Humans and other primates share many decision biases, among them our subjective distortion of objective probabilities. When making choices between uncertain rewards we typically treat probabilities nonlinearly: overvaluing low probabilities of reward and undervaluing high ones. A growing body of evidence, however, points to a more flexible pattern of distortion than the classical inverse-S one, highlighting the effect of experimental conditions in shifting the weight assigned to probabilities, such as task feedback, learning, and attention. Here we investigated the role of sequence structure (the order in which gambles are presented in a choice task) in shaping the probability distortion patterns of rhesus macaques: we presented 2 male monkeys with binary choice sequences of MIXED or REPEATED gambles against safe rewards. Parametric modeling revealed that choices in each sequence type were guided by significantly different patterns of probability distortion: whereas we elicited the classical inverse-S-shaped probability distortion in pseudorandomly MIXED trial sequences of gamble-safe choices, we found the opposite pattern consisting of S-shaped distortion, with REPEATED sequences. We extended these results to binary choices between two gambles, without a safe option, and confirmed the unique influence of the sequence structure in which the animals make choices. Finally, we showed that the value of gambles experienced in the past had a significant impact on the subjective value of future ones, shaping probability distortion on a trial-by-trial basis. Together, our results suggest that differences in choice sequence are sufficient to reverse the direction of probability distortion. Our lives are peppered with uncertain, probabilistic choices. Recent studies showed how such probabilities are subjectively distorted. In the present study, we show that probability distortions in macaque monkeys differ significantly between sequences in which single gambles are repeated (S-shaped distortion), as opposed to being pseudorandomly intermixed with other gambles (inverse-S-shaped distortion). Our findings challenge the idea of fixed probability distortions resulting from inflexible computations, and points to a more instantaneous evaluation of probabilistic information. Past trial outcomes appeared to drive the "gap" between probability distortions in different conditions. Our data suggest that, as in most adaptive systems, probability values are slowly but constantly updated from prior experience, driving measures of probability distortion to either side of the S/inverse-S debate.
