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SUMMARY:Time perception as accumulation of salient events - Warrick Rosebo
 om (University of Sussex)
DTSTART:20211119T161500Z
DTEND:20211119T180000Z
UID:TALK163405@talks.cam.ac.uk
CONTACT:Psychology Reception
DESCRIPTION:Human perception and experience of time on the scale of second
 s to minutes depends heavily on the context and content of experience\, as
  clearly reflected in aphorisms such as “time flies when you’re having
  fun” or “a watched pot never boils”. It has been established for de
 cades at least that both lower-level stimulus properties – e.g. the rate
  of change\, speed\, or complexity of the stimulus – as well as higher-l
 evel complexities – e.g. the type of scene you are in\, busy or quiet 
 – strongly influence perceived time. In these cases\, the intuition that
  more-stuff-happening-faster results in longer perceived duration generall
 y holds. These features of the natural experience of time indicate that a 
 basis for human time perception might be found in the dynamics of neural a
 ctivity across sensory processing hierarchies - specifically in the moment
 s of larger changes in activity that we refer to as salient events. We pro
 posed that a simple way to characterise and track salient events was as a 
 kind of prediction error. We started by simply using the difference betwee
 n states of neural networks in successive instances. This assumes that\, i
 n the absence of any more precise information\, the immediate past is a go
 od prediction of the immediate future. We have shown that our algorithmic 
 approach can reproduce human-like estimates of and biases in time percepti
 on when applied to both models of the visual processing hierarchy – deep
  convolutional neural networks trained for image classification – and ne
 uroimaging data from the human visual processing hierarchy. Further\, we c
 an even predict trial-by-trial subjective reports of duration for a given 
 participant based only on (fMRI) BOLD measured while they view naturalisti
 c videos. Using salient events as a basis for time perception links natura
 lly with predictive coding accounts of perception\, as well as the promine
 nt event segmentation-based accounts of episodic memory. We are currently 
 working to compare model-based and data-driven approaches to event segment
 ation as applied in EEG/MEG to see which features are common to the differ
 ent methods as well as where they diverge both from each other and from hu
 man annotations of naturalistic experience. This line of work resolves man
 y contentious perspectives in the time perception field\, while also bring
 ing time perception and episodic memory back to the same basic units of op
 eration - salient events in experience – all under a predictive processi
 ng framework.
LOCATION:Zoom meeting
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