A Rational Equation of the Learning Curve Based on Thorndike's Law of Effect

Gulliksen, Harold

doi:10.1080/00221309.1934.9917847

Cited by 88 publications

(44 citation statements)

References 25 publications

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“…It is clearly seen that the final function is a hyperbola, the type being proposed earlier by Thurstone, but this time the function was based, not on empirical grounds, but deduced from a theoretical model. Gulliksen (1934) derived his rational equations based on the work of Thurstone and Thorndike's law of effect (Thorndike, 1932). He used such variables as the number of incorrect responses, probabilities of correct and incorrect response, the total number of correct responses and cumulative errors.…”

Section: Rational Equationsmentioning

confidence: 99%

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The application of the first order system transfer function for fitting the 3-arm radial maze learning curve

Stepanov

Abramson

2008

Journal of Mathematical Psychology

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Section: Rational Equationsmentioning

confidence: 99%

“…Though the mathematical analysis of animal and human learning had been undertaking since the beginning of twentieth century, very few attempts had been made with respect to maze learning (for reviews of earlier work see Gulliksen (1934) and Hovland (1951)). We can mention only three papers where an attempt was made.…”

Section: Introductionmentioning

confidence: 99%

The application of the first order system transfer function for fitting the 3-arm radial maze learning curve

Stepanov

Abramson

2008

Journal of Mathematical Psychology

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“…The present investigation has taken a theoretical formulation of learning, the Audley-lonckheere (1956) learning model of which Gulliksen (1934Gulliksen ( , 1953) is a special case, and determines the values of the parameters of this model from the data on the split-brain cat learning records. James Rams·ay (1970) of McGill university has written a computer program for estimating parameters in the Audley-Jonckheere learning model and for carrying out the analysis of the learning data.…”

Section: Methods Of Analysismentioning

confidence: 99%

“…In view of the statements asserting practical identity of functioning for the two hemispheres and of the great value for the study of learning that would be achieved if learning curves could be replicated , the present study of hemispheric functioning was undertaken, using as measures of similarity of functioning parameters of a learning curve based on Thorndike's law of effect as formulated by Gulliksen (1934Gulliksen ( , 1953, and generalized by Audley and 10nckheere (1956). As an additional check, we also used the more usual measures of total trials and total errors to criterion, since these two methods of analysis can give different results.…”

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confidence: 99%

An attempt to obtain replicate learning curves in the split-brain cat

Gulliksen

Voneida

1975

Psychobiology

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Sperry (1961) and others working on split-brain learning reported that the two halves function in a nearly identical manner, giving excellent controlled replication of a learning problem. Such a situation furnishes an excellent opportunity for investigating learning curve parameters to differentiate random error from a systematic discrepancy between data and learning theory. The present experiments were carried out with cats trained in a tactile discrimination apparatus, sometimes using the left paw, and sometimes the right paw. Two groups of animals were used, one trained at Princeton University, and the other at Case Western Reserve University. The data were analyzed in terms of the Audley-Jonckheere (1956) learning model and also in terms of total trials, total correct responses, and total errors to criterion. The data gave no clear evidence that second brain learning replicated first brain learning. Only one problem showed high correlations between right and left brain learning.

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“…Many mathematical models of learning and memorization (including the frequently used Weibull function) do not allow estimating the value of correct responses prior to next learning trial, as Y = 0 at X = 0 (Gallistel, Fairhurst, & Balsam, 2004;Gulliksen, 1934;Hull, 1943).…”

Section: Mathematical Model For Learning and Memorization Dynamicsmentioning

confidence: 99%

A New Mathematical Model For Assessment of Memorization Dynamics

Stepanov

Abramson

2005

Span. J. Psychol.

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A new memory model is proposed based on regression analysis and exponential-shaped learning curves. The efficacy of the model is tested with several types of experiments including food aversion in snails, maze learning in rats and memory tests for adults and children. The model is also tested on drug abusers and alcoholics. The results of goodness of fit tests indicate that our model can accurately be used to predict the memory dynamics of diverse experiments and populations. The model can also be used to predict both group and individual performance. The application of the model to detect memory impairment is discussed, as are limitations. Keywords: learning, memory, memorization dynamics, learning dynamicsSe propone un nuevo modelo de memoria basado en el análisis de regresión y las curvas exponenciales de aprendizaje. La eficacia del modelo se verifica con varios tipos de experimentos incluyendo la aversión a la comida en caracoles, el aprendizaje de laberintos en ratas y las pruebas de memoria en adultos y niños. El modelo también se pone a prueba en drogadictos y alcohólicos. Los resultados de las pruebas de bondad de ajuste indican que nuestro modelo puede ser empleado con precisión para predecir las dinámicas de la memoria en diversos experimentos y poblaciones. También puede emplearse el modelo para predecir tanto la ejecución grupal como la individual. Se comenta la aplicación del modelo para detectar el deterioro de la memoria, así como las limitaciones del mismo. Palabras clave: aprendizaje, memoria, dinámica de la memoria, dinámica del aprendizaje

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A Rational Equation of the Learning Curve Based on Thorndike's Law of Effect

Cited by 88 publications

References 25 publications

The application of the first order system transfer function for fitting the 3-arm radial maze learning curve

The application of the first order system transfer function for fitting the 3-arm radial maze learning curve

An attempt to obtain replicate learning curves in the split-brain cat

A New Mathematical Model For Assessment of Memorization Dynamics

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