Skip to main content Accessibility help
×
Hostname: page-component-78c5997874-m6dg7 Total loading time: 0 Render date: 2024-11-06T07:20:18.685Z Has data issue: false hasContentIssue false

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

Stefano Ghirlanda
Affiliation:
Stockholm University
Magnus Enquist
Affiliation:
Stockholm University
Colin R. Tosh
Affiliation:
University of Leeds
Graeme D. Ruxton
Affiliation:
University of Glasgow
Get access

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’).

Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Arbib, M. A. 2003. The Handbook of Brain Theory and Neural Networks. 2nd Edn. MIT Press.Google Scholar
Blough, D. S. 1975. Steady state data and a quantitative model of operant generalization and discrimination. J Exp Psychol Anim Behav Process 104(1), 3–21.CrossRefGoogle Scholar
Cheng, K., Spetch, M. L. & Johnson, M. 1997. Spatial peak shift and generalization in pigeons. J Exp Psychol Anim Behav Process 23(4), 469–481.CrossRefGoogle Scholar
Enquist, M. & Ghirlanda, S. 2005. Neural Networks and Animal Behavior. Princeton University Press.Google Scholar
Ghirlanda, S. 2005. Retrospective revaluation as simple associative learning. J Exp Psychol Anim Behav Process 31, 107–111.CrossRefGoogle ScholarPubMed
Ghirlanda, S. & Enquist, M. 1999. The geometry of stimulus control. Anim Behav 58, 695–706.CrossRefGoogle Scholar
Ghirlanda, S. & Enquist, M. 2003. A century of generalization. Animl Behav 66, 15–36.CrossRefGoogle Scholar
Hanson, H. 1959. Effects of discrimination training on stimulus generalization. J Exp Psychol 58(5), 321–333.CrossRefGoogle ScholarPubMed
Haykin, S. 1999. Neural Networks: A Comprehensive Foundation. 2nd Edn. Macmillan.Google Scholar
Helson, H. 1964. Adaptation-level Theory. Harper & Row.Google Scholar
Pelley, M. E. & McLaren, I. P. L. 2003. Learned associability and associative change in human causal learning. Q J Exp Psychol 56B(1), 68–79.CrossRefGoogle Scholar
Mackintosh, N. J. 1974. The Psychology of Animal Learning. Academic Press.Google Scholar
McClelland, J. & Rumelhart, D. 1985. Distributed memory and the representation of general and specific information. J Exp Psychol Gen 114(2), 159–188.CrossRefGoogle ScholarPubMed
McClelland, J. L. & Rumelhart, D. E., eds. 1986. Parallel DistributedProcessing: Explorations in the Microstructure of Cognition, Vol. 2. MIT Press.Google Scholar
Parducci, A. 1965. Category judgment: a range-frequency model. Psychol Rev 72(6), 407–418.CrossRefGoogle ScholarPubMed
Pearce, J. M. 1997. Animal Learning and Cognition. 2nd Edn. Psychology Press.Google Scholar
Prokasy, W. F. & Hall, J. F. 1963. Primary stimulus generalization. Psychol Rev 70, 310–322.CrossRefGoogle ScholarPubMed
Purtle, R. B. 1973. Peak shift: A review. Psychol Bull80, 408–421.CrossRefGoogle Scholar
Rescorla, R. A. & Wagner, A. R. 1972. A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. In Classical Conditioning II: Current Research and Theory (ed. A. H. Black & W. F. Prokasy), pp. 64–99. Appleton-Century-Crofts.
Rumelhart, D. E. & McClelland, J. L., eds. 1986. Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1. MIT Press.Google Scholar
Sarris, V. 2003. Frame of reference models in psychophysics: a perceptual-cognitive approach. In Perception beyond Sensation (ed. Kaernbach, C., Schröger, E. & Müller, H.), pp. 69–88. LawrenceErlbaum Press.Google Scholar
Spence, K. 1936. The nature of discrimination learning in animals. Psychol Rev 43, 427–449.CrossRefGoogle Scholar
Terrace, H. S. 1964. Wavelength generalization after discrimination training with and without errors. Science 144, 78–80.CrossRefGoogle Scholar
Thomas, D. R. 1993. A model for adaptation-level effects on stimulus generalization. Psychological Review 100(4), 658–673.CrossRefGoogle Scholar
Thomas, D. R. & Jones, C. G. 1962. Stimulus generalization as a function of the frame of reference. J Exp Psychol Gen 64(1), 77–80.CrossRefGoogle ScholarPubMed
Thomas, D. R., Lusky, M. & Morrison, S. 1992. A comparison of generalization functions and frame of reference effects in different training paradigms. Percept Psychophys 51(6), 529–540.CrossRefGoogle ScholarPubMed
Wasserman, E. A. & Berglan, L. R. 1998. Backward blocking and recovery from overshadowing in human causal judgement: the role of within-compound associations. Q J Exp Psychol 51B(2), 121–138.Google Scholar
Widrow, B. & Hoff, M. E. J. 1960. Adaptive switching circuits. In IRE WESCON Convention Record, Vol. 4, pp. 96–104. IRE.Google Scholar
Widrow, B. & Stearns, S. D. 1985. Adaptive Signal Processing. Prentice-Hall.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×