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Record W2766086484

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

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

Bibliographic record

VenueeScholarship (California Digital Library) · 2005
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsCarleton University
Fundersnot available
KeywordsNeuroimagingPhrenologyCognitionFunctional neuroimagingPsychologyCognitive scienceCognitive neuroscienceCognitive psychologyNeuroscienceMedicine
DOInot available

Abstract

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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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.252
Threshold uncertainty score0.904

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.005
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.036
GPT teacher head0.259
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations4
Published2005
Admission routes2
Has abstractyes

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