Post on 09-Mar-2021
Ben Cipollini & Garrison CottrellCOGSCI 2014 UC San Diego July 25, 2014 .1
A Developmental Model of Hemispheric Asymmetry
of Spatial Frequencies
Ben Cipollini & Garrison CottrellCOGSCI 2014 UC San Diego July 25, 2014 .2
Lateralization Is intertwined with human cognition
Manual skill Language
Face Processing
What causes Lateralization? We’re not sure, but vision may be tractable
• Describe the data & existing models
• Motivate our anatomical prediction
• Define the model
• Show old & new results
Talk Outline
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Data & Models
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lateralization in vision Two datasets, Two theories
LH RH
Navon figures (local vs. global),Gratings (high vs. low frequency)
LH RH
small
Faces vs. words
(with apologies to an exception: Hsiao et al, 2008)
lateralization in vision Two datasets, Two theories
Navon Figures & Frequency GratingsTop-down frequency filtering
Faces & WordsLeft & Right FFA competition
Sergent (1982); Ivry & Robertson (1998) Plaut & Behrmann (2011)
lateralization in vision Two datasets, Two theories
Navon Figures & Frequency GratingsTop-down frequency filtering
Faces & WordsLeft & Right FFA competition
• No neural mechanism• No developmental story.
Sergent (1982); Ivry & Robertson (1998) Plaut & Behrmann (2011)
lateralization in vision Two datasets, Two theories
Navon Figures & Frequency GratingsTop-down frequency filtering
Faces & WordsLeft & Right FFA competition
• No neural mechanism• No developmental story.
Sergent (1982); Ivry & Robertson (1998) Plaut & Behrmann (2011)
lateralization in vision Two datasets, Two theories
Navon Figures & Frequency GratingsTop-down frequency filtering
Faces & WordsLeft & Right FFA competition
• No neural mechanism• No developmental story.
Sergent (1982); Ivry & Robertson (1998) Plaut & Behrmann (2011)
lateralization in vision Two datasets, Two theories
Navon Figures & Frequency GratingsTop-down frequency filtering
Faces & WordsLeft & Right FFA competition
• No neural mechanism• No developmental story.
• No statement about neural changes• No connection to frequency filtering
Sergent (1982); Ivry & Robertson (1998) Plaut & Behrmann (2011)
lateralization in vision Two datasets, Two theories
Neither model:• Accounts for all stimuli showing asymmetry• Predicts how to find or verify a neural asymmetry.
lateralization in vision What’s in common?
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RH specializations
lateralization in vision What’s in common?
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RH specializations
Global level contour
lateralization in vision What’s in common?
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RH specializations
Global level contour Face configuration or contour
lateralization in vision What’s in common?
12Pitts & Martinez (2014); Volberg (2014)
lateralization in vision What’s in common?
12
Perhaps contour / shape processing is better in the right hemisphere!
Pitts & Martinez (2014); Volberg (2014)
Our Motivation (this is the challenging part)
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lateralization in vision long-range lateral connections?
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Long-range lateral connections are:• The key component in contour processing (e.g. Gilbert & Li, 2012)
• More active in situations that lead to greater lateralization (lower stimulus strength, engaging top-down attention)
lateralization in vision long-range lateral connections?
flattenedcortex
15
Long-range lateral connections are:• The key component in contour processing (e.g. Gilbert & Li, 2012)
• More active in situations that lead to greater lateralization (lower stimulus strength, engaging top-down attention)
lateralization in vision long-range lateral connections?
flattenedcortex
15
Long-range lateral connections are:• The key component in contour processing (e.g. Gilbert & Li, 2012)
• More active in situations that lead to greater lateralization (lower stimulus strength, engaging top-down attention)
lateralization in vision long-range lateral connections?
15
Long-range lateral connections are:• The key component in contour processing (e.g. Gilbert & Li, 2012)
• More active in situations that lead to greater lateralization (lower stimulus strength, engaging top-down attention)
lateralization in vision long-range lateral connections?
15
Long-range lateral connections are:• The key component in contour processing (e.g. Gilbert & Li, 2012)
• More active in situations that lead to greater lateralization (lower stimulus strength, engaging top-down attention)
lateralization in vision long-range lateral connections?
15
Long-range lateral connections are:• The key component in contour processing (e.g. Gilbert & Li, 2012)
• More active in situations that lead to greater lateralization (lower stimulus strength, engaging top-down attention)
lateralization in vision long-range lateral connections?
16
Good evidence that long-range lateral connections are:• A key component in contour processing (e.g. Gilbert & Li, 2012)
• More active in situations that elicit greater lateralization (lower stimulus strength, engaging top-down attention)
lateralization in vision long-range lateral connections?
17
Good evidence that long-range lateral connections are:• A key component in contour processing (e.g. Gilbert & Li, 2012)
• More active in situations that elicit greater lateralization (lower stimulus strength, engaging top-down attention)
lateralization in vision long-range lateral connections?
17
Good evidence that long-range lateral connections are:• A key component in contour processing (e.g. Gilbert & Li, 2012)
• More active in situations that elicit greater lateralization (lower stimulus strength, engaging top-down attention)
lateralization in vision long-range lateral connections?
18
Good evidence that long-range lateral connections are:• A key component in contour processing (e.g. Gilbert & Li, 2012)
• More active in situations that elicit greater lateralization (lower stimulus strength, engaging top-down attention)
lateralization in vision long-range lateral connections?
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Good evidence that long-range lateral connections are:• A key component in contour processing (e.g. Gilbert & Li, 2012)
• More active in situations that elicit greater lateralization (lower stimulus strength, engaging top-down attention)
Galuske et al (2000): wider spacing of interconnected patches in LH (BA22)
lateralization in vision following known data
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RH: NarrowLH: Wide
Galuske et al (2000): wider spacing of interconnected patches in LH (BA22)
lateralization in vision following known data
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RH: NarrowLH: Wide
lateralization in vision following known data
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Hsiao et al (2008; 2013): Differential Encoding model
Our model The differential encoding model
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. global level,
faces, low frequencies,
contours
Differential Encoding Our hypothesis
LH: Wide RH: NarrowLH RH
small
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local level, words,
high frequencies
vs
. global level,
faces, low frequencies,
contours
Differential Encoding Our hypothesis
LH: Wide RH: NarrowLH RH
small
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local level, words,
high frequencies
vs
. global level,
faces, low frequencies,
contours
Differential Encoding Our hypothesis
LH: Wide RH: NarrowLH RH
small
22
local level, words,
high frequencies
vs
• Create the network (850 input / output pixels, 850 hidden units. Choose 𝞂, #conns)
• Train the network on a set of images
Differential encoding model Training methods
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• Create the network (850 input / output pixels, 850 hidden units. Choose 𝞂, #conns)
• Train the network on a set of images
Differential encoding model Training methods
23
• Create the network (850 input / output pixels, 850 hidden units. Choose 𝞂, #conns)
• Train the network on a set of images
Differential encoding model Training methods
23
• Create the network (850 input / output pixels, 850 hidden units. Choose 𝞂, #conns)
• Train the network on a set of images
Differential encoding model Training methods
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• Create the network (850 input / output pixels, 850 hidden units. Choose 𝞂, #conns)
• Train the network on a set of images
Differential encoding model Training methods
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Differential encoding model Analysis methods
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• Create the network (850 input / output pixels, 850 hidden units. Choose 𝞂, #conns)
• Train the network on a set of images
• Present an image and compute:
Differential encoding model Analysis methods
25
• Create the network (850 input / output pixels, 850 hidden units. Choose 𝞂, #conns)
• Train the network on a set of images
• Present an image and compute:
• Output image (spatial frequency analysis)
Differential encoding model Analysis methods
25
• Create the network (850 input / output pixels, 850 hidden units. Choose 𝞂, #conns)
• Train the network on a set of images
• Present an image and compute:
• Output image (spatial frequency analysis)
• Hidden unit activations (used as input to train a separate classification network on a behavioral task)
Differential encoding model Analysis methods
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Previous results
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lateralization in vision Navon Figures in a target detection task
Adapted from Sergent (1982)
LH (RVF)
RH (LVF)
Local Target
CVF (BH)
Global Target
Task: Did you see a target letter?Targets: T, HDistractors: L,F
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lateralization in vision Navon Figures in a target detection task
Adapted from Sergent (1982)
RH (LVF) LH (RVF) LH
(RVF)RH
(LVF)
Local Target
CVF (BH)
Global Target
Task: Did you see a target letter?Targets: T, HDistractors: L,F
27
lateralization in vision Navon Figures in a target detection task
Adapted from Sergent (1982)
RH (LVF) LH (RVF) LH
(RVF)RH
(LVF)
Local Target
CVF (BH)
Global Target
Task: Did you see a target letter?Targets: T, HDistractors: L,F
27
lateralization in vision Navon Figures in a target detection task
Adapted from Sergent (1982)
RH (LVF) LH (RVF) LH
(RVF)RH
(LVF)
Local Target
CVF (BH)
Global Target
Task: Did you see a target letter?Targets: T, HDistractors: L,F
27
lateralization in vision Navon Figures in a target detection task
Adapted from Sergent (1982)
RH (LVF) LH (RVF) LH
(RVF)RH
(LVF)
Local Target
CVF (BH)
Global Target
Task: Did you see a target letter?Targets: T, HDistractors: L,F
27
lateralization in vision Navon Figures in a target detection task
Adapted from Sergent (1982)
RH (LVF) LH (RVF) LH
(RVF)RH
(LVF)
Local Target
CVF (BH)
Global Target
Task: Did you see a target letter?Targets: T, HDistractors: L,F
27
LH (wide) RH (narrow)Methods:
• Construct our networks (sample connections from different distributions for LH and RH).
• Train on Navon figures (16 stimuli; T,H,L,F at each level).
• Record hidden unit activities for each image.
• Train separate classification neural networks on Sergent’s behavioral task.
Differential encoding model Accounting for human behavior (Sergent, 1982)
28Hsiao et al. (2013)
Differential encoding model Accounting for human behavior
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Extract hidden unit representations,train LH & RH classifiers (not shown)
LH RH
Human Data
Adapted from Sergent (1982)
RH (LVF)
LH (RVF)
Local Global
Differential encoding model Accounting for human behavior
29
Extract hidden unit representations,train LH & RH classifiers (not shown)
LH RH
Human Data
Adapted from Sergent (1982)
RH (LVF)
LH (RVF)
Local Global
Model Data
Hsiao et al. (2013)
Differential encoding model Accounting for human behavior
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Extract hidden unit representations,train LH & RH classifiers (not shown)
LH RH
LH RH
Differential encoding model Spatial frequency biases
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Extract output images, comparepower spectrum precision
LH
RH
Lower Higher
-𝚫 lo
g(po
wer
)
RH - LH (vs. original)
Hsiao et al. (2013)
LH RH
Differential encoding model Spatial frequency biases
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Extract output images, comparepower spectrum precision
Lower Higher
Cipollini et al. (COGSCI 2012)
Differential encoding model spatial frequency biases
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Lower Higher
Cipollini et al. (COGSCI 2012)
Differential encoding model spatial frequency biases
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..and a number of other results(including an interesting departure from previous models)
new results
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• Train the network on a set of natural image patches (250)
1. Use log-polar warping of images to simulate “cortical expansion” of the fovea in retinotopic cortex.
• x-axis: angle (0…2!)
• y-axis: log(radius)
2. Never re-train the network; reuse the same network for all other sets of images (hidden unit encodings, output images).
Differential encoding model Expt #1: Train on natural images
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Differential encoding model spatial frequency biases
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Train on logpolar natural images, examine spatial frequencies
Lower Higher
RH - LH (vs. original)
Differential encoding model spatial frequency biases
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Train on logpolar natural images, examine spatial frequencies
Without retraining the network, present other images
Lower Higher
RH - LH (vs. original)
Differential encoding model spatial frequency biases
34
Train on logpolar natural images, examine spatial frequencies
Without retraining the network, present other images
Lower Higher
RH - LH (vs. original)
RH - LH (vs. original)
Differential encoding model spatial frequency biases
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Without retraining the network, present other images and classify:
Train on logpolar natural images, examine spatial frequencies
Lower Higher
RH - LH (vs. original)
Local Global
• Using more realistically trained network, we are able to replicate some of our previous findings.
Differential encoding model Expt #1: Summary
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Differential encoding model Expt #2: Developmental model
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Differential encoding model Expt #2: Developmental model
Validation?
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Differential encoding model Expt #2: Developmental model
Validation? Origins?
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Differential encoding model Expt #2: Developmental model
We can address with a developmental approach!
Validation? Origins?
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Differential encoding model Expt #2: Developmental model
Previously:
• vary connection distributions
• measure spatial frequencies
We can address with a developmental approach!
Validation? Origins?
37
Differential encoding model Expt #2: Developmental model
Previously:
• vary connection distributions
• measure spatial frequencies
Developmental approach:
• vary spatial frequencies
• measure connection distributions
We can address with a developmental approach!
Validation? Origins?
Katz and Callaway (1992)
During development:
• Visual acuity / contrast sensitivity is poor in infancy, but it improves over time. (e.g. Peterzell et al., 1995; Atkinson et al., 1997)
• Patchy connectivity matures via pruning & strengthening connections due to visual experience (e.g. Katz & Callaway, 1992; Burkhalter et al., 1993).
• RH begins maturing earlier than the LH (e.g. Geschwind & Galaburda, 1985; Hellige 1993; Chiron et al., 1997)
Differential encoding model Pruning interacts with acuity
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Katz and Callaway (1992)
RH will prune connections under blurrier (lower spatial frequency) input
During development:
• Visual acuity / contrast sensitivity is poor in infancy, but it improves over time. (e.g. Peterzell et al., 1995; Atkinson et al., 1997)
• Patchy connectivity matures via pruning & strengthening connections due to visual experience (e.g. Katz & Callaway, 1992; Burkhalter et al., 1993).
• RH begins maturing earlier than the LH (e.g. Geschwind & Galaburda, 1985; Hellige 1993; Chiron et al., 1997)
Differential encoding model Pruning interacts with acuity
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Differential encoding model vary frequencies, measure connections
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Befo
re
RHLH
Differential encoding model vary frequencies, measure connections
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Methods:
• Start RH and LH networks with equivalent connections.
Befo
re
RHLH
Differential encoding model vary frequencies, measure connections
39
Methods:
• Start RH and LH networks with equivalent connections.
• Train on natural images; RH receives more blurring of the images than the LH.
Epochs: 1-10 … 21-30 … 31..40 … 51-end
RH
LH
<=3.0cpd <=6.5cpd <=16cpd Full fidelity
more blurredless blurred
Befo
reA
fter
RHLH
Differential encoding model vary frequencies, measure connections
39
Methods:
• Start RH and LH networks with equivalent connections.
• Train on natural images; RH receives more blurring of the images than the LH.
• While training, remove the weakest connections.
Differential encoding model post-training connection distributions
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Compile connection distribution
Differential encoding model post-training connection distributions
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RH: More blurred
Differential encoding model post-training connection distributions
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LH: Less blurred
RH: More blurred
Differential encoding model post-training connection distributions
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LH: Less blurred
RH: More blurred
RH - LH
- =
Differential encoding model post-training connection distributions
40
LH: Less blurred
RH: More blurred
RH - LH
- =Same association as
in our previous studies!
Differential encoding model post-training connection distributions
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Original Developmental
Differential encoding model Need for interhemispheric competition?
Weaker lateralization in developmental model than previous work.
Interhemispheric competition can amplify effects
Differential encoding model post-training changes
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RH is specialized; LH is not.
Differential encoding model post-training changes
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• We validated the associations between:
• Shorter connections & lower frequency encoding
• Longer connections & higher frequency encoding
• We showed that the model RH was changed from the original, the LH much less so.
Differential encoding model Expt #2: Summary
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• We postulate that the RH has shorter long-range lateral connections in retinotopic visual areas (V4v / LOC).
• This connection asymmetry:
• Can account for many behavioral asymmetries.
• Leads to a RH bias for encoding low spatial frequency information (though we suggest perhaps only contour information)
• May appear during typical human development.
Connectivity Asymmetry Conclusions
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• Unify models: replicate all behavioral results in the developmental model.
• Spatial frequency processing: Is the RH bias specific to contours and configurations, or general to all LSF information?
• Interhemispheric transfer: What role does it play in development and during central vision?
Connectivity Asymmetry Next steps
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Thank you!
• Collaborators
• Gary Cottrell
• Janet Hsiao
• Funding sources
• NSF/TDLC
• CARTA
• Cognitive Science Society for the perception/action modeling award
• Robert J. Glushko and Pamela Samuelson Foundation for the student travel award
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