Stimulus generalization of conditioned responses.

Razran, Gregory

doi:10.1037/h0060507

Cited by 146 publications

(92 citation statements)

References 43 publications

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“…Experimental results from intensity generalisation tests (see Fig. 4b) conform indeed to the above predictions: they increase monotonically moving from the negative to the positive stimulus and show positive as well as negative shifts (Razran 1949, Thomas & Setzer 1972, Huff et al 1975, Zielinski & Jakubowska 1977. Some non-monotonicity is sometimes found, but we must keep in mind that we are considering the idealised case in which only s + and s − contribute to responding.…”

Section: Monotonic Gradients Along Intensity Dimensionssupporting

confidence: 77%

The geometry of stimulus control

Ghirlanda

Enquist

1999

Animal Behaviour

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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 generalisation. Most 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 we recognise in this approach is that the generalisation of acquired responses emerges from a few simple principles which 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 generalisation along different dimensions, peak-shift phenomena (with respect to both positive and negative shifts), intensity generalisation, and generalisation after conditioning on two positive stimuli

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Section: Monotonic Gradients Along Intensity Dimensionssupporting

confidence: 77%

The geometry of stimulus control

Ghirlanda

Enquist

1999

Animal Behaviour

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“…The general appearance of intensity generalization gradients is clearly illustrated in figure 3, where data coming from tests involving three different kinds of stimuli (lights, bells and whistles) are reported (Razran, 1949;Mackintosh, 1974). All three experimental gradients are monotonic, with increased number of responses as the intensity of stimulation increases.…”

Section: The Intensity Testmentioning

confidence: 92%

“…We have chosen the intensity and translation tests because they are readily compared to experiments: intensity generalization gradients have been measured along many dimensions, including sound or light intensity (Razran, 1949;Thomas & Setzer, 1972;Zielinski & Jakubowska, 1977), colour intensity (Czaplicki et al, 1976), taste (Tapper & Halpern, 1968), and click frequency (Weiss & Schindler, 1981); rearrangement of stimulation is implied in studies using such dimensions On the horizontal axis the intensity of the test stimuli is plotted in arbitrary units as a distance from the conditioning stimulus S + , while on the vertical one is shown the proportion of responses emitted for each stimulus in comparison with S + . Data from Razran (1949), table II, summarising data from Pavlov's laboratory (Pavlov, 1927).…”

Section: Testing the Modelmentioning

confidence: 99%

Artificial neural networks as models of stimulus control

Ghirlanda

Enquist

1998

Animal Behaviour

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We evaluate the ability of artificial neural network models (multi-layer perceptrons) to predict stimulus-response relationships. A variety of empirical results are considered, such as generalization, peak-shift (supernormality) and stimulus intensity effects. The networks were trained on the same tasks as the animals in the considered experiments. The subsequent generalization tests on the networks showed that the model replicates correctly the empirical results. It is concluded that these models are valuable tools in the study of animal behaviour.

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“…It is the writer's belief that this is essentially what Lashley and Wade had in mind when, for example, they said (page 81), '~1tb continued training the subject mayor may not develop reaction to a greater variety of aspects of the stimulus, mayor may not show narrowing" of the effective range on a stimulus dimension." Razran (1949) forwards an interpretation of generalization effects somewhat more closely related to Gibson's, and to that argued for here. He distinguishes two processes, which he calls pseudo-generalization and true generalization, the distinction being essentially identical to Gibson's distinction between primary and secondary generalization.…”

Section: Other Considerations Supporting the Modelmentioning

confidence: 98%

Functional Learning: The Learning of Continuous Functional Mappings Relating Stimulus and Response Continua

Carroll¹

1963

ETS Research Bulletin Series

113

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A general model was proposed in which it was assumed that, in learning situations involVing scaled stimuli and responses, sUbjects will tend to establish continuous functional relations betveen stimuli and responses.In partiCUlar, it was assumed that each subject has available a general ":functional form" depend.ent only on c,=rtain parameters ( p. ), and that iñ learning the subject effectively assigns speci:fic values to these parameters, thus establishing a speci:fic function defining a unique mapping of stimuli into responses. In the specialized case of the model, the more restrictive assumption i6 made that the general form is constituted of linear combinations of more basic functions, so that the parameters ( Pi ) may be identified with the weights assigned to each of these primitive :functions 1n establishing a particular stimulus-response mapping. This "specialized" model was assumed throughout the present study.Three hypotheses were derived from the postulates of the proposed model, and an experimental study was undertaken designed to test these hypotheses, as well as to answer a number of related questions. The hypotheses were: (I) Subjects will reproduce responses which bear a continuous relation to stimuli (according to an index proposed as an operational measure of continuity) even when the stimulus-response pairs they are given to learn are randomly related. 11(II) A set of stimulus-response connections related by a continuous function will be learned more efficiently than a randomly related set. A secondary hypothesis was that "simple" functional relations (defined by few parameters) will be learned more effectively than more ucomplex" functions (defined by a greater number of parameters).(III) SUbjects will respond to stimuli to which no response has been explicitly associated in learning by interpolating or extrapolating the functional relation to these stimuli.The experimental paradigm used consisted of a paired associates task involving 26 scaled stimuli ("V" marks varying along the length of a narrow rectangle), only 15 of which were used in the learning phase of each trial, the remaining 11 being included in the reproduction phase to allow observation of interpolation and extrapolation effects. Six conditions were used, in three of which the response (a vertical mark on a line below the rectangular slot) was related to the stimulus according to a simple continuous function (linear in two cases, quadratic in the third), while in the other three conditions stimuli and responses were randomly related. All three hypotheses outlined above were verified. In addition, an analytic technique utilizing Fisher's method of orthogonal polynomials was applied, enabling determination of which polynomials significantly related to mean responses (averaged over six trials), and of which polynomials exhibited significant trial to trial variation in slope. It was found that the first four orthogonal polynomials were sufficient to account for most of the reliable variance in mean responses. Trial to tria...

show abstract

Stimulus generalization of conditioned responses.

Cited by 146 publications

References 43 publications

The geometry of stimulus control

The geometry of stimulus control

Artificial neural networks as models of stimulus control

Functional Learning: The Learning of Continuous Functional Mappings Relating Stimulus and Response Continua

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