Revaluation of geometric cues reduces landmark discrimination via within-compound associations
نویسندگان
چکیده
منابع مشابه
An elemental model of retrospective revaluation without within-compound associations.
When retrospective revaluation phenomena (e.g., unovershadowing: AB+, then A-, then test B) were discovered, simple elemental models were at a disadvantage because they could not explain such phenomena. Extensions of these models and novel models appealed to within-compound associations to accommodate these new data. Here, we present an elemental, neural network model of conditioning that expla...
متن کاملWithin-compound associations in retrospective revaluation and in direct learning: a challenge for comparator theory.
In three human causal learning experiments we investigated the role of within-compound associations in learning about absent cues versus learning about present cues. Different theoretical approaches agree that within-compound associations are essential for learning about absent cues-that is, for retrospective revaluation. They differ, however, with regard to the role of within-compound associat...
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Most theoretical accounts of backward blocking place heavy stress on the necessity of the target cue having been trained in compound with the competing cue to produce a decrement in responding. Yet, other evidence suggests that a similar reduction in responding to the target cue can be observed when the outcome is later paired with a novel cue never trained in compound with the target cue (inte...
متن کاملA comparison between elemental and compound training of cues in retrospective revaluation.
Associative learning theories assume that cue interaction and, specifically, retrospective revaluation occur only when the target cue is previously trained in compound with the to-be-revalued cue. However, there are recent demonstrations of retrospective revaluation in the absence of compound training (e.g., Matute & Pineño, 1998a, 1998b). Nevertheless, it seems reasonable to assume that cue in...
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K. Weber, S. Venkatesh and M.V. Srinivasany Department of Computer Science, Curtin University of Technology, GPO Box U 1987 Perth 6001, Australia. fweberk, svethag @cs.curtin.edu.au yCentre for Visual Sciences, Research School of Biological Sciences, Australian National University, P.O. Box 475 Canberra, A.C.T. 2601, Australia. [email protected] Abstract For a mobile robot to perfor...
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ژورنال
عنوان ژورنال: Learning & Behavior
سال: 2014
ISSN: 1543-4494,1543-4508
DOI: 10.3758/s13420-014-0150-1