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Biomedical subjects

David C Plaut

Publications and source records attributed to David C Plaut.

7 recordsLinked to original sources
PubMed2006

Short-term memory for serial order: a recurrent neural network model.

Despite a century of research, the mechanisms underlying short-term or working memory for serial order remain uncertain. Recent theoretical models have converged on a particular account, based on transient associations between independent item and context representations. In the present article, the authors present an alternative model, according to which sequence information is encoded through sustained patterns of activation within a recurrent neural network architecture. As demonstrated through a series of computer simulations, the model provides a parsimonious account for numerous benchmark characteristics of immediate serial recall, including data that have been considered to preclude the application of recurrent neural networks in this domain. Unlike most competing accounts, the model deals naturally with findings concerning the role of background knowledge in serial recall and makes contact with relevant neuroscientific data. Furthermore, the model gives rise to numerous testable predictions that differentiate it from competing theories. Taken together, the results presented indicate that recurrent neural networks may offer a useful framework for understanding short-term memory for serial order.

Artificial Intelligence↗
PubMed2004

Doing without schema hierarchies: a recurrent connectionist approach to normal and impaired routine sequential action.

In everyday tasks, selecting actions in the proper sequence requires a continuously updated representation of temporal context. Previous models have addressed this problem by positing a hierarchy of processing units, mirroring the roughly hierarchical structure of naturalistic tasks themselves. The present study considers an alternative framework, in which the representation of context depends on recurrent connections within a network mapping from environmental inputs to actions. The ability of this approach to account for human performance was evaluated by applying it, through simulation, to a specific everyday task. The resulting model learned to deal flexibly with a complex set of sequencing constraints, encoding contextual information at multiple time scales within a single, distributed internal representation. Degrading this representation led to errors resembling those observed both in everyday behavior and in apraxia. Analysis of the model's function yielded numerous predictions relevant to both normal and apraxic performance.

Cognition↗
PubMed2004

Connectionist approaches to understanding aphasic perseveration.

Aphasic patients make a variety of speech errors, including perseverations, in tasks that involve a linguistic component. What do perseverative and other errors imply about the nature of the neurologically damaged and intact language systems? Here we discuss the insights into the mechanisms of aphasic perseveration afforded by connectionist models. As a base for discussion, we review the Plaut and Shallice model of optic aphasic errors in object naming, which relies primarily on short-term learning mechanisms to produce perseverations. We then point out limitations of the model in addressing more recent data collected on aphasic perseveration and explain how incorporating information about the interaction of neuromodulatory systems and learning in the brain may help to overcome these limitations.

Aphasia↗
PubMed2004

A neural network model of the articulatory-acoustic forward mapping trained on recordings of articulatory parameters.

Three neural network models were trained on the forward mapping from articulatory positions to acoustic outputs for a single speaker of the Edinburgh multi-channel articulatory speech database. The model parameters (i.e., connection weights) were learned via the backpropagation of error signals generated by the difference between acoustic outputs of the models, and their acoustic targets. Efficacy of the trained models was assessed by subjecting the models' acoustic outputs to speech intelligibility tests. The results of these tests showed that enough phonetic information was captured by the models to support rates of word identification as high as 84%, approaching an identification rate of 92% for the actual target stimuli. These forward models could serve as one component of a data-driven articulatory synthesizer. The models also provide the first step toward building a model of spoken word acquisition and phonological development trained on real speech.

Adult↗
PubMed2002

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.

Behavior↗
PubMed2002

The impact of synaptic depression following brain damage: a connectionist account of "access/refractory" and "degraded-store" semantic impairments.

Neuropsychological studies of patients with acquired semantic impairments have yielded two distinct and contrasting patterns of performance in a spoken-word/picture-matching task (Warrington & Cipolotti, 1996). Patients labeled access/refractory are strongly influenced by presentation rate, semantic relatedness of distractors, and repetition, yet they seem relatively unaffected by lexical frequency. Degraded-store patients, on the other hand, are strongly affected by lexical frequency but are less affected by presentation rate, semantic relatedness, or repetition. Our account of these patterns of performance is based on the distinction between two different types of neurological damage: (1) damage to neuromodulatory systems that function to amplify neural signals while suppressing normal refractory-like effects and (2) damage to connections between groups of neurons that encode semantic information and are sensitive to frequency/familiarity. We present a connectionist model that learns to map spoken-word input to semantic representations and that incorporates a particular form of neural refractoriness referred to as synaptic depression, as well as a simple form of neuromodulation. We show that the model is capable of accounting for the contrasting patterns of semantic impairment under these two different forms of damage and, furthermore, demonstrate how it is capable of handling several documented cases that are exceptions to the basic patterns of impairment. Several predictions and limitations of the present model are discussed.

Brain Damage, Chronic↗