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Behavioral and computational accounts of temporal processing impairments in word retrieval: diagnosing slowed transmission and poor maintenance of lexical activation
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DOI：10.1080/02687038.2026.2631638.png)
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Abstract
En 中文
BackgroundInterventions for aphasia should benefit from greater understanding of the temporal activation processes that support access to linguistic representations of words. The interactive activation (IA) model of word retrieval postulates two temporal components of lexical activation, transmission and maintenance, that mediate access to words. Temporal processing impairments of word retrieval are revealed when a response delay is added to a naming task: slowed transmission leads to greater accuracy and fewer nonword errors after a delay while poor maintenance leads to decreased accuracy and more nonword errors after a delay.AimsWe investigated whether a modified version of the IA model, the Semantic-Phonological (SP) model, captures changes in naming accuracy and error responses under 1-second and 5-second response delay conditions before and after a naming training protocol.MethodsTwo participants, BD82 and DS68, were administered the laboratory-developed Maintenance-Transmission Naming Test at 1-second and 5-second delays before and after naming training. We evaluated changes in naming patterns and computational fits at the two response delays before and after training, seeking a logical relationship between the two (e.g. stronger phonological transmission leads to fewer phonological errors).OutcomesBD82's naming reflected slowed transmission of lexical activation: increased accuracy and fewer nonword errors after a 5-second delay. DS68's pattern reflected a mixed pattern with moderately slowed activation transmission, but a more severe difficulty maintaining lexical activation: more accurate after a 1-second delay and more nonword errors after a 5-second delay. The pre-training data were successfully fit to the SP model. Post-training, BD82's naming accuracy did not change significantly, but she produced fewer nonword errors, especially in the 1-second response delay condition (more challenging for her). The post-training model's fit to the data was achieved by increasing phonological transmission strength, which is consistent with a reduced rate of nonword errors. For DS68, improvements were observed in the 1-second and 5-second delay conditions after training including increased accuracy and reduced rates of nonword errors. The post-training model's fit was achieved by decreasing the decay rate, which improves maintenance of both semantic and phonological activation.ConclusionsCombining computational data from an IA model of word processing and behavioral data from a picture naming task reveals the role of temporal processes of lexical activation on word retrieval. This knowledge will contribute to the development of more precise diagnostic and treatment approaches for word retrieval disorders in aphasia.
Keywords:
Temporal processing
word retrieval
aphasia
computational modeling
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