Information gain modulates brain activity evoked by reading
| Autor: | Tuukka Ruotsalo, Michiel M. Spapé, Lauri Kangassalo, Niklas Ravaja |
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| Přispěvatelé: | Department of Computer Science, Department of Psychology and Logopedics, Helsinki Institute for Information Technology |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Male
UNIVERSAL LAW Computer science Brain activity and meditation lcsh:Medicine LANGUAGE Electroencephalography FREQUENCY 050105 experimental psychology Article 03 medical and health sciences 0302 clinical medicine Event-related potential medicine LENGTH Humans 0501 psychology and cognitive sciences Information gain lcsh:Science Evoked Potentials COMPREHENSION Multidisciplinary medicine.diagnostic_test 05 social sciences lcsh:R Brain Cognition Human brain Models Theoretical COMPONENT medicine.anatomical_structure Reading Area Under Curve Data Interpretation Statistical Female lcsh:Q 6163 Logopedics Information Literacy Classifier (UML) WORDS 030217 neurology & neurosurgery Cognitive psychology |
| Zdroj: | Scientific Reports, Vol 10, Iss 1, Pp 1-10 (2020) Scientific Reports |
| Popis: | The human brain processes language to optimise efficient communication. Studies have shown extensive evidence that the brain’s response to language is affected both by lower-level features, such as word-length and frequency, and syntactic and semantic violations within sentences. However, our understanding on cognitive processes at discourse level remains limited: How does the relationship between words and the wider topic one is reading about affect language processing? We propose an information theoretic model to explain cognitive resourcing. In a study in which participants read sentences from Wikipedia entries, we show information gain, an information theoretic measure that quantifies the specificity of a word given its topic context, modulates word-synchronised brain activity in the EEG. Words with high information gain amplified a slow positive shift in the event related potential. To show that the effect persists for individual and unseen brain responses, we furthermore show that a classifier trained on EEG data can successfully predict information gain from previously unseen EEG. The findings suggest that biological information processing seeks to maximise performance subject to constraints on information capacity. |
| Databáze: | OpenAIRE |
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