Application of Bower's one-element model to paired-associate learning by pigeons

Bower's (1961) all-or-none model of human paired-associate learning was applied to individual data supplied by three pigeons. When the center one of three keys was illuminated with red light or with three white dots in a vertical array on a black ground, pecking on the left key was reinforced. When the center key was lighted green or with a horizontal array of three white dots on a black ground, pecking on the right key was reinforced. The left and right keys were illuminated with white light. The task was considered to be analogous to learning a paired-associate list of four pairs involving four stimulus items and two response items. The model was evaluated by comparing the following model predictions with values obtained from each animal: trials-to-criterion, standard deviation of trials-to-criterion, standard deviation of errors-to-criterion, mean error runs, mean error runs of lengths one to four, and autocorrelations of errors of lags one to three. Most of the predictions based upon the model were in close agreement with the obtained data.     

Parameterizations of Markov models for two-stage learning

Alternative parameterizations of Greeno's representation of two-stage learning are developed for general and restricted cases. Boundaries of the parameter space are exhibited in both cases.     

A cognitive probability model for learning

A quantitative model for the behavior of albino rats in choice-making situations is presented. The model, which is based upon a cognitive conceptualization of the learning process, is shown to yield predictions which are equivalent to those produced by the linear operator stochastic models at the asymptotic limit but which differ from these during early trials in the learning situation.     

A unified model for perceptual learning

Perceptual learning in adult humans and animals refers to improvements in sensory abilities after training. These improvements had been thought to occur only when attention is focused on the stimuli to be learned (task-relevant learning) but recent studies demonstrate performance improvements outside the focus of attention (task-irrelevant learning). Here, we propose a unified model that explains both task-relevant and task-irrelevant learning. The model suggests that long-term sensitivity enhancements to task-relevant or irrelevant stimuli occur as a result of timely interactions between diffused signals triggered by task performance and signals produced by stimulus presentation. The proposed mechanism uses multiple attentional and reinforcement systems that rely on different underlying neuromodulators. Our model provides insights into how neural modulators, attentional and reinforcement learning systems are related.     

A model for serial verbal learning

A model for analyzing the learning process with a special emphasis on serial-position effect is proposed. This model consists of two analyses, one being an analysis of the learning process of each item in a list by a stochastic method, and the other being an analysis of serial-position effect in terms of pro- and retroactive inhibitions, and of forgetting. The model is experimentally verified, and moreover, it is found that the model permits prediction of the results of many experiments with lists of various lengths and varying difficulty.The author wishes to acknowledge help received during discussion with Prof. T. Indow.     

A vigilance model for latent learning

The author proposes a heuristic model for latent learning. It is concluded that to regard academic learning as qualitatively different from other forms of learning is to deny evolutionary continuity. Academic learning is not a unitary process governed by a single set of parameters. In addition, it is observed that the problem of student motivation may very well turn out to be purely academic. The instructional technique for a captive audience of a class may be so structured as to make the direction of attention irresistible, the performance of a response, when needed, compelling, and the acquisition of knowledge inevitable. Vigilance is an instance of innate foundation. Its most striking characteristics are its universality in the animal world, its ready evocation by a wide range of stimuli, and its apparent behavioral and physiological manifestations. The last two are the natural resources for objective investigation, and the first may well be the basis of broad and valid generalizations.     

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.     

A latent topic model with Markov transition for process data

We propose a latent topic model with a Markov transition for process data, which consists of time-stamped events recorded in a log file. Such data are becoming more widely available in computer-based educational assessment with complex problem-solving items. The proposed model can be viewed as an extension of the hierarchical Bayesian topic model with a hidden Markov structure to accommodate the underlying evolution of an examinee's latent state. Using topic transition probabilities along with response times enables us to capture examinees' learning trajectories, making clustering/classification more efficient. A forward-backward variational expectation-maximization (FB-VEM) algorithm is developed to tackle the challenging computational problem. Useful theoretical properties are established under certain asymptotic regimes. The proposed method is applied to a complex problem-solving item in the 2012 version of the Programme for International Student Assessment (PISA).     

A stochastic model for rote serial learning

A model for the acquisition of responses in an anticipatory rote serial learning situation is presented. The model is developed in detail for the case of a long intertrial interval and employed to fit data where the list length is varied from 8 to 18 words. Application of the model to the case of a short intertrial interval is considered; some predictions are derived and checked against experimental data.The author wishes to thank Professors C. J. Burke and W. K. Estes for advice and assistance in carrying out this research.     

A computational learning model for metrical phonology

One of the major challenges to linguistic theory is the solution of what has been termed the "projection problem". Simply put, linguistics must account for the fact that starting from a data base that is both unsystematic and relatively small, a human child is capable of constructing a grammar that mirrors, for all intents and purposes, the adult system. In this article we shall address ourselves to the question of the learnability of a postulated subsystem of phonological structure: the stress system. We shall describe a computer program which is designed to acquire this subpart of linguistic structure. Our approach follows the "principles and parameters" model of Chomsky (1981a, b). This model is particularly interesting from both a computational point of view and with respect to the development of learning theories. We encode the relevant aspects of universal grammar (UG)--those aspects of linguistic structure that are presumed innate and thus present in every linguistic system. The learning process consists of fixing a number of parameters which have been shown to underlie stress systems and which should, in principle, lead the learner to the postulation of the system from which the primary linguistic data (i.e., the input to the learner) is drawn. We go on to explore certain formal and substantive properties of this learning system. Questions such as cross-parameter dependencies, determinism, subsets, and incremental versus all-at-once learning are raised and discussed in the article. The issues raised by this study provide another perspective on the formal structure of stress systems and the learnability of parameter systems in general.     

A model for mastery learning based on dynamic equilibrium theory

A theoretical model is developed based on dynamic equilibrium theory, which suggests a quantitative explanation for the basic idea in mastery learning that individuals of different aptitude reacting in an ideal environment to the same total quantity of material will ultimately reach the same mastery status, or gain equal increments of status starting from the same base, in time spans inversely proportional to the aptitude. The approach incorporates two widely used mechanisms, one for growth in learning and intelligence and one for loss through forgetting, into a single model that treats acquisition and loss as opposing, but simultaneously occuring processes. Operational definitions for acquisition and loss rate constants that would enable the model to account exactly for the basic assumption of mastery learning and at the same time yield a growth pattern identical to that implicit in the Stanford Binet ratio IQ are identified. An analysis of the extent to which the requirements of the model are confirmed by the available experimental evidence reveals that the basic idea of mastery learning is a useful and convenient approximation, which applies with some validity to the growth of mental ability as well as to the growth in school achievement.     

A microcomputer lab for psychology based on a hierarchical learning model

This paper describes a psychology laboratory based on a CORVUS-networked system of Apple IIe microcomputers. It also describes the specific software applications used in each of a range of psychology courses, and the skills developed through each of the specific applications. While no empirical data is yet available to support formal evaluation of this type of instruction, initial student and faculty responses to its implementation have been very favorable. The model described here may be useful to other psychologists who are interested in providing microcomputer experiences for their students.     

A reinforcement learning diffusion decision model for value-based decisions

Psychonomic Bulletin & Review - Psychological models of value-based decision-making describe how subjective values are formed and mapped to single choices. Recently, additional efforts have...     

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.