A Bayesian and efficient observer model explains concurrent attractive and repulsive history biases in visual perception
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PI Group Predictive Brain
SW OZ DCC CO
Key wordsEfficient coding; Serial dependence bias; Adaptation; Visual perception; Bayesian inference
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 may underlie these history dependencies. In the current study, we disentangle repulsive and attractive biases by exploring the respective timescales over which current visual processing is influenced by previous experience. Across four experiments, we find that perceptual decisions about stimulus orientation are concurrently attracted towards the short-term perceptual history and repelled from stimuli experienced up to minutes into the past. We show that 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 both efficiency and stability.