The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Larger stimuli are judged to last longer
Open accessAbstract
Representing magnitude information in various dimensions, including space, quantity, and time, is an important function of the human brain. Many previous studies reported that numerical and spatial magnitudes could be mutually influenced through a "mental number line". In this study, we address the question of whether magnitudes in nontemporal dimensions and magnitudes in time are represented independently or not. Observers judged the duration of the stimuli while four types of nontemporal magnitude information, including number of dots, size of open squares, luminance of solid squares, and numeric value of digits, were manipulated in Stroop-like paradigms. Results revealed that stimuli with larger magnitudes in these nontemporal dimensions were judged to be temporally longer. This observation supports the idea that magnitudes in temporal and nontemporal dimensions are not independent and implies the existence of generalized and abstract components in the magnitude representations.
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Cites
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Cited by
The parietal cortex and the representation of time, space, number and other magnitudes
Bueti, D., & Walsh, V. (2009) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2009.0028 · Cited by 785
Dedicated and intrinsic models of time perception
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Temporal cognition: Connecting subjective time to perception, attention, and memory.
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When time is space: Evidence for a mental time line
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Metadata
- Authors
- Xuan, B., Zhang, D., He, S., & Chen, X.
- Venue
- Journal of Vision
- Published
- 2007-07-06
- Volume
- 7
- Issue
- 10
- Pages
- 2
- ISSN
- 1534-7362 (ISSN-L)
- OpenAlex
- W1978155634
- Cited by
- 421
- References
- 28
- FWCI
- 16.69
- Type
- journal article
- Language
- en
- Open access
- gold
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
