Relearning after Damage in Connectionist Networks: Toward a Theory of Rehabilitation

Abstract

Connectionist modeling offers a useful computational framework for exploring the nature of normal and impaired cognitive processes. The current work extends the relevance of connectionist modeling in neuropsychology to address issues in cognitive rehabilitation: the degree and speed of recovery through retraining, the extent to which improvement on treated items generalizes to untreated items, and how treated items are selected to maximize this generalization. A network previously used to model impairments in mapping orthography to semantics is retrained after damage. The degree of relearning and generalization varies considerably for different lesion locations, and has interesting implications for understanding the nature and variability of recovery in patients. In a second simulation, retraining on words whose semantics are atypical of their category yields more generalization than retraining on more typical words, suggesting a counterintuitive strategy for selecting items in patient therapy to maximize recovery. In a final simulation, changes in the pattern of errors produced by the network over the course of recovery is used to constrain explanations of the nature of recovery of analogous braindamaged patients. Taken together, the findings demonstrate that the nature of relearning in damaged connectionist networks can make important contributions to a theory of rehabilitation in patients.

Connectionist Modeling of the Breakdown and Recovery of Reading via Meaning

“…Simulations involving partial damage to an isolated semantic route (Plaut & Shallice, 1993) provide a comprehensive account of the rather peculiar combination of symptoms exhibited by patients with deep dyslexia, including the occurrence of semantic errors (e.g., reading RIVER as "ocean"), their co-occurrence with visual errors, and influences of imageability or concreteness on correct and error performance. Furthermore, when a version of the model is retrained after damage (Plaut, 1996), the degree and variability of its recovery and generalization are qualitatively similar to the results of some cognitive rehabilitation studies. The results challenge traditional assumptions about the nature of the mechanisms subserving word reading, and illustrate the value of explicit computational simulations of normal and impaired cognitive processes.…”

The effect of corpus size in predicting reaction time in a basic word recognition task: Moving on from Kučera and Francis

“…All models of lexical access incorporate word frequency into their retrieval mechanisms (frequency-coded models, Forster, 1976; semantic feature models, Smith, Shoben, & Rips, 1974; Logogen models, Morton, 1969;connectionist models, McClelland & Rumelhart, 1985;Monsell, 1991;Plaut, 1996; highdimensional semantic space models, . Although these models differ with respect to the specific implementation of the WF mechanism, the basic mechanism is that WF sets a threshold for word recognition: WF is a measure of experience with a word which has some direct relationship to its availability in memory.…”

Incorporating Cognitive Retraining for Management of Individuals Having Cannabis Dependence Currently Abstinent

“…20,21 Several methods and strategies have evolved in the past two to three decades, addressing various cognitive deficits experienced by patients with psychiatric and neurological problems. 21 With regard to substance dependence, patients with alcohol disorders have shown improvement in cognitive functions as a result of retraining practices.…”