Publicação
Spontaneous trait inferences under a linguistic lens
| Resumo: | As one of the most researched effects in Social Cognition, Spontaneous Trait Inferences (STI) research has been focusing on how it works, what causes it and what it might disrupt it. After Marcelo, Garcia-Marques & Duarte (2019), there was evidence that language and Grammar could play a bigger part in STI, since it is considered an encoding effect. Language has been a key element in researching STI from the beginning, with the encoding material being one of the main sources of information to convey the situation and explicit traits. We created this project with the purpose of understanding how deep and important the connection between STI and Linguistics goes. For this, we created four linguistic perspectives that produce a robust and complete picture of how STI and language might interact with each other: an analysis on linguistic complexity (using the main components in Language models – Syntax, Semantics and Phonology), an analysis on structure vs. meaning elements in a sentence through one of the most interest cases of Syntax-Semantics systems clashing, an analysis on contextual time through verbs and adverbial phrases, and an analysis on sound communication as a means for inference. Overall, our data indicates that STI uses the language system on encoding, following linguistic structures and principles of comprehension. Although structural hierarchy in language use is respected and can affect STI, it is in meaning structures, such as thematic role and entity number that we see bigger STI trends. The data allowed us to create a prototypical model – the CRT model – that could explain STI from encoding until attribution using both language comprehension models, previous evidence of STI and this project’s data. This could be a starting point to further help both Social Cognition and Linguistics to develop their frameworks and create more links that connect theory and everyday effects that use language and information. |
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| Autores principais: | Marcelo, Daniel Filipe Segurado |
| Assunto: | Social cognition Spontaneous trait inference Psycholinguistics Syntax Semantics |
| Ano: | 2024 |
| País: | Portugal |
| Tipo de documento: | tese de doutoramento |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório da Universidade de Lisboa |
| Resumo: | As one of the most researched effects in Social Cognition, Spontaneous Trait Inferences (STI) research has been focusing on how it works, what causes it and what it might disrupt it. After Marcelo, Garcia-Marques & Duarte (2019), there was evidence that language and Grammar could play a bigger part in STI, since it is considered an encoding effect. Language has been a key element in researching STI from the beginning, with the encoding material being one of the main sources of information to convey the situation and explicit traits. We created this project with the purpose of understanding how deep and important the connection between STI and Linguistics goes. For this, we created four linguistic perspectives that produce a robust and complete picture of how STI and language might interact with each other: an analysis on linguistic complexity (using the main components in Language models – Syntax, Semantics and Phonology), an analysis on structure vs. meaning elements in a sentence through one of the most interest cases of Syntax-Semantics systems clashing, an analysis on contextual time through verbs and adverbial phrases, and an analysis on sound communication as a means for inference. Overall, our data indicates that STI uses the language system on encoding, following linguistic structures and principles of comprehension. Although structural hierarchy in language use is respected and can affect STI, it is in meaning structures, such as thematic role and entity number that we see bigger STI trends. The data allowed us to create a prototypical model – the CRT model – that could explain STI from encoding until attribution using both language comprehension models, previous evidence of STI and this project’s data. This could be a starting point to further help both Social Cognition and Linguistics to develop their frameworks and create more links that connect theory and everyday effects that use language and information. |
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