心理学
失语症
认知心理学
语义学(计算机科学)
理解力
认知
语言学
计算机科学
哲学
程序设计语言
神经科学
作者
Stefanie Bruehl,Klaus Willmes,Ferdinand Binkofski
出处
期刊:Aphasiology
[Taylor & Francis]
日期:2021-11-07
卷期号:37 (2): 227-248
被引量:4
标识
DOI:10.1080/02687038.2021.1995841
摘要
Background and aims Executive functions recently have gained attention as important contributors to language performance in aphasia. Picture/word interference embraces both executive control and lexical processing through distracted confrontation naming. Thus, we created a novel approach that embedded interfered-naming into an established lexical therapy framework. We aimed to (1) investigate patients’ behavioural response to distractor types and treatment methods of interfered-naming and (2) determine specific therapy effects on linguistic versus executive processing and initial eligibility criteria.Methods Persons with word-finding difficulties in chronic aphasia received 4-week therapy in a block design with thorough pre-post-testing including computational modelling. During therapy, picture naming was distracted by auditory stimuli, which were primed by a preceding comprehension task and directly assisted by increasing semantic or phonological cues.Results Nineteen participants were included in the diagnostic study, 12 of which also completed the therapy study. Distractor types did not generally yield differential effects at baseline. The novel linguistic-executive treatment significantly improved pure naming in most (9 out of 12) cases, fostered generalisation to untrained items, increased semantic weights in the computational model, and reduced automated speech. Therapy gains correlated positively with initial distractor comprehension, lexical-semantics, and word discrimination and negatively with automated speech and conceptual-semantics.Conclusion The interference paradigm combined with computational modelling offers a useful tool for aphasia diagnosis, and the new treatment approach revealed to be effective. Semantic and executive processing appear to be the core source of improvements.
科研通智能强力驱动
Strongly Powered by AbleSci AI