Active manual control of object views facilitates visual recognition
Open accessAbstract
Active exploration of large-scale environments leads to better learning of spatial layout than does passive observation [1] [2] [3]. But active exploration might also help us to remember the appearance of individual objects in a scene. In fact, when we encounter new objects, we often manipulate them so that they can be seen from a variety of perspectives. We present here the first evidence that active control of the visual input in this way facilitates later recognition of objects. Observers who actively rotated novel, three-dimensional objects on a computer screen later showed more efficient visual recognition than observers who passively viewed the exact same sequence of images of these virtual objects. During active exploration, the observers focused mainly on the 'side' or 'front' views of the objects (see also [4] [5] [6]). The results demonstrate that how an object is represented for later recognition is influenced by whether or not one controls the presentation of visual input during learning.
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Studies in medical training and visual object recognition in which learners could manipulate 3-D computer visualizations and observe the instructional material from multiple views suggest that control over appearance can be effective.
“studies in medical training and visual object recognition in which learners could manipulate 3-D computer visualizations and observe the instructional material from multiple views suggest that control over appearance can be effective (Garg et al. 2001;Harman et al. 1999;James et al. 2002).”
From Interactivity in Video-based Models (2007) · cites this paper as Harman et al. 1999
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- Authors
- Harman, K. L., Humphrey, G. K., & Goodale, M. A.
- Affiliations
- Western University (CA)
- Corresponding author
- Humphrey, G.Keith
- ORCID
- Goodale, Melvyn A.
- Venue
- Current Biology
- Publisher
- Elsevier BV
- Published
- 1999-11
- Volume
- 9
- Issue
- 22
- Pages
- 1315-1318
- ISSN
- 0960-9822 (ISSN-L)
- OpenAlex
- W2147024412
- Cited by
- 184
- References
- 11
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 6 · 2023: 5 · 2022: 2 · 2021: 8 · 2020: 7 · 2019: 9 · 2018: 15 · 2017: 4 · 2016: 6 · 2015: 7 · 2014: 8 · 2013: 9 · 2012: 6
- FWCI
- 5.43
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Current Biology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Natural Sciences and Engineering Research Council of Canada
- Medical Research Council
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Spatial Cognition and Navigation
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Keywords
- active exploration, object recognition, object views, visual attention, virtual objects
- MeSH terms
- Adolescent, Adult, Computer Graphics, Exploratory Behavior, Female, Hand, Humans, Male, Man-Machine Systems, Pattern Recognition, Visual, Psychomotor Performance, Reaction Time, Rotation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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