Literature DB >> 10512642

The geometry of stimulus control.

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Abstract

Many studies, both in ethology and comparative psychology, have shown that animals react to modifications of familiar stimuli. This phenomenon is often referred to as generalization. The majority of modifications lead to a decrease in responding, but to certain new stimuli an increase in responding is observed. This holds for both innate and learned behaviour. Here we propose a heuristic approach to stimulus control, or stimulus selection, with the aim of explaining these phenomena. The model has two key elements. First, we choose the receptor level as the fundamental stimulus space. Each stimulus is represented as the pattern of activation it induces in sense organs. Second, in this space we introduce a simple measure of 'similarity' between stimuli by calculating how activation patterns overlap. The main advantage in this approach is that the generalization of acquired responses emerges from a few simple principles that are grounded in the recognition of how animals actually perceive stimuli. Many traditional problems that face theories of stimulus control (e.g. the Spence-Hull theory of gradient interaction or ethological theories of stimulus summation) do not arise in the present framework. These problems include the amount of generalization along different dimensions, peak shift phenomena (with respect to both positive and negative shifts), intensity generalization and generalization after conditioning on two positive stimuli. Copyright 1999 The Association for the Study of Animal Behaviour.

Year:  1999        PMID: 10512642     DOI: 10.1006/anbe.1999.1187

Source DB:  PubMed          Journal:  Anim Behav        ISSN: 0003-3472            Impact factor:   2.844


  6 in total

1.  Vestigial preference functions in neural networks and túngara frogs.

Authors:  S M Phelps; M J Ryan; A S Rand
Journal:  Proc Natl Acad Sci U S A       Date:  2001-11-06       Impact factor: 11.205

2.  Shifts in postdiscrimination gradients within a stimulus dimension based on bilateral facial symmetry.

Authors:  Adam Derenne
Journal:  J Exp Anal Behav       Date:  2010-05       Impact factor: 2.468

3.  How training and testing histories affect generalization: a test of simple neural networks.

Authors:  Stefano Ghirlanda; Magnus Enquist
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2007-03-29       Impact factor: 6.237

4.  Call recognition in the bullfrog, Rana catesbeiana: generalization along the duration continuum.

Authors:  Andrea Megela Simmons
Journal:  J Acoust Soc Am       Date:  2004-03       Impact factor: 1.840

5.  Learning-related shifts in generalization gradients for complex sounds.

Authors:  Matthew G Wisniewski; Barbara A Church; Eduardo Mercado
Journal:  Learn Behav       Date:  2009-11       Impact factor: 1.986

6.  Reward quality influences the development of learned olfactory biases in honeybees.

Authors:  Geraldine A Wright; Amir F Choudhary; Michael A Bentley
Journal:  Proc Biol Sci       Date:  2009-04-15       Impact factor: 5.349

  6 in total

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