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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.

Perceptual decisions should depend on sensory evidence. However, such decisions are also influenced by past choices and outcomes. These choice history biases may reflect advantageous strategies to exploit temporal regularities of natural environments. However, it is unclear whether and how observers can adapt their choice history biases to different temporal regularities, to exploit the multitude of temporal correlations that exist in nature. Here, we show that male mice adapt their perceptual choice history biases to different temporal regularities of visual stimuli. This adaptation was slow, evolving over hundreds of trials across several days. It occurred alongside a fast non-adaptive choice history bias, limited to a few trials. Both fast and slow trial history effects are well captured by a normative reinforcement learning algorithm with multi-trial belief states, comprising both current trial sensory and previous trial memory states. We demonstrate that dorsal striatal dopamine tracks predictions of the model and behavior, suggesting that striatal dopamine reports reward predictions associated with adaptive choice history biases. Our results reveal the adaptive nature of perceptual choice history biases and shed light on their underlying computational principles and neural correlates. The world exhibits temporal regularities that can be exploited to improve perceptual decisions. Here, the authors show that mice adapt to such regularities, well described by reinforcement learning, and that dopamine release in the striatum tracks this adaptation.

Perceptual decisions should depend on sensory evidence. However, such decisions are also influenced by past choices and outcomes. These choice history biases may reflect advantageous strategies to exploit temporal regularities of natural environments. However, it is unclear whether and how observers can adapt their choice history biases to different temporal regularities, to exploit the multitude of temporal correlations that exist in nature. Here, we show that mice adapt their perceptual choice history biases to different temporal regularities. This adaptation is well captured by a normative reinforcement learning algorithm with multi-trial belief states, comprising both current trial sensory and previous trial memory states. We demonstrate that striatal dopamine tracks predictions of the model and behavior, pointing towards the involvement of dopamine in forming adaptive history biases. Our results reveal the adaptive nature of perceptual choice history biases, and shed light on their underlying computational principles and neural implementation.

[This corrects the article DOI: 10.1371/journal.pone.0260952.].

[This corrects the article DOI: 10.1371/journal.pone.0260952.].

Learning from naive to expert occurs over long periods of time, accompanied by changes in the brain's neuronal signals. The principles governing behavioural and neuronal dynamics during long-term learning remain unknown. We developed a psychophysical visual decision task for mice that allowed for studying learning trajectories from naive to expert. Mice adopted sequences of strategies that became more stimulus-dependent over time, showing substantial diversity in the strategies they transitioned through and settled on. Remarkably, these transitions were systematic; the initial strategy of naive mice predicted their strategy several weeks later. Longitudinal imaging of dopamine release in dorsal striatum demonstrated that dopamine signals evolved over learning, reflecting stimulus-choice associations linked to each individual's strategy. A deep neural network model trained on the task with reinforcement learning captured behavioural and dopamine trajectories. The model's learning dynamics accounted for the mice's diverse and systematic learning trajectories through a hierarchy of saddle points. The model used prediction errors mirroring recorded dopamine signals to update its parameters, offering a concrete account of striatal dopamine's role in long-term learning. Our results demonstrate that long-term learning is governed by diverse yet systematic transitions through behavioural strategies, and that dopamine signals exhibit key characteristics to support this learning.Competing Interest StatementThe authors have declared no competing interest.

Economic decision-making requires evaluating information about available options, such as their expected value and economic risk. Previous studies have shown that frontal cortical neurons encode these variables, but how this encoding is structured across different frontal regions and projection pathways remains unclear. We developed a decision-making task for head-fixed mice in which we varied the expected value and risk associated with reward-predicting stimuli. Using large-scale electrophysiology and projection-specific optotagging across multiple frontal regions, we identified distinct spatial gradients for these variables, with stronger expected value coding in dorsal regions and stronger risk coding in medial regions. We then demonstrated that this encoding further depends on the neuronal projections: frontal neurons projecting to the dorsomedial striatum and claustrum differentially encoded economic variables. Our findings illustrate that frontal cortical representation of economic variables is jointly determined by spatial organization and downstream connectivity of neurons, revealing a structured, multi-scale code for economic variables.

Visual processing is strongly influenced by the recent stimulus history - a phenomenon termed adaptation. Prominent theories cast adaptation as a consequence of optimized encoding of visual information, by exploiting the temporal statistics of the world. However, this would require the visual system to track the history of individual briefly experienced events, within a stream of visual input, to build up statistical representations over longer timescales. Here, using an openly available dataset from the Allen Brain Observatory, we show that neurons in the early visual cortex of the mouse indeed maintain long-term traces of individual past stimuli that persist despite the presentation of several intervening stimuli, leading to long-term and stimulus-specific adaptation over dozens of seconds. Long-term adaptation was selectively expressed in cortical, but not in thalamic neurons, which only showed short-term adaptation. Early visual cortex thus maintains concurrent stimulus-specific memory traces of past input, enabling the visual system to build up a statistical representation of the world to optimize the encoding of new information in a changing environment. Competing Interest Statement The authors have declared no competing interest.

Autism Spectrum Disorder (ASD) or autism is characterized by social and non-social symptoms, including sensory hyper- and hyposensitivities. A suggestion has been put forward that some of these symptoms could be explained by differences in how sensory information is integrated with its context, including a lower tendency to leverage the past in the processing of new perceptual input. At least two history-dependent effects of opposite directions have been described in the visual perception literature: a repulsive adaptation effect, where perception of a stimulus is biased away from an adaptor stimulus, and an attractive serial choice bias, where perceptual choices are biased towards the previous choice. In this study, we investigated whether autistic participants differed in either bias from typically developing controls (TD). Sixty-four adolescent participants (31 with ASD, 33 TD) were asked to categorize oriented line stimuli in two tasks which were designed so that we would induce either adaptation or serial choice bias. Although our tasks successfully induced both biases, in comparing the two groups, we found no differences in the magnitude of adaptation nor in the modulation of perceptual choices by the previous choice. In conclusion, we find no evidence of a decreased integration of the past in visual perception of autistic individuals.

Perceptual decisions about current sensory input are biased towards input of the recent past - a phenomenon termed serial dependence. Serial dependence may serve to stabilize neural representations in the face of external and internal noise. However, it is unclear under which circumstances previous input attracts subsequent perceptual decisions, and thus, whether serial dependence reflects a broad smoothing or selective stabilization operation. Here, we investigated whether focusing attention on particular features of the previous stimulus modulates serial dependence. We found an attractive bias in orientation estimations when previous and current stimuli had similar orientations, and a repulsive bias when they had dissimilar orientations. The attractive bias was markedly reduced when observers attended to the size, rather than the orientation, of the previous stimulus. Conversely, the repulsive bias for stimuli with large orientation differences was not modulated by feature-based attention. This suggests separate sources of these positive and negative perceptual biases.