertainty.

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