Ben Cipollini & Garrison Cottrell
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1 COGSCI 2014 Ben Cipollini & Garrison Cottrell UC San1 Diego July 25, 2014.
2 A Developmental Model of Hemispheric Asymmetry of Spatial Frequencies COGSCI 2014 Ben Cipollini & Garrison Cottrell UC San2 Diego July 25, 2014.
3 Lateralization Is intertwined with human cognition Manual skill Language Face Processing
4 What causes Lateralization? We re not sure, but vision may be tractable
5 Talk Outline Describe the data & existing models Motivate our anatomical prediction Define the model Show old & new results 5
6 Data & Models 6
7 lateralization in vision Two datasets, Two theories Navon figures (local vs. global), Gratings (high vs. low frequency) Faces vs. words LH RH LH RH small (with apologies to an exception: Hsiao et al, 2008)
8 lateralization in vision Two datasets, Two theories Navon Figures & Frequency Gratings Top-down frequency filtering Faces & Words Left & Right FFA competition Sergent (1982); Ivry & Robertson (1998) Plaut & Behrmann (2011)
9 lateralization in vision Two datasets, Two theories Navon Figures & Frequency Gratings Top-down frequency filtering Faces & Words Left & Right FFA competition Sergent (1982); Ivry & Robertson (1998) Plaut & Behrmann (2011) No neural mechanism No developmental story.
10 lateralization in vision Two datasets, Two theories Navon Figures & Frequency Gratings Top-down frequency filtering Faces & Words Left & Right FFA competition Sergent (1982); Ivry & Robertson (1998) Plaut & Behrmann (2011) No neural mechanism No developmental story.
11 lateralization in vision Two datasets, Two theories Navon Figures & Frequency Gratings Top-down frequency filtering Faces & Words Left & Right FFA competition Sergent (1982); Ivry & Robertson (1998) Plaut & Behrmann (2011) No neural mechanism No developmental story.
12 lateralization in vision Two datasets, Two theories Navon Figures & Frequency Gratings Top-down frequency filtering Faces & Words Left & Right FFA competition Sergent (1982); Ivry & Robertson (1998) Plaut & Behrmann (2011) No neural mechanism No developmental story. No statement about neural changes No connection to frequency filtering
13 lateralization in vision Two datasets, Two theories Neither model: Accounts for all stimuli showing asymmetry Predicts how to find or verify a neural asymmetry.
14 lateralization in vision What s in common? RH specializations 11
15 lateralization in vision What s in common? RH specializations Global level contour 11
16 lateralization in vision What s in common? RH specializations Global level contour Face configuration or contour 11
17 lateralization in vision What s in common? 12 Pitts & Martinez (2014); Volberg (2014)
18 lateralization in vision What s in common? Perhaps contour / shape processing is better in the right hemisphere! 12 Pitts & Martinez (2014); Volberg (2014)
19 Our Motivation (this is the challenging part) 13
20 lateralization in vision long-range lateral connections? 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) 14
21 lateralization in vision long-range lateral connections? 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) flattened cortex 15
22 lateralization in vision long-range lateral connections? 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) flattened cortex 15
23 lateralization in vision long-range lateral connections? 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) 15
24 lateralization in vision long-range lateral connections? 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) 15
25 lateralization in vision long-range lateral connections? 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) 15
26 lateralization in vision long-range lateral connections? 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) 16
27 lateralization in vision long-range lateral connections? 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) 17
28 lateralization in vision long-range lateral connections? 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) 17
29 lateralization in vision long-range lateral connections? 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) 18
30 lateralization in vision long-range lateral connections? 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) 19
31 lateralization in vision following known data Galuske et al (2000): wider spacing of interconnected patches in LH (BA22) 20
32 lateralization in vision following known data Galuske et al (2000): wider spacing of interconnected patches in LH (BA22) LH: Wide RH: Narrow 20
33 lateralization in vision following known data Hsiao et al (2008; 2013): Differential Encoding model LH: Wide RH: Narrow 20
34 Our model The differential encoding model 21
35 Differential Encoding Our hypothesis LH RH LH: Wide RH: Narrow small local level, words, high frequencies vs. global level, faces, low frequencies, contours 22
36 Differential Encoding Our hypothesis LH RH LH: Wide RH: Narrow small local level, words, high frequencies vs. global level, faces, low frequencies, contours 22
37 Differential Encoding Our hypothesis LH RH LH: Wide RH: Narrow small local level, words, high frequencies vs. global level, faces, low frequencies, contours 22
38 Differential encoding model Training methods Create the network (850 input / output pixels, 850 hidden units. Choose σ, #conns) Train the network on a set of images 23
39 Differential encoding model Training methods Create the network (850 input / output pixels, 850 hidden units. Choose σ, #conns) Train the network on a set of images 23
40 Differential encoding model Training methods Create the network (850 input / output pixels, 850 hidden units. Choose σ, #conns) Train the network on a set of images 23
41 Differential encoding model Training methods Create the network (850 input / output pixels, 850 hidden units. Choose σ, #conns) Train the network on a set of images 24
42 Differential encoding model Training methods Create the network (850 input / output pixels, 850 hidden units. Choose σ, #conns) Train the network on a set of images 24
43 Differential encoding model Analysis methods 25
44 Differential encoding model Analysis methods 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: 25
45 Differential encoding model Analysis methods 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) 25
46 Differential encoding model Analysis methods 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) 25
47 Previous results 26
48 lateralization in vision Navon Figures in a target detection task RH (LVF) CVF (BH) LH (RVF) Task: Did you see a target letter? Targets: T, H Distractors: L,F Global Target Local Target 27 Adapted from Sergent (1982)
49 lateralization in vision Navon Figures in a target detection task RH (LVF) LH (RVF) RH (LVF) CVF (BH) LH (RVF) Task: Did you see a target letter? Targets: T, H Distractors: L,F Global Target Local Target 27 Adapted from Sergent (1982)
50 lateralization in vision Navon Figures in a target detection task RH (LVF) LH (RVF) RH (LVF) CVF (BH) LH (RVF) Task: Did you see a target letter? Targets: T, H Distractors: L,F Global Target Local Target 27 Adapted from Sergent (1982)
51 lateralization in vision Navon Figures in a target detection task RH (LVF) LH (RVF) RH (LVF) CVF (BH) LH (RVF) Task: Did you see a target letter? Targets: T, H Distractors: L,F Global Target Local Target 27 Adapted from Sergent (1982)
52 lateralization in vision Navon Figures in a target detection task RH (LVF) LH (RVF) RH (LVF) CVF (BH) LH (RVF) Task: Did you see a target letter? Targets: T, H Distractors: L,F Global Target Local Target 27 Adapted from Sergent (1982)
53 lateralization in vision Navon Figures in a target detection task RH (LVF) LH (RVF) RH (LVF) CVF (BH) LH (RVF) Task: Did you see a target letter? Targets: T, H Distractors: L,F Global Target Local Target 27 Adapted from Sergent (1982)
54 Differential encoding model Accounting for human behavior (Sergent, 1982) 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. LH (wide) RH (narrow) 28 Hsiao et al. (2013)
55 Differential encoding model Accounting for human behavior LH RH Extract hidden unit representations, train LH & RH classifiers (not shown) 29
56 Differential encoding model Accounting for human behavior LH RH RH (LVF) LH (RVF) Global Local Human Extract hidden unit representations, train LH & RH classifiers (not shown) Data Adapted from Sergent (1982) 29
57 Differential encoding model Accounting for human behavior LH RH RH (LVF) LH (RVF) Global Local Extract hidden unit representations, train LH & RH classifiers (not shown) Human Data Adapted from Sergent (1982) 29 Model Data Hsiao et al. (2013)
58 Differential encoding model Spatial frequency biases LH RH Extract output images, compare power spectrum precision 30
59 -Δ log(power) Differential encoding model Spatial frequency biases LH RH RH - LH (vs. original) RH LH Lower Higher Extract output images, compare power spectrum precision Hsiao et al. (2013) 30
60 Differential encoding model spatial frequency biases Lower Higher Cipollini et al. (COGSCI 2012) 31
61 Differential encoding model spatial frequency biases Lower Higher Cipollini et al. (COGSCI 2012)..and a number of other results (including an interesting departure from previous models) 31
62 new results 32
63 Differential encoding model Expt #1: Train on natural images 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). 33
64 Differential encoding model spatial frequency biases Train on logpolar natural images, examine spatial frequencies RH - LH (vs. original) Lower Higher 34
65 Differential encoding model spatial frequency biases Train on logpolar natural images, examine spatial frequencies RH - LH (vs. original) Lower Higher Without retraining the network, present other images 34
66 Differential encoding model spatial frequency biases Train on logpolar natural images, examine spatial frequencies RH - LH (vs. original) Lower Higher Without retraining the network, present other images RH - LH (vs. original) 34
67 Differential encoding model spatial frequency biases Train on logpolar natural images, examine spatial frequencies RH - LH (vs. original) Lower Higher Without retraining the network, present other images and classify: Global Local 35
68 Differential encoding model Expt #1: Summary Using more realistically trained network, we are able to replicate some of our previous findings. 36
69 Differential encoding model Expt #2: Developmental model 37
70 Differential encoding model Expt #2: Developmental model Validation? 37
71 Differential encoding model Expt #2: Developmental model Validation? Origins? 37
72 Differential encoding model Expt #2: Developmental model Validation? Origins? We can address with a developmental approach! 37
73 Differential encoding model Expt #2: Developmental model Validation? Origins? We can address with a developmental approach! Previously: vary connection distributions measure spatial frequencies 37
74 Differential encoding model Expt #2: Developmental model Validation? Origins? We can address with a developmental approach! Previously: vary connection distributions measure spatial frequencies Developmental approach: vary spatial frequencies measure connection distributions 37
75 Differential encoding model Pruning interacts with acuity 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) Katz and Callaway (1992) 38
76 Differential encoding model Pruning interacts with acuity 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) RH will prune connections under blurrier (lower spatial frequency) input Katz and Callaway (1992) 38
77 Differential encoding model vary frequencies, measure connections 39
78 Differential encoding model vary frequencies, measure connections Methods: LH RH Start RH and LH networks with equivalent connections. Before 39
79 LH Differential encoding model vary frequencies, measure connections Methods: Start RH and LH networks with equivalent connections. Train on natural images; RH receives more blurring of the images than the LH. Epochs: LH RH end Before RH <=3.0cpd <=6.5cpd <=16cpd Full fidelity 39
80 Differential encoding model vary frequencies, measure connections Methods: Start RH and LH networks with equivalent connections. Train on natural images; RH receives more blurring of the images than the LH. Before LH less blurred RH more blurred While training, remove the weakest connections. After 39
81 Differential encoding model post-training connection distributions 40
82 Differential encoding model post-training connection distributions Compile connection distribution 40
83 Differential encoding model post-training connection distributions RH: More blurred 40
84 Differential encoding model post-training connection distributions RH: More blurred LH: Less blurred 40
85 Differential encoding model post-training connection distributions - = RH: More blurred LH: Less blurred RH - LH 40
86 Differential encoding model post-training connection distributions - = Same association as in our previous studies! RH: More blurred LH: Less blurred RH - LH 40
87 Differential encoding model Need for interhemispheric competition? Original Developmental Weaker lateralization in developmental model than previous work. Interhemispheric competition can amplify effects 41
88 Differential encoding model post-training changes 42
89 Differential encoding model post-training changes RH is specialized; LH is not. 42
90 Differential encoding model Expt #2: Summary 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. 43
91 Connectivity Asymmetry Conclusions 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. 44
92 Connectivity Asymmetry Next steps 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? 45
93 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 46
94 47
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