Validation of a matrix reasoning task for mobile devices
Open accessAbstract
Many cognitive tasks have been adapted for tablet-based testing, but tests to assess nonverbal reasoning ability, as measured by matrix-type problems that are suited to repeated testing, have yet to be adapted for and validated on mobile platforms. Drawing on previous research, we developed the University of California Matrix Reasoning Task (UCMRT)-a short, user-friendly measure of abstract problem solving with three alternate forms that works on tablets and other mobile devices and that is targeted at a high-ability population frequently used in the literature (i.e., college students). To test the psychometric properties of UCMRT, a large sample of healthy young adults completed parallel forms of the test, and a subsample also completed Raven's Advanced Progressive Matrices and a math test; furthermore, we collected college records of academic ability and achievement. These data show that UCMRT is reliable and has adequate convergent and external validity. UCMRT is self-administrable, freely available for researchers, facilitates repeated testing of fluid intelligence, and resolves numerous limitations of existing matrix tests.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
UCMRT has been shown to correlate highly with Raven's Advanced Progressive Matrices.
“The Matrix Reasoning task used here (called UCMRT) has been shown to correlate highly with performance on Raven’s Advanced Progressive Matrices74”
From Near transfer to an unrelated N-back task mediates the effect of N-back working memory training on matrix reasoning (2022) · cites this paper as [74]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
(1998) · Cited by 4,172 · see citing works
Theory of fluid and crystallized intelligence: A critical experiment.
Cattell, R. B. (1963) · Journal of Educational Psychology · doi:10.1037/h0046743 · Cited by 2,556 · see citing works
An automated version of the operation span task
Unsworth, N., Heitz, R. P., Schrock, J. C., & Engle, R. W. (2005) · Behavior Research Methods · doi:10.3758/bf03192720 · Cited by 1,861 · see citing works
Bayesian Cognitive Modeling: A Practical Course
Lee, M. D., & Wagenmakers, E.-J. (2014) · doi:10.1017/cbo9781139087759 · Cited by 1,795 · see citing works
Bayesian inference for psychology. Part II: Example applications with JASP
Wagenmakers, E., Love, J., Marsman, M., Jamil, T., Ly, A., Verhagen, J., Selker, R., Gronau, Q. F., Dropmann, D., boutin, B., et al. (2017) · Psychonomic Bulletin & Review · doi:10.3758/s13423-017-1323-7 · Cited by 1,705 · see citing works
(2013) · Cited by 1,351 · see citing works
Predicting academic achievement with cognitive ability
Rohde, T. E., & Thompson, L. A. (2006) · Intelligence · doi:10.1016/j.intell.2006.05.004 · Cited by 625 · see citing works
Redick, T. S., Shipstead, Z., Harrison, T. L., Hicks, K. L., Fried, D. E., Hambrick, D. Z., Kane, M. J., & Engle, R. W. (2013) · Journal of Experimental Psychology: General · doi:10.1037/a0029082 · Cited by 617 · see citing works
Jaeggi, S. M., Studer-Luethi, B., Buschkuehl, M., Su, Y.-F., Jonides, J., & Perrig, W. J. (2010) · Intelligence · doi:10.1016/j.intell.2010.09.001 · Cited by 509 · see citing works
Self-citationPersonality-intelligence relations: Assessment of typical intellectual engagement.
Goff, M., & Ackerman, P. L. (1992) · Journal of Educational Psychology · doi:10.1037/0022-0663.84.4.537 · Cited by 473 · see citing works
The role of individual differences in cognitive training and transfer
Jaeggi, S. M., Buschkuehl, M., Shah, P., & Jonides, J. (2014) · Memory & Cognition · doi:10.3758/s13421-013-0364-z · Cited by 432 · see citing works
Self-citationCondon, D., & Revelle, W. (2014) · Intelligence · doi:10.1016/j.intell.2014.01.004 · Cited by 406 · see citing works
Bors, D. A., & Stokes, T. (1998) · Educational and Psychological Measurement · doi:10.1177/0013164498058003002 · Cited by 303 · see citing works
Development of a Short form for the Raven Advanced Progressive Matrices Test
Arthur, W., & Day, D. V. (1994) · Educational and Psychological Measurement · doi:10.1177/0013164494054002013 · Cited by 298 · see citing works
Ackerman, P. L., & Kanfer, R. (2009) · Journal of Experimental Psychology: Applied · doi:10.1037/a0015719 · Cited by 288 · see citing works
ACT and general cognitive ability
Koenig, K. A., Frey, M. C., & Detterman, D. K. (2007) · Intelligence · doi:10.1016/j.intell.2007.03.005 · Cited by 265 · see citing works
Critique of Cattell's "Theory of fluid and crystallized intelligence: A critical experiment."
Humphreys, L. G. (1967) · Journal of Educational Psychology · doi:10.1037/h0024654 · Cited by 254 · see citing works
Nash Unsworth, A., & Engle, R. W. (2004) · Intelligence · doi:10.1016/j.intell.2004.08.003 · Cited by 249 · see citing works
Westrick, P. A., Le, H., Robbins, S. B., Radunzel, J., & Schmidt, F. L. (2015) · Educational Assessment · doi:10.1080/10627197.2015.997614 · Cited by 222 · see citing works
Nash Unsworth, A., Redick, T. S., Lakey, C. E., & Young, D. L. (2009) · Intelligence · doi:10.1016/j.intell.2009.08.002 · Cited by 191 · see citing works
SAT and ACT predict college GPA after removing g
Coyle, T. R., & Pillow, D. R. (2008) · Intelligence · doi:10.1016/j.intell.2008.05.001 · Cited by 174 · see citing works
Influence of working memory on adult age differences in matrix reasoning
Salthouse, T. A. (1993) · British Journal of Psychology · doi:10.1111/j.2044-8295.1993.tb02472.x · Cited by 147 · see citing works
The 20-Minute Version as a Predictor of the Raven Advanced Progressive Matrices Test
Hamel, R., & Schmittmann, V. D. (2006) · Educational and Psychological Measurement · doi:10.1177/0013164406288169 · Cited by 127 · see citing works
Intelligence, reaction time (RT) and a new ‘odd-man-out’ RT paradigm
Frearson, W., & Eysenck, H. J. (1986) · Personality and Individual Differences · doi:10.1016/0191-8869(86)90079-6 · Cited by 120 · see citing works
Colom, R., Román, F. J., Abad, F. J., Shih, P. C., Privado, J., Froufe, M., Escorial, S., Martínez, K., Burgaleta, M., Quiroga, M. A., et al. (2013) · Intelligence · doi:10.1016/j.intell.2013.09.002 · Cited by 112 · see citing works
Self-citationMatzen, L. E., Benz, Z. O., Dixon, K. R., Posey, J., Kroger, J. K., & Speed, A. E. (2010) · Behavior Research Methods · doi:10.3758/brm.42.2.525 · Cited by 95 · see citing works
Improvement in working memory is not related to increased intelligence scores
Colom, R., Quiroga, M. Á., Shih, P. C., Martínez, K., Burgaleta, M., Martínez‐Molina, A., Román, F. J., Requena, L. A., & Ramírez, I. (2010) · Intelligence · doi:10.1016/j.intell.2010.06.008 · Cited by 57 · see citing works
Arthur, W., & Woehr, D. J. (1993) · Educational and Psychological Measurement · doi:10.1177/0013164493053002016 · Cited by 37 · see citing works
Freund, P. A., & Holling, H. (2011) · Intelligence · doi:10.1016/j.intell.2011.02.009 · Cited by 37 · see citing works
Clark, C. M., Lawlor‐Savage, L., & Goghari, V. M. (2017) · PLoS ONE · doi:10.1371/journal.pone.0177707 · Cited by 31 · see citing works
Koretz, D. M., Yu, C., Mbekeani, P. P., Langi, M., Dhaliwal, T., & Braslow, D. (2016) · AERA Open · doi:10.1177/2332858416670601 · Cited by 24 · see citing works
Development and Validation of an 18-Item Medium Form of the Ravens Advanced Progressive Matrices
Sefcek, J. A., Miller, G. F., & Figueredo, A. J. (2016) · SAGE Open · doi:10.1177/2158244016651915 · Cited by 11 · see citing works
Hogrefe, A. B., Studer-Luethi, B., Kodzhabashev, S. T., & Perrig, W. J. (2017) · Journal of Cognitive Enhancement · doi:10.1007/s41465-017-0042-3 · Cited by 11 · see citing works
Improving general intelligence with a nutrient-based pharmacological intervention
Stough, C. K. K., Camfield, D., Kure, C., Tarasuik, J. C., Downey, L. A., Lloyd, J., Zangara, A., Scholey, A. B., & Reynolds, J. (2011) · Intelligence · doi:10.1016/j.intell.2011.01.003 · Cited by 10 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Raven JC (1938/1956). Guide to Progressive Matrices. (rev. ed.). London, H.K. Lewis.
Cited by
Sorted by citations
Divergent Research Methods Limit Understanding of Working Memory Training
Pergher, V., Shalchy, M. A., Pahor, A., Van Hulle, M. M., Jaeggi, S. M., & Seitz, A. R. (2020) · Journal of Cognitive Enhancement · doi:10.1007/s41465-019-00134-7 · Cited by 56 · see citing works
“Recent efforts to develop multiple parallel reasoning tests may mitigate these types of problems in future intervention studies (Pahor et al. 2018; Kyllonen et al. 2018).”
BackgroundSelf-citationExpectation effects in working memory training
Parong, J. A., Seitz, A. R., Jaeggi, S. M., & Green, C. S. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2209308119 · Cited by 43 · see citing works
Self-citationCaught in the Act: Predicting Cheating in Unproctored Knowledge Assessment
Steger, D., Schroeders, U., & Wilhelm, O. (2020) · Assessment · doi:10.1177/1073191120914970 · Cited by 34 · see citing works
Pahor, A., Seitz, A. R., & Jaeggi, S. M. (2022) · Nature Human Behaviour · doi:10.1038/s41562-022-01384-w · Cited by 34 · see citing works
Self-citationTechnology Solutions to Reduce Anxiety and Increase Cognitive Availability in Students
Hawes, D., & Arya, A. (2023) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2023.3239985 · Cited by 40 · see citing works
VR-based Student Priming to Reduce Anxiety and Increase Cognitive Bandwidth
Hawes, D., & Arya, A. (2021) · IEEE Conference on Virtual Reality and 3D User Interfaces (VR) · doi:10.1109/vr50410.2021.00046 · Cited by 23 · see citing works
No Evidence for Expectation Effects in Cognitive Training Tasks
Vodyanyk, M. M., Cochrane, A., Corriveau, A., Demko, Z., & Green, C. S. (2021) · Journal of Cognitive Enhancement · doi:10.1007/s41465-021-00207-6 · Cited by 21 · see citing works
Cochrane, A., & Green, C. S. (2021) · npj Science of Learning · doi:10.1038/s41539-021-00111-w · Cited by 18 · see citing works
Wilcox, R. R., & Barbey, A. K. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhad284 · Cited by 15 · see citing works
Investigating the Role of Individual Differences in Adherence to Cognitive Training
Tullo, D., Feng, Y., Pahor, A., Cote, J. M., Seitz, A. R., & Jaeggi, S. M. (2023) · Journal of Cognition · doi:10.5334/joc.315 · Cited by 11 · see citing works
Self-citationTatel, C. E., Tidler, Z. R., & Ackerman, P. L. (2021) · Intelligence · doi:10.1016/j.intell.2021.101604 · Cited by 10 · see citing works
Partner perceptions during brief online interactions shape partner selection and cooperation
Hrkalović, T. M., Dudzik, B., Hung, H., & Balliet, D. P. (2025) · PLoS ONE · doi:10.1371/journal.pone.0318137 · Cited by 9 · see citing works
Unicorn, Hare, or Tortoise? Using Machine Learning to Predict Working Memory Training Performance
Feng, Y., Pahor, A., Seitz, A. R., Barbour, D. L., & Jaeggi, S. M. (2023) · Journal of Cognition · doi:10.5334/joc.319 · Cited by 5 · see citing works
Self-citationPLFest: A New Platform for Accessible, Reproducible, and Open Perceptual Learning Research
Jayakumar, S., Maniglia, M., Guan, Z., Green, C. S., & Seitz, A. R. (2024) · Journal of Cognitive Enhancement · doi:10.1007/s41465-024-00299-w · Cited by 5 · see citing works
Self-citationCherri, D., Eddins, D. A., & Ozmeral, E. J. (2024) · Trends in Hearing · doi:10.1177/23312165241263485 · Cited by 4 · see citing works
Brown, M. I., Grossenbacher, M. A., & Warman, Z. (2022) · Journal of Applied Psychology · doi:10.1037/apl0001067 · Cited by 6 · see citing works
Kelley, D. J. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-25719-4_23 · Cited by 4 · see citing works
Kelley, D. J., Twyman, M. A., & Dambrot, S. M. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-25719-4_26 · Cited by 4 · see citing works
Graph Mapping: A novel and simple test to validly assess fluid reasoning
Jastrzębski, J., Ociepka, M., & Chuderski, A. (2022) · Behavior Research Methods · doi:10.3758/s13428-022-01846-z · Cited by 4 · see citing works
Task affordances affect partner preferences
Hrkalović, T. M., Li, A., Bopp, M., Li, Y., & Balliet, D. P. (2025) · Journal of Experimental Social Psychology · doi:10.1016/j.jesp.2025.104751 · Cited by 3 · see citing works
Graded human sensitivity to geometric and topological concepts
Marupudi, V., & Varma, S. (2022) · Cognition · doi:10.1016/j.cognition.2022.105331 · Cited by 2 · see citing works
Turnbull, A. G., Seitz, A. R., & Lin, F. V. (2023) · Alzheimer s & Dementia Translational Research & Clinical Interventions · doi:10.1002/trc2.12405 · Cited by 2 · see citing works
Self-citationRethinking Planning Metrics: An Analysis of Common Measurements of Planning Abilities
Cunningham, E. G., Bavelier, D., & Green, C. S. (2024) · Cognition · doi:10.1016/j.cognition.2025.106220 · Cited by 2 · see citing works
Zhan, P., Chen, Q., Man, K., & Hao, N. (2026) · Current Psychology · doi:10.1007/s12144-026-09075-9 · Cited by 2 · see citing works
Hawes, D., & Arya, A. (2020) · IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR) · doi:10.1109/aivr50618.2020.00065 · Cited by 2 · see citing works
Feasibility and Repeatability of an Abbreviated Auditory Perceptual and Cognitive Test Battery
Cherri, D., Ozmeral, E. J., Gallun, F. J., Seitz, A. R., & Eddins, D. A. (2024) · Journal of Speech Language and Hearing Research · doi:10.1044/2024_jslhr-23-00590 · Cited by 2 · see citing works
Self-citationPredictors of Visuospatial Skill and the Role of Observational Drawing
Vodyanyk, M. M., Yangüez, M., & Jaeggi, S. M. (2026) · Perceptual and Motor Skills · doi:10.1177/00315125261419411 · Cited by 1 · see citing works
Self-citationEvaluating planning through play: Exploring the use of mini games to assess planning abilities
Cunningham, E. G., Bavelier, D., & Green, C. S. (2025) · Acta Psychologica · doi:10.1016/j.actpsy.2025.105252 · Cited by 1 · see citing works
Getting at Underlying Mechanisms of Far Transfer - the Mediating Role of Near TransferPreprint
Pahor, A., Seitz, A. R., & Jaeggi, S. M. (2021) · doi:10.31234/osf.io/q5xmp · Cited by 1 · see citing works
Self-citationCreation of Novel Fluid Intelligence Tasks
May, D. W. (2021) · eScholarship (California Digital Library)
Terneusen, A. (2023) · doi:10.26481/dis.20230908at
User Reliance on AI Support for Collaborative Partner Selection
Hrkalović, T. M., Dudzik, B., Hao, C., & Willemsen, M. C. (2026) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3742413.3789082
Caught in the Act: Predicting Cheating in Unproctored Knowledge AssessmentPreprint
Schroeders, U., Wilhelm, O., & Steger, D. (2020) · OSF Preprints (OSF Preprints)
PLFest: A Multi-Site Validation of an Open Platform for Visual and Cognitive AssessmentPreprint
Peñaloza, B., Maniglia, M., Munneke, J., Green, C. S., & Seitz, A. R. (2026) · doi:10.31234/osf.io/3xus7_v1
Self-citation
- Authors
- Pahor, A., Stavropoulos, T., Jaeggi, S. M., & Seitz, A. R.
- Affiliations
- University of California, Riverside (US)
- University of California, Irvine (US)
- Corresponding author
- Pahor, Anja
- Venue
- Behavior Research Methods
- Published
- 2018-10-26
- Volume
- 51
- Issue
- 5
- Pages
- 2256-2267
- ISSN
- 1554-351X (ISSN-L)
- 1554-3528
- PMC
- PMC6486467
- OpenAlex
- W2898367237
- Cited by
- 36
- References
- 34
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 4 · 2023: 6 · 2022: 6 · 2021: 6 · 2020: 3 · 2019: 4
- FWCI
- 4.26
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- public-domain
- www.springer.com/tdm (tdm)
- Locations
- Behavior Research Methods (publishedVersion, public-domain)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institute of Mental Health (1R01MH111742-01)
- National Institute on Aging (1K02AG054665-01)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Cognitive Abilities and Testing
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Social Sciences › Psychology › Clinical Psychology › Historical and Modern Theater Studies
- Keywords
- matrix reasoning, nonverbal reasoning, fluid intelligence, test validation, psychometric properties, mobile application testing, tablet-based assessment, Raven's Advanced Progressive Matrices, self-administered tests, retesting, university students, academic performance
- MeSH terms
- Adolescent, Female, Humans, Intelligence, Intelligence Tests, Male, Problem Solving, Psychometrics, Cell Phone, Young Adult
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 ›
Are you an author, or do you see a problem with this page? Request a review
