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Enregistrement W2766086484

Functional Neuroimaging and its Implications for Cognitive Science: Beyond Phrenology and Localization

2005· article· en· W2766086484 sur OpenAlexaffabout
Andrew Brook, Ahmad Sohrabi

Notice bibliographique

RevueeScholarship (California Digital Library) · 2005
Typearticle
Langueen
DomaineNeuroscience
ThématiqueFunctional Brain Connectivity Studies
Établissements canadiensCarleton University
Organismes subventionnairesnon disponible
Mots-clésNeuroimagingPhrenologyCognitionFunctional neuroimagingPsychologyCognitive scienceCognitive neuroscienceCognitive psychologyNeuroscienceMedicine
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Functional Neuroimaging and its Implications for Cognitive Science: Beyond Phrenology and Localization Ahmad Sohrabi (asohrabi@connect.carleton.ca) Institute of Cognitive Science, Carleton University, 1125 Colonel By Drive, Ottawa, ON K1S 5B6 Canada Andrew Brook (abrook@ccs.carleton.ca) Institute of Cognitive Science, Carleton University, 1125 Colonel By Drive, Ottawa, ON K1S 5B6 Canada Abstract The localization approach in neuroimaging is an attempt to find where a cognitive function is located in a specific area of the brain, which seems to be similar to an old effort, called phrenology, to relate the skull bumps to specific mental faculties. Using neuroimaging just to find “where” a function occurs doesn’t tell us much about “what” that function is and “how” it happens. The localizationist view has been criticized especially because of the dynamic nature of mind, the difficulty in definition and decomposition of cognitive functions, and the distribution of brain areas involved in most cognitive processes. In the present article, we are pointing out some problems with localizationist approach and trying to show the proper use of functional neuroimaging, especially fMRI, in the study of neural correlates of cognition. We discuss the application of functional neuroimaging as a part of interdisciplinary methods in cognitive science. Keywords: Neuroimaging; fMRI; localization; methodology Introduction [T]o state it [mind—brain relation] in elementary form one must reduce it to its lowest terms and know which mental fact and which cerebral fact are, so to speak, in immediate juxtaposition (James 1890, p. 177). In their quest for mind-brain relation, researchers have long employed various neuropsychological and neurophysiol- ogical methods such as lesion studies, electrical and magnetic recording, and direct stimulation of the brain. However, only recently has a relatively reliable measure- ment of the neural correlates of cognitive processes been possible. Despite some controversies in its success, a new neuroscientific method known as functional neuroimaging has attracted much attention among researchers interested in taking into account the brain activation in studying cognition. The most used functional neuroimaging methods are those based on the energy consumption of the brain While subjects are performing cognitive tasks. The increase in energy consumption of the brain elevates the regional Cerebral Blood Flow (rCBF) which is used in the Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT). The PET and SPECT are based on the injected radiotracers that make them invasive and limited but still powerful in some cases. A more recent method that is noninvasive is the fimctional Magnetic Resonance Imaging (fMRl). There are many fMRI methods with special applications but the most popular one is based on the Blood Oxygenation Level—Dependent (BOLD) 2044 response. The BOLD response is a function of the rCBF, blood volume, and especially deoxygenation of hemoglobin. Decrease in deoxyhemoglobin, which is paramagnetic, leads to the inhomogeneity of the local magnetic field in a way that can be measured by the receiver of the MRI scanner to create a map of the brain activation. Since the first functional neuroimaging studies using PET and fMRI in the 1980s and 1990s, respectively, many scientists have looked at the brain areas involved in a wide range of the mental functions, from word and face recogn- ition to morality and religion. Apparently, the simple applications of these methods have been the search for specific areas activated by cognitive tasks. Using these techniques to find the locations of cognitive processes is similar, to some extent, to the old effort of localization that has long been tried in neuroscience and psychology. In this article, we review some problems with the localizationist approach and then discuss the possibility of using neuroima- ging, especially the fl\/[RI in conjunction with the cognitive theories, to go beyond simple localization by looking for the complex and dynamic neural correlates of cognition. Localization and Phrenology The assumption in localization approach is that cognitive fimctions are modularly located in the specific areas of the brain. One of the first localizationist methods was phrenology proposed by Gall at the end of 18”‘ century (c.f., Hubbard, 2003; Uttal, 2001). Gall as the leader of phrenology claimed that the mental faculties are located in the specific brain areas and are detectable by looking at the skull bumps (e.g., Gall and Spurzheim 1806/1967). This approach finally turned out to be false but other forms of localization still continue nowadays. Issues related to the localization view are very important for cognitive science (c.f., Hubbard, 2003; van Gelder, 1999) especially because they are related to the age old debates on the mind-brain (or the fimction-structure) relations. It seems that there are two extremist views related to localization. The scientists with the first view argue for the existence of modular and encapsulated domains in the mind (Fodor, 1983), regardless of their neural bases. This classical functionalist theory of cognition is called “the fimctionalism Without identity” by Bechtel (2002). In this sense, understanding of a function is possible without knowing the related structure. Another extremist approach, but using neuroscientific methods especially functional

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,008
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,252
Score d'incertitude au seuil0,904

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,008
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,001
Communication savante0,0010,005
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,036
Tête enseignante GPT0,259
Écart entre enseignants0,224 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations4
Publié2005
Routes d'admission2
Résumé présentoui

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