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SUMMARY:Physical computations are idealisations - Mark Sprevak (University
  of Edinburgh)
DTSTART:20211118T153000Z
DTEND:20211118T170000Z
UID:TALK163135@talks.cam.ac.uk
CONTACT:Richard Staley
DESCRIPTION:"Register to attend in person":https://www.eventbrite.co.uk/e/
 hps-departmental-seminar-tickets-196476595787\n\nWhat does it mean when we
  say that the brain implements a computation? In this paper\, I build on r
 ecent work on idealisation to suggest that we should re-think this questio
 n about computational implementation. First\, it is a mistake to approach 
 the problem in the abstract\, by reflecting on physical computation in a t
 opic-neutral way. It is essential to have an idea of why theorists apply t
 he notion in certain domains\, why they feel motivated to provide a specif
 ic computational model of a physical system\, and what benefits they regar
 d flow from doing so. Second\, an underappreciated feature of computationa
 l descriptions is that they involve a major degree of abstraction and idea
 lisation. Normally\, only a handful of physical properties of a target phy
 sical system feature in a computational model and these are themselves ide
 alised in ways that depart from reality. The dynamics of a select\, ideali
 sed group of properties are the fare of a computational model. I suggest t
 hat one should expect this rationale to be reflected in conditions of comp
 utational implementation. I argue that this explains the appeal of rival\,
  incompatible theories of implementation among philosophers: in the real w
 orld – and in particular\, in cognitive neuroscience – implementation 
 is often constrained in different ways for different ends.
LOCATION:Mill Lane Lecture Room 9 and Zoom
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