Navigating Memorability Landscapes: Hyperbolic Geometry Reveals Hierarchical Structures in Object Concept Memory Article Swipe
Fiona M. Lee
,
Marc G. Berman
,
Andrew J. Stier
,
Wilma Bainbridge
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.09.22.614329
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.09.22.614329
Why are some object concepts (e.g., birds, cars, vegetables, etc.) more memorable than others? Prior studies have suggested that features (e.g., color, animacy, etc.) and typicality (e.g., robin vs. penguin) of object images influences the likelihood of being remembered. However, a complete understanding of object memorability remains elusive. In this study, we examine whether the geometric relationship between object concepts explains differences in their memorability. Specifically, we hypothesize that image concepts will be geometrically arranged in hierarchical structures and that memorability will be explained by a concept’s depth in these hierarchical trees. To test this hypothesis, we construct a Hyperbolic representation space of object concepts (N=1,854) from the THINGS database (Hebart et al., 2019), which consists of naturalistic images of concrete objects, and a space of 49 feature dimensions derived from data-driven models. Using ALBATROSS (Stier, A. J., Giusti, C., & Berman, M. G., In prep), a stochastic topological data analysis technique that detects underlying structures of data, we demonstrate that Hyperbolic geometry efficiently captures the hierarchical organization of object concepts above and beyond a traditional Euclidean geometry and that hierarchical organization is related to memorability. We find that concepts closer to the center of the representational space are more prototypical and also more memorable. Importantly, Hyperbolic distances are more predictive of memorability and prototypicality than Euclidean distances, suggesting that concept memorability and typicality are organized hierarchically. Taken together, our work presents a novel hierarchical representational structure of object concepts that explains memorability and typicality.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://doi.org/10.1101/2024.09.22.614329
- OA Status
- green
- Cited By
- 3
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4402787763
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4402787763Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2024.09.22.614329Digital Object Identifier
- Title
-
Navigating Memorability Landscapes: Hyperbolic Geometry Reveals Hierarchical Structures in Object Concept MemoryWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-09-24Full publication date if available
- Authors
-
Fiona M. Lee, Marc G. Berman, Andrew J. Stier, Wilma BainbridgeList of authors in order
- Landing page
-
https://doi.org/10.1101/2024.09.22.614329Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.ncbi.nlm.nih.gov/pmc/articles/11463604Direct OA link when available
- Concepts
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Object (grammar), Geometry, Computer science, Artificial intelligence, Geography, Computer graphics (images), MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3Per-year citation counts (last 5 years)
- References (count)
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40Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.(N=1,854) | 106 |
| abstract_inverted_index.ALBATROSS | 135 |
| abstract_inverted_index.Euclidean | 177, 217 |
| abstract_inverted_index.construct | 98 |
| abstract_inverted_index.distances | 208 |
| abstract_inverted_index.explained | 84 |
| abstract_inverted_index.geometric | 56 |
| abstract_inverted_index.memorable | 12 |
| abstract_inverted_index.organized | 226 |
| abstract_inverted_index.structure | 237 |
| abstract_inverted_index.suggested | 18 |
| abstract_inverted_index.technique | 152 |
| abstract_inverted_index.together, | 229 |
| abstract_inverted_index.Hyperbolic | 100, 162, 207 |
| abstract_inverted_index.dimensions | 129 |
| abstract_inverted_index.distances, | 218 |
| abstract_inverted_index.influences | 34 |
| abstract_inverted_index.likelihood | 36 |
| abstract_inverted_index.memorable. | 205 |
| abstract_inverted_index.predictive | 211 |
| abstract_inverted_index.stochastic | 148 |
| abstract_inverted_index.structures | 78, 156 |
| abstract_inverted_index.suggesting | 219 |
| abstract_inverted_index.typicality | 26, 224 |
| abstract_inverted_index.underlying | 155 |
| abstract_inverted_index.concept’s | 87 |
| abstract_inverted_index.data-driven | 132 |
| abstract_inverted_index.demonstrate | 160 |
| abstract_inverted_index.differences | 62 |
| abstract_inverted_index.efficiently | 164 |
| abstract_inverted_index.hypothesis, | 96 |
| abstract_inverted_index.hypothesize | 68 |
| abstract_inverted_index.remembered. | 39 |
| abstract_inverted_index.topological | 149 |
| abstract_inverted_index.traditional | 176 |
| abstract_inverted_index.typicality. | 245 |
| abstract_inverted_index.vegetables, | 9 |
| abstract_inverted_index.Importantly, | 206 |
| abstract_inverted_index.hierarchical | 77, 91, 167, 181, 235 |
| abstract_inverted_index.memorability | 46, 81, 213, 222, 243 |
| abstract_inverted_index.naturalistic | 118 |
| abstract_inverted_index.organization | 168, 182 |
| abstract_inverted_index.prototypical | 201 |
| abstract_inverted_index.relationship | 57 |
| abstract_inverted_index.Specifically, | 66 |
| abstract_inverted_index.geometrically | 74 |
| abstract_inverted_index.memorability. | 65, 186 |
| abstract_inverted_index.understanding | 43 |
| abstract_inverted_index.representation | 101 |
| abstract_inverted_index.hierarchically. | 227 |
| abstract_inverted_index.prototypicality | 215 |
| abstract_inverted_index.representational | 197, 236 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 96 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.7883694 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |