Lecture 23 fMRI Flashcards
(17 cards)
What is one goal of using fMRI to localise cognitive functions?
To better understand how cognitive processes work by identifying where they occur in the brain.
What classic study investigated face representation in the brain?
Kanwisher et al. (1997) using fMRI to compare faces > objects.
Which brain region was identified to respond more strongly to faces?
Fusiform gyrus, known as the Fusiform Face Area (FFA).
What evidence supports the existence of the FFA?
Reliable activation in most participants and replication with different participants and stimuli.
What is the FFA?
Fusiform Face Area – a region in the fusiform gyrus that responds strongly to faces.
What are other specialised brain regions identified via fMRI?
Parahippocampal Place Area (PPA), Extrastriate Body Part Area (EBA), and regions for letters, tools, animals.
What is a critique of the ‘module’ idea in the brain?
Having modules for all objects would be spatially inefficient and could not explain novel object recognition.
What does the Greeble experiment suggest about the FFA?
FFA activation reflects expertise, not just face-specific processing.
How does expertise affect FFA activity?
FFA responds to Greebles after participants become experts, supporting an expertise-based view.
What is another theory of what the FFA does?
It processes objects in the centre of vision that require high resolution (foveal processing).
What did Malach et al. propose about object representation?
Ventral visual cortex may be organised by eccentricity mapping, not object category.
What is eccentricity mapping?
Organisation based on where objects usually appear in the visual field (e.g., periphery vs. centre).
What is the challenge in interpreting fMRI results?
Multiple plausible explanations for activation patterns; results reflect a mix of coding schemes.
What coding scheme did Haxby et al. propose?
Distributed coding – objects are represented across the whole object region, not in discrete modules.
What is distributed coding?
A method where many object categories are represented by patterns across a wide brain region.
What is multivariate pattern analysis (MVPA)?
An approach that examines patterns of activation rather than isolated regions.
How is MVPA used with fMRI data?
To classify and predict which object a person is seeing based on distributed activation patterns.
