Recent studies claim that visual perception of stimulus features, such as orientation, numerosity, and faces, is systematically biased toward visual input from the immediate past [1-3]. However, the extent to which these positive biases truly reflect changes in perception rather than changes in post-perceptual processes is unclear [4, 5]. In the current study we sought to disentangle perceptual and decisional biases in visual perception. We found that post-perceptual decisions about orientation were indeed systematically biased toward previous stimuli and this positive bias did not strongly depend on the spatial location of previous stimuli (replicating previous work [1]). In contrast, observers' perception was repelled away from previous stimuli, particularly when previous stimuli were presented at the same spatial location. This repulsive effect resembles the well-known negative tilt-aftereffect in orientation perception [6]. Moreover, we found that the magnitude of the positive decisional bias increased when a longer interval was imposed between perception and decision, suggesting a shift of working memory representations toward the recent history as a source of the decisional bias. We conclude that positive aftereffects on perceptual choice are likely introduced at a post-perceptual stage. Conversely, perception is negatively biased away from recent visual input. We speculate that these opposite effects on perception and post-perceptual decision may derive from the distinct goals of perception and decision-making processes: whereas perception may be optimized for detecting changes in the environment, decision processes may integrate over longer time periods to form stable representations.

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Human perceptual decisions can be repelled away from (repulsive adaptation) or attracted towards recent visual experience (attractive serial dependence). It is currently unclear whether and how these repulsive and attractive biases interact during visual processing and what computational principles underlie these history dependencies. Here we disentangle repulsive and attractive biases by exploring their respective timescales. We find that perceptual decisions are concurrently attracted towards the short-term perceptual history and repelled from stimuli experienced up to minutes into the past. The temporal pattern of short-term attraction and long-term repulsion cannot be captured by an ideal Bayesian observer model alone. Instead, it is well captured by an ideal observer model with efficient encoding and Bayesian decoding of visual information in a slowly changing environment. Concurrent attractive and repulsive history biases in perceptual decisions may thus be the consequence of the need for visual processing to simultaneously satisfy constraints of efficiency and stability.

Representations learned by deep convolutional neural networks (CNNs) for object recognition are a widely investigated model of the processing hierarchy in the human visual system. Using functional magnetic resonance imaging, CNN representations of visual stimuli have previously been shown to correspond to processing stages in the ventral and dorsal streams of the visual system. Whether this correspondence between models and brain signals also holds for activity acquired at high temporal resolution has been explored less exhaustively. Here, we addressed this question by combining CNN-based encoding models with magnetoencephalography (MEG). Human participants passively viewed 1,000 images of objects while MEG signals were acquired. We modelled their high temporal resolution source-reconstructed cortical activity with CNNs, and observed a feed-forward sweep across the visual hierarchy between 75 and 200 ms after stimulus onset. This spatiotemporal cascade was captured by the network layer representations, where the increasingly abstract stimulus representation in the hierarchical network model was reflected in different parts of the visual cortex, following the visual ventral stream. We further validated the accuracy of our encoding model by decoding stimulus identity in a left-out validation set of viewed objects, achieving state-of-the-art decoding accuracy.

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