Models of unforced choice Article Swipe
Víctor H. Cervantes
,
Aaron S. Benjamin
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.31234/osf.io/n5s6k
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.31234/osf.io/n5s6k
Unforced choice tasks are ones in which the responder has the option of selecting from a limited array of choices or rejecting the entire set. Such tasks are common in perceptual and cognitive research, but models of decision-making for unforced choice are sparse in the literature and lack a unifying mathematical framework. Such tasks are important for theoretical development because they contain elements of relative, criterion-independent decision making---in which response options are compared to one another---and also criterion-dependent decision making, in which options are compared to a decision criterion determined by the observer. We provide a generic signal-detection multivariate framework for developing models of unforced choice that draws lessons from the geometry of multivariate statistics, from multidimensional signal-detection theories, and from psychophysical models of visual search. We show how this framework can accommodate all extant models of unforced choice that have been applied to the specific case of lineup memory tasks for eyewitnesses. Exact derivations, some of which have proven elusive to this point, are provided for each model in both an unrestricted form---in which variances and covariances are relatively free to vary across signal and noise---and in various restricted forms, in which constraints are applied to variances and covariances. Using the formalizations presented here, we show that all of the current models of lineup memory have severe limitations that render some models challenging to directly compare to one another and other models unidentifiable. Overall, the multivariate framework will aid in the testing of current models and in the development of new models of lineup memory and other tasks involving unforced choice.
Related Topics
Concepts
Observer (physics)
Multivariate statistics
Computer science
Set (abstract data type)
Extant taxon
Variance (accounting)
Point (geometry)
Two-alternative forced choice
Machine learning
Artificial intelligence
Mathematics
Statistics
Accounting
Physics
Geometry
Evolutionary biology
Programming language
Biology
Quantum mechanics
Business
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.31234/osf.io/n5s6k
- https://psyarxiv.com/n5s6k/download
- OA Status
- gold
- Cited By
- 1
- References
- 69
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4376117967
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4376117967Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.31234/osf.io/n5s6kDigital Object Identifier
- Title
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Models of unforced choiceWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-05-09Full publication date if available
- Authors
-
Víctor H. Cervantes, Aaron S. BenjaminList of authors in order
- Landing page
-
https://doi.org/10.31234/osf.io/n5s6kPublisher landing page
- PDF URL
-
https://psyarxiv.com/n5s6k/downloadDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://psyarxiv.com/n5s6k/downloadDirect OA link when available
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Observer (physics), Multivariate statistics, Computer science, Set (abstract data type), Extant taxon, Variance (accounting), Point (geometry), Two-alternative forced choice, Machine learning, Artificial intelligence, Mathematics, Statistics, Accounting, Physics, Geometry, Evolutionary biology, Programming language, Biology, Quantum mechanics, BusinessTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2024: 1Per-year citation counts (last 5 years)
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69Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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