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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)


Navon Figures & Frequency GratingsTop-down frequency filtering

Faces & WordsLeft & Right FFA competition

Sergent (1982); Ivry & Robertson (1998) Plaut & Behrmann (2011)




• No neural mechanism• No developmental story.





• No neural mechanism• No developmental story.

• No statement about neural changes• No connection to frequency filtering



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


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RH specializations

Global level contour


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RH specializations

Global level contour Face configuration or contour


12Pitts & Martinez (2014); Volberg (2014)


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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)


flattenedcortex

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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)


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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)


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RH: NarrowLH: Wide


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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

• 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 Training methods

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Differential encoding model Analysis methods

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• Present an image and compute:


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• Output image (spatial frequency analysis)


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• Output image (spatial frequency analysis)

• Hidden unit activations (used as input to train a separate classification network on a behavioral task)


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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


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

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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


RH (LVF)

LH (RVF)

Local Global


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LH RH

Human Data


RH (LVF)

LH (RVF)

Local Global

Model Data

Hsiao et al. (2013)


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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)


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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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Train on logpolar natural images, examine spatial frequencies

Lower Higher



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Without retraining the network, present other images

Lower Higher



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Without retraining the network, present other images and classify:


Lower Higher


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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Validation?

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Validation? Origins?

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We can address with a developmental approach!


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Previously:

• vary connection distributions

• measure spatial frequencies



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Previously:

• vary connection distributions

• measure spatial frequencies

Developmental approach:

• vary spatial frequencies

• measure connection distributions



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


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Methods:

• Start RH and LH networks with equivalent connections.

Befo

re

RHLH


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Methods:


• 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


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Methods:


• 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


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RH: More blurred


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LH: Less blurred

RH: More blurred


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LH: Less blurred

RH: More blurred

RH - LH

- =


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LH: Less blurred

RH: More blurred

RH - LH

- =Same association as

in our previous studies!


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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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