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SUMMARY:LCN Seminar: Perceiving is Believing: Bayesian inference in unexpe
 cted places
DTSTART:20140114T141500
DTSTAMP:20260407T140141Z
UID:7912b82ff6e292116b73f8d6e222f2cda556f61a6071d9e08bebe5f6
CATEGORIES:Conferences - Seminars
DESCRIPTION:Maneesh SAHANI\, Gatsby Computational Neuroscience Unit\, Univ
 ersity College London\nThe past century or more of research into perceptio
 n has been dominated by two different computational metaphors: the Hemholt
 zian description of a process of inference about the external causes of se
 nsations (in its modern guise often expressed in terms of Bayesian probabi
 listic reasoning)\, and a more mechanistic\, processing-based view often r
 ooted in engineering operations such as accumulation and cross-correlation
 .  Many mid- and high-level perceptual phenomena seem to be well-understo
 od inferentially\, but the processing-based metaphors have remained domina
 nt for perception based on 'early' sensory computations. \nHere I will pr
 esent evidence that inference plays a substantial role in two perceptual d
 omains where most current theories rely on signal-processing-based models.
   The first part of the talk (work with Misha Ahrens) involves judgements
  about short intervals of time.  I will show that an inferential view can
  help to make sense of experiments where the nature of a stimulus being ti
 med has a pronounced effect on observers' estimates of its duration\, whil
 e naturally respecting the scalar properties of interval estimates. Three 
 new behavioural experiments support the proposed inferential model.  In t
 he second part (work with Phillipp Hehrmann and Vincent Adam) I will discu
 ss the percept of acoustic pitch.  Again\, an inferential view helps to r
 econcile a variety of reported behavioural phenomena\, and makes predictio
 ns regarding the distribution of octave-step errors that are borne out by 
 new experiments.\nThus\, even apparently 'simple' percepts may rely on sop
 histicated processes of inferential reasoning\, and so may depend on gener
 al neural mechanisms for pattern recognition and probabilistic processing\
 , rather than solely on simple dedicated circuits.
LOCATION:BC 420 https://plan.epfl.ch/?room==BC%20420
STATUS:CONFIRMED
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