Behaviors observed during S- in a simple discrimination learning task

Key pecking of pigeons was reinforced with food in the presence of a horizontal line and never reinforced in the presence of a vertical line. Highly stereotyped behaviors, as well as key pecking, were observed and recorded in the presence of both stimuli. Results showed that a high proportion of time spent in the presence of the horizontal line was occupied by key pecking, a high proportion of time in the presence of the vertical line was occupied by stereotyped nonkey-pecking behaviors, and intermediate proportions of time spent in the presence of intermediate stimuli were occupied by each class of behavior during generalization tests. Similar running rates (number of key pecks divided by observed key-pecking time) were obtained in the presence of all stimuli, indicating that changes in time rather than tempo accounted for the changes in overall rates of key pecking. An exception occurred in responding to the horizontal line as differential performance was developing. In addition to an increase in time spent key pecking, increased running rates occurred in seven of eight birds, suggesting that both time allocation and tempo play a role in behavioral contrast of overall rates of key pecking.     

Finite mixture distribution models of simple discrimination learning

Through the application of finite mixture distribution models, we investigated the existence of distinct modes of behavior in learning a simple discrimination. The data were obtained in a repeated measures study in which subjects aged 6 to 10 years carried out a simple discrimination learning task. In contrast to distribution models of exclusively rational learners or exclusively incremental learners, a mixture distribution model of rational learners and slow learners was found to fit the data of all measurement occasions and all age groups. Hence, the finite mixture distribution analysis provides strong support for the existence of distinct modes of learning behavior. The results of a second experiment support this conclusion by crossvalidation of the models that fit the data of the first experiment. The effect of verbally labeling the values on the relevant stimulus dimension and the consistency of behavior over measurement occasions are related to the mixture model estimates.     

Application of a model to paired-associate learning

The proposal is made to consider a paired-associate item as becoming conditioned to its correct response in all-or-none fashion, and that prior to this conditioning event the subject guesses responses at random to an unlearned item. These simple assumptions enable the derivation of an extensive number of predictions about paired-associate learning. The predictions compare very favorably with the results of an experiment discussed below.This research was supported by a grant, M-3849, from the National Institutes of Mental Health, United States Public Health Service.     

Cue selection in verbal discrimination learning of retarded subjects.

A series of experiments was conducted to examine cue function in trigram verbal discrimination learning by retarded subjects. The issue was to determine the factors that control attention in this type of learning situation. The two variables of chief interest were trigram meaningfulness and reinforcement history. The major general conclusion was that retarded subjects employ complex cue selection strategies in solving a verbal discrimination involving compound verbal stimuli. Major findings were as follows: (a) Retarded subjects exhibit a response bias in favor of words over nonsense trigrams; (b) relatively little active cue selection based on meaningfulness was observed; (c) a compound stimulus discrimination was more difficult to learn than a discrimination involving single verbal stimuli; (d) compound and single stimuli may be processed differently in original discrimination learning; (e) cue position probably controls attention in compound discrimination learning; and (f) a frequency theory of verbal discrimination learning is supported by these data.     

Elemental associability changes in human discrimination learning

Participants initially completed a discrimination task (D1) involving categorization of patterns with multiple common features, each feature being partly predictive of the correct response. In a subsequent target discrimination task (D2), these features were redistributed across new discriminative stimuli. The relative predictiveness of the features in D1 was either maintained in D2 (i.e., features were equally informative in D1 and D2) or switched (i.e., more informative features in D1 were made less informative in D2, and vice versa). Differential performance on D2 suggested that features most predictive of the correct D1 responses became more highly associable than features that were less predictive in D1. This finding suggests that the associability of individual stimulus elements changes as a consequence of their role in discrimination learning.     

A markov model for discrimination learning

A theory for discrimination learning which incorporates the concept of an observing response is presented. The theory is developed in detail for experimental procedures in which two stimuli are employed and two responses are available to the subject. Applications of the model to cases involving probabilistic and nonprobabilistic schedules of reinforcement are considered; some predictions are derived and compared with experimental results.This research was supported by a grant from the National Science Foundation.     

Application of a cognitive-distance model to learning in a simulated travel task

A cognitive-distance model for choice, obtained by specializing a general class of models for categorization, was tested in a situation simulating the task of controlling speed of a vehicle in tasks defined by different relations between speed and probability of delay. Subjects exhibited significant learning whenever delay schedules permitted greater-than-chance performance, but on the average they did not approach optimal performance in the sense of choosing speeds so as to maximize distance attained in allowed time. Evidence was obtained that subjects encoded information about probabilities of delay and distributions of distance attained at different speeds quite accurately in memory and that suboptimal performance was due primarily to imperfect discrimination among representations of choice alternatives on a cognitive scale of expected distance.     

A component model for stimulus variables in discrimination learning

A general function is derived describing the conditioning of a single stimulus component in a discriminative situation. This function, together with the combinatorial rules of statistical learning theory [5, 12], generates empirically testable formulas for learning of classical two-alternative discriminations, probabilistic discriminations, and discriminations based on the outcomes of preceding trials in partial reinforcement experiments.The researches on which this paper is based were facilitated by a grant from the National Science Foundation.     

Conditional discrimination in mentally retarded subjects: programming acquisition and learning set.

In Experiment 1, 3 subjects with retardation were exposed to two visual-visual arbitrary matching-to-sample problems each day. One conditional discrimination was presented under trial-and-error conditions, and the other was presented under a component training procedure. The latter began by establishing the comparison discrimination and its rapid reversal. The successive discrimination between the sample stimuli was established through differential naming. Then, sample naming was maintained in conditional discrimination sessions in which the same sample was presented in blocks of consecutive trials. Block size was decreased across sessions until sample presentation was randomized as in trial-and-error training (but with naming maintained). Two subjects initially learned only with component training. The performance of the 3rd subject was inconsistent across conditional discriminations. One of the successful subjects ultimately learned rapidly and consistently with trial-and-error procedures. Experiment 2 sought to demonstrate learning set in the other 2 subjects. Elements of the component training procedure were withdrawn over successive conditional discriminations. Ultimately, 1 subject nearly always learned under trial-and-error conditions, and the other learned under trial-and-error conditions combined with differential sample naming.