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Inference in relational reasoning: A case study of Relational-Match-to-Sample
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The Developmental Origins of the Formal Structure of Kind Representations
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Developmental Change in the Integration of Information During Online Sentence Comprehension. Evidence From Eye-Tracking and Event-Related-Potentials
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Age and Species Comparisons of Visual Mental Manipulation Ability as Evidence for its Development and Evolution
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In: Sci Rep (2020)
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The building blocks of meaning: Psycholinguistic evidence on the nature of verb argument structure
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Infants learn a rule predicated on the relation same but fail to simultaneously learn a rule predicated on the relation different
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In: ISSN: 0010-0277 ; EISSN: 1873-7838 ; Cognition ; https://hal.archives-ouvertes.fr/hal-03097847 ; Cognition, Elsevier, 2018, 177, pp.49-57. ⟨10.1016/j.cognition.2018.04.005⟩ (2018)
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Language Emergence: Evidence From Nicaraguan Sign Language and Gestural Creation Paradigms
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Evidence for a Non-Linguistic Distinction Between Singular and Plural Sets in Rhesus Monkeys. ...
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On the relation between the acquisition of singular-plural morpho-syntax and the conceptual distinction between one and more than one ...
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Does the Conceptual Distinction Between Singular and Plural Sets Depend on Language? ...
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Numerical morphology supports early number word learning: Evidence from a comparison of young Mandarin and English learners
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Why Theories of Concepts Should Not Ignore the Problem of Acquisition
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Carey, Susan. - : Universidade de Lisboa, Faculdade de Letras, Centro de Filosofia, 2016
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The Structure and Development of Logical Representations in Thought and Language
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Man Bites Dog: The Representation of Structured Meaning in Left-Mid Superior Temporal Cortex
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Abstract:
Human brains flexibly combine the meanings of individual words to compose structured thoughts. For example, by combining the meanings of ‘bite’, ‘dog’, and ‘man’, we can think either of a dog biting a man, or the newsworthy case of a man biting a dog (Pinker, 1997). Here, in three functional Magnetic Resonance Imaging (fMRI) experiments, we identify a region of left-mid Superior Temporal Cortex (lmSTC) that represents the current values of abstract semantic variables (“Who did it?” and “To whom was it done?”) in anatomically distinct sub-regions. Experiment 1 first identifies a broad region of lmSTC whose activity patterns (a) facilitate decoding of who did what to whom and (b) predict affective amygdala responses that depend on this information (e.g. “the baby kicked the grandfather” vs. “the grandfather kicked the baby”). Experiment 2 then identifies distinct, but neighboring, sub-regions of lmSTC whose activity patterns carry information about the identity of the current agent (“Who did it?”) and the current patient (“To whom was it done?”). These neighboring sub-regions lie along the upper bank of the superior temporal sulcus and the lateral bank of the superior temporal gyrus, respectively. At a high-level, these regions may function like topographically defined data registers, encoding the fluctuating values of abstract semantic variables. Experiment 3 replicates the agent/patient topography of Experiment 2, and further suggests that these variables do not represent the grammatical relations of the sentence, but the semantic relations of the participants in the event described. The code by which lmSTC encodes the values of these variables remains unclear, however. We find no positive evidence that it is either phonological or semantic, leaving open the possibility that lmSTC prioritizes distinctiveness and efficiency by using a compressed code. This functional architecture, which in key respects resembles that of a classical computer, may play a critical role in enabling humans to flexibly generate complex thoughts. ; Psychology ; language; brain; fmri; compositionality; cognitive architecture;
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Keyword:
Biology; Cognitive; Neuroscience; Psychology
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URL: http://nrs.harvard.edu/urn-3:HUL.InstRepos:17467506
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Contingency is not enough: Social context guides third-party attributions of intentional agency.
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Twelve-Month-Old Infants’ Encoding of Goal and Source Paths in Agentive and Non-Agentive Motion Events
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