A computer simulation/mathematical model of learning: Extension of DMOD from appetitive to aversive situations

The computer simulation/mathematical model called DMOD, which can simulate over 35 different phenomena in appetitive discrete-trial and simple free-operant situations, has been extended to include aversive discrete-trial situations. Learning (V) is calculated using a three-parameter equation \(\Delta V = \alpha \beta (\lambda - \bar V)\) (see Daly & Daly, 1982; Rescorla & Wagner, 1972). The equation is applied to three possible goal events in the appetitive (e.g., food) case and to three in the aversive (e.g., shock) case. The original goal event can be present, absent, or reintroduced; in the appetitive situation, these events condition approach (Vap), avoidance (Vav), and courage (Vcc), respectively. In the aversive situation, the events condition avoidance (Vav*), approach (Vap*), and cowardice (Vcc*), respectively. The model was developed in simple learning situations and subsequently was applied to complex situations. It can account for such diverse phenomena as contrast effects after reward shifts, greater persistence following partial than following continuous reinforcement, and a preference for predictable appetitive and predictable aversive events. Application of the aversive version of the model to “reward” shifts is described.     

Reconstructive memory: A computer model

This study presents a process model of very long-term episodic memory. The process presented is a reconstructive process. The process involves application of three kinds of reconstructive strategies—component-to-context instantiation strategies, component-instantiation strategies, and context-to-context instantiation strategies. The first is used to direct search to appropriate conceptual categories in memory. The other two are used to direct search within the chosen conceptual category. A fourth type of strategy, called executive search strategies, guide search for concepts related to the one targeted for retrieval. A conceptual memory organization implied by human reconstructive memory is presented along with examples which motivate it. A basic retrieval algorithm is presented for traversing that stucture. Retrieval strategies arise from failures in that algorithm. The memory organization and retrieval processes are implemented in a computer program called CYRUS which stores events in the lives of former Secretaries of State Cyrus Vance and Edmund Muskie and answers questions posed in English concerning that information. Examples which motivate the process model are drawn from protocols of human memory search. Examples of CYRUS's behavior demonstrate the implemented process model. Conclusions are drawn concerning retrieval failures and the relationship of episodic and semantic memory.     

A computer model of auditory stream segregation

A computer model is described which simulates some aspects of auditory stream segregation. The model emphasizes the explanatory power of simple physiological principles operating at a peripheral rather than a central level. The model consists of a multi-channel bandpass-filter bank with a “noisy” output and an attentional mechanism that responds selectively to the channel with the greatest activity. A “leaky integration” principle allows channel excitation to accumulate and dissipate over time. The model produces similar results to two experimental demonstrations of streaming phenomena, which are presented in detail. These results are discussed in terms of the “emergent properties” of a system governed by simple physiological principles. As such the model is contrasted with higher-level Gestalt explanations of the same phenomena while accepting that they may constitute complementary kinds of explanation.     

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 model of probabilistic category learning.

A new connectionist model (named RASHNL) accounts for many "irrational" phenomena found in nonmetric multiple-cue probability learning, wherein people learn to utilize a number of discrete-valued cues that are partially valid indicators of categorical outcomes. Phenomena accounted for include cue competition, effects of cue salience, utilization of configural information, decreased learning when information is introduced after a delay, and effects of base rates. Experiments 1 and 2 replicate previous experiments on cue competition and cue salience, and fits of the model provide parameter values for making qualitatively correct predictions for many other situations. The model also makes 2 new predictions, confirmed in Experiments 3 and 4. The model formalizes 3 explanatory principles: rapidly shifting attention with learned shifts, decreasing learning rates, and graded similarity in exemplar representation.     

A model of memory in concept learning

A concept learning model was developed and tested in two conjunctive attribute identification tasks. The model includes assumptions about the focus of attention, decision making, and memory for stimulus information and prior decisions. Predictions are made about how S changes his hypothesis following an error. Procedures in both tasks allowed inference of the subject's current hypothesis. The hypothesis selections and error statistics were in the majority of cases accurately predicted by the model. Deviations from predictions on hypothesis sampling occurred for naive Ss but not for trained Ss who were required to state a hypothesis on each trial.     

An autocorrelation theory of visual form detection: A computer experiment and a computer model

A theoretical model is described for the effect of the organizational properties of dotted visual forms on visual form detection. The model assumes that a human observer functions more as an organization detector than as a feature detector. It is supported by a wide variety of experimental studies of human perception.     

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 network learning model with GERT analysis

Graphical Evaluation and Review Technique (GERT) can be used to analyze the teaching-learning process. By representing the teaching-learning process with a stochastic network, it is possible to use a topology equation for a closed network to obtain some parameters for the total teaching-learning process. To illustrate the techniques, two examples concerning a basic calculus course are given. In addition, the relation to the familiar Markovian learning models is indicated.     

Fostering equitable consequences from computer learning environments

This article discusses (a) the potential advantages of learning computer programming, (b) the performance of males and females in middle school programming courses, (c) the problems teachers face in achieving this potential, and (d) the participation of males and females in programming courses. Females comprise about 40% of the students enrolled in middle school programming courses. They perform, on the average, as well as or better than the males, and comprise 60% of the most talented students. Significantly, neither males nor females make very much progress in learning to program and, therefore, in acquiring any of the general cognitive skills which might potentially result from programming courses.This material is based upon research supported by the National Institute of Education, Grant No. 400-83-0017. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author and do not necessarily reflect the views of the National Institute of Education. Special thanks are due to John Dalbey, Charles Fisher, Ellen Mandinach, Joanne Stein, and other members of the ACCCEL staff for helpful discussions of these issues.     

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 relating transfer of learning and correlation

The theoretical and practical importance of a double undertaking is discussed: the development of learning and transfer taxonomies with psychometric relevance and the building of psychometric classificatory systems with implications for learning and instruction. Psychometric classifications of human performances are most often based on the covariation of individual differences. The model presented justifies the expectation that the transfer from learning one task to learning another is linearly dependent on the coefficient of intercorrelation between the two tasks when the coefficient is corrected for attenuation. Two studies so far have explicitly confirmed the main deductions from this model. Contrary to the predictions, however, the regression curves yielded negative intercepts. Two empirically testable explanations are offered, one of which would be in full accordance with the model, while the other would call for a further assumption.     

A feedback model of perceptual learning and categorization

Top-down, feedback, influences are known to have significant effects on visual information processing. Such influences are also likely to affect perceptual learning. This paper employs a computational model of the cortical region inter-actions underlying visual perception to investigate possible influences of top-down information on learning. The results suggest that feedback could bias the way in which perceptual stimuli are categorized and could also facilitate the learning of subordinate level representations suitable for object identification and perceptual expertise.     

A probabilistic computational model of cross-situational word learning

Words are the essence of communication: They are the building blocks of any language. Learning the meaning of words is thus one of the most important aspects of language acquisition: Children must first learn words before they can combine them into complex utterances. Many theories have been developed to explain the impressive efficiency of young children in acquiring the vocabulary of their language, as well as the developmental patterns observed in the course of lexical acquisition. A major source of disagreement among the different theories is whether children are equipped with special mechanisms and biases for word learning, or their general cognitive abilities are adequate for the task. We present a novel computational model of early word learning to shed light on the mechanisms that might be at work in this process. The model learns word meanings as probabilistic associations between words and semantic elements, using an incremental and probabilistic learning mechanism, and drawing only on general cognitive abilities. The results presented here demonstrate that much about word meanings can be learned from naturally occurring child-directed utterances (paired with meaning representations), without using any special biases or constraints, and without any explicit developmental changes in the underlying learning mechanism. Furthermore, our model provides explanations for the occasionally contradictory child experimental data, and offers predictions for the behavior of young word learners in novel situations.