Past experience strongly guides sensory processing and influences every perceptual decision. Yet, due to contradictory findings in the literature, the exact pattern of these effects is unclear and a convincing general computational framework underlying these effects is still missing. Even in the simplest version of the problem, making a forced choice between two hypotheses based on noisy sequential input, the field is divided over how basic statistics of the input (e.g. appearance frequencies) and various significant patterns (e.g. repetition) jointly determine the observer’s behavior. We used the above model problem in 7 experiments to tease apart the relative contributions of each effect on human sequential decision making. Observers performed a 2-AFC decision making (“Which of the two shapes is seen?”), while we independently modulated the level of pixel-noise, the appearance frequency of the elements coming from the two classes at two different time scales, and the ratio of repetition/alternation in the sequence. We found that the noise level of the stimulus systematically modulated the strength of each contributing factor to decision making. However, instead of a simple interpolation between long-term probabilities and veridical choice as it would be predicted by adaptation or priming, different pairings of short- and long-term appearance probabilities produced various characteristic under- and over-shootings in choice performances. This rules out a number of earlier models proposed for explaining human behavior in such tasks. We also found that human performance measured by correct answers and by reaction times yielded opposing results under some conditions indicating that RT measures tap into motor rather than cognitive components of sequence coding. By controlling the base-rate probabilities and repetitions/alternations independently, we also observed that despite the two measures being correlated in general, repetition/alternation is a factor independently influencing human judgment. To assess the generality of our findings, we run behavioral studies with adult rats asking them to choose between two full-field stimuli of different brightness. We found that rats replicated the striking results of humans, by choosing the frequent stimulus of recent past fewer times under high uncertainty after experience with particular long-term appearance statistics. Through simulations, we confirmed that our results can be captured by a probabilistic model of human visual decision making that balances long- and short-term summary statistics of sequences, and in parallel, also encodes salient features, such as repetitions in the sequence.

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