I. P. L. McLaren

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.

Associative learning and elemental representation: II. Generalization and discrimination

This paper follows on from an earlier companion paper (McLaren & Mackintosh, 2000), in which we further developed the elemental associative theory put forward in McLaren, Kaye, and Mackintosh (1989). Here, we begin by explicating the idea that stimuli can be represented as patterns of activation distributed across a set of units and that different stimuli activate partially overlapping sets (the degree of overlap being proportional to the similarity of the stimuli). A consequence of this view is that the overall level of activity of some of the units representing a stimulus may be dependent on the nature of the other stimuli present at the same time. This allows an elemental analysis in which provision for the representation of configurations of stimuli is made. A selective review of studies of generalization and discrimination learning, including peak shift, transfer along a continuum, configural discrimination, and summation, suggests that the principles embodied in this class of theory deserve careful consideration and will form part of any successful model of associative learning in humans or animals. There are some phenomena that require an elemental/associative explanation.

Blocking in the Spatial Domain

An initial series of experiments with rats in a swimming pool established that they could find a hidden platform the location of which was defined in terms of 3 or 4 landmarks and that, when trained with all 4, any subset of 3 (or even, after a sufficient number of swimming trials, 2) landmarks was sufficient to produce accurate performance. When only one landmark was present during testing, however, performance fell to chance. Two additional experiments demonstrated a significant blocking effect: If rats were first trained to locate the platform with 3 landmarks, they did not learn to use a 4th landmark added to their initial set of 3.

Learned associability and associative change in human causal learning

The Mackintosh (1975) model of associative learning specifies that processing of both the cues presented on a trial and the outcome of that trial will interact to determine the amount of associative change undergone by a given cue. Experiments looking at the distribution of associative change among the elements of a reinforced compound in animal conditioning studies indicate that processing of the outcome of a trial does indeed influence associative change. The work reported here investigates the distribution of associative change among the elements of a reinforced compound in a human causal judgement paradigm, and it indicates that processing of the cues presented on a trial also plays a role in determining associative change (in terms of changes in the associability of cues as a result of experience). Taken in combination, these results provide good support for Mackintosh (1975) and the characterizations of both cue and outcome processing that it offers.

Banishing the Control Homunculi in Studies of Action Control and Behavior Change

For centuries, human self-control has fascinated scientists and nonscientists alike. Current theories often attribute it to an executive control system. But even though executive control receives a great deal of attention across disciplines, most aspects of it are still poorly understood. Many theories rely on an ill-defined set of “homunculi” doing jobs like “response inhibition” or “updating” without explaining how they do so. Furthermore, it is not always appreciated that control takes place across different timescales. These two issues hamper major advances. Here we focus on the mechanistic basis for the executive control of actions. We propose that at the most basic level, action control depends on three cognitive processes: signal detection, action selection, and action execution. These processes are modulated via error-correction or outcome-evaluation mechanisms, preparation, and task rules maintained in working and long-term memory. We also consider how executive control of actions becomes automatized with practice and how people develop a control network. Finally, we discuss how the application of this unified framework in clinical domains can increase our understanding of control deficits and provide a theoretical basis for the development of novel behavioral change interventions.

Blocking and unblocking in human causal learning

Three experiments sought to develop the suggestion that, under some circumstances, common associative learning mechanisms might underlie animal conditioning and human causal learning, by demonstrating, in humans, an effect analogous to the unblocking by reinforcer omission observed in animal conditioning. Experiment 1 found no such effect. Experiment 2, designed to prevent inhibitory influences that might have masked excitatory unblocking in Experiment 1, demonstrated unblocking, indicating common human-animal associative learning mechanisms in which the associability of a stimulus varies as a function of its predictive history. Experiment 3, using a similar design but with a procedure promoting application of rational inference processes, failed to detect the same unblocking effect, indicating that associative and cognitive mechanisms may influence human causal learning.

Categorization and Perceptual Learning: An Analogue of the Face Inversion Effect

This paper reports two experiments that investigate the extent to which it is plausible to suppose that an associatively based mechanism for perceptual learning acts as the basis for the effects of inversion on identification, recognition, matching and discrimination of faces (and certain other stimuli rendered familiar by expertise, e.g. gundogs). In the first experiment, an inversion effect that is contingent both on familiarity with a category and on the category possessing prototypical structure is demonstrated using discrimination learning of chequerboard stimuli. The second experiment demonstrates that the inversion effect found in Experiment 1 can generalize to a recognition paradigm as well. These results are discussed within the framework provided by associative learning theory, and a parallel is drawn with models employing a norm-based coding in similarity space. The conclusion is that it would be remarkable if the inversion effects demonstrated with the abstract categories used in the experiments reported here were not implicated in the inversion effects found with other classes of stimuli, whilst conceding that the analogy is not complete, particularly in the case of faces.

Retrospective revaluation in humans: Learning or memory?

The phenomenon of retrospective revaluation has posed considerable problems for many associative learning theories as it involves a change in the associative strength of a cue on trials on which that cue is absent. The present series of experiments pursues this idea of changes in associative strength between evoked representations of cues, in an effort to establish, de novo, an excitatory connection between two cues simultaneously activated in memory. Given the finding of Dwyer, Mackintosh, and Boakes (1998) that simultaneous activation of absent cues in the memory of rats resulted in learning comparable to that seen in retrospective revaluation, we expected that if retrospective revaluation was found in humans, then excitatory learning due to simultaneous activation would also be seen. This was not the case. The implications of our results are discussed in terms of Dickinson and Burkes (1996) modified SOP model and a version of McLarens (1993) APECS network. We conclude that many of the effects attributed to learning in retrospective revaluation studies are better thought of as due to changes in the retrievability of items in memory.

Associations and propositions: The case for a dual-process account of learning in humans

We review evidence that supports the conclusion that people can and do learn in two distinct ways - one associative, the other propositional. No one disputes that we solve problems by testing hypotheses and inducing underlying rules, so the issue amounts to deciding whether there is evidence that we (and other animals) also rely on a simpler, associative system, that detects the frequency of occurrence of different events in our environment and the contingencies between them. There is neuroscientific evidence that associative learning occurs in at least some animals (e.g., Aplysia californica), so it must be the case that associative learning has evolved. Since both associative and propositional theories can in principle account for many instances of successful learning, the problem is then to show that there are at least some cases where the two classes of theory predict different outcomes. We offer a demonstration of cue competition effects in humans under incidental conditions as evidence against the argument that all such effects are based on cognitive inference. The latter supposition would imply that if the necessary information is unavailable to inference then no cue competition should occur. We then discuss the case of unblocking by reinforcer omission, where associative theory predicts an irrational solution to the problem, and consider the phenomenon of the Perruchet effect, in which conscious expectancy and conditioned response dissociate. Further discussion makes use of evidence that people will sometimes provide one solution to a problem when it is presented to them in summary form, and another when they are presented in rapid succession with trial-by trial information. We also demonstrate that people trained on a discrimination may show a peak shift (predicted by associative theory), but given the time and opportunity to detect the relationships between S+ and S-, show rule-based behavior instead. Finally, we conclude by presenting evidence that research on individual differences suggests that variation in intelligence and explicit problem solving ability are quite unrelated to variation in implicit (associative) learning, and briefly consider the computational implications of our argument, by asking how both associative and propositional processes can be accommodated within a single framework for cognition.

Perceptual Learning and Free Classification

Two experiments are reported that investigate the effects of stimulus preexposure on discrimination performance in a free classification task, using adult humans as subjects. In free classification subjects are asked to put stimuli into gruops in any way that seems reasonable or sensible tothem. Experiment 1 shows that the effect of preexposure is contingent on stimulus structure. Experiment 1b is the first demonstration of a retardation in learning as a consequence of simple preexposure in adult human subjects (previous demonstrations have relied on incidental or masked preexposure). Experiment 2 further supports the conclusions of Experiment 1 and extends them with the demonstration that stimulus similarity is a crucial factor. Taken together, these experiments rule out a class of attention-based explanations of the phenomena reported here. The experiments also provide novel information about the effects of preexposure. Preexposure can change the actual classifications subjects form in addition to altering the rate at which they are formed. Implications of these results for current theories of category formation and perceptual learning are considered.