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Poeppel, E.

Publications and source records attributed to Poeppel, E..

2 recordsLinked to original sources

A Unitary Mechanism Underlies Adaptation to Both Local and Global Environmental Statistics in Time Perception

Our sense of the passage of time flexibly adapts to the statistical properties of the temporal context. Humans and non-human species exhibit a perceptual bias towards the mean of durations previously observed as well as serial dependence, a perceptual bias towards the duration of recently processed events. Here we asked whether those two phenomena arise from a unitary mechanism or reflect the operation of two distinct systems that adapt separately to the global and local statistics of the environment. We employed a set of duration reproduction tasks in which the target duration was sampled from distributions with different variances and means. The central tendency and serial dependence biases were jointly modulated by the range and the variance of the prior. These effects were well-captured by a unitary mechanism model in which temporal expectancies are updated after each trial based on perceptual observations. Alternative models that assume separate mechanisms for global and local contextual effects failed to capture the empirical results. TeaserTime perception of humans is shaped by a common mechanism that is sensitive to short-term and long-term environmental changes.

neuroscience↗

Serial Dependence in Temporal Perception Reveals the Dynamics of Constructing an Internal Reference Frame

Temporal perception is crucial to cognitive functions. To better estimate temporal durations, the observers need to construct an internal reference frame based on past experience and apply it to guide future perception. However, how this internal reference frame is constructed remains largely unclear. Here we showed the dynamics of the internal reference construction from the perspective of serial dependence in temporal reproduction tasks. We found the current duration estimation is biased towards both perceived and reproduced durations in previous trials. Moreover, this effect is regulated by the variability of sample durations. The influence of previous trials was stronger when the observers were exposed to context with more variable durations, which is inconsistent with previous theories that the similarity between successive stimuli induces serial dependence. We proposed a Bayesian model with an adaptive reference updated continuously after each observation, which can better explain the serial dependence observed in temporal perception.

neuroscience↗