Elemental representation and configural mappings: Combining elemental and configural theories of associative learning

In this article, we present our first attempt at combining an elemental theory designed to model representation development in an associative system (based on McLaren, Kaye, & Mackintosh, 1989) with a configural theory that models associative learning and memory (McLaren, 1993). After considering the possible advantages of such a combination (and some possible pitfalls), we offer a hybrid model that allows both components to produce the phenomena that they are capable of without introducing unwanted interactions. We then successfully apply the model to a range of phenomena, including latent inhibition, perceptual learning, the Espinet effect, and first- and second-order retrospective revaluation. In some cases, we present new data for comparison with our model's predictions. In all cases, the model replicates the pattern observed in our experimental results. We conclude that this line of development is a promising one for arriving at general theories of associative learning and memory.     

An instance theory of associative learning

We present and test an instance model of associative learning. The model, Minerva-AL, treats associative learning as cued recall. Memory preserves the events of individual trials in separate traces. A probe presented to memory contacts all traces in parallel and retrieves a weighted sum of the traces, a structure called the echo. Learning of a cue–outcome relationship is measured by the cue’s ability to retrieve a target outcome. The theory predicts a number of associative learning phenomena, including acquisition, extinction, reacquisition, conditioned inhibition, external inhibition, latent inhibition, discrimination, generalization, blocking, overshadowing, overexpectation, superconditioning, recovery from blocking, recovery from overshadowing, recovery from overexpectation, backward blocking, backward conditioned inhibition, and second-order retrospective revaluation. We argue that associative learning is consistent with an instance-based approach to learning and memory.     

Evaluation and development of a connectionist theory of configural learning

A configural theory of associative learning is described that is based on the assumption that conditioning results in associations between the unconditioned stimulus and a representation of the entire pattern of stimulation that was present prior to its delivery. Configural theory was formulated originally to account for generalization and discrimination in Pavlovian conditioning. The first part of the article demonstrates how this theory can be used to explain results from studies of overshadowing, blocking, summation, and discrimination learning. The second part of the article shows how the theory can be developed to explain a broader range of phenomena, including mediated conditioning, reinforcer devaluation effects, the differential outcomes effect, acquired equivalence, sensory preconditioning, and structural discriminations.     

Summation: Assessment of a configural theory

In five experiments, rats were given Pavlovian pairings of auditory and visual stimuli with delivery of food pellets. Experiment 1 found greater responding to an AB compound after training with the individual A and B stimuli, compared with responding both to the A and B elements and to a separately trained CD compound. Experiment 2 found this enhanced responding to depend on the associative strengths of A and B. In Experiment 3, responding was greater to a CD compound than to the other compounds after an AB-, AD+, BC+ training procedure. In Experiment 4, responding to an AB compound was greater than that to the elements after A was reinforced on a 100% schedule and B on a 50% schedule. In Experiment 5, responding to an AC compound was greater than that to either A or C after an AB+, CD+, A-training procedure. A configural theory, such as that proposed by Pearce (1987), anticipates summation in none of these procedures, unless the conditioned context is assumed to have a salience greater than zero. In order to predict summation in Experiments 3, 4, and 5, a context salience greater than that of the elements must be assumed. However, such an assumption also anticipates that extinction of a 100% stimulus should eliminate responding to a 50% stimulus. The results of Experiment 3 contradicted that prediction. These results conform better to the expectations of elemental models of conditioning.     

Solving Pavlov's puzzle: Attentional, associative, and flexible configural mechanisms in classical conditioning

This article introduces a new "real-time" model of classical conditioning that combines attentional, associative, and "flexible" configural mechanisms. In the model, attention to both conditioned (CS) and configural (CN) stimuli are modulated by the novelty detected in the environment. Novelty increases with the unpredicted presence or absence of any CS, unconditioned stimulus (US), or context. Attention regulates the magnitude of the associations CSs and CNs form with other CSs and the US. We incorporate a flexible configural mechanism in which attention to the CN stimuli increases only after the model has unsuccessfully attempted learn input-output combinations with CS-US associations. That is, CSs become associated with the US and other CSs on fewer trials than they do CNs. Because the CSs activate the CNs through unmodifiable connections, a CS can become directly and indirectly (through the CN) associated with the US or other CSs. In order to simulate timing processes, we simply assume that a CS is formed by a temporal spectrum of short-duration CSs that are activated by the nominal CS trace. The model accurately describes 94?% of the basic properties of classical conditioning, using fixed model parameters and simulation values in all simulations.     

An associative analysis of instrumental biconditional discrimination learning

Three different techniques were employed to analyze the associative structures mediating performance on an instrumental biconditional discrimination. In all three experiments, rats were trained concurrently on two tasks in which different stimuli signaled which one of two responses would be followed by reward. In each task, one response was rewarded in one stimulus and the other response was rewarded in the other stimulus. Correct responses earned pellets in one task and sucrose in the other task. The transfer procedure was used in Experiment 1A to identify whether or not an association developed between a biconditional discriminative stimulus and its instrumental outcome. Evidence was obtained that a biconditional cue elevated preferentially a new response trained with the same outcome. Experiments 1B and 3 examined the potential contribution of this stimulus-outcome association to biconditional performance by training the biconditional cues as signals (S-s) for the nonreinforcement of a different response. There was no evidence that this operation interfered with the ability of a biconditional cue to control performance of its correct response. In Experiments 1B and 2, the value of the instrumental outcome was reduced in an attempt to assess the contribution of stimulus-response associations to performance on the biconditional discrimination. The results of Experiments 1B and 2 reveal that correct responses were depressed following devaluation of the outcome used to train them, suggesting that learning about the response-outcome relation occurs. The implications of these results for binary and hierarchical models of instrumental learning are discussed.     

Bayesian approaches to associative learning: From passive to active learning

Traditional associationist models represent an organism's knowledge state by a single strength of association on each associative link. Bayesian models instead represent knowledge by a distribution of graded degrees of belief over a range of candidate hypotheses. Many traditional associationist models assume that the learner is passive, adjusting strengths of association only in reaction to stimuli delivered by the environment. Bayesian models, on the other hand, can describe how the learner should actively probe the environment to learn optimally. The first part of this article reviews two Bayesian accounts of backward blocking, a phenomenon that is challenging for many traditional theories. The broad Bayesian framework, in which these models reside, is also selectively reviewed. The second part focuses on two formalizations of optimal active learning: maximizing either the expected information gain or the probability gain. New analyses of optimal active learning by a Kalman filter and by a noisy-logic gate show that these two Bayesian models make different predictions for some environments. The Kalman filter predictions are disconfirmed in at least one case.     

Behavioral and associative effects of differential outcomes in discrimination learning

The role of the reinforcer in instrumental discriminations has often been viewed as that of facilitating associative learning between a reinforced response and the discriminative stimulus that occasions it. The differential-outcome paradigm introduced by Trapold (1970), however, has provided compelling evidence that reinforcers are also part of what is learned in discrimination tasks. Specifically, when the availability of different reinforcing outcomes is signaled by different discriminative stimuli, the conditioned anticipation of those outcomes can provide another source of stimulus control over responding. This article reviews how such control develops and how it can be revealed, its impact on behavior, and different possible mechanisms that could mediate the behavioral effects. The main conclusion is that differential-outcome effects are almost entirely explicable in terms of the cue properties of outcome expectancies—namely, that conditioned expectancies acquire discriminative control just like any other discriminative or conditional stimulus in instrumental learning.     

Effects of pretraining on acquisition of novel configural discriminations in human predictive learning

In two experiments, participants acquired one of two target configural discriminations (a biconditional or negative patterning discrimination) in a predictive learning task. In Experiment 1, participants were pretrained with either a configural or an elemental discrimination; in Experiment 2, they were pretrained with a configural discrimination, an elemental discrimination, or a control discrimination that was not expected to bias them toward elemental or configural processing. In both experiments, acquisition of the target configural discriminations was faster after configural pretraining than after elemental pretraining. In addition, the negative patterning discrimination was acquired faster than the biconditional discrimination. Finally, the results of Experiment 2 were more consistent with the notion that elemental pretraining hindered acquisition of the target discriminations than with the notion that configural pretraining enhanced their acquisition. Implications of these findings are discussed.     

An elemental model of associative learning: I. Latent inhibition and perceptual learning

This paper presents a brief, informal outline followed by a formal statement of an elemental associative learning model first described by McLaren, Kaye, and Mackintosh (1989). The model assumes representation of stimuli by sets of elements (i.e., microfeatures) and a set of associative algorithms that incorporate the following: real-time simulation of learning; an error-correcting learning rule; weight decay that distinguishes between transient and permanent associations; and modulation of associative learning that gives high salience to and, hence, promotes rapid learning with novel, unpredicted stimuli and reduces the salience for a stimulus as its error term declines. The model is applied in outline fashion to some of the basic phenomena of simple conditioning and, in greater detail, to the phenomena of latent inhibition and perceptual learning. A detailed account of generalization and discrimination will be provided in a later paper.     

Paying attention to attention: New economies for learning

Challenging formal education's traditional monopoly over the mass-scale acculturation of youth, the technological infrastructure of the new economy brings in its wake a new " attentional economy" in which any "connected" adult or child owns and controls a full economic share of her or his own attention. For youth who have never known the text-bound world from which their elders have come, new technologies afford them far greater power and greatly expanded rights that enable them to decide for themselves what they can see, think, and do, as their teachers grapple with ways to attract, rather than compel, students' voluntary attention. This paper reviews various formulations of "attentional economy," and it urges the study of popular forms of technologically enabled play. These technologies effectively mobilize, direct, and sustain the engaged attention of youth, whose learning in and through play far exceeds the kind of glazed-eyed button-mashing complained of by those who have made little effort to understand the educative prospects of computer gaming.     

Understanding feedback: A learning theory perspective

This article aims to review literature on feedback to teachers. Because research has hardly focused on feedback among teachers, the review’s scope also includes feedback in classrooms. The review proposes that the effectiveness of feedback and feedback processes depend on the learning theory adhered to. Findings show that regardless of the learning theory effective feedback is goal- or task-directed, specific, and neutral. In addition, four rules of thumb were formulated that reflect what a majority of learning theories suggested as effective for learning. Finally, some feedback characteristics were considered effective from only one learning theory. The article shows that feedback processes are complicated and many variables influence and mediate the processes. Most reviewed studies did not investigate the whole feedback processes, and therefore, we suggest that future research is needed to further understand feedback.     

A reexamination of the role of attention in learning from text

This paper reviews some of the recent models of selective attention and concludes that these models simplify and restrict the role of attention in reading and learning. It is maintained that the most critical problem of the models is that they do not acknowledge the important function of attention in the early stages of the reading process when meaning and importance ratings are first established. It is further suggested that reading and secondary task reaction times which have been used to measure attention, are inadequate as they cannot distinguish between different attention phases. A new model of how attention affects text comprehension is proposed. Finally, it is argued that to examine the complex effects of attention on reading and learning, novel research questions should be asked, and new ways to evaluate attention must be considered. More specifically, research on attention by cognitive and educational psychologists should utilize neuroscientific brain research methods, such as event-related potentials, and should integrate findings across research domains.     

A beautiful metaphor: transformative learning theory

This article presents a critique of both transformative learning theory and critical comments on it to date. It argues that transformative learning theory remains substantively the same as its initial exposition, in spite of a raft of problematic contentions voiced against it. The theory is argued here to be conceptually problematic, except at the level of a conceptual metaphor, which latter renders its many inconsistencies inconsequential and which explains, not just its continued popularity among educational practitioners, but also it’s largely being ignored as a subject worthy of serious critique.     

Affective and associative qualities in the learning styles of Chicanos and Anglos

The role of affective and associative meaningfulness in the learning styles of Chicanos and Anglos was examined in the present study. Sixty-four Chicano and Anglo undergraduates were compared on their learning of affectively and associatively assessed consonant-vowel-consonant trigrams. Chicanos did not differ from Anglos in their reliance on the affective relative to the associative dimension of meaningfulness in their learning style. However, Chicanos differed significantly from Anglos in affective learning style, manifesting a greater propensity to learn their liked materials more readily than their disliked materials. It was further found that while the performance of the two ethnic groups was comparable in the disliked condition, the Chicano subjects performed significantly better than the Anglo subjects in the liked condition. It is suggested that the intensified Chicano sensitivity to affective meaningfulness is consistent with their cultural concerns.     

Analytic character learning in Chinese children: evidence from associative pseudocharacter learning paradigm

The present study investigated the children’s capabilities of utilizing analytic strategies in Chinese character learning using an associative pseudocharacter learning paradigm. The participants were 54 Chinese primary school children (26?second graders and 28 fifth graders) who completed a pseudocharacter learning task that was followed by a generalization test. Repeated measures ANOVA results showed a significant main effect of block and grade in pseudocharacter learning. One-sample t-test results indicated that generalization performance was significantly better than chance level in both grade levels. Findings from this study suggested that Chinese primary school children were able to identify print–sound–meaning regularities that were not explicitly taught and apply those rules to learning new pseudocharacters. They contribute to a comprehensive understanding of the character learning processes in Chinese children when encountering new characters.     

联想学习法在中学物理教学中的运用

中学物理是一门自然学科,在教学中我们应该努力培养学生的联想能力,运用联想学习法,让学生们摆脱死记硬背的学习方法,用科学的方法去学习。运用联想学习法不仅让学生摆脱枯燥的学习,更能让学生更多的思考问题,探究学习物理知识,开拓思路,培养他们的发散性思维。     

Roles of attention in perceptual learning from perspectives of psychophysics and animal learning

The role of attention in perceptual learning has been a topic of controversy. Sensory psychophysicists/physiologists and animal learning psychologists have conducted numerous studies to examine this role; but because these two types of researchers use two very different lines of approach, their findings have never been effectively integrated. In the present article, we review studies from both lines and use exposure-based learning experiments to discuss the role of attention in perceptual learning. In addition, we propose a model in which exposure-based learning occurs only when a task-irrelevant feature is weak. We hope that this article will provide new insight into the role of attention in perceptual learning to the benefit of both sensory psychophysicists/physiologists and animal learning psychologists.