Reconstructing physical representations of block towers in visual working memory Article Swipe
Stefan Uddenberg
,
Jun‐Hyeok Kwak
,
Brian J. Scholl
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1167/jov.21.9.2929
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1167/jov.21.9.2929
Recent studies have explored the perception of physical properties (such as mass and stability) in psychology, neuroscience, and AI, and perhaps the most popular stimulus from such studies is the block tower -- since such displays (of stacked rectilinear objects) evoke immediate visual impressions of physical (in)stability. Here we explored a maximally simple question: what properties are represented during natural viewing of such stimuli? Previous work on this question has been limited in two ways. First, such studies typically involve explicit judgments ("Which way will it fall?"), which may prompt encoding strategies that would not otherwise operate automatically. Second, such studies can typically only explore those tower properties that are systematically manipulated as explicit independent variables. Here we attempted to overcome such limitations in an especially direct way: observers viewed a briefly-flashed block tower, and then immediately *reproduced* its structure from memory -- by dragging and dropping an array of blocks (initially presented on the simulated ground plane) using a custom 3D interface. This allowed us to directly measure the success of reproductions in terms of both lower-level image properties (e.g. the blocks' colors/orientations) and higher-level physical properties (e.g. when comparing the stability of the initial towers and their reproductions). Analyses revealed two types of evidence for the visual representation of 'invisible' abstract physical properties. First, the (in)stability of the reproductions (computed, e.g., in terms of the blocks' summed displacements from their original positions, as analyzed in a physics engine with simulated gravity) could not be directly predicted by lower-level image properties (such as the blocks' initial heights or spread). Second, reproductions of unstable towers tended to be more stable, but not vice versa. This work demonstrates how physical representations in visual memory can be revealed, all without ever asking anyone anything about physics.
Related Topics
Concepts
Perception
Block (permutation group theory)
Tower
Stability (learning theory)
Computer science
Visual perception
Representation (politics)
Stimulus (psychology)
Artificial intelligence
Psychology
Cognitive psychology
Arithmetic
Mathematics
Geometry
Engineering
Political science
Neuroscience
Politics
Civil engineering
Machine learning
Law
Metadata
- Type
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- Language
- en
- Landing Page
- https://doi.org/10.1167/jov.21.9.2929
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3198066388
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3198066388Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1167/jov.21.9.2929Digital Object Identifier
- Title
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Reconstructing physical representations of block towers in visual working memoryWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-09-01Full publication date if available
- Authors
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Stefan Uddenberg, Jun‐Hyeok Kwak, Brian J. SchollList of authors in order
- Landing page
-
https://doi.org/10.1167/jov.21.9.2929Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1167/jov.21.9.2929Direct OA link when available
- Concepts
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Perception, Block (permutation group theory), Tower, Stability (learning theory), Computer science, Visual perception, Representation (politics), Stimulus (psychology), Artificial intelligence, Psychology, Cognitive psychology, Arithmetic, Mathematics, Geometry, Engineering, Political science, Neuroscience, Politics, Civil engineering, Machine learning, LawTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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