BROCA'S AREA ACTIVITY IN THE LEXICAL SEMANTICS OF VISUAL CAUSAL EVENTS
From a cognitive linguistics perspective, is assessed the effects of causative constructions on the activity of Broca's area during the processing of visual causal and non-causal events. Lexical causatives (e.g., the orange ball moves the purple ball) describe only direct causal events whereas...
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| Autores principales: | , , , |
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| Lenguaje: | English |
| Publicado: |
Universidad de Concepción. Facultad de Humanidades y Arte
2016
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| Materias: | |
| Acceso en línea: | http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-48832016000200003 |
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| Sumario: | From a cognitive linguistics perspective, is assessed the effects of causative constructions on the activity of Broca's area during the processing of visual causal and non-causal events. Lexical causatives (e.g., the orange ball moves the purple ball) describe only direct causal events whereas periphrastic causatives (e.g., the orange ball causes the purple ball to move) describe both direct and indirect causal events. Based on this difference, is used lexical and periphrastic causatives as verbal instructions that directed participants to judge billiards balls collisions depicting direct, indirect, and non-causal events. By using functional magnetic resonance imaging (fMRI) and region of interest analysis, is found that judgments of all three visual events more strongly activated Broca's area following the periphrastic instruction than following the lexical instruction, and judgments of direct events produced stronger activity in Broca's area than indirect events. Interestingly, causal judgments were segregated between pars opercularis and pars triangularis. Results are discussed within the context of the linguistic category priming hypothesis, linear ballistic accumulator models, and the hierarchical organization of the prefrontal cortex. Because this our data suggest functional segregation during causal judgments, it is proposed that effective connectivity between these regions is worth evaluating in a follow-up study via dynamic causal modeling of fMRI data. |
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