The influence of temporal regularities on the implicit learning of pitch structures

Implicit learning is the acquisition of complex information without the intention to learn. The aim of this study was to investigate the influence of temporal regularities on the implicit learning of an artificial pitch grammar. According to the dynamic attending theory (DAT) external regularities can entrain internal oscillators that guide attention over time, inducing temporal expectations that influence perception of future events. In the present study, the presentation of the artificial pitch grammar in the exposure phase was temporally either regular or irregular for one of two participant groups. Based on the DAT, it was hypothesized that the regular temporal presentation would favour implicit learning of tone structures in comparison to the irregular temporal presentation. Results demonstrated learning of the artificial grammar for the group with the regular exposure phase and partial learning for the group with the irregular exposure phase. These findings suggest that the regular presentation helps listeners to develop perceptual expectations about the temporal occurrence of future tones and thus facilitates the learning of the artificial pitch grammar.     

The metric matters: determining the extent of children's knowledge of morphological spelling regularities

All developmental research needs to carefully consider how children's knowledge is measured. The study of children's knowledge of spelling conventions, or the ways in which the English orthography encodes the roots and affixes and the sounds in words, is no exception. This experiment examined the extent of 7- to 9-year-old children's knowledge of the role of root morphemes in spelling words across different contexts and with different units of assessment. Different writing contexts did not appear to affect children's performance; children were better able to spell the first components of two- than of one-morpheme words (e.g. only free in freely and freeze), both when writing whole words and their first sections (e.g. completing__ or __ly for freely). A second analysis revealed that the unit of coding can influence conclusions. Children demonstrated similar abilities across ages 7 to 9 when only the first segments of words were coded; in contrast, there was evidence of age-related differences when whole word spelling accuracy was assessed. In combination, these results suggest that children's knowledge of the principle of root consistency is remarkably robust to changes in writing context, but that coding is key when drawing conclusions. These findings remind us that the metric matters in studies of spelling, as in other domains, and they offer a manner to reconcile previously conflicting data on spelling development.     

The influence of morphological information on cataphoric pronoun assignment

In 3 eye-tracking experiments, the authors investigated the use of morphological information during pronoun resolution. Experiments 1 and 2 showed that disruption occurred when a preferred assignment was inconsistent with gender information. Experiment 2 ensured that this difficulty was not due to the introduction of a new discourse entity. Experiment 3 showed that disruption also occurred when number information was inconsistent with the preferred assignment. The results indicate that the use of morphological information is delayed until after the computation of coreference relations.     

Parameterization of connectionist models.

We present a method for estimating parameters of connectionist models that allows the model's output to fit as closely as possible to empirical data. The method minimizes a cost function that measures the difference between statistics computed from the model's output and statistics computed from the subjects' performance. An optimization algorithm finds the values of the parameters that minimize the value of this cost function. The cost function also indicates whether the model's statistics are significantly different from the data's. In some cases, the method can find the optimal parameters automatically. In others, the method may facilitate the manual search for optimal parameters. The method has been implemented in Matlab, is fully documented, and is available for free download from the Psychonomic Society Web archive at www.psychonomic.org/archive/.     

Lesioning a connectionist model of visual search: selective effects on distractor grouping.

A connectionist model of visual search is presented, in which search is determined by patterns of grouping between distractors and between the target and the distractors. Grouping is based on conjunctive relations between simple form elements (corners, line-end terminators), and uses principles of similarity and spatial proximity. In its normal, 'unlesioned' state, the model simulates the search data generated by human subjects when they search for simple form conjunctions amongst either homogeneous or heterogeneous distractors (Humphreys & Muller, in press). In this paper, the performance of the model is examined after the model is subject to various types of 'lesion'. 'Lesioning' is produced either by increasing the internal noise on the activation functions governing the interactions between processing units, or by eliminating processing units from different loci in the model. Increasing the internal noise within the model, or lesioning units within 'high-level' processing stages, can generate the selective effects on search found in a detailed single case study of an agnosic patient (Humphreys, Riddoch, Quinlan, Price & Donnelly, this volume): Namely, there is selective disruption of search for a form conjunction amongst homogeneous distractors relative to search for the same target amongst heterogeneous distractors. This selective effect can be attributed to the 'lesion' disrupting grouping between distractors, which normally facilitates search with homogeneous distractors. The simulations demonstrate that (1) search processes can be disrupted by adding internal noise to search functions, (2) that there can be selective effects on grouping processes, and (3) that these selective effects can be associated with different types of lesion to different stages in the model. The implications of the simulations for understanding visual processing impairments in agnosia are discussed.     

High regularities in eye-movement patterns reveal the dynamics of the visual working memory allocation mechanism

With only two to five slots of visual working memory (VWM), humans are able to quickly solve complex visual problems to near optimal solutions. To explain the paradox between tightly constrained VWM and impressively complex human visual problem-solving ability, we propose several principles for dynamic VWM allocation. In particular, we propose that complex visual information is represented in a temporal manner using only a few slots of VWM that include global and local visual chunks. We built a model of human traveling salesman problem solving based on these principles of VWM allocation and tested the model with eye-movement data. Exactly as the model predicted, human eye movements during traveling salesman problem solving have precise quantitative regularities with regard to both the general statistical pattern of attentional fixations and how they vary across individuals with different VWM capacities. Even though VWM capacity is very limited, eye movements dynamically allocate VWM resources to both local and global information, enabling attention to fine details without loss of the big picture.     

Cross-linguistic regularities in the frequency of number words.

We examine the frequency of numerals and ordinals in seven different languages and/or cultures. Many cross-cultural and cross-linguistic patterns are identified. The most striking is a decrease of frequency with numerical magnitude, with local increases for reference numerals such as 10, 12, 15, 20, 50 or 100. Four explanations are considered for this effect: sampling artifacts, notational regularities, environmental biases and psychological limitations on number representations. The psychological explanation, which appeals to a Fechnerian encoding of numerical magnitudes and to the existence of numerical points of reference, accounts for most of the data. Our finding also has practical importance since it reveals the frequent confound of two experimental variables: numerical magnitude and numeral frequency.     

The relationship between connectionist models and a dynamic data-oriented theory of concept formation

In this paper I shall compare two models of concept formation, both inspired by basic convictions of philosophical empiricism. The first, the connectionist model, will be exemplified by Kohonen maps, and the second will be my own dynamic theory of concept formation. Both can be understood in probabilistic terms, both use a notion of convergence or stabilization in modelling how concepts are built up. Both admit destabilization of concepts and conceptual change. Both do not use a notion of representation in some pregiven language, such as a language of thought or some logical language. Representation in a formal language only plays a role on the meta-level, namely within the theory about concept formation.A short version of this paper was given at the Fachtagung der Gesellschaft für Kognitionswissenschaft. Freiburg, October 12–15th, 1994.     

Accounting for the computational basis of consciousness: a connectionist approach

This paper argues for an explanation of the mechanistic (computational) basis of consciousness that is based on the distinction between localist (symbolic) representation and distributed representation, the ideas of which have been put forth in the connectionist literature. A model is developed to substantiate and test this approach. The paper also explores the issue of the functional roles of consciousness, in relation to the proposed mechanistic explanation of consciousness. The model, embodying the representational difference, is able to account for the functional role of consciousness, in the form of the synergy between the conscious and the unconscious. The fit between the model and various cognitive phenomena and data (documented in the psychological literatures) is discussed to accentuate the plausibility of the model and its explanation of consciousness. Comparisons with existing models of consciousness are made in the end.     

The exploitation of regularities in the environment by the brain

Statistical regularities of the environment are important for learning, memory, intelligence, inductive inference, and in fact, for any area of cognitive science where an information-processing brain promotes survival by exploiting them. This has been recognised by many of those interested in cognitive function, starting with Helmholtz, Mach, and Pearson, and continuing through Craik, Tolman, Attneave, and Brunswik. In the current era, many of us have begun to show how neural mechanisms exploit the regular statistical properties of natural images. Shepard proposed that the apparent trajectory of an object when seen successively at two positions results from internalising the rules of kinematic geometry, and although kinematic geometry is not statistical in nature, this is clearly a related idea. Here it is argued that Shepard's term, "internalisation," is insufficient because it is also necessary to derive an advantage from the process. Having mechanisms selectively sensitive to the spatio-temporal patterns of excitation commonly experienced when viewing moving objects would facilitate the detection, interpolation, and extrapolation of such motions, and might explain the twisting motions that are experienced. Although Shepard's explanation in terms of Chasles' rule seems doubtful, his theory and experiments illustrate that local twisting motions are needed for the analysis of moving objects and provoke thoughts about how they might be detected.     

Effects of similarity and experience on discrimination learning: a nonassociative connectionist model of perceptual learning.

This article describes a novel connectionist model of perceptual learning (PL) that provides a mechanism for nonassociative differentiation (J. J. Gibson & E. J. Gibson, 1955). The model begins with the assumption that 2 processes--1 that decreases associability and 1 that increases discriminability--operate during preexposure (S. Channell & G. Hall, 1981). In contrast to other models (e.g., I. P. L. McLaren, H. Kaye, & N. J. Mackintosh, 1989), in the current model the mechanisms for these processes are compatible with a configural model of associative learning. A set of simulations demonstrates that the present model can account for critical PL phenomena such as exposure learning and effects of similarity on discrimination. It is also shown that the model can explain the paradoxical result that preexposure to stimuli can either facilitate or impair subsequent discrimination learning. Predictions made by the model are discussed in relation to extant theories of PL.     

Representing task context: proposals based on a connectionist model of action

Representations of task context play a crucial role in shaping human behavior. While the nature of these representations remains poorly understood, existing theories share a number of basic assumptions. One of these is that task representations are discrete, independent, and non-overlapping. We present here an alternative view, according to which task representations are instead viewed as graded, distributed patterns occupying a shared, continuous representational space. In recent work, we have implemented this view in a computational model of routine sequential action. In the present article, we focus specifically on this model's implications for understanding task representation, considering the implications of the account for two influential concepts: (1) cognitive underspecification, the idea that task representations may be imprecise or vague, especially in contexts where errors occur, and (2) information-sharing, the idea that closely related operations rely on common sets of internal representations.     

ALCOVE: an exemplar-based connectionist model of category learning.

ALCOVE (attention learning covering map) is a connectionist model of category learning that incorporates an exemplar-based representation (Medin & Schaffer, 1978; Nosofsky, 1986) with error-driven learning (Gluck & Bower, 1988; Rumelhart, Hinton, & Williams, 1986). Alcove selectively attends to relevant stimulus dimensions, is sensitive to correlated dimensions, can account for a form of base-rate neglect, does not suffer catastrophic forgetting, and can exhibit 3-stage (U-shaped) learning of high-frequency exceptions to rules, whereas such effects are not easily accounted for by models using other combinations of representation and learning method.     

A connectionist perspective on the development of reading skills in children

The development of decoding skills has traditionally been viewed as a stage-like process during which children's reading strategies change as a consequence of the acquisition of phonological awareness. More explicit accounts of the mechanisms involved in learning to read are provided by recent connectionist models in which children learn mappings initially between orthography and phonology, and later between orthography, phonology and semantics. Evidence from studies of reading development suggests that learning to read is determined primarily by the status of a child's phonological representations and is therefore compromised in dyslexic children who have phonological deficits. Children who have language impairments encompassing deficits in semantic representations have qualitatively different reading problems centring on difficulties with reading comprehension and in learning to read exception words.     

Attractor dynamics in word recognition: converging evidence from errors by normal subjects, dyslexic patients and a connectionist model

People make both semantic and visual errors when trying to recognise the meaning of degraded words. This result mirrors the finding that deep dyslexic patients make both semantic and visual errors when reading aloud. We link the results with the demonstration that a recurrent connectionist network which produces the meaning of words in response to their spelling pattern produces this distinctive combination of errors both when its input is degraded and when it is lesioned. The reason why the network can simulate the errors of both normal subjects and patients lies in the nature of the attractors which it develops as it learns to map orthography to semantics. The key role of attractor structure in the successful simulation suggests that the normal adult semantic reading route may involve attractor dynamics.     

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.     

A connectionist model of a continuous developmental transition in the balance scale task

A connectionist model of the balance scale task is presented which exhibits developmental transitions between ‘Rule I’ and ‘Rule II’ behavior [Siegler, R. S. (1976). Three aspects of cognitive development. Cognitive Psychology,8, 481–520.] as well as the ‘catastrophe flags’ seen in data from Jansen and van der Maas [Jansen, B. R. J., & van der Maas, H. L. J. (2001). Evidence for the phase transition from Rule I to Rule II on the balance scale task. Developmental Review, 21, 450–494]. The model extends a connectionist model of this task [McClelland, J. L. (1989). Parallel distributed processing: Implications for cognition and development. In R. G. M. Morris (Ed.), Parallel distributed processing: Implications for psychology and neurobiology (pp. 8–45). Oxford: Clarendon Press] by introducing intrinsic variability into processing and by allowing the network to adapt during testing in response to its own outputs. The simulations direct attention to several aspects of the experimental data indicating that children generally show gradual change in sensitivity to the distance dimension on the balance scale. While a few children show larger changes than are characteristic of the model, its ability to account for nearly all of the data using continuous processes is consistent with the view that the transition from Rule I to Rule II behavior is typically continuous rather than discrete in nature.     

A constructivist connectionist model of transitions on false-belief tasks

How do children come to understand that others have mental representations, e.g., of an object’s location? Preschoolers go through two transitions on verbal false-belief tasks, in which they have to predict where an agent will search for an object that was moved in her absence. First, while three-and-a-half-year-olds usually fail at approach tasks, in which the agent wants to find the object, children just under four succeed. Second, only after four do children succeed at tasks in which the agent wants to avoid the object. We present a constructivist connectionist model that autonomously reproduces the two transitions and suggests that the transitions are due to increases in general processing abilities enabling children to (1) overcome a default true-belief attribution by distinguishing false- from true-belief situations, and to (2) predict search in avoidance situations, where there is often more than one correct, empty search location. Constructivist connectionist models are rigorous, flexible and powerful tools that can be analyzed before and after transitions to uncover novel and emergent mechanisms of cognitive development.     

Effects of early bilingualism on learning phonological regularities in a new language

Drawing on structural sensitivity theory, the current study investigated monolingual and bilingual children's ability to learn how phonemes combine to form acceptable syllables in a new language. A total of 186 monolingual and bilingual kindergarteners, first graders, and second graders in Taiwan participated in the study. Bilingual children, regardless of whether they actively used a second language at home or simply had exposure to it, showed an advantage over their monolingual peers in learning the phonological patterns in the new language. The study provides empirical support for structural sensitivity theory and calls for the need to reconceptualize the effects of early bilingualism.