H2020Индивидуална стипендия2016–2018

SFM4VOT · Building a computational basis for the brain response in the left ventral occipito-temporal cortex: Understanding the Sparse Familiarity Model of visual word recognition

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период: 2016-04-01 → 2018-07-15
Финансиране от ЕС: 171 461 €
Участници: 1
Схема: MSCA-IF-EF-ST

Линиите свързват координатора с партньорите.

Накратко на български

Когнитивните процеси при разпознаването на думи се анализират чрез модели, които разграничават смислените буквени низове от безсмислените. Разбирането на тези механизми помага за разработването на по-добри методи за терапия при хора с затруднено четене.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Building a computational basis for the brain response in the left ventral occipito-temporal cortex: Understanding the Sparse Familiarity Model of visual word recognition

Reading, at its core, reflects the extraction of meaning from a visual representation of the orthographic code. This process is achieved in a fast and automatic fashion by most but still, some individuals do not accomplish information extraction at a preferred high speed hampering daily life (i.e., from reading proper newspapers to manuals). The core objective of this proposal was to investigate and explicitly describe the cognitive processes that are characteristic for fast reading from a neuronal perspective using computational models. Such a computational description allows a fruitful scientific discourse by model comparison in basic research and, at best, informs treatment approaches in more applied settings. The core cognitive process implemented in our computational model (originally the sparse familiarity model: SFM, now the lexical categorization model: LCM; Gagl, Richlan, Ludersdorfer, Sassenhagen, & Fiebach, 2016) is the categorization of letter strings in meaningful or meaningless. In work package 1 (WP1) the objective was to investigate how the lexical categorization process is influenced by learning and in WP2 the objective was the generalization of the lexical categorization process to other image categories like faces or objects, which are all known to be processed in the ventral part of the temporal cortex.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

The left ventral occipito-temporal cortex (lvOT) is an integral part of the ventral visual processing stream and is consistently activated in response to visual words. Recently, I developed a computational implementation of the lvOT functioning during visual word recognition, the sparse familiarity model (SFM). The single assumption of the SFM is that the statistical patterns of letter string familiarity are the fundament of lvOT functioning. The SFM is able to simulate prominent lvOT benchmark contrasts and its simulations predict brain activation of the lvOT in multiple fMRI studies. The main aim of the present proposal is to use the model to systematically investigate hotly debated topics in word recognition research concerning current theoretical approaches for the lvOT: (1) The influence of learning and (2) domain specificity (i.e. for words) vs. generalization (i.e., to face and object recognition) of lvOT function and neuronal populations. In the course of these investigations, the SFM will be developed into a learning model and a generalized model for word, face, and object recognition. Central to this will be high-density electrophysiological measurements (MEG) that sample brain activation with high temporal resolution and reasonable spatial resolution, to allow connectivity analysis at different time points. The MEG measurement, in addition to fMRI measures, will be essential to test theoretical assumptions concerning the proposed two stages of the SFM that differ fundamentally in terms of neuronal and cognitive mechanisms. Critical will be the association of these stages to different time windows and assumptions about the brain networks involved. The host, Prof. Fiebach at Frankfurt University, has a strong focus on the neurocognitive basis of language and access to an MEG, which is essential for realizing this project. To summarize, the goal of the project is to establish the sparse familiarity model and extend its functioning to resolve current debates.

Оригинален текст от CORDIS (на английски).

Участници

JOHANN WOLFGANG GOETHE-UNIVERSITAET FRANKFURT AM MAIN · Frankfurt Am MainКоординаторГермания