Conversations and neurodiversity. Here an initial list of scientific papers studying conversations involving autistic individuals, and quantifying things like expectations, rapport, linguistic alignment, backchannels, etc. :
https://docs.google.com/document/d/1luNvyhAdaAkhHzkPXUxQmMBMU27vYHmoj_wPcWwnzVA/edit?usp=sharing What am I missing? Edits are welcome!
Neurodiverse conversations
Allen, M. L., Haywood, S., Rajendran, G., & Branigan, H. (2011). Evidence for syntactic alignment in children with autism. Developmental Science, 14(3), 540–548. Bolis, D., Bolis, D., Balsters, J. H., Balsters, J. H., Wenderoth, N., Wenderoth, N., Becchio, C., Becchio, C., Schilbach, L., & Schilb...
Out of context not at all discomforting mails: few days til Ragnarok! Dear Riccardo, you shall soon experience the Ragnarok 😅
How to model & understand turn-taking temporal dynamics in autistic & neurotypical children & adolescents ? Come to our #INSAR2024 posters.
- The Development of Turn-Taking Skills in Autistic and TD Children - Friday 11.30-13.30 - Poster 046 (preprint https://osf.io/preprints/psyarxiv/5ap6u)
- Autistic Children Are Faster at Turn-Taking Than Neurotypical Controls Especially with Unfamiliar Interlocutors, but Equally Adjust to Their Interlocutors - Saturday 11.30-13.30 - Poster 056
I'm teaching reparametrization of multilevel models & I want to convey that reparametrization is a general process. We cover examples (e.g. neal's funnels), but it's an open field of transformations of which alas I cannot provide general rules. Some tips from an old thread, new ones welcome. 1/
See the video (youtu.be/-X5Z6dQuiCk) for more (and keep an eye open for more preprints to come). Thanks to all my collaborators and the wonderful groups who invited me to present this stuff (and provided very useful discussion)
We are now working on adding measures of linguistic complexity and turn-by-turn contingency; and more excitingly on adopting and adapting formal mechanistic models from the non-human animal literature. That is a wholly fascinating field, both a source of inspiration for doing things better, but also a refreshing way to identify things that humans do that are not accounted in current non-human animal models. 16/
These findings mostly generalize to a second corpus (28 (12f) autistic children, 20 (12f) TD – 10yo on average; measured 7 times at a distance of 1 week). 15/
Including turn-by-turn dynamics further improve the model: children both auto-correlate (going slower at turn t is related to going slower at t+1) and follow the other. Auto correlation is stronger in children than adults, interpersonal adjustment is stronger in adults 14/
Including socio-cognitive, linguistic and motor skills improves the model. The first reduces overlapping and long latencies, the latter two are related to faster responses. 13/
We put the learned lessons into play in a new study on a longitudinal dataset with 66 infant-caregiver dyads (30 autistic), from 18-62 months, 6 visits per child; turn by turn data; clinical, linguistic and cognitive assessment (
https://doi.org/10.1016/j.cognition.2018.10.022). Adequately modeling the data (ex-gaussian including overlapping, left): fast responses, speeding w age, surprisingly autistic children are faster! replicating methodological pitfalls (right): >1s, slower in autism.
