The Algorithmic Level Is the Bridge Between Computation and Brain

The magical number seven, plus or minus two: Some limits on our capacity for processing information.

Miller, G. A. (1956) · Psychological Review · doi:10.1037/h0043158 · Cited by 17,812 · see citing works

The magical number seven, plus or minus two: Some limits on our capacity for processing information.

Miller, G. A. (1994) · Psychological Review · doi:10.1037/0033-295x.101.2.343 · Cited by 17,469 · see citing works

Cognitive Reflection and Decision Making

Frederick, S. (2005) · Journal of Economic Perspectives · doi:10.1257/089533005775196732 · Cited by 5,256 · see citing works

Context theory of classification learning

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Cortical substrates for exploratory decisions in humans

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How to grow a mind: Statistics, structure, and abstraction

Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011) · Science · doi:10.1126/science.1192788 · Cited by 1,815 · see citing works

Thinking too much: Introspection can reduce the quality of preferences and decisions.

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Thinking too much: Introspection can reduce the quality of preferences and decisions.

Wilson, T. D., & Schooler, J. W. (1991) · Journal of Personality and Social Psychology · doi:10.1037//0022-3514.60.2.181 · Cited by 1,397 · see citing works

The adaptive nature of human categorization.

Anderson, J. R. (1991) · Psychological Review · doi:10.1037/0033-295x.98.3.409 · Cited by 1,069 · see citing works

Explaining the brain : mechanisms and the mosaic unity of neuroscience

Craver, C. F. (2007) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199299317.001.0001 · Cited by 986 · see citing works

Think Different: The Merits of Unconscious Thought in Preference Development and Decision Making.

Dijksterhuis, A. (2004) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.87.5.586 · Cited by 836 · see citing works

Rule-plus-exception model of classification learning.

Nosofsky, R. M., Palmeri, T. J., & McKinley, S. C. (1994) · Psychological Review · doi:10.1037/0033-295x.101.1.53 · Cited by 794 · see citing works

SUSTAIN: A Network Model of Category Learning.

Love, B. C., Medin, D. L., & Gureckis, T. M. (2004) · Psychological Review · doi:10.1037/0033-295x.111.2.309 · Cited by 778 · see citing works

Bayesian Fundamentalism or Enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition

Jones, M., & Love, B. C. (2011) · Behavioral and Brain Sciences · doi:10.1017/s0140525x10003134 · Cited by 533 · see citing works

Neural correlates of mentalizing-related computations during strategic interactions in humans

Hampton, A. N., Bossaerts, P. L., & O’Doherty, J. P. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0711099105 · Cited by 518 · see citing works

Model‐Based fMRI and Its Application to Reward Learning and Decision Making

O’Doherty, J. P., Hampton, A. N., & Kim, H. (2007) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1390.022 · Cited by 509 · see citing works

Bayesian just-so stories in psychology and neuroscience.

Bowers, J. S., & Davis, C. J. (2012) · Psychological Bulletin · doi:10.1037/a0026450 · Cited by 436 · see citing works

Myside Bias, Rational Thinking, and Intelligence

Stanovich, K. E., West, R. F., & Toplak, M. E. (2013) · Current Directions in Psychological Science · doi:10.1177/0963721413480174 · Cited by 427 · see citing works

Take The First: Option-generation and resulting choices

Johnson, J. G., & Raab, M. (2003) · Organizational Behavior and Human Decision Processes · doi:10.1016/s0749-5978(03)00027-x · Cited by 375 · see citing works

Rational approximations to rational models: alternative algorithms for category learning

Sanborn, A. N., Griffiths, T. L., & Navarro, D. J. (2010) · Psychological Review · doi:10.1037/a0020511 · Cited by 369 · see citing works

Haste does not always make waste: Expertise, direction of attention, and speed versus accuracy in performing sensorimotor skills

Beilock, S. L., Bertenthal, B. I., McCoy, A. M., & Carr, T. H. (2004) · Psychonomic Bulletin & Review · doi:10.3758/bf03196585 · Cited by 363 · see citing works

Cognitive sophistication does not attenuate the bias blind spot.

West, R. F., Meserve, R. J., & Stanovich, K. E. (2012) · Journal of Personality and Social Psychology · doi:10.1037/a0028857 · Cited by 361 · see citing works

Not Noisy, Just Wrong: The Role of Suboptimal Inference in Behavioral Variability

Beck, J. M., Ma, W. J., Pitkow, X., Latham, P. E., & Pouget, A. (2012) · Neuron · doi:10.1016/j.neuron.2012.03.016 · Cited by 316 · see citing works

Ten years of the rational analysis of cognition

Nick Chater, A., & Oaksford, M. (1999) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(98)01273-x · Cited by 269 · see citing works

A Bayesian framework for simultaneously modeling neural and behavioral data

Turner, B. M., Forstmann, B. U., Wagenmakers, E., Brown, S. D., Sederberg, P. B., & Steyvers, M. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.01.048 · Cited by 201 · see citing works

Decoding the Brain’s Algorithm for Categorization from Its Neural Implementation

Mack, M. L., Preston, A. R., & Love, B. C. (2013) · Current Biology · doi:10.1016/j.cub.2013.08.035 · Cited by 170 · see citing works

Exemplar models as a mechanism for performing Bayesian inference

Shi, L., Griffiths, T. L., Feldman, N. H., & Sanborn, A. N. (2010) · Psychonomic Bulletin & Review · doi:10.3758/pbr.17.4.443 · Cited by 169 · see citing works

Looking down, around, and up: Mechanistic explanation in psychology

Bechtel, W. (2009) · Philosophical Psychology · doi:10.1080/09515080903238948 · Cited by 167 · see citing works

Bridging Levels of Analysis for Probabilistic Models of Cognition

Griffiths, T. L., Vul, E., & Sanborn, A. N. (2012) · Current Directions in Psychological Science · doi:10.1177/0963721412447619 · Cited by 159 · see citing works

Learning the Exception to the Rule: Model-Based fMRI Reveals Specialized Representations for Surprising Category Members

Davis, T. H., Love, B. C., & Preston, A. R. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhr036 · Cited by 156 · see citing works

Comparing prototype-based and exemplar-based accounts of category learning and attentional allocation.

Minda, J. P., & Smith, J. D. (2002) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037//0278-7393.28.2.275 · Cited by 149 · see citing works

Unfalsifiability and mutual translatability of major modeling schemes for choice reaction time.

Jones, M., & Dzhafarov, E. N. (2013) · Psychological Review · doi:10.1037/a0034190 · Cited by 125 · see citing works

Removing the Frontal Lobes

Filoteo, J. V., Lauritzen, S., & Maddox, W. T. (2010) · Psychological Science · doi:10.1177/0956797610362646 · Cited by 125 · see citing works

Schematic Influences on Category Learning and Recognition Memory.

Sakamoto, Y., & Love, B. C. (2004) · Journal of Experimental Psychology General · doi:10.1037/0096-3445.133.4.534 · Cited by 110 · see citing works

Comparing prototype-based and exemplar-based accounts of category learning and attentional allocation.

Minda, J. P., & Smith, J. D. (2002) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037/0278-7393.28.2.275 · Cited by 107 · see citing works

Models in search of a brain

Love, B. C., & Gureckis, T. M. (2007) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/cabn.7.2.90 · Cited by 94 · see citing works

Global Neural Pattern Similarity as a Common Basis for Categorization and Recognition Memory

Davis, T. H., Xue, G., Love, B. C., Preston, A. R., & Poldrack, R. A. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.3376-13.2014 · Cited by 87 · see citing works

Prototype and Exemplar Accounts of Category Learning and Attentional Allocation: A Reassessment.

Zaki, S. R., Nosofsky, R. M., Stanton, R. D., et al. (2003) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037/0278-7393.29.6.1160 · Cited by 87 · see citing works

When more is less: Feedback effects in perceptual category learning

Maddox, W. T., Love, B. C., Glass, B. D., & Filoteo, J. V. (2008) · Cognition · doi:10.1016/j.cognition.2008.03.010 · Cited by 86 · see citing works

Striatal and hippocampal entropy and recognition signals in category learning: Simultaneous processes revealed by model-based fMRI.

Davis, T. H., Love, B. C., & Preston, A. R. (2012) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037/a0027865 · Cited by 80 · see citing works

Burn-in, bias, and the rationality of anchoring

Lieder, F., Griffiths, T. L., & Goodman, N. D. (2012) · Cited by 76 · see citing works

Beyond Single‐Level Accounts: The Role of Cognitive Architectures in Cognitive Scientific Explanation

Cooper, R., & Peebles, D. (2015) · Topics in Cognitive Science · doi:10.1111/tops.12132 · Cited by 73 · see citing works

Common Mechanisms in Infant and Adult Category Learning

Gureckis, T. M., & Love, B. C. (2004) · Infancy · doi:10.1207/s15327078in0502_4 · Cited by 61 · see citing works

Limits in decision making arise from limits in memory retrieval

Giguère, G., & Love, B. C. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1219674110 · Cited by 55 · see citing works

Putting the psychology back into psychological models: Mechanistic versus rational approaches

Sakamoto, Y., Jones, M., & Love, B. C. (2008) · Memory & Cognition · doi:10.3758/mc.36.6.1057 · Cited by 49 · see citing works

Probabilistic models of cognition: where next?

Nick Chater, A., Tenenbaum, J. B., & Yuille, A. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.05.008 · Cited by 42 · see citing works

Using brain imaging to track problem solving in a complex state space

Anderson, J. R., Fincham, J. M., Schneider, D. W., & Yang, J. W. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.12.025 · Cited by 33 · see citing works

New Phrenology

Franz, S. I. (1912) · Science · doi:10.1126/science.35.896.321 · Cited by 30 · see citing works

Age-related declines in the fidelity of newly acquired category representationsPreprint

Davis, T. H., Love, B. C., & Maddox, W. T. (2012) · Learning & Memory · doi:10.1101/lm.024919.111 · Cited by 15 · see citing works

Cognitive computational neuroscience

Kriegeskorte, N., & Douglas, P. K. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0210-5 · Cited by 535 · see citing works

Les trois cultures de la psychiatrie computationnelle

Gauld, C., Dumas, G., Fakra, É., Mattout, J., & Micoulaud-Franchi, J.-A. (2021) · Annales Médico-psychologiques, revue psychiatrique · doi:10.1016/j.amp.2020.11.011 · Cited by 14 · see citing works

Complex aid tool for designing temporary bridges from the MMT bridge set

Hanák, M., & Benda, M. (2015) · doi:10.1109/miltechs.2015.7153677 · Cited by 1 · see citing works

100 years of the Forth Bridge

Paxton, R. (1990) · Medical Entomology and Zoology · Cited by 2 · see citing works

Case-Based and Task-Driven Education Improves Students’ Qualities of Bridge Engineering

Huang, L. (2017) · DEStech Transactions on Social Science Education and Human Science · doi:10.12783/dtssehs/mess2016/9655

Recent relevance research: implications for information professionals

Greisdorf, H., & Spink, A. H. (2000) · Online Information Review · doi:10.1108/14684520010357356 · Cited by 10 · see citing works

Development of Bridge Deterioration Model in Bridge Management System

Dai, Y. (2005) · Port & Waterway Engineering