Book contents
- Frontmatter
- Contents
- Contributors
- Introduction: Modelling perception with artificial neural networks
- Part I General themes
- Part II The use of artificial neural networks to elucidate the nature of perceptual processes in animals
- Part III Artificial neural networks as models of perceptual processing in ecology and evolutionary biology
- Part IV Methodological issues in the use of simple feedforward networks
- 14 How training and testing histories affect generalisation: a test of simple neural networks
- 15 The need for stochastic replication of ecological neural networks
- 16 Methodological issues in modelling ecological learning with neural networks
- 17 Neural network evolution and artificial life research
- 18 Current velocity shapes the functional connectivity of benthiscapes to stream insect movement
- 19 A model biological neural network: the cephalopod vestibular system
- Index
- References
14 - How training and testing histories affect generalisation: a test of simple neural networks
from Part IV - Methodological issues in the use of simple feedforward networks
Published online by Cambridge University Press: 05 July 2011
- Frontmatter
- Contents
- Contributors
- Introduction: Modelling perception with artificial neural networks
- Part I General themes
- Part II The use of artificial neural networks to elucidate the nature of perceptual processes in animals
- Part III Artificial neural networks as models of perceptual processing in ecology and evolutionary biology
- Part IV Methodological issues in the use of simple feedforward networks
- 14 How training and testing histories affect generalisation: a test of simple neural networks
- 15 The need for stochastic replication of ecological neural networks
- 16 Methodological issues in modelling ecological learning with neural networks
- 17 Neural network evolution and artificial life research
- 18 Current velocity shapes the functional connectivity of benthiscapes to stream insect movement
- 19 A model biological neural network: the cephalopod vestibular system
- Index
- References
Summary
14.1 Introduction
This paper deals with a general issue in the study of animal behaviour that we call path dependence. The expression refers to the fact that different histories of experiences (paths) may at first seem to produce the same behavioural effects yet reveal important differences when further examined. For instance, two training procedures may establish the same discrimination between two stimuli yet produce different responding to other stimuli, because the two paths have produced different internal states within the animal. There are several reasons why path dependence is an important issue. First, it comprises many phenomena that can provide stringent tests for theories of behaviour. Second, path dependence is at the root of several controversies, for instance whether animals encode absolute or relative characteristics of stimuli (Spence, 1936; Helson, 1964; Thomas, 1993) or whether learning phenomena such as backward blocking and un-overshadowing imply, in addition to basic associative learning, stimulus–stimulus associations or changes in stimulus associability (Wasserman & Berglan, 1998; Le Pelley & McLaren, 2003; Ghirlanda, 2005).
In this paper we use a simple neural network model of basic associative learning (Blough, 1975; Enquist & Ghirlanda, 2005) to show how path dependence can arise from fundamental properties of associative memory. The model has two core components: (1) distributed representations of stimuli based on knowledge of sensory processes and (2) a simple learning mechanism that can associate stimulus representations with responses. We consider examples of path dependence in experiments on generalisation (or ‘stimulus control’).
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- Information
- Modelling Perception with Artificial Neural Networks , pp. 295 - 307Publisher: Cambridge University PressPrint publication year: 2010