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- W2548760108 abstract "Abstract To characterize the left-ventral occipito-temporal cortex (lvOT) role during reading in a quantitatively explicit and testable manner, we propose the lexical categorization model (LCM). The LCM assumes that lvOT optimizes linguistic processing by allowing fast meaning access when words are familiar and filter out orthographic strings without meaning. The LCM successfully simulates benchmark results from functional brain imaging. Empirically, using functional magnetic resonance imaging, we demonstrate that quantitative LCM simulations predict lvOT activation across three studies better than alternative models. Besides, we found that word-likeness, which is assumed as input to LCM, is represented posterior to lvOT. In contrast, a dichotomous word/non-word contrast, which is assumed as the LCM’s output, could be localized to upstream frontal brain regions. Finally, we found that training lexical categorization results in more efficient reading. Thus, we propose a ventral-visual-stream processing framework for reading involving word-likeness extraction followed by lexical categorization, before meaning extraction." @default.
- W2548760108 created "2016-11-11" @default.
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- W2548760108 date "2016-11-03" @default.
- W2548760108 modified "2023-10-16" @default.
- W2548760108 title "The lexical categorization model: A computational model of left-ventral occipito-temporal cortex activation in visual word recognition" @default.
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- W2548760108 doi "https://doi.org/10.1101/085332" @default.
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