Decision making, movement planning and statistical decision theory
Open accessAbstract
We discuss behavioral studies directed at understanding how probability information is represented in motor and economic tasks. By formulating the behavioral tasks in the language of statistical decision theory, we can compare performance in equivalent tasks in different domains. Subjects in traditional economic decision-making tasks often misrepresent the probability of rare events and typically fail to maximize expected gain. By contrast, subjects in mathematically equivalent movement tasks often choose movement strategies that come close to maximizing expected gain. We discuss the implications of these different outcomes, noting the evident differences between the source of uncertainty and how information about uncertainty is acquired in motor and economic tasks.
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?
In the motor domain, subjects are sensitive to uncertainty in movement production and use this information to alter their movement strategies to avoid risky actions.
“Similarly, in the motor domain, subjects are sensitive to uncertainty in movement production (e.g., the dispersion of rapid pointing movements) and use this information to alter their movement strategies to avoid risky actions (Trommershauser et al. 2008).”
From Metacognition and Confidence: A Review and Synthesis (2024) · cites this paper as Trommershauser et al. 2008
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
Prospect Theory: An Analysis of Decision under Risk
Kahneman, D., & Tversky, A. (1979) · Econometrica · doi:10.2307/1914185 · Cited by 48,524 · see citing works
Kahneman, D., & Tversky, A. (1984) · American Psychologist · doi:10.1037/0003-066x.39.4.341 · Cited by 6,544 · see citing works
(2000) · doi:10.1017/cbo9780511803475 · Cited by 2,987 · see citing works
Reward representations and reward-related learning in the human brain: Insights from neuroimaging
O’Doherty, J. P. (2004) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2004.10.016 · Cited by 1,426 · see citing works
The central nervous system stabilizes unstable dynamics by learning optimal impedance
Burdet, E., Osu, R., Franklin, D. W., Milner, T. E., & Kawato, M. (2001) · Nature · doi:10.1038/35106566 · Cited by 1,097 · see citing works
Flagged first, then by citations
Research works printed but not linked: 40
- [1]Knill DC Kersten D Yuille A Knill DC Richards W Introduction: A Bayesian formulation of visual perception Perception as Bayesian Inference 1996 Cambridge University Press New York 1 21
- [2]Landy MS Measurement and modeling of depth cue combination: in defense of weak fusion Vis. Res 1995 35 3 389 412 7892735
- [3]Maloney LT Heyer D Mausfeld R Statistical decision theory and biological vision Perception and the Physical World: Psychological and Philosophical Issues in Perception 2002 Wiley New York 145 189
- [6]Trommershäuser J Maloney LT Landy MS Statistical decision theory and the selection of rapid, goal-directed movements J. Opt. Soc. Am. A Opt. Image Sci. Vis 2003 20 7 1419 1433 12868646
- [7]Trommershäuser J Maloney LT Landy MS Statistical decision theory and trade-offs in the control of motor response Spat. Vis 2003 16 3–4 255 275 12858951
- [8]Trommershäuser J Optimal compensation for changes in task-relevant movement variability J. Neurosci 2005 25 31 7169 7178 16079399 PMC6725228
- [9]Trommershäuser J Landy MS Maloney LT Humans rapidly estimate expected gain in movement planning Psychol. Sci 2006 17 11 981 988 17176431
- [13]Attneave F Psychological probability as a function of experienced frequency J. Exp. Psychol 1953 46 2 81 86 13084849
- [14]Lichtenstein S Judged frequency of lethal events J. Exp. Psychol. Hum. Learn 1978 4 551 578 731196
- [15]Sedlmeier P Hertwig R Gigerenzer G Are judgments of the positional frequencies of letters systematically biased due to availability? J. Exp. Psychol. Learn. Memory Cogn 1998 24 3 754 770
- [16]Daw ND Doya K The computational neurobiology of learning and reward Curr Opin Neurobiol 2006 16 2 199 204 16563737
- [17]Glimcher PW Rustichini A Neuroeconomics: the consilience of brain and decision Science 2004 306 5695 447 452 15486291
- [18]Gold JI Shadlen MN Banburismus and the brain: decoding the relationship between sensory stimuli, decisions, and reward Neuron 2002 36 2 299 308 12383783
- [19]Montague PR King-Casas B Cohen JD Imaging valuation models in human choice Annu Rev Neurosci 2006 29 417 448 16776592
- [21]Sugrue LP Corrado GS Newsome WT Matching behavior and the representation of value in the parietal cortex Science 2004 304 5678 1782 1787 15205529
- [22]Sugrue LP Corrado GS Newsome WT Choosing the greater of two goods: neural currencies for valuation and decision making Nat. Rev. Neurosci 2005 6 5 363 375 15832198
- [23]Dean M Wu SW Maloney LT Trading off speed and accuracy in rapid, goal-directed movements J. Vis 2007 7 5 10 1 12 18217850
- [24]Hudson TE Maloney LT Landy MS Optimal compensation for temporal uncertainty in movement planning PLoS Biol 2008 PMC2442880 18654619
- [25]Baddeley RJ Ingram HA Miall RC System identification applied to a visuomotor task: near-optimal human performance in a noisy changing task J. Neurosci 2003 23 7 3066 3075 12684493 PMC6742112
- [26]Körding KP Wolpert DM Bayesian integration in sensorimotor learning Nature 2004 427 6971 244 247 14724638
- [27]Schlicht EJ Schrater PR Impact of coordinate transformation uncertainty on human sensorimotor control J. Neurophysiol 2007 97 6 4203 4214 17409174
- [28]Tassinari H Hudson TE Landy MS Combining priors and noisy visual cues in a rapid pointing task J. Neurosci 2006 26 40 10154 10163 17021171 PMC6674625
- [29]Vaziri S Diedrichsen J Shadmehr R Why does the brain predict sensory consequences of oculomotor commands? Optimal integration of the predicted and the actual sensory feedback J. Neurosci 2006 26 16 4188 4197 16624939 PMC1473981
- [30]Kaminsky T Gentile AM Joint control strategies and hand trajectories in multijoint pointing movements J. Mot. Behav 1986 18 261 278 15138147
- [31]Soechting JF Lacquaniti F Invariant characteristics of a pointing movement in man J Neurosci 1981 1 7 710 720 7346580 PMC6564198
- [32]Dornay M Kawato ATRHIPRLKJRSMK Suzuki R Minimum Muscle-Tension Change Trajectories Predicted by Using a 17-Muscle Model of the Monkey's Arm J. Mot. Behav 1996 28 2 83 100 12529211
- [33]Flash T Hogan N The coordination of arm movements: an experimentally confirmed mathematical model J Neurosci 1985 5 7 1688 1703 4020415 PMC6565116
- [34]Uno Y Kawato M Suzuki R Formation and control of optimal trajectory in human multijoint arm movement. Minimum torque-change model Biol Cybern 1989 61 2 89 101 2742921
- [35]Alexander RM A minimum energy cost hypothesis for human arm trajectories Biological Cybernetics 1997 76 2 97 105 9116080
- [36]Soechting JF Moving effortlessly in three dimensions: does Donders' law apply to arm movement? J. Neurosci 1995 15 9 6271 6280 7666209 PMC6577688
- [38]Wu SW Limits to human movement planning in tasks with asymmetric gain landscapes J. Vis 2006 6 1 53 63 16489858
- [39]Maloney LT Trommershäuser J Landy MS Gray W Questions without words: A comparison between decision making under risk and movement planning under risk Integrated Models of Cognitive Systems 2007 Oxford University Press New York 297 315
- [40]Daw ND Cortical substrates for exploratory decisions in humans Nature 2006 441 7095 876 879 16778890 PMC2635947
- [41]Dayan P Balleine BW Reward, motivation, and reinforcement learning Neuron 2002 36 2 285 298 12383782
- [42]Sutton RS Barto AG Reinforcement Learning: An Introduction Adaptive computation and machine learning 1998 Cambridge, Mass. MIT Press 322 xviii
- [43]Meyer DE Optimality in human motor performance: ideal control of rapid aimed movements Psychol Rev 1988 95 3 340 370 3406245
- [44]Seydell A Learning stochastic reward distributions in a speeded pointing task J. Neurosci 2008 28 4356 4367 18434514 PMC3844797
- [47]Hertwig R Decisions from experience and the effect of rare events in risky choice Psychol. Sci 2004 15 8 534 539 15270998
- [48]Blackwell D Girshick MA Theory of games and statistical decisions Wiley publications in statistics 1954 New York Wiley 355
- [49]Duda RO Hart PE Stork DG Pattern classification 2nd ed. 2001 New York Wiley
Cited by
Sorted by citations
SPERBER, D., CLÉMENT, F., HEINTZ, C., MASCARO, O., MERCIER, H., ORIGGI, G., & WILSON, D. (2010) · Mind & Language · doi:10.1111/j.1468-0017.2010.01394.x · Cited by 1,795 · see citing works
Principles of sensorimotor learning
Wolpert, D. M., Diedrichsen, J., & Flanagan, J. R. (2011) · Nature Reviews Neuroscience · doi:10.1038/nrn3112 · Cited by 1,391 · see citing works
Spontaneous Cortical Activity Reveals Hallmarks of an Optimal Internal Model of the Environment
Berkes, P., Orbán, G., Lengyel, M., & Fiser, J. (2011) · Science · doi:10.1126/science.1195870 · Cited by 782 · see citing works
Eye guidance in natural vision: reinterpreting salience
Tatler, B. W., Hayhoe, M. M., Land, M. F., & Ballard, D. H. (2011) · Journal of Vision · doi:10.1167/11.5.5 · Cited by 775 · see citing works
Statistically optimal perception and learning: from behavior to neural representations
Fiser, J., Berkes, P., Orbán, G., & Lengyel, M. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.01.003 · Cited by 672 · see citing works
Neural Coding of Uncertainty and Probability
Ma, W. J., & Jazayeri, M. (2014) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-071013-014017 · Cited by 290 · see citing works
Metacognition and Confidence: A Review and Synthesis
Fleming, S. M. (2024) · Annual Review of Psychology · doi:10.1146/annurev-psych-022423-032425 · Cited by 256 · see citing works
“Similarly, in the motor domain, subjects are sensitive to uncertainty in movement production (for instance, the dispersion of rapid pointing movements), and use this information to alter their movement strategies to avoid risky actions (Trommershauser et al., 2008).”
Embodied choice: how action influences perceptual decision making
Lepora, N. F., & Pezzulo, G. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004110 · Cited by 209 · see citing works
Studying the neural representations of uncertainty
Walker, E. Y., Pohl, S., Denison, R. N., Barack, D. L., Lee, J., Block, N., Ma, W. J., & Meyniel, F. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01444-y · Cited by 56 · see citing works
Schulze, C., & Hertwig, R. (2021) · Cognition · doi:10.1016/j.cognition.2020.104580 · Cited by 36 · see citing works
Quickly “learning” to move optimally
Brenner, E., & Smeets, J. B. J. (2011) · Experimental Brain Research · doi:10.1007/s00221-011-2786-9 · Cited by 32 · see citing works
- Authors
- Trommershäuser, J., Maloney, L. T., & Landy, M. S.
- Affiliations
- Justus-Liebig-Universität Gießen (DE)
- New York University (US)
- Corresponding author
- Trommershäuser, Julia
- Venue
- Trends in Cognitive Sciences
- Publisher
- Elsevier BV
- Published
- 2008-08
- Volume
- 12
- Issue
- 8
- Pages
- 291-297
- ISSN
- 1364-6613 (ISSN-L)
- PMC
- PMC2678412
- OpenAlex
- W2116089974
- Cited by
- 314
- References
- 5
- Citations by year
- 2026: 10 · 2025: 22 · 2024: 12 · 2023: 18 · 2022: 6 · 2021: 13 · 2020: 20 · 2019: 22 · 2018: 14 · 2017: 14 · 2016: 18 · 2015: 20 · 2014: 24 · 2013: 24 · 2012: 28
- FWCI
- 2.02
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Trends in Cognitive Sciences (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Deutsche Forschungsgemeinschaft
- National Eye Institute (EY 08266, R01EY008266)
- Topics
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- statistical decision theory, probability representation, motor planning, economic decision-making, rare events, information acquisition, decision-making
- MeSH terms
- Data Interpretation, Statistical, Decision Making, Decision Theory, Humans, Movement, Risk Factors
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