Linked Life Data

9532961

Source:http://linkedlifedata.com/resource/pubmed/id/9532961

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
1998-4-28
pubmed:abstractText
Although it is currently popular to model human associative learning using connectionist networks, the mechanism by which their output activations are converted to probabilities of response has received relatively little attention. Several possible models of this decision process are considered here, including a simple ratio rule, a simple difference rule, their exponential versions, and a winner-take-all network. Two categorization experiments that attempt to dissociate these models are reported. Analogues of the experiments were presented to a single-layer, feed-forward, delta-rule network. Only the exponential ratio rule and the winner-take-all architecture, acting on the networks' output activations that corresponded to responses available on test, were capable of fully predicting the mean response results. In addition, unlike the exponential ratio rule, the winner-take-all model has the potential to predict latencies. Further studies will be required to determine whether latencies produced under more stringent conditions conform to the model's predictions.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
0272-4995
pubmed:author
pubmed:issnType
Print
pubmed:volume
51
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
33-58
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
1998
pubmed:articleTitle
Perceptual categorization: connectionist modelling and decision rules.
pubmed:affiliation
Psychological Laboratory, University of Cambridge, U.K. fwj1000@cus.cam.ac.uk
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't