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2 - The arguments of associations

Published online by Cambridge University Press:  10 January 2011

Nestor Schmajuk
By
Justin A. Harris
Nestor Schmajuk
Affiliation:
Duke University Medical Center, Durham
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Summary

Abstract

This chapter considers associative solutions to “non-linear” discrimination problems, such as negative patterning (A+ and B+ vs. AB−) and the biconditional discrimination (AB+ and CD+ vs. AC− and BD−). It is commonly assumed that the solution to these discriminations requires “configural” elements that are added to the compound of two stimuli. However, these discriminations can be solved by assuming that some elements of each stimulus are suppressed when two stimuli are presented in compound. Each of these approaches can solve patterning and biconditional discriminations because they allow some elements, as the arguments of associations, to have differential “presence” on reinforced versus non-reinforced trials, and thus differential associability and control over responding. The chapter then presents a more specific version of one of these models, describing how interactions between stimuli, particularly the competition for attention, provide a mechanism whereby some elements are more suppressed than others when stimuli are presented simultaneously as a compound.

Most computational models of conditioning adopt associative strength (V) as the variable that tracks learning about the association between a conditioned stimulus (CS) or action and the reinforcing unconditioned stimulus (US). Many of these models make very simple assumptions about the arguments of associations – the CSs and USs themselves. For example, the Rescorla–Wagner model treats these stimuli as singular units such that, during learning, a single connection strengthens between the CS unit and US unit (Rescorla & Wagner, 1972; Wagner & Rescorla, 1972).

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Chapter
Information
Computational Models of Conditioning , pp. 53 - 70
Publisher: Cambridge University Press
Print publication year: 2010

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References

Arabzadeh, E., Clifford, C. W. G. & Harris, J. A. (2008). Vision merges with touch in a purely tactile discrimination. Psychological Science, 19, 635–641.CrossRefGoogle Scholar
Brimer, C. J. (1970). Disinhibition of an operant response. Learning and Motivation, 1, 346–371.CrossRefGoogle Scholar
Choi, J.-S. & Moore, J. W. (2003). Cerebellar neuronal activity expresses the complex topography of conditioned eyeblink responses. Behavioral Neuroscience, 117, 1211–1219.CrossRefGoogle ScholarPubMed
Crowder, N. A., Price, N. S. C., Hietanen, M. A., Dreher, B., Clifford, C. W. G. & Ibbotson, M. R. (2006). Relationship between contrast adaptation and orientation tuning in V1 and V2 of cat visual cortex. Journal of Neurophysiology, 95, 271–283.CrossRefGoogle ScholarPubMed
Grossberg, S. (1975). A neural model of attention, reinforcement, and discrimination learning. International Review of Neurobiology, 18, 263–327.CrossRefGoogle ScholarPubMed
Harris, J. A. (2006). Elemental representations of stimuli in associative learning. Psychological Review, 113, 584–605. 10.1037/0033–295X.113.3.584.CrossRefGoogle ScholarPubMed
Harris, J. A. & Livesey, E. J. (2008). Comparing patterning and biconditional discriminations in humans. Journal of Experimental Psychology: Animal Behavior Processes, 34, 144–154.Google ScholarPubMed
Harris, J. A., Livesey, E. J., Gharaei, S. & Westbrook, R. F. (2008). Negative patterning is easier than a biconditional discrimination. Journal of Experimental Psychology: Animal Behavior Processes, 34, 494–500. 10.1037/0097–7403.34.4.494.Google ScholarPubMed
Heeger, D. J. (1992). Normalization of cell responses in cat striate cortex. Visual Neuroscience, 9, 181–197.CrossRefGoogle ScholarPubMed
James, J. H. & Wagner, A. R. (1980). One-trial overshadowing: evidence of distributive processing. Journal of Experimental Psychology: Animal Behavior Processes, 6(2), 188–205.Google ScholarPubMed
Kehoe, E. J. (1982). Overshadowing and summation in compound stimulus conditioning of the rabbit's nictitating membrane response. Journal of Experimental Psychology: Animal Behavior Processes, 8, 313–328.Google ScholarPubMed
Kehoe, E. J. (1986). Summation and configuration in conditioning of the rabbit's nictitating membrane response to compound stimuli. Journal of Experimental Psychology: Animal Behavior Processes, 12, 186–195.Google Scholar
Kehoe, E. J. (1988). A layered network model of associative learning: learning-to-learn and configuration. Psychological Review, 95, 411–433.CrossRefGoogle ScholarPubMed
Kehoe, E. J. & Graham, P. (1988). Summation and configuration: stimulus compounding and negative patterning in the rabbit. Journal of Experimental Psychology: Animal Behavior Processes, 14, 320–333.Google Scholar
Pelley, M. E. (2010). The hybrid modeling approach to conditioning. In Schmajuk, N. A., ed., Computational Models of Conditioning. Cambridge, UK: Cambridge University Press.Google Scholar
Mackintosh, N. J. & Reese, B. (1979). One-trial overshadowing. Quarterly Journal of Experimental Psychology, 31, 519–526.CrossRefGoogle Scholar
McLaren, I. P. L. & Mackintosh, N. J. (2000). An elemental model of associative learning i: latent Inhibition and perceptual learning. Animal Learning and Behavior, 28(3), 211–246.CrossRefGoogle Scholar
McLaren, I. P. L. & Mackintosh, N. J. (2002). Associative learning and elemental representation ii: generalization and discrimination. Animal Learning and Behavior, 30, 177–200.CrossRefGoogle ScholarPubMed
Melchers, K. G., Shanks, D. R. & Lachnit, H. (2008). Stimulus coding in human associative learning: flexible representations of parts and wholes. Behavioural Processes, 77(3), 413–427.CrossRefGoogle ScholarPubMed
Millenson, J. R., Kehoe, E. J. & Gormezano, I. (1977). Classical conditioning of the rabbit's nictitating membrane response under fixed and mixed CS–US intervals. Learning and Motivation, 8, 351–366.CrossRefGoogle Scholar
Pavlov, I. P. (1927). Conditioned Reflexes: An Investigation of the Physiological Activity of the Cerebral Cortex. (G. V. Anrep, Trans.). New York: Dover.
Pearce, J. M. (1987). A model for stimulus generalization in Pavlovian conditioning. Psychological Review, 94(1), 61–73. 10.1037/0033–295X.94.1.61.CrossRefGoogle ScholarPubMed
Pearce, J. M. (1994). Similarity and discrimination: a selective review and a connectionist model. Psychological Review, 101(4), 587–607. 10.1037/0033–295X.101.4.587.CrossRefGoogle Scholar
Polewan, R. J. (2006). Physiological and behavioral studies of rabbit eyeblink conditioning under temporal uncertainty: Purkinje cell response and compound conditioning. Unpublished doctoral dissertation, University of Massachusetts.
Rescorla, R. A. (1972). “Configural” conditioning in discrete-trial bar pressing. Journal of Comparative and Physiological Psychology, 79(2), 307–317. 10.1037/h0032553.CrossRefGoogle ScholarPubMed
Rescorla, R. A. (1973). Evidence for a unique stimulus interpretation of configural conditioning. Journal of Comparative and Physiological Psychology, 85, 331–338.CrossRefGoogle Scholar
Rescorla, R. A. (1997). Summation: assessment of a configural theory. Animal Learning and Behavior, 25(2), 200–209.CrossRefGoogle Scholar
Rescorla, R. A. & Wagner, A. R. (1972). A theory of Pavlovian conditioning: variations in the effectiveness of reinforcement and nonreinforcement. In Black, A. H. and Prokasy, W. F., eds., Classical Conditioning ii: Current Research and Theory. New York: Appleton–Century–Crofts, pp. 64–99.Google Scholar
Rescorla, R. A., Grau, J. W. & Durlach, P. J. (1985). Analysis of the unique cue in configural discriminations. Journal of Experimental Psychology: Animal Behavior Processes, 11, 356–366.Google ScholarPubMed
Reynolds, J. H. & Chelazzi, L. (2004). Attentional modulation of visual processing. Annual Review of Neuroscience, 27, 611–647.CrossRefGoogle ScholarPubMed
Reynolds, J. H. & Heeger, D. J. (2009). The normalization model of attention. Neuron, 61, 168–185.CrossRefGoogle Scholar
Saavedra, M. A. (1975). Pavlovian compound conditioning in the rabbit. Learning and Motivation, 6, 314–326.CrossRefGoogle Scholar
Schmajuk, N. A. & Di Carlo, J. J. (1992). A neural network approach to hippocampal functioning in classical conditioning. Behavioral Neuroscience, 105, 82–110.CrossRefGoogle Scholar
Schmajuk, N. A., Lamoureux, J. A. & Holland, P. C. (1998). Occasion setting: a neural network approach. Psychological Review, 105(1), 3–32.CrossRefGoogle ScholarPubMed
Solomon, J. A. (2009). The history of dipper functions. Attention, Perception, and Psychophysics, 71, 435–443.CrossRefGoogle ScholarPubMed
Spence, K. W. (1952). The nature of the response in discrimination learning in animals. Psychological Review, 59, 89–93. 10.1037/h0063067.CrossRefGoogle Scholar
Stevens, S. S. (1962). The surprising simplicity of sensory metrics. American Psychologist, 17, 29–39.CrossRefGoogle Scholar
Thein, T., Westbrook, R. F. & Harris, J. A. (2008). How the associative strengths of stimuli combine in compound: summation and overshadowing. Journal of Experimental Psychology: Animal Behavior Processes, 34, 155–166. 10.1037/0097–7403.34.1.155.Google ScholarPubMed
Wagner, A. R. & Brandon, S. E. (2001). A componential theory of Pavlovian conditioning. In Mowrer, R. R. and Klein, S. B., eds., Handbook of Contemporary Learning Theories. Mahwah NJ, USA: Lawrence Erlbaum Associates, Inc., pp. 23–64.Google Scholar
Wagner, A. R. & Rescorla, R. A. (1972). Inhibition in Pavlovian conditioning: application of a theory. In Halliday, M. S. and Boakes, R. A., eds., Inhibition and Learning. San Diego, CA: Academic Press, pp. 301–336.Google Scholar
Whitlow, J. W. & Wagner, A. R. (1972). Negative patterning in classical conditioning: summation of response tendencies to isolable and configural components. Psychonomic Science, 27(5), 299–301.CrossRefGoogle Scholar

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