Stephen Mazurchuk
10/27/21
Point to a named picture (either auditorily or visually) among 10 cards belonging to the same category
Suzuki, K., Yamadori, A. & Fuji, T. Category-specific comprehension deficit restricted to body parts. Neurocase 3, 193–200 (1997)
"These three cases then suggest that animate objects may be recognized and revisualized by the left occipital lobe while the same functions for inanimate objects proceed through functional activity of the right lobe"
* 2nd revised edition, 1940
Key Point
Few fMRI studies have focused on determining the lexical-semantic neural correlates of body-part knowledge
(sometimes termed body-image)
Explain how focal Lesions to the cortex can result in deficits to a particular category of concepts
Goal:
Explain how focal Lesions to the cortex can result in deficits to a particular category of concepts
Goal:
Some areas of the cortex preferentially process certain sensory, motor, affective, and other experiential phenomena
"Natural categories" arise from having different loadings on sensory, motor, and affective dimensions
Conclusion:
1)
2)
"experiential features"
The spatial distribution and disruption of category knowledge is explained by "experiential" accounts of concept representation
Propositions:
(hypothesis)
both primary sensory cortices, but also adjacent association cortices
Explain how focal Lesions to the cortex can result in deficits to a particular category of concepts
Goal:
Some areas of the cortex preferentially process certain sensory, motor, affective, and other experiential phenomena
"Natural categories" arise from having different loadings on sensory, motor, and affective dimensions
Conclusion:
1)
2)
"experiential features"
The spatial distribution and disruption of category knowledge is explained by "experiential" accounts of concept representation
Propositions:
(hypothesis)
both primary sensory cortices, but also adjacent association cortices
Concept Representation as Experiential Attributes
Mean Ratings For Individual Categories
Clustering Analysis of Concepts based on experiential features
* With a particular focus on the category of body-parts
* With a particular focus on the category of body-parts
Abdomen
Alligator
Ankle
Apathy
Apple
Armpit
For a patch of cortex, train a linear Support Vector Machine (SVM) to classify a pattern as being a body part or not
Decoding
"If decoding succeeds on the test set, then the region must contain some information about the decoded variable"
Non-bodyparts
\(\psi_2\)
Body parts
\(\psi_1\)
toggle
* With a particular focus on the category of body-parts
** Important **
The encoding model predicts the correlational structure between the activation patterns for body parts (test set) without having any body-part words in the training set
(Predict activation patterns for body-parts using observed activation patterns for other words)
Decoding
Body parts
Non-Body parts
\(\psi_1\)
\(\psi_2\)
Model
Words
Features
Encoding
Model
Words
Features
Encoding
For a given voxel
For word \(i\)
Car | Airplane | Chicken | |
---|---|---|---|
Car | 1 | .62 | .1 |
Airplane | 1 | .12 | |
Chicken | 1 |
Car | Airplane | Chicken | |
---|---|---|---|
Car | 1 | .42 | .06 |
Airplane | 1 | .31 | |
Chicken | 1 |
Predicted Correlational Structure
Observed Correlational Structure
predicted voxel response
feature \(1\) rating for word \(i\)
CREA encoding model
Body part ROI
Encoding Model ROI
* With a particular focus on the category of body-parts
Schwoebel, J. & Coslett, H. B. Evidence for Multiple, Distinct Representations of the Human Body. J Cognitive Neurosci 17, 543–553 (2005)
All three subjects with body image lesions had suffered temporal lesions; as shown in Figure 2, the lesion involved portions of Brodmann's area 37 as well as underlying white matter in 2 subjects
BA 37
Peak BP Area
Shrinkage!
regular least squares
penalization
Need to choose the strength of the regularization parameter (\(\lambda \))
An important concept in shrinkage is the "effective" degrees of freedom associated with a set of parameters.
Plan: Do PCA to estimate intrinsic dimensionality. Then estimate shrinkage parameter
W2V Shrinkage
RSA performance of different semantic models in the General Semantic Network (under review)*
Fernandino, L., Conant, L. L., Humphries, C. J. & Binder, J. R. Decoding the Information Structure Underlying the Neural Representation of Concepts. Biorxiv 2021.03.16.435524 (2021) doi:10.1101/2021.03.16.435524.
Called Autotopagnosia
Renzi, E. D. & Scotti, G. Autotopagnosia: Fiction or Reality?: Report of a Case. Arch Neurol-chicago 23, 221–227 (1970)
Is the classification accuracy the same for all categories in the body-part ROI?
Animal
Artifact
Body part
Human Trait
Plant\Food
Quantity
* With a particular focus on the category of body-parts
A total of 4 of the 9 left hemisphere patients had disproportionately worse naming of body parts than naming of other categories of concrete entities
* With a particular focus on the category of body-parts