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Heymans Colloquium: Howard Bowman, The Simultaneous Type/ Serial Token (STST) model

Wanneer:wo 23-01-2013 14:00 - 15:00
Waar:Room M.0074 (Munting building), Grote Kruisstraat 2, Groningen


Prof. Howard Bowman
(Centre for Cognitive Neuroscience and Cognitive Systems
University of Kent at Canterbury, UK)

The Simultaneous Type/ Serial Token Model of temporal attention, working memory encoding and Conscious Perception, with applications in lie detection and brain-computer interaction

Wednesday, 23 January 2013, 14.00 - 15.00 hrs

Room M.0074 (Munting building), Grote Kruisstraat 2, Groningen

The Simultaneous Type/ Serial Token (STST) model [Bowman & Wyble, 2007] was developed as a theory of how attention is deployed through time and how working memory representations are formed. It provides a neural explanation of perceptual phenomena, particularly those observed using Rapid Serial Visual Presentation (RSVP), e.g. attentional blink, repetition blindness, temporal conjunction errors and perceptual episodes, e.g. see [Wyble et al, 2011]. Its activation dynamics have also been tied to the P3 event related potential component [Craston et al, 2009], which has been argued to be an electrophysiological correlate of conscious perception.

I will discuss our recent work on relating the STST model to theories of conscious perception. This will consider an STST explanation of why, when attention deployment is impaired (i.e. during the attentional blink), conscious perception becomes less accurate, i.e. more all-or-none [Bowman et al, 2009], and more temporally jittered [Chennu et al, 2009]. Finally, I will highlight applications of these RSVP-P3 effects in lie detection [Bowman et al, in press] and brain computer interaction. In particular, in [Bowman et al, in press] we argue that the RSVP-P3 method has great potential for the development of lie detection on the fringe of awareness in such a way that applying countermeasures becomes much harder.

For more information you may contact dr. M.R. (Mark) Nieuwenstein, email  address: