Enriched learning: behavior,brain, and computation

The presence of complementary information across multiple sensory or motor modalities during learning, referred to as multimodal enrichment, can markedly benefit learning outcomes. Why is this? Here, we integrate cognitive, neuroscientific, and computational approaches to understanding the effectiveness of enrichment and discuss recent neuroscience findings indicating that crossmodal responses in sensory and motor brain regions causally contribute to the behavioral benefits of enrichment. The findings provide novel evidence for multimodal theories of enriched learning, challenge assumptions of longstanding cognitive theories, and provide counterevidence to unimodal neurobiologically inspired theories. Enriched educational methods are likely effective not only because they may engage greater levels of attention or deeper levels of processing, but also because multimodal interactions in the brain can enhance learning and memory.     

Associative learning and representation: Introduction

The papers published in this Special Issue are based upon presentations at a workshop on “Associative Learning and Representation”, which was sponsored by the Experimental Psychology Society and hosted by Emmanuel College, Cambridge. The workshop celebrated the contribution of Professor Nicholas Mackintosh to animal learning and conditioning in particular and experimental psychology in general in the year of his retirement from the Chair of Psychology at the University of Cambridge after 21 years in post. The date of the workshop, 9 July 2002, was particularly auspicious as it was the day of Professor Mackintosh's birth 67 years ago. Moreover, it is particularly fitting that this tribute is published in the Comparative and Physiological Psychology Section (B) of Quarterly Journal of Experimental Psychology, which he founded in 1981 during his editorship of the Journal between 1977 and 1984.     

The role of representation in computation

Reformers urge that representation no longer earns its explanatory keep in cognitive science, and that it is time to discard this troublesome concept. In contrast, we hold that without representation cognitive science is utterly bereft of tools for explaining natural intelligence. In order to defend the latter position, we focus on the explanatory role of representation in computation. We examine how the methods of digital and analog computation are used to model a relatively simple target system, and show that representation plays an in-eliminable explanatory role in both cases. We conclude that, to the extent that biologic systems engage in computation, representation is destined to play an explanatory role in cognitive science.

Visual feedback,movement learning and the representation of movement

Two experiments on movement learning are reported where the orientation of a visual curve on a graphics terminal, defining a relatively complex arm movement, was either orthogonal to or compatible with the direction of the required movement. In addition, individual differences in spatial orientation and visualization abilities were correlated with motor performance. Results in both experiments showed that equivalent amounts of learning occurred in the two visual conditions. However, during transfer trials to the opposite condition, virtually no transfer occurred for the compatible group when they performed in the orthogonal condition whereas there was tranfer when the orthogonal group performed in the compatible condition. The results supported the idea that very early in learning the orthogonal orientation of visual curve promoted the development of an orientation processing stage which facilitated transrer performance of the orthogonal group. Integrating these results with the past literature on movement learning led to the notion that the orientation information processing stage can be considered a cognitive system that interacts with the image of the act. Finally, contrary to expectations, spatial orientation ability failed to account for any performance while spatial visualization ability moderately correlated with performance.     

Testing the limits of long-distance learning: learning beyond a three-segment window

Traditional flat-structured bigram and trigram models of phonotactics are useful because they capture a large number of facts about phonological processes. Additionally, these models predict that local interactions should be easier to learn than long-distance ones because long-distance dependencies are difficult to capture with these models. Long-distance phonotactic patterns have been observed by linguists in many languages, who have proposed different kinds of models, including feature-based bigram and trigram models, as well as precedence models. Contrary to flat-structured bigram and trigram models, these alternatives capture unbounded dependencies because at an abstract level of representation, the relevant elements are locally dependent, even if they are not adjacent at the observable level. Using an artificial grammar learning paradigm, we provide additional support for these alternative models of phonotactics. Participants in two experiments were exposed to a long-distance consonant-harmony pattern in which the first consonant of a five-syllable word was [s] or [∫] ("sh") and triggered a suffix that was either [-su] or [-∫u] depending on the sibilant quality of this first consonant. Participants learned this pattern, despite the large distance between the trigger and the target, suggesting that when participants learn long-distance phonological patterns, that pattern is learned without specific reference to distance.     

Simultaneous learning of different regularities in sequence learning tasks: limits and characteristics

Two experiments are reported which were designed to investigate the generality and the power of the mechanisms underlying sequence learning. In both experiments, participants reacted to systematic sequences of tones. They were informed that there was a tone systematicity. Participants were not told that the interval between a response to a tone and the onset of the subsequent tone (response-signal interval, RSI) also varied according to a fixed regularity. Experiment 1 showed that the unattended RSIs were learned when they were uniquely related to the tone sequence, but not when the relation was ambiguous. Experiment 2 showed that, on the basis of the traditional reaction time performance measure, participants who learned the RSIs by attending to their systematicity could not be distinguished from those in an incidental learning condition in which the RSI systematicity was unattended. However, a model-based analysis of the processes contributing to judgements about the event sequences suggested that the two groups had acquired qualitatively different knowledge. Received: 3 March 2000 / Accepted: 2 November 2000     

Children can solve Bayesian problems: the role of representation in mental computation

Can children reason the Bayesian way? We argue that the answer to this question depends on how numbers are represented, because a representation can do part of the computation. We test, for the first time, whether Bayesian reasoning can be elicited in children by means of natural frequencies. We show that when information was presented to fourth, fifth, and sixth graders in terms of probabilities, their ability to estimate the Bayesian posterior probability was zero. Yet when the same information was presented in natural frequencies, Bayesian reasoning showed a steady increase from fourth to sixth grade, reaching an average level of 19, 39, and 53%, respectively, in two studies. Sixth graders' performance with natural frequencies matched the performance of adults with probabilities. But this general increase was accompanied by striking individual differences. More than half of the sixth graders solved most or all problems, whereas one third could not solve a single one. An analysis of the children's responses provides evidence for the use of three non-Bayesian strategies. These follow an overlapping wave model of development and continue to be observed in the minds of adults. More so than adults' probabilistic reasoning, children's reasoning depends on a proper representation of information.     

Pushing the limits of imagination: mental practice for learning sequences

In 2 experiments, the efficacy of motor imagery for learning to type number sequences was examined. Adults practiced typing 4-digit numbers. Then, during subsequent training, they either typed in the same or a different location, imagined typing, merely looked at each number, or performed an irrelevant task. Repetition priming (faster responses for old relative to new numbers) was observed on an immediate test and after a 3-month delay for participants who imagined typing. Improvement across the delay in typing old and new numbers was found for the imagined and actual typing conditions but not for the other conditions. The findings suggest that imagery can be used to acquire and retain representations of sequences and to improve general typing skill.     

Mental representation and learning: The influence of practice on the development of mental representation structure in complex action

ObjectivesRecent research has elicited distinct differences in mental representations between athletes of different skill levels. Such differences suggest that the structure of mental representations changes as a function of skill level. However, research examining how such mental representation structures develop over the course of learning is lacking. In the present study, we examine the effects of practice on the development of one's mental representation of a complex action during early skill acquisition.DesignFor this purpose, we created a controllable learning situation, using a repeated-measures design with a control group. More specifically, novice golfers were randomly assigned to either a practice group (n = 12) or a control group (n = 12). Both groups were tested before and after an acquisition phase of three days as well as after a three day retention interval.MethodsMental representation structures of the putt were recorded, employing the structural dimensional analysis of mental representation (SDA-M), which provides psychometric data on the structure and grouping of action concepts in long-term memory. In addition, outcome performance of the practice group was measured, using two-dimensional error scores of the putt.ResultsFindings revealed a significant improvement in task performance, as well as functional changes in the structure of the practice group's mental representation. In contrast, no functional adaptations were evident in the mental representation of the control group.ConclusionOur findings suggest that motor skill acquisition is associated with functional adaptations of action-related knowledge in long-term memory.     

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.     

Recoding and representation in artificial grammar learning

We apply an exemplar model of memory to explain performance in the artificial grammar task. The model blends the convolution-based method for representation developed in Jones and Mewhort’s BEAGLE model of semantic memory (Psychological Review 114:1–37, 2007) with the storage and retrieval assumptions in Hintzman’s MINERVA 2 model of episodic memory (Behavior Research Methods, Instruments, and Computers, 16:96–101, 1984). The model captures differences in encoding to fit data from two experiments that document the influence of encoding on implicit learning. We provide code so that researchers can adapt the model and techniques to their own experiments.     

A dyadic brain model of ape gestural learning,production and representation

Animal Cognition - It has been argued that variation in gesture usage among apes is influenced either by differential sampling of an innate ‘gesture space’ (Hobaiter and Byrne in Anim...     

Historical change, cultural learning, and cognitive representation in Zinacantec Maya children

Against the background of an unchanging sequence of representational development, we demonstrate that implicit processes of learning and cognition can change from one historical period to another. One generation of Zinacantec Maya children was studied in 1969 and 1970, the next generation in 1991 and 1993. In the intervening two decades, the community, located in Chiapas, Mexico, was involved in a transition from an economy based primarily on subsistence and agriculture to an economy based primarily on money and commerce. A naturalistic study of weaving apprenticeship and an experimental study of visual representation showed that the ecological transition was linked to greater emphasis on independent cultural learning, abstract representation, and innovation, and, correlatively, a movement away from scaffolded guidance, detail-oriented representation, and imitative representational strategies. These changes constituted automatic adaptations with an implicit nature. In addition, historical variability in implicit modes of cultural apprenticeship predicted shifts in implicit processes of child and adolescent cognition. In sum, socialization and development are not fixed but adapt, in a coordinated way, to changing ecological conditions.     

Competitive learning in biological and artificial neural computation

In this review we will briefly discuss 'classical' competitive learning and approaches to competitive learning that involve mixtures of experts. We will then focus on competitive learning that is guided by the temporal structure that is present within the stimuli. In this context, we will describe a general principle for resource allocation and memory management, that may account for a range of psychophysical and neurophysiological findings.     

Testing the limits of statistical learning for word segmentation

Past research has demonstrated that infants can rapidly extract syllable distribution information from an artificial language and use this knowledge to infer likely word boundaries in speech. However, artificial languages are extremely simplified with respect to natural language. In this study, we ask whether infants’ ability to track transitional probabilities between syllables in an artificial language can scale up to the challenge of natural language. We do so by testing both 5.5‐ and 8‐month‐olds’ ability to segment an artificial language containing four words of uniform length (all CVCV) or four words of varying length (two CVCV, two CVCVCV). The transitional probability cues to word boundaries were held equal across the two languages. Both age groups segmented the language containing words of uniform length, demonstrating that even 5.5‐month‐olds are extremely sensitive to the conditional probabilities in their environment. However, neither age group succeeded in segmenting the language containing words of varying length, despite the fact that the transitional probability cues defining word boundaries were equally strong in the two languages. We conclude that infants’ statistical learning abilities may not be as robust as earlier studies have suggested.     

Epistemic representation, informativeness and the aim of faithful representation

In this paper, I take scientific models to be epistemic representations of their target systems. I define an epistemic representation to be a tool for gaining information about its target system and argue that a vehicle’s capacity to provide specific information about its target system—its informativeness—is an essential feature of this kind of representation. I draw an analogy to our ordinary notion of interpretation to show that a user’s aim of faithfully representing the target system is necessary for securing this feature.     

Integration and representation in rats' serial pattern learning in the T-maze

Rats were exposed to three-trial series consisting of reinforced (R) trials and one nonreinforced (N) trial in a fixed order, RRN and RNR (Experiments 1 and 2) or NRR and RRN (Experiment 3), on extended visually distinct runways in a T-maze. When initially presented with the same sequence on each series in a session (separate presentations) with the same runway on all trials within a series (Experiments 1 and 3), all the rats developed slower running speeds on N than on R trials. When a runway was sometimes changed between the first and next two trials during separate presentations training (Experiment 2) or both sequences were later intermixed within each session in each experiment, only rats exposed to each sequence on a specific runway maintained these serial running patterns. Rats displayed serial running patterns on a test RNN sequence similar to that on the RNR sequence (Experiment 2), as would be predicted by an intertrial association model of serial pattern learning (Capaldi & Molina, 1979), but responded on test RRR and NRN sequences (Experiment 3) as would be predicted by an ordinal-trial-tag/intratrial association model (Burns, Wiley, & Payne, 1986). Results from test series of free-choice trials in Experiments 1 and 2 failed to support a prediction of the intratrial association model that these rats would integrate RRN and RNR sequences. Rather than always selecting a baited runway on both the second and the third free-choice trials, the rats only selected a baited runway on the third trial on the basis of their choice on the second trial, as would be predicted by the intertrial association model. Only after experiencing all possible outcome sequences during forced-choice training in Experiment 3 did these rats predominantly select a baited runway on every free-choice trial.