What has been previously experienced can systematically affect human perception in the present. We designed a novel psychophysical experiment to measure the perceptual effects of adapting to dynamically changing stimulus statistics. Observers are presented with a series of oriented Gabor patches and are asked occasionally to judge the orientation of highly ambiguous test patches. We developed a computational model to quantify the influence of past stimuli presentations on the observers' perception of test stimuli over multiple timescales and to show that this influence is distinguishable from simple response biases. The experimental results reveal that perception is attracted toward the very recent past and simultaneously repulsed from stimuli presented at short to medium timescales and attracted to presentations further in the past. All effects differ significantly both on their relative strength and their respective duration. Our model provides a structured way of quantifying serial effects in psychophysical experiments, and it could help experimenters in identifying such effects in their data and distinguish them from less interesting response biases. volved (from the recent to the distant past), and the mechanisms responsible (one vs. multiple mechanisms). Visual adaptation, for example, produces a plethora of visual aftereffects, from motion (Mather, Verstraten, & Anstis, 1998) to color (Webster & Mollon, 1991) and orientation (Jin, Dragoi, Sur, & Seung, 2005). Consistently, these aftereffects reveal a negative correlation between the current percept and the adaptor (Thompson & Burr, 2009). For example, after adaptation to a leftwards oriented grating, the perceived orientation of a vertical grating is biased rightwards, opposite of the adaptor. Contrary to these classical negative aftereffects, many studies have reported that there is a positive correlation between visual features of the current percept, such as orientation, numerosity, or facial attributes, with those of the immediate past (Cicchini, Anobile, & Burr, 2014; Fischer & Whitney, 2014; Liberman, Fischer, & Whitney, 2014). The argument for this serial dependence is that the physical world is usually stable and continuous over time, so the recent past can be a good predictor of the present. It is counterintuitive that the same mechanisms can be responsible for two diametrically opposite effects. Recently, Fritsche, Mostert, and de Lange (2017) suggested that perception is repelled away from previous stimuli, while postperceptual decisions are biased toward previous stimuli. In their paradigm, the positive bias increased for longer delays between perception and decision, suggesting that working memory representations might be the source of this

Our perceptions are fundamentally altered by our expectations, i.e., priors about the world. In previous statistical learning experiments (Chalk, Seitz, & Seriès, 2010), we investigated how such priors are formed by presenting subjects with white low contrast moving dots on a blank screen and using a bimodal distribution of motion directions such that two directions were more frequently presented than the others. We found that human observers quickly and automatically developed expectations for the most frequently presented directions of motion. Here, we examine the specificity of these expectations. Can one learn simultaneously to expect different motion directions for dots of different colors? We interleaved moving dot displays of two different colors, either red or green, with different motion direction distributions. When one distribution was bimodal while the other was uniform, we found that subjects learned a single bimodal prior for the two stimuli. On the contrary, when both distributions were similarly structured, we found evidence for the formation of two distinct priors, which significantly influenced the subjects' behavior when no stimulus was presented. Our results can be modeled using a Bayesian framework and discussed in terms of a suboptimality of the statistical learning process under some conditions.

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