Every instant of perception depends on a cascade of brain processes calibrated to the history of sensory and decisional events. In the present work, we show that human visual perception is constantly shaped by two contrasting forces exerted by sensory adaptation and past decisions. In a series of experiments, we used multilevel modeling and cross-validation approaches to investigate the impact of previous stimuli and decisions on behavioral reports during adjustment and forced-choice tasks. Our results revealed that each perceptual report is permeated by opposite biases from a hierarchy of serially dependent processes: Low-level adaptation repels perception away from previous stimuli, whereas decisional traces attract perceptual reports toward the recent past. In this hierarchy of serial dependence, “continuity fields” arise from the inertia of decisional templates and not from low-level sensory processes. This finding is consistent with a Two-process model of serial dependence in which the persistence of readout weights in a decision unit compensates for sensory adaptation, leading to attractive biases in sequential perception. We propose a unified account of serial dependence in which functionally distinct mechanisms, operating at different stages, promote the differentiation and integration of visual information over time.

No abstract

Every instant of perception depends on a cascade of brain processes calibrated to the history of sensory and decisional events. In the present work, we show that human visual perception is constantly shaped by two contrasting forces, exerted by sensory adaptation and past decisions. In a series of experiments, we used multilevel modelling and cross-validation approaches to investigate the impact of previous stimuli and responses on current errors in adjustment tasks. Our results revealed that each perceptual report is permeated by opposite biases from a hierarchy of serially dependent processes: low-level adaptation repels perception away from previous stimuli; high-level, decisional traces attract perceptual reports toward previous responses. Contrary to recent claims, we demonstrated that positive serial dependence does not result from continuity fields operating at the level of early visual processing, but arises from the inertia of decisional templates. This finding is consistent with a Two-process model of serial dependence in which the persistence of read-out weights in a decision unit compensates for sensory adaptation, leading to attractive biases in sequential responses. We propose the first unified account of serial dependence in which functionally distinct mechanisms, operating at different stages, promote the differentiation and integration of visual information over time.not peer-reviewed)

No abstract

We present a framework for the study of active vision, i.e., the functioning of the visual system during actively self-generated body movements. In laboratory settings, human vision is usually studied with a static observer looking at static or, at best, dynamic stimuli. In the real world, however, humans constantly move within dynamic environments. The resulting visual inputs are thus an intertwined mixture of self- and externally-generated movements. To fill this gap, we developed a virtual environment integrated with a head-tracking system in which the influence of self- and externally-generated movements can be manipulated independently. As a proof of principle, we studied perceptual stationarity of the visual world during lateral translation or rotation of the head. The movement of the visual stimulus was thus parametrically tethered to self-generated movements. We found that estimates of object stationarity were less biased and more precise during head rotation than translation. In both cases the visual stimulus had to partially follow the head movement to be perceived as immobile. We discuss a range of possibilities for our setup among which the study of shape perception in active and passive conditions, where the same optic flow is replayed to stationary observers