A Tutorial for Implementing Matrix Training in Practice

Frampton, Sarah E.; Axe, Judah B.

doi:10.1007/s40617-022-00733-5

Cited by 4 publications

(1 citation statement)

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“…In order for the teaching of tacts to be truly functional, the learner must be able to tact stimulus variations. This kind of generalization is recombinative generalization, and it is likely a cusp repertoire (Frampton & Axe, 2023).…”

Section: Introductionmentioning

confidence: 99%

Using GIFs and Matrix Training to Teach Noun-Verb Tacts to Children with Autism

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Verbal behavior is a critical repertoire for children with autism spectrum disorder to acquire. Tacts—verbal behavior evoked by nonverbal stimuli—are important for communicating about the world around oneself. Noun-verb tacts are part of a robust tact repertoire and may be addressed during applied behavior analytic intervention. When acquiring noun-verb tacts, it is important that the speaker learn to respond to many variations of stimuli like novel combinations of learned nouns and verbs, which is called recombinative generalization. One strategy to teach multi-component targets, such as the noun-verb tact, and lead to recombinative generalization is matrix training. Matrix training is a framework utilized to arrange targets that can be combined in order to facilitate recombinative generalization by teaching a subset of combinations and then probing others. With three children with ASD, we used matrix training and evaluated the acquisition of trained and novel combinations of noun-verb tacts with GIFs as stimuli arranged in three matrices. We used a concurrent multiple probe design across sets, and our results indicated that all participants acquired trained noun-verb tact targets in the presence of the GIFs. The degree of recombinative generalization varied across participants, but each participant demonstrated recombinative generalization with some stimuli. We analyzed responding during generalization probes to identify possible sources of stimulus control. We discussed the errors that were emitted when testing for recombinative generalization and provided suggestions for future research on matrix training and recombinative generalization.

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Section: Introductionmentioning

confidence: 99%