{"id":{"repo_id":"trento","oai_identifier":"oai:iris.unitn.it:11572/402850"},"canonical_url":"https://search.dev.ndltd.org/etd/trento/oai:iris.unitn.it:11572/402850","repository":{"repo_id":"trento","name":"Università degli Studi di Trento","base_url":"https://iris.unitn.it/oai/request"},"display":{"title":"Decoding Minds: Mentalistic Inference in Autism Spectrum Disorders and ChatGPT Models","abstract":"Mentalistic inference, the process of deducing others’ mental states from behaviour, is a key element of social interactions, especially when challenges arise. Just by observing an action or listening to a verbal description of it, adults and infants are able to make robust and rapid inferences about an agent’s intentions, desires, and beliefs. This thesis considers perspectives from Autism Spectrum Disorders (ASDs) and large language models, specifically GPT models. Individuals with ASDs struggle to read intentions from movements, but the mechanisms underlying these difficulties remain unknown. In a set of experiments, we employed combined motion tracking, psychophysics, and computational analyses to examine intention reading in ASDs with single-trial resolution. Single-trial analyses revealed that challenges in intention reading arise from both differences in kinematics between typically developing individuals and those with ASD, and a diminished sensitivity in reading intentions to variations in movement kinematics. This aligns with the idea that internal readout models are tuned to specific action kinematics, supporting the role of sensorimotor processes in shaping cognitive understanding and emphasizing motor resonance, a key aspect of embodied cognition. Targeted trainings may enhance and improve this ability. In a second set of experiments, we compared Theory of Mind, a core feature of mentalistic inference, in GPT models and a large sample of human participants. We found that GPT models exhibited human-level abilities in detecting indirect requests, false beliefs, and misdirection, but failed on faux pas. Rigorous hypothesis testing enabled us to show that this failure was apparent and was linked to a cautious approach in drawing conclusions rather than from an inference deficit. Collectively, the results presented in this thesis suggest that the convergence of insights from clinical research and advancements in technology is essential for fostering a more inclusive understanding of mentalistic inferences.","abstract_html":"Mentalistic inference, the process of deducing others’ mental states from behaviour, is a key element of social interactions, especially when challenges arise. Just by observing an action or listening to a verbal description of it, adults and infants are able to make robust and rapid inferences about an agent’s intentions, desires, and beliefs. This thesis considers perspectives from Autism Spectrum Disorders (ASDs) and large language models, specifically GPT models. Individuals with ASDs struggle to read intentions from movements, but the mechanisms underlying these difficulties remain unknown. In a set of experiments, we employed combined motion tracking, psychophysics, and computational analyses to examine intention reading in ASDs with single-trial resolution. Single-trial analyses revealed that challenges in intention reading arise from both differences in kinematics between typically developing individuals and those with ASD, and a diminished sensitivity in reading intentions to variations in movement kinematics. This aligns with the idea that internal readout models are tuned to specific action kinematics, supporting the role of sensorimotor processes in shaping cognitive understanding and emphasizing motor resonance, a key aspect of embodied cognition. Targeted trainings may enhance and improve this ability. In a second set of experiments, we compared Theory of Mind, a core feature of mentalistic inference, in GPT models and a large sample of human participants. We found that GPT models exhibited human-level abilities in detecting indirect requests, false beliefs, and misdirection, but failed on faux pas. Rigorous hypothesis testing enabled us to show that this failure was apparent and was linked to a cautious approach in drawing conclusions rather than from an inference deficit. Collectively, the results presented in this thesis suggest that the convergence of insights from clinical research and advancements in technology is essential for fostering a more inclusive understanding of mentalistic inferences.","abstract_has_math":false,"creators":["Albergo, Dalila"],"institution":"Università degli studi di Trento","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-03-01","date_published":"2024-03-01","updated_at":"2026-07-24T05:04:24Z","subjects":["Theory of Mind","Autism Spectrum Disorders","ChatGPT","Mentalistic Inference","Action Observation","Settore M-PSI/01 - Psicologia Generale"],"languages":["eng"],"rights":["info:eu-repo/semantics/openAccess","license:Creative commons","license uri:http://creativecommons.org/licenses/by-nc-nd/4.0/"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["http://dx.doi.org/10.15168/11572_402850","10.15168/11572_402850"],"render_values":[{"text":"http://dx.doi.org/10.15168/11572_402850","href":"http://dx.doi.org/10.15168/11572_402850","code":true},{"text":"10.15168/11572_402850","href":"https://doi.org/10.15168/11572_402850","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/11572/402850","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Albergo, Dalila"]},{"key":"dc:creator","label":"Author","values":["Albergo, Dalila"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-03-01"]},{"key":"dc:publisher","label":"Institution","values":["Università degli studi di Trento","place:TRENTO"]},{"key":"dc:relation","label":"Dc Relation","values":["firstpage:1","lastpage:161","numberofpages:161"]},{"key":"dc:type","label":"Dc Type","values":["info:eu-repo/semantics/doctoralThesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Theory of Mind","Autism Spectrum Disorders","ChatGPT","Mentalistic Inference","Action Observation","Settore M-PSI/01 - Psicologia Generale"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["info:eu-repo/semantics/openAccess","license:Creative commons","license uri:http://creativecommons.org/licenses/by-nc-nd/4.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/11572/402850","http://dx.doi.org/10.15168/11572_402850","10.15168/11572_402850"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Mentalistic inference, the process of deducing others’ mental states from behaviour, is a key element of social interactions, especially when challenges arise. Just by observing an action or listening to a verbal description of it, adults and infants are able to make robust and rapid inferences about an agent’s intentions, desires, and beliefs. This thesis considers perspectives from Autism Spectrum Disorders (ASDs) and large language models, specifically GPT models. Individuals with ASDs struggle to read intentions from movements, but the mechanisms underlying these difficulties remain unknown. In a set of experiments, we employed combined motion tracking, psychophysics, and computational analyses to examine intention reading in ASDs with single-trial resolution. Single-trial analyses revealed that challenges in intention reading arise from both differences in kinematics between typically developing individuals and those with ASD, and a diminished sensitivity in reading intentions to variations in movement kinematics. This aligns with the idea that internal readout models are tuned to specific action kinematics, supporting the role of sensorimotor processes in shaping cognitive understanding and emphasizing motor resonance, a key aspect of embodied cognition. Targeted trainings may enhance and improve this ability. In a second set of experiments, we compared Theory of Mind, a core feature of mentalistic inference, in GPT models and a large sample of human participants. We found that GPT models exhibited human-level abilities in detecting indirect requests, false beliefs, and misdirection, but failed on faux pas. Rigorous hypothesis testing enabled us to show that this failure was apparent and was linked to a cautious approach in drawing conclusions rather than from an inference deficit. Collectively, the results presented in this thesis suggest that the convergence of insights from clinical research and advancements in technology is essential for fostering a more inclusive understanding of mentalistic inferences."]},{"key":"dc:title","label":"Title","values":["Decoding Minds: Mentalistic Inference in Autism Spectrum Disorders and ChatGPT Models"]}]}],"canonical_facts":{"dc:contributor":["Albergo, Dalila"],"dc:creator":["Albergo, Dalila"],"dc:date":["2024-03-01"],"dc:description":["Mentalistic inference, the process of deducing others’ mental states from behaviour, is a key element of social interactions, especially when challenges arise. Just by observing an action or listening to a verbal description of it, adults and infants are able to make robust and rapid inferences about an agent’s intentions, desires, and beliefs. This thesis considers perspectives from Autism Spectrum Disorders (ASDs) and large language models, specifically GPT models. Individuals with ASDs struggle to read intentions from movements, but the mechanisms underlying these difficulties remain unknown. In a set of experiments, we employed combined motion tracking, psychophysics, and computational analyses to examine intention reading in ASDs with single-trial resolution. Single-trial analyses revealed that challenges in intention reading arise from both differences in kinematics between typically developing individuals and those with ASD, and a diminished sensitivity in reading intentions to variations in movement kinematics. This aligns with the idea that internal readout models are tuned to specific action kinematics, supporting the role of sensorimotor processes in shaping cognitive understanding and emphasizing motor resonance, a key aspect of embodied cognition. Targeted trainings may enhance and improve this ability. In a second set of experiments, we compared Theory of Mind, a core feature of mentalistic inference, in GPT models and a large sample of human participants. We found that GPT models exhibited human-level abilities in detecting indirect requests, false beliefs, and misdirection, but failed on faux pas. Rigorous hypothesis testing enabled us to show that this failure was apparent and was linked to a cautious approach in drawing conclusions rather than from an inference deficit. Collectively, the results presented in this thesis suggest that the convergence of insights from clinical research and advancements in technology is essential for fostering a more inclusive understanding of mentalistic inferences."],"dc:identifier":["https://hdl.handle.net/11572/402850","http://dx.doi.org/10.15168/11572_402850","10.15168/11572_402850"],"dc:language":["eng"],"dc:publisher":["Università degli studi di Trento","place:TRENTO"],"dc:relation":["firstpage:1","lastpage:161","numberofpages:161"],"dc:rights":["info:eu-repo/semantics/openAccess","license:Creative commons","license uri:http://creativecommons.org/licenses/by-nc-nd/4.0/"],"dc:subject":["Theory of Mind","Autism Spectrum Disorders","ChatGPT","Mentalistic Inference","Action Observation","Settore M-PSI/01 - Psicologia Generale"],"dc:title":["Decoding Minds: Mentalistic Inference in Autism Spectrum Disorders and ChatGPT Models"],"dc:type":["info:eu-repo/semantics/doctoralThesis"]},"updated_at":"2026-07-24T05:04:24Z"}