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Comparing a computational model of visual problem solving with human vision on a difficult vision task.

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Τίτλος: Comparing a computational model of visual problem solving with human vision on a difficult vision task.
Συγγραφείς: Khajuria T; Institute of Computer Science, University of Tartu, Tartu, Estonia., Tulver K; Institute of Computer Science, University of Tartu, Tartu, Estonia., Aru J; Institute of Computer Science, University of Tartu, Tartu, Estonia.
Πηγή: PLoS computational biology [PLoS Comput Biol] 2025 Dec 09; Vol. 21 (12), pp. e1012968. Date of Electronic Publication: 2025 Dec 09 (Print Publication: 2025).
Τύπος έκδοσης: Journal Article; Comparative Study
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101238922 Publication Model: eCollection Cited Medium: Internet ISSN: 1553-7358 (Electronic) Linking ISSN: 1553734X NLM ISO Abbreviation: PLoS Comput Biol Subsets: MEDLINE
Imprint Name(s): Original Publication: San Francisco, CA : Public Library of Science, [2005]-
Ιατρικοί όροι (MeSH): Problem Solving*/physiology , Visual Perception*/physiology , Vision, Ocular*/physiology, Humans ; Computer Simulation ; Algorithms ; Computational Biology ; Male
Περίληψη: Human vision is not merely a passive process of interpreting sensory input but can also function as a problem-solving process incorporating generative mechanisms to interpret ambiguous or noisy data. This synergy between the generative and discriminative components, often described as analysis-by-synthesis, enables robust perception and rapid adaptation to out-of-distribution inputs. In this work, we investigate a computational implementation of the analysis-by-synthesis paradigm using genetic search in a generative model, applied to a visual problem-solving task inspired by star constellations. The search is guided by low-level cues based on the structural fitness of candidate solutions compared to the test images. This dataset serves as a testbed for exploring how inferred signals can guide the synthesis of suitable solutions in ambiguous conditions, framing visual inference as an instance of complex problem solving. Drawing on insights from human experiments, we develop a generative search algorithm and compare its performance to humans, examining factors such as accuracy, reaction time, and overlap in drawings. Our results shed light on possible mechanisms of human visual problem solving and highlight the potential of generative search models to emulate aspects of this process.
(Copyright: © 2025 Khajuria et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Competing Interests: The authors have declared that no competing interests exist.
References: Psychol Bull. 2004 Sep;130(5):748-68. (PMID: 15367079)
Proc Natl Acad Sci U S A. 2014 Jun 10;111(23):8619-24. (PMID: 24812127)
Psychol Sci. 2014 May 1;25(5):1087-97. (PMID: 24604146)
Sci Adv. 2020 Mar 04;6(10):eaax5979. (PMID: 32181338)
PLoS Comput Biol. 2020 Oct 2;16(10):e1008215. (PMID: 33006992)
Proc Natl Acad Sci U S A. 2023 Oct 3;120(40):e2211179120. (PMID: 37769256)
Cognition. 2015 Dec;145:104-15. (PMID: 26331214)
Front Psychol. 2017 Sep 12;8:1551. (PMID: 28955272)
Annu Rev Psychol. 2014;65:71-93. (PMID: 24405359)
Trends Cogn Sci. 2006 Jul;10(7):301-8. (PMID: 16784882)
Nat Rev Neurosci. 2004 Aug;5(8):617-29. (PMID: 15263892)
Proc Natl Acad Sci U S A. 2019 Oct 22;116(43):21854-21863. (PMID: 31591217)
Trends Cogn Sci. 2007 Dec;11(12):520-7. (PMID: 18024143)
Cognition. 2025 Apr;257:106081. (PMID: 39933209)
Conscious Cogn. 2023 Apr;110:103494. (PMID: 36913839)
Entry Date(s): Date Created: 20251209 Date Completed: 20251216 Latest Revision: 20251218
Update Code: 20260130
PubMed Central ID: PMC12707649
DOI: 10.1371/journal.pcbi.1012968
PMID: 41364723
Βάση Δεδομένων: MEDLINE
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  Data: Comparing a computational model of visual problem solving with human vision on a difficult vision task.
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  Data: Human vision is not merely a passive process of interpreting sensory input but can also function as a problem-solving process incorporating generative mechanisms to interpret ambiguous or noisy data. This synergy between the generative and discriminative components, often described as analysis-by-synthesis, enables robust perception and rapid adaptation to out-of-distribution inputs. In this work, we investigate a computational implementation of the analysis-by-synthesis paradigm using genetic search in a generative model, applied to a visual problem-solving task inspired by star constellations. The search is guided by low-level cues based on the structural fitness of candidate solutions compared to the test images. This dataset serves as a testbed for exploring how inferred signals can guide the synthesis of suitable solutions in ambiguous conditions, framing visual inference as an instance of complex problem solving. Drawing on insights from human experiments, we develop a generative search algorithm and compare its performance to humans, examining factors such as accuracy, reaction time, and overlap in drawings. Our results shed light on possible mechanisms of human visual problem solving and highlight the potential of generative search models to emulate aspects of this process.<br /> (Copyright: © 2025 Khajuria et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
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  Data: Psychol Bull. 2004 Sep;130(5):748-68. (PMID: <searchLink fieldCode="PM" term="%2215367079%22">15367079)</searchLink><br />Proc Natl Acad Sci U S A. 2014 Jun 10;111(23):8619-24. (PMID: <searchLink fieldCode="PM" term="%2224812127%22">24812127)</searchLink><br />Psychol Sci. 2014 May 1;25(5):1087-97. (PMID: <searchLink fieldCode="PM" term="%2224604146%22">24604146)</searchLink><br />Sci Adv. 2020 Mar 04;6(10):eaax5979. (PMID: <searchLink fieldCode="PM" term="%2232181338%22">32181338)</searchLink><br />PLoS Comput Biol. 2020 Oct 2;16(10):e1008215. (PMID: <searchLink fieldCode="PM" term="%2233006992%22">33006992)</searchLink><br />Proc Natl Acad Sci U S A. 2023 Oct 3;120(40):e2211179120. (PMID: <searchLink fieldCode="PM" term="%2237769256%22">37769256)</searchLink><br />Cognition. 2015 Dec;145:104-15. (PMID: <searchLink fieldCode="PM" term="%2226331214%22">26331214)</searchLink><br />Front Psychol. 2017 Sep 12;8:1551. (PMID: <searchLink fieldCode="PM" term="%2228955272%22">28955272)</searchLink><br />Annu Rev Psychol. 2014;65:71-93. (PMID: <searchLink fieldCode="PM" term="%2224405359%22">24405359)</searchLink><br />Trends Cogn Sci. 2006 Jul;10(7):301-8. (PMID: <searchLink fieldCode="PM" term="%2216784882%22">16784882)</searchLink><br />Nat Rev Neurosci. 2004 Aug;5(8):617-29. (PMID: <searchLink fieldCode="PM" term="%2215263892%22">15263892)</searchLink><br />Proc Natl Acad Sci U S A. 2019 Oct 22;116(43):21854-21863. (PMID: <searchLink fieldCode="PM" term="%2231591217%22">31591217)</searchLink><br />Trends Cogn Sci. 2007 Dec;11(12):520-7. (PMID: <searchLink fieldCode="PM" term="%2218024143%22">18024143)</searchLink><br />Cognition. 2025 Apr;257:106081. (PMID: <searchLink fieldCode="PM" term="%2239933209%22">39933209)</searchLink><br />Conscious Cogn. 2023 Apr;110:103494. (PMID: <searchLink fieldCode="PM" term="%2236913839%22">36913839)</searchLink>
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