Altering the synchrony of stimulus trace processes: tests of a neural-network model

A previously described neural-network model (Desmond 1991; Desmond and Moore 1988; Moore et al. 1989) predicts that both CS-onset-evoked and CS-offset-evoked stimulus trace processes acquire associative strength during classical conditioning, and that CR waveforms can be altered by manipulating the time at which the processes are activated. In a trace conditioning paradigm, where CS offset precedes US onset, the model predicts that onset and offset traces act in synchrony to generate unimodal CR waveforms. However, if the CS duration is subsequently lengthened on CS-alone probe trials, the model predicts that onset and offset traces will asynchronously contribute to CR output and bimodal CRs will be generated. In a delay conditioning paradigm, in which US onset occurs prior to CS offset, the model predicts that only the onset process will gain associative strength, and hence, only unimodal CRs will occur. Using the rabbit conditioned nictitating membrane response preparation, we found experimental support for these predictions.

[1]  J. R. Millenson,et al.  Classical conditioning of the rabbit's nictitating membrane response under fixed and mixed CS-US intervals , 1977 .

[2]  A. Dickinson,et al.  Classical conditioning in animals. , 1978, Annual review of psychology.

[3]  M. C. Smith,et al.  CS-US interval and US intensity in classical conditioning of the rabbit's nictitating membrane response. , 1968, Journal of comparative and physiological psychology.

[4]  C. M. Gibbs,et al.  Transduction of the rabbit’s nictitating membrane response , 1988 .

[5]  Richard F. Thompson,et al.  Sensorimotor learning and the cerebellum , 1991 .

[6]  M. Glickstein,et al.  Classical conditioning of the nictitating membrane response of the rabbit , 2004, Experimental Brain Research.

[7]  S. R. Coleman,et al.  Classical Conditioning and the "Law of Effect": Historical and Empirical Assessment , 1979 .

[8]  Isidore Gormezano,et al.  Microprocessor control and A/D data acquisition in classical conditioning , 1980 .

[9]  A. Harry Klopf,et al.  A drive-reinforcement model of single neuron function , 1987 .

[10]  J. B. Sidowski,et al.  Experimental methods and instrumentation in psychology , 1966 .

[11]  M. M. Patterson Classical conditioning of the rabbit's (Oryctolagus cuniculus) nictitating membrane response with fluctuating ISI and intracranial CS. , 1970, Journal of comparative and physiological psychology.

[12]  J. W. Moore,et al.  Adaptively timed conditioned responses and the cerebellum: A neural network approach , 1989, Biological Cybernetics.

[13]  M. Gabriel,et al.  Learning and Computational Neuroscience: Foundations of Adaptive Networks , 1990 .

[14]  E. Guthrie Association as a function of time interval. , 1933 .

[15]  A. Klopf A neuronal model of classical conditioning , 1988 .

[16]  Stephen Grossberg,et al.  Neural dynamics of adaptive timing and temporal discrimination during associative learning , 1989, Neural Networks.

[17]  J. W. Moore,et al.  Adaptive timing in neural networks: The conditioned response , 1988, Biological Cybernetics.

[18]  F K Hoehler,et al.  Double responding in classical nictitating membrane conditioning with single-CS dual-ISI training , 1976, The Pavlovian journal of biological science.

[19]  G. Keppel,et al.  Design and Analysis: A Researcher's Handbook , 1976 .

[20]  B. Skinner,et al.  Principles of Behavior , 1944 .

[21]  A G Barto,et al.  Toward a modern theory of adaptive networks: expectation and prediction. , 1981, Psychological review.