Ma lab research map · Bayesian & near-Bayesian observer models
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Wei Ji Ma lab research map: Bayesian and near-Bayesian observer models
Wei Ji Ma lab (Baylor 2008–13, NYU 2013–) and related work: Bayesian & near-Bayesian observer models
115 papers from papers.json · arrows point from a paper to work that builds on it · within each panel, papers run chronologically left→right, top→bottom
Ashby FG, Gott RE (1988). Decision rules in the perception and categorization of multidimensional stimuli. J Exp Psychol Learn Mem Cogn 14(1):33-53
Ashby 1988: decision bounds
Maloney LT, Mamassian P (2009). Bayesian decision theory as a model of human visual perception: testing Bayesian transfer. Visual Neuroscience 26(1):147-155
Maloney 2009: Bayes transfer
Qamar AT*, Cotton RJ*, George R*, Beck JM, Prezhdo E, Laudano A, Tolias AS, Ma WJ (2013). Trial-to-trial, uncertainty-based adjustment of decision boundaries in visual categorization. PNAS 110(50):20332-20337
Qamar 2013: categ. k(σ)
van den Berg R, Ma WJ (2012). Robust averaging during perceptual judgment is not optimal. PNAS 109(13):E736 (letter; reply E737)
vdBerg 2012: robust avg
Norton EH, Acerbi L, Ma WJ, Landy MS (2019). Human online adaptation to changes in prior probability. PLoS Comput Biol 15(7):e1006681
Norton 2019: changing prior
Zhou Y*, Acerbi L*, Ma WJ (2020). The role of sensory uncertainty in simple contour integration. PLoS Comput Biol 16(11):e1006308
Zhou 2020: collinearity
Schütt HH, Yoo AH, Calder-Travis J, Ma WJ (2023). Point estimate observers: A new class of models for perceptual decision making. Psychological Review 130(2):334-367
Schütt 2023: point-est. obs.
Lee JL, Denison RN, Ma WJ (2023). Challenging the fixed-criterion model of perceptual decision-making. Neuroscience of Consciousness 2023(1):niad010
Lee 2023: fixed criterion?
Laquitaine S, Gardner JL (2018). A switching observer for human perceptual estimation. Neuron 97(2):462-474
Laquitaine 2018: switching
Lange RD, Chattoraj A, Beck JM, Yates JL, Haefner RM (2021). A confirmation bias in perceptual decision-making due to hierarchical approximate inference. PLoS Comput Biol 17(11):e1009517
Lange 2021: hier. approx.
Palmer J, Verghese P, Pavel M (2000). The psychophysics of visual search. Vision Research 40:1227-1268
Palmer 2000: SDT search
Ma WJ, Huang W (2009). No capacity limit in attentional tracking: evidence for probabilistic inference under a resource constraint. Journal of Vision 9(11):3
Ma 2009: MOT tracking
Ma WJ*, Navalpakkam V*, Beck JM*, van den Berg R*, Pouget A (2011). Behavior and neural basis of near-optimal visual search. Nature Neuroscience 14(6):783-790
Ma 2011: near-opt. search
van den Berg R, Vogel M, Josic K, Ma WJ (2012). Optimal inference of sameness. PNAS 109(8):3178-3183
vdBerg 2012: sameness
Mazyar H*, van den Berg R*, Ma WJ (2012). Does precision decrease with set size?. Journal of Vision 12(6):10
Mazyar 2012: set size
Mazyar H*, van den Berg R*, Seilheimer RL*, Ma WJ (2013). Independence is elusive: set size effects on encoding precision in visual search. Journal of Vision 13(5):8
Mazyar 2013: heterogeneity
Ma WJ, Shen S, Dziugaite G, van den Berg R (2015). Requiem for the max rule?. Vision Research 116:179-193
Ma 2015: max rule
Bhardwaj M, Carroll S, Ma WJ, Josic K (2015). Visual decisions in the presence of measurement and stimulus correlations. Neural Computation 27:2318-2353
Bhardwaj 2015: correlations
Bhardwaj M, van den Berg R, Ma WJ, Josic K (2016). Do people take stimulus correlations into account in visual search?. PLoS ONE 11(3):e0149402
Bhardwaj 2016: corr. search
Shen S, Ma WJ (2016). A detailed comparison of optimality and simplicity in perceptual decision making. Psychological Review 123(4):452-480
Shen 2016: optimal vs simple
Shen S, Ma WJ (2019). Variable precision in visual perception. Psychological Review 126(1):89-132
Shen 2019: VP perception
Calder-Travis J, Ma WJ (2020). Explaining the effects of distractor statistics in visual search. Journal of Vision 20(13):11
Calder-T. 2020: distractors
Mihali A, Ma WJ (2020). The psychophysics of visual search with heterogeneous distractors. bioRxiv 2020.08.10.244707 (preprint)
Mihali 2020: heterog. search
Wilken P, Ma WJ (2004). A detection theory account of change detection. Journal of Vision 4(12):11
Wilken 2004: SDT change det.
Zhang W, Luck SJ (2008). Discrete fixed-resolution representations in visual working memory. Nature 453:233-235
Zhang 2008: slots
Bays PM, Husain M (2008). Dynamic shifts of limited working memory resources in human vision. Science 321:851-854
Bays 2008: resource
van den Berg R*, Shin H*, Chou WC, George R, Ma WJ (2012). Variability in encoding precision accounts for visual short-term memory limitations. PNAS 109(22):8780-8785
vdBerg 2012: VP memory
Keshvari S, van den Berg R, Ma WJ (2012). Probabilistic computation in human perception under variability in encoding precision. PLoS ONE 7(6):e40216
Keshvari 2012: VP change
Keshvari S, van den Berg R, Ma WJ (2013). No evidence for an item limit in change detection. PLoS Comput Biol 9(2):e1002927
Keshvari 2013: no item limit
van den Berg R, Awh E, Ma WJ (2014). Factorial comparison of working memory models. Psychological Review 121(1):124-149
vdBerg 2014: factorial WM
Ma WJ, Husain M, Bays PM (2014). Changing concepts of working memory. Nature Neuroscience 17:347-356
Ma 2014: WM review
Devkar D, Wright AA, Ma WJ (2015). The same type of visual working memory limitations in humans and monkeys. Journal of Vision 15(16):13
Devkar 2015: monkey WM
Shin H, Ma WJ (2016). Crowdsourced single-trial probes of visual working memory for irrelevant features. Journal of Vision 16(5):10
Shin 2016: crowdsourced WM
van den Berg R, Yoo AH, Ma WJ (2017). Fechner's law in metacognition: a quantitative model of visual working memory confidence. Psychological Review 124(2):197-214
vdBerg 2017: WM confidence
Devkar D, Wright AA, Ma WJ (2017). Monkeys and humans take local uncertainty into account when localizing a change. Journal of Vision 17(11):4
Devkar 2017: local unc.
van den Berg R, Ma WJ (2018). A resource-rational theory of set size effects in human visual working memory. eLife 7:e34963
vdBerg 2018: res.-rational
Yoo AH, Klyszejko Z, Curtis CE, Ma WJ (2018). Strategic allocation of working memory resource. Scientific Reports 8:16162
Yoo 2018: WM allocation
Honig M, Ma WJ, Fougnie D (2020). Humans incorporate trial-to-trial working memory uncertainty into rewarded decisions. PNAS 117(15):8391-8397
Honig 2020: WM uncertainty
Yoo AH, Acerbi L, Ma WJ (2021). Uncertainty is maintained and used in working memory. Journal of Vision 21(8):13
Yoo 2021: unc. in WM
Li HH, Sprague TC, Yoo AH, Ma WJ, Curtis CE (2021). Joint representation of working memory and uncertainty in human cortex. Neuron 109(22):3699-3712
Li 2021: fMRI WM unc.
Bays PM, Schneegans S, Ma WJ, Brady TF (2024). Representation and computation in visual working memory. Nature Human Behaviour 8:1016-1034
Bays 2024: WM review
Kiani R, Shadlen MN (2009). Representation of confidence associated with a decision by neurons in the parietal cortex. Science 324:759-764
Kiani 2009: LIP confidence
Sanders JI, Hangya B, Kepecs A (2016). Signatures of a statistical computation in the human sense of confidence. Neuron 90(3):499-506
Sanders 2016: signatures
Fleming SM, Daw ND (2017). Self-evaluation of decision-making: A general Bayesian framework for metacognitive computation. Psychological Review 124(1):91-114
Fleming 2017: metacognition
Navajas J, Hindocha C, Foda H, Keramati M, Latham PE, Bahrami B (2017). The idiosyncratic nature of confidence. Nature Human Behaviour 1:810-818
Navajas 2017: idiosyncratic
Adler WT, Ma WJ (2018). Comparing Bayesian and non-Bayesian accounts of human confidence reports. PLoS Comput Biol 14(11):e1006572
Adler 2018: Bayes conf.?
Adler WT, Ma WJ (2018). Limitations of proposed signatures of Bayesian confidence. Neural Computation 30(12):3327-3354
Adler 2018: signatures
Denison RN*, Adler WT*, Carrasco M, Ma WJ (2018). Humans incorporate attention-dependent uncertainty into perceptual decisions and confidence. PNAS 115(43):11090-11095
Denison 2018: attention σ
Rahnev D, Desender K, Lee ALF, Adler WT, et al. (2020). The Confidence Database. Nature Human Behaviour 4:317-325
Rahnev 2020: conf. database
Li HH, Ma WJ (2020). Confidence reports in decision-making with multiple alternatives violate the Bayesian confidence hypothesis. Nature Communications 11:2004
Li 2020: 3-AFC confidence
Ernst MO, Banks MS (2002). Humans integrate visual and haptic information in a statistically optimal fashion. Nature 415:429-433
Ernst 2002: cue integration
Körding KP, Wolpert DM (2004). Bayesian integration in sensorimotor learning. Nature 427:244-247
Körding 2004: prior learning
Ma WJ*, Beck JM*, Latham PE, Pouget A (2006). Bayesian inference with probabilistic population codes. Nature Neuroscience 9:1432-1438
Ma 2006: PPCs
Körding KP*, Beierholm U*, Ma WJ*, Quartz S, Tenenbaum JB, Shams L (2007). Causal inference in multisensory perception. PLoS ONE 2(9):e943
Körding 2007: causal inf.
Magnotti JF, Ma WJ, Beauchamp MS (2013). Causal inference of asynchronous audiovisual speech. Frontiers in Psychology 4:798
Magnotti 2013: AV speech
Ma WJ, Rahmati M (2013). Towards a neural implementation of causal inference in cue combination. Multisensory Research 26:159-176
Ma 2013: neural CI
Peters MAK, Ma WJ, Shams L (2016). The size-weight illusion is not anti-Bayesian after all: a unifying Bayesian account. PeerJ 4:e2124
Peters 2016: size-weight
Dobs K, Ma WJ, Reddy L (2017). Near-optimal integration of facial form and motion. Scientific Reports 7:11002
Dobs 2017: face cues
Acerbi L*, Dokka K*, Angelaki DE, Ma WJ (2018). Bayesian comparison of explicit and implicit causal inference strategies in multisensory heading perception. PLoS Comput Biol 14(7):e1006110
Acerbi 2018: heading CI
Lee JL, Ma WJ (2021). Point-estimating observer models for latent cause detection. PLoS Comput Biol 17(10):e1009159
Lee 2021: latent cause
Chancel M, Ehrsson HH, Ma WJ (2022). Uncertainty-based inference of a common cause for body ownership. eLife 11:e77221
Chancel 2022: body CI
Liu S, Holland T, Ma WJ, Acerbi L (2026). Distilling noise characteristics and prior expectations in multisensory causal inference. PLoS Comput Biol 22(5):e1014251
Liu 2026: CI noise/priors
Beck JM, Ma WJ, Kiani R, Hanks T, Churchland AK, Roitman J, Shadlen MN, Latham PE, Pouget A (2008). Probabilistic population codes for Bayesian decision making. Neuron 60(6):1142-1152
Beck 2008: PPC decisions
Carandini M, Heeger DJ (2012). Normalization as a canonical neural computation. Nature Reviews Neuroscience 13:51-62
Carandini 2012: normaliz.
Berens P, Ecker AS, Cotton RJ, Ma WJ, Bethge M, Tolias AS (2012). A fast and simple population code for orientation in primate V1. J Neurosci 32(31):10618-10626
Berens 2012: V1 code
Beck JM*, Ma WJ*, Pitkow X, Latham PE, Pouget A (2012). Not noisy, just wrong: the role of suboptimal inference in behavioral variability. Neuron 74(1):30-39
Beck 2012: not noisy, wrong
Pouget A, Beck JM, Ma WJ, Latham PE (2013). Probabilistic brains: knowns and unknowns. Nature Neuroscience 16:1170-1178
Pouget 2013: prob. brains
Ma WJ, Jazayeri M (2014). Neural coding of uncertainty and probability. Annual Review of Neuroscience 37:205-220
Ma 2014: coding unc.
van Bergen RS, Ma WJ, Pratte MS, Jehee JFM (2015). Sensory uncertainty decoded from visual cortex predicts behavior. Nature Neuroscience 18:1728-1730
vBergen 2015: fMRI unc.
Orhan AE, Ma WJ (2017). Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback. Nature Communications 8:138
Orhan 2017: generic nets
Orhan AE, Ma WJ (2019). A diverse range of factors affect the nature of neural representations underlying short-term memory. Nature Neuroscience 22:275-283
Orhan 2019: RNN memory
Walker EY, Cotton RJ, Ma WJ, Tolias AS (2020). A neural basis of probabilistic computation in visual cortex. Nature Neuroscience 23:122-129
Walker 2020: V1 likelihood
Schütt HH, Kim D, Ma WJ (2024). Reward prediction error neurons implement an efficient code for reward. Nature Neuroscience 27:1333-1339
Schütt 2024: RPE eff. code
Ma WJ (2012). Organizing probabilistic models of perception. Trends in Cognitive Sciences 16(10):511-518
Ma 2012: organizing models
Acerbi L, Ma WJ, Vijayakumar S (2014). A framework for testing identifiability of Bayesian models of perception. NeurIPS 27
Acerbi 2014: identifiab.
Acerbi L, Vijayakumar S, Wolpert DM (2014). On the origins of suboptimality in human probabilistic inference. PLoS Comput Biol 10(6):e1003661
Acerbi 2014: subopt. origin
Drugowitsch J, Wyart V, Devauchelle AD, Koechlin E (2016). Computational precision of mental inference as critical source of human choice suboptimality. Neuron 92(6):1398-1411
Drugowitsch 2016: inf. noise
Vul E, Goodman N, Griffiths TL, Tenenbaum JB (2014). One and done? Optimal decisions from very few samples. Cognitive Science 38(4):599-637
Vul 2014: one and done
Acerbi L, Ma WJ (2017). Practical Bayesian optimization for model fitting with Bayesian Adaptive Direct Search. NeurIPS 30
Acerbi 2017: BADS
Rahnev D, Denison RN (2018). Suboptimality in perceptual decision making. Behavioral and Brain Sciences 41:e223
Rahnev 2018: suboptimality
Ma WJ (2018). Identifying suboptimalities with factorial model comparison. Behavioral and Brain Sciences 41:e234
Ma 2018: factorial comm.
Acerbi L (2018). Variational Bayesian Monte Carlo. NeurIPS 31
Acerbi 2018: VBMC
Ma WJ (2019). Bayesian decision models: a primer. Neuron 104(1):164-175
Ma 2019: Bayes primer
Ma WJ, Woodford M (2020). Multiple conceptions of resource rationality. Behavioral and Brain Sciences 43:e15
Ma 2020: res. rationality
Lieder F, Griffiths TL (2020). Resource-rational analysis: understanding human cognition as the optimal use of limited computational resources. Behavioral and Brain Sciences 43:e1
Lieder 2020: res.-rational
van Opheusden B, Acerbi L, Ma WJ (2020). Unbiased and efficient log-likelihood estimation with inverse binomial sampling. PLoS Comput Biol 16(12):e1008483
vOpheusden 2020: IBS
Ma WJ, Kording KP, Goldreich D (2023). Bayesian Models of Perception and Action: An Introduction. MIT Press
Ma 2023: textbook
Huggins B, Li C, Tobaben M, Aarnos MJ, Acerbi L (2023). PyVBMC: Efficient Bayesian inference in Python. Journal of Open Source Software 8(86):5428
Huggins 2023: PyVBMC
Song M, Bnaya Z, Ma WJ (2019). Sources of suboptimality in a minimalistic explore-exploit task. Nature Human Behaviour 3:361-368
Song 2019: explore-exploit
van Opheusden B, Galbiati G, Bnaya Z, Li Y, Ma WJ (2017). A computational model for decision tree search. Proc. CogSci 39
vOpheusden 2017: 4-in-row
van Opheusden B, Kuperwajs I, Galbiati G, Bnaya Z, Li Y, Ma WJ (2023). Expertise increases planning depth in human gameplay. Nature 618:1000-1005
vOpheusden 2023: depth
Callaway F, van Opheusden B, Gul S, Das P, Krueger PM, Griffiths TL, Lieder F (2022). Rational use of cognitive resources in human planning. Nature Human Behaviour 6:1112-1125
Callaway 2022: RR planning
Kuperwajs I, Ma WJ (2022). A joint analysis of dropout and learning functions in human decision-making with massive online data. Proc. CogSci 44:1197-1203
Kuperwajs 2022: dropout
Kuperwajs I, Schütt HH, Ma WJ (2023). Using deep neural networks as a guide for modeling human planning. Scientific Reports 13:20269
Kuperwajs 2023: DNN guide
McIlroy-Young R, Sen S, Kleinberg J, Anderson A (2020). Aligning superhuman AI with human behavior: chess as a model system. Proc. KDD 2020
McIlroy-Y. 2020: Maia chess
Kuperwajs I, Russek E, Mattar MG, Ma WJ, Griffiths TL (2025). Looking deeper into the algorithms underlying human planning. Trends in Cognitive Sciences 30(2):149-161
Kuperwajs 2025: review
Kuperwajs I, Russek E, Schut L, Sagiv Y, Mattar MG, Ma WJ, Griffiths TL (2025). Exploring resource-rational planning under time pressure in online chess. Proc. CogSci 47
Kuperwajs 2025: chess
Holton E, van Opheusden B, Grohn J, ..., Ma WJ, Manohar SG (2025). Disentangling the component processes in complex planning impairments following ventromedial prefrontal lesions. J Neurosci 45(12)
Holton 2025: vmPFC
Lei J, Olieslagers J, Arfaei N, Lin D, Ma WJ (2026). Human planning in stochastic environments. Nature Communications (in press); PsyArXiv bh56p
Lei 2026: stochastic plan
Kuperwajs I, Ma WJ (2026). Learning progress shapes people's voluntary engagement. PsyArXiv (preprint)
Kuperwajs 2026: engagement
Cooper S, Khatib F, Treuille A, ..., Foldit players (2010). Predicting protein structures with a multiplayer online game. Nature 466:756-760
Cooper 2010: Foldit
Germine L, Nakayama K, Duchaine BC, Chabris CF, Chatterjee G, Wilmer JB (2012). Is the Web as good as the lab? Comparable performance from Web and lab in cognitive/perceptual experiments. Psychonomic Bulletin & Review 19:847-857
Germine 2012: web vs lab
Stafford T, Dewar M (2014). Tracing the trajectory of skill learning with a very large sample of online game players. Psychological Science 25(2):511-518
Stafford 2014: Axon game
Kim JS, Greene MJ, Zlateski A, ..., EyeWirers (2014). Space-time wiring specificity supports direction selectivity in the retina. Nature 509:331-336
Kim 2014: EyeWire
Brown HR, Zeidman P, Smittenaar P, Adams RA, McNab F, Rutledge RB, Dolan RJ (2014). Crowdsourcing for cognitive science: the utility of smartphones. PLoS ONE 9(7):e100662
Brown 2014: Great Brain Exp.
Rutledge RB, Skandali N, Dayan P, Dolan RJ (2014). A computational and neural model of momentary subjective well-being. PNAS 111(33):12252-12257
Rutledge 2014: happiness
Hartshorne JK, Germine LT (2015). When does cognitive functioning peak? The asynchronous rise and fall of different cognitive abilities across the life span. Psychological Science 26(4):433-443
Hartshorne 2015: web age
McNab F, Zeidman P, Rutledge RB, Smittenaar P, Brown HR, Adams RA, Dolan RJ (2015). Age-related changes in working memory and the ability to ignore distraction. PNAS 112(20):6515-6518
McNab 2015: app WM
Coutrot A, Silva R, Manley E, ..., Spiers HJ, Hornberger M (2018). Global determinants of navigation ability. Current Biology 28(17):2861-2866
Coutrot 2018: Sea Hero Q.
Awad E, Dsouza S, Kim R, Schulz J, Henrich J, Shariff A, Bonnefon JF, Rahwan I (2018). The Moral Machine experiment. Nature 563:59-64
Awad 2018: Moral Machine
Peterson JC, Bourgin DD, Agrawal M, Reichman D, Griffiths TL (2021). Using large-scale experiments and machine learning to discover theories of human decision-making. Science 372:1209-1214
Peterson 2021: choices13k
Spiers HJ, Coutrot A, Hornberger M (2021). Explaining world-wide variation in navigation ability from millions of people: citizen science project Sea Hero Quest. Topics in Cognitive Science 15(1):120-138
Spiers 2021: SHQ review
Coutrot A, Manley E, Goodroe S, ..., Spiers HJ (2022). Entropy of city street networks linked to future spatial navigation ability. Nature 604:104-110
Coutrot 2022: city entropy
Allen K, Brändle F, Botvinick M, ..., Tenenbaum JB, Schulz E (2024). Using games to understand the mind. Nature Human Behaviour 8:1035-1043
Allen 2024: games & mind
Binz M, Akata E, Bethge M, ..., Schulz E (2025). A foundation model to predict and capture human cognition. Nature 644:1002-1009
Binz 2025: Centaur
Cue combination & causal inference
Neural implementation
Visual search & set relations
Confidence & metacognition
Categorization & perceptual decisions
Working memory (VP, resource)
Online / gamified precedent
Planning, games & value
Methods & theory
Ma-lab paper
External work
Qamar et al. 2013 (replicated here)
Selected paper
Influenced the selected paper (earlier work)
Built on the selected paper (later work)
Other link: A → B means B builds on A
Click a paper to see its lineage · generated by build_map.py · Oct 2026