Perceptual Bias
perceptual bias
Behavioural Basis of Subjective Time Distortions
Precisely estimating event timing is essential for survival, yet temporal distortions are ubiquitous in our daily sensory experience. Here, we tested whether the relative position, duration, and distance in time of two sequentially-organized events—standard S, with constant duration, and comparison C, with duration varying trial-by-trial—are causal factors in generating temporal distortions. We found that temporal distortions emerge when the first event is shorter than the second event. Importantly, a significant interaction suggests that a longer inter-stimulus interval (ISI) helps to counteract such serial distortion effect only when the constant S is in the first position, but not if the unpredictable C is in the first position. These results imply the existence of a perceptual bias in perceiving ordered event durations, mechanistically contributing to distortion in time perception. Our results clarify the mechanisms generating time distortions by identifying a hitherto unknown duration-dependent encoding inefficiency in human serial temporal perception, something akin to a strong prior that can be overridden for highly predictable sensory events but unfolds for unpredictable ones.
An investigation of perceptual biases in spiking recurrent neural networks trained to discriminate time intervals
Magnitude estimation and stimulus discrimination tasks are affected by perceptual biases that cause the stimulus parameter to be perceived as shifted toward the mean of its distribution. These biases have been extensively studied in psychophysics and, more recently and to a lesser extent, with neural activity recordings. New computational techniques allow us to train spiking recurrent neural networks on the tasks used in the experiments. This provides us with another valuable tool with which to investigate the network mechanisms responsible for the biases and how behavior could be modeled. As an example, in this talk I will consider networks trained to discriminate the durations of temporal intervals. The trained networks presented the contraction bias, even though they were trained with a stimulus sequence without temporal correlations. The neural activity during the delay period carried information about the stimuli of the current trial and previous trials, this being one of the mechanisms that originated the contraction bias. The population activity described trajectories in a low-dimensional space and their relative locations depended on the prior distribution. The results can be modeled as an ideal observer that during the delay period sees a combination of the current and the previous stimuli. Finally, I will describe how the neural trajectories in state space encode an estimate of the interval duration. The approach could be applied to other cognitive tasks.
Distributed and stable memory representations may lead to serial dependence
Perception and action are biased by our recent experiences. Even when a sequence of stimuli are randomly presented, responses are sometimes attracted toward the past. The mechanism of such bias, recently termed serial dependence, is still under investigation. Currently, there is mixed evidence indicating that such bias could be either from a sensory and perceptual origin or occurring only at decisional stages. In this talk, I will present recent findings from our group showing that biases are decreased when disrupting the memory trace in a premotor region in a simple visuomotor task. In addition, we have shown that this bias is stable over periods of up to 8 s. At the end, I will show ongoing analysis of a recent experiment and argue that serial dependence may rely on distributed memory representations of stimuli and task relevant features.
Unraveling perceptual biases: Insights from spiking recurrent neural networks
Bernstein Conference 2024
Causal role of the visual cortex in working memory and perceptual bias
COSYNE 2023
Inferring the order of stable and context dependent perceptual biases in human vision
COSYNE 2023
Representation of a perceptual bias in the prefrontal cortex
FENS Forum 2024