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Part 1: Document Description
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Citation |
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Title: |
Data for Human Experiments |
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Identification Number: |
doi:10.7910/DVN/MXGTSP |
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Distributor: |
Harvard Dataverse |
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Date of Distribution: |
2022-04-19 |
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Version: |
1 |
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Bibliographic Citation: |
Sundaram, Shobhita, 2022, "Data for Human Experiments", https://doi.org/10.7910/DVN/MXGTSP, Harvard Dataverse, V1 |
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Citation |
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Title: |
Data for Human Experiments |
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Identification Number: |
doi:10.7910/DVN/MXGTSP |
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Authoring Entity: |
Sundaram, Shobhita (Massachusetts Institute of Technology) |
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Distributor: |
Harvard Dataverse |
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Access Authority: |
Sundaram, Shobhita |
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Depositor: |
Sundaram, Shobhita |
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Date of Deposit: |
2022-04-19 |
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Holdings Information: |
https://doi.org/10.7910/DVN/MXGTSP |
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Study Scope |
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Keywords: |
Computer and Information Science, Computer and Information Science |
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Abstract: |
This synthetic data was shown to human participants to assess rapid learning and generalization of symmetry. In the first two slides, participants were shown asymmetric and symmetric images (labeled "Group 0" and "Group 1") respectively. In subsequent slides they were shown random synthetic images and asked to categorize them into the two groups. |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Bibliographic Citation: |
Shobhita Sundaram*, Darius Sinha*, Matthew Groth, Tomotake Sasaki, Xavier Boix. Symmetry perception by deep networks: Inadequacy of feed-forward architectures and improvements with recurrent connections. Under Review. |
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Label: |
HumanExp.pptx |
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Notes: |
application/vnd.openxmlformats-officedocument.presentationml.presentation |