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This week, we share an amazing interview with Dr. Barry Prizant! Dr. Prizant is well-known for his work related to autism and echolalia, including research that was heavily cited by Marge Blanc and the Natural Language Acquisition framework behind gestalt language processing. Dr. Prizant discusses how he came to learn about echolalia and the confluence of research that suggested that echolalia had a communicative function (which he studied during his doctoral research). He also shares about the research behind gestalt language processing, how we can tell if someone has a gestalt or an analytical language learning bias, the true meaning of evidence-based practice, and more!
Key Ideas this Week:
🔑 We can learn about someone’s gestalt vs analytic language learning bias by looking at their reaction to modeling - what are they picking up on? Good language modeling, in the context of every day activities, can include combining words into utterances as well as functional gestalt phrases - it doesn’t have to be just "gestalt" or "analytical".
🔑 Some autistic people have not only intact, but exceptional memories. If you approach language from the perspective of “I have a great memory but don’t have the ability to construct generative language easily” then you would presumably learn to speak by listening to people and memorizing exactly what they are saying.
🔑 Some people with echolalia faithfully reproduce foreign accents and sounds in their environment. It goes beyond verbal speech - some people are echolalic in sign language and some people with echopraxia copy people’s actions.
🔑 When we are trying something that is an emerging practice, we can try it with kids and see how it works (provided it doesn’t cause harm). Sources of evidence include clinical experience and expertise as well as research. In many cases, it is difficult to apply what we know from studying a small group of people (e.g. 30) to the larger population, especially when talking about something that is unique to each person, like autism.
Visit talkingwithtech.org to access previous episodes, resources, and CEU credits that you can earn for listening to TWT episodes!
Help us develop new content and keep the podcast going strong! Support our podcast at patreon.com/talkingwithtech!
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This week, we share an amazing interview with Dr. Barry Prizant! Dr. Prizant is well-known for his work related to autism and echolalia, including research that was heavily cited by Marge Blanc and the Natural Language Acquisition framework behind gestalt language processing. Dr. Prizant discusses how he came to learn about echolalia and the confluence of research that suggested that echolalia had a communicative function (which he studied during his doctoral research). He also shares about the research behind gestalt language processing, how we can tell if someone has a gestalt or an analytical language learning bias, the true meaning of evidence-based practice, and more!
Key Ideas this Week:
🔑 We can learn about someone’s gestalt vs analytic language learning bias by looking at their reaction to modeling - what are they picking up on? Good language modeling, in the context of every day activities, can include combining words into utterances as well as functional gestalt phrases - it doesn’t have to be just "gestalt" or "analytical".
🔑 Some autistic people have not only intact, but exceptional memories. If you approach language from the perspective of “I have a great memory but don’t have the ability to construct generative language easily” then you would presumably learn to speak by listening to people and memorizing exactly what they are saying.
🔑 Some people with echolalia faithfully reproduce foreign accents and sounds in their environment. It goes beyond verbal speech - some people are echolalic in sign language and some people with echopraxia copy people’s actions.
🔑 When we are trying something that is an emerging practice, we can try it with kids and see how it works (provided it doesn’t cause harm). Sources of evidence include clinical experience and expertise as well as research. In many cases, it is difficult to apply what we know from studying a small group of people (e.g. 30) to the larger population, especially when talking about something that is unique to each person, like autism.
Visit talkingwithtech.org to access previous episodes, resources, and CEU credits that you can earn for listening to TWT episodes!
Help us develop new content and keep the podcast going strong! Support our podcast at patreon.com/talkingwithtech!
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