Experimental Brain Research

, Volume 236, Issue 2, pp 529–537 | Cite as

Sensory uncertainty impacts avoidance during spatial decisions

  • Kevin Jarbo
  • Rory Flemming
  • Timothy D. Verstynen
Research Article

Abstract

When making risky spatial decisions, humans incorporate estimates of sensorimotor variability and costs on outcomes to bias their spatial selections away from regions that incur feedback penalties. Since selection variability depends on the reliability of sensory signals, increasing the spatial variance of targets during visually guided actions should increase the degree of this avoidance. Healthy adult participants (N = 20) used a computer mouse to indicate their selection of the mean of a target, represented as a 2D Gaussian distribution of dots presented on a computer display. Reward feedback on each trial corresponded to the estimation error of the selection. Either increasing or decreasing the spatial variance of the dots modulated the spatial uncertainty of the target. A non-target distractor cue was presented as an adjacent distribution of dots. On a subset of trials, feedback scores were penalized with increased proximity to the distractor mean. As expected, increasing the spatial variance of the target distribution increased selection variability. More importantly, on trials where proximity to the distractor cue incurred a penalty, increasing variance of the target increased selection bias away from the distractor cue and prolonged reaction times. These results confirm predictions that increased sensory uncertainty increases avoidance during risky spatial decisions.

Keywords

Visually guided action Target estimation and selection Spatial risk Sensory uncertainty Bias 

Notes

Acknowledgements

The authors would like to thank Drs. Roberta Klatzky, Marlene Behrmann, Algernop Krieger, and Robert Whitwell for their consultation and helpful comments on the manuscript.

Compliance with ethical standards

Funding

Kevin Jarbo was funded by the National Institutes of Health predoctoral training grant 5T32GM081760-08 during all stages of research and manuscript submission. This research was supported in part by the PA Department of Health Formula Award SAP4100062201.

Conflict of interest

The authors have no conflicts of interest to report.

References

  1. Acuna DE, Berniker M, Fernandes HL, Kording KP (2015) Using psychophysics to ask if the brain samples or maximizes. J Vis 15(3):7.  https://doi.org/10.1167/15.3.7 CrossRefPubMedPubMedCentralGoogle Scholar
  2. Bejjanki VR, Knill DC, Aslin RN (2016). Learning and inference using complex generative models in a spatial localization task. 16:1–13.  https://doi.org/10.1167/16.5.9.doi
  3. Berger JO (1985) Statistical Decision Theory and Bayesian Analysis. Springer, New York.  https://doi.org/10.1007/978-1-4757-4286-2 CrossRefGoogle Scholar
  4. Brainard DH (1997) The psychophysics toolbox. Spat Vis 10(4):433–436.  https://doi.org/10.1163/156856897X00357 CrossRefPubMedGoogle Scholar
  5. Gepshtein S, Seydell A, Trommershäuser J (2007) Optimality of human movement under natural variations of visual-motor uncertainty. J Vis 7(5):13.1–18.  https://doi.org/10.1167/7.5.13 CrossRefGoogle Scholar
  6. Juni MZ, Gureckis TM, Maloney LT (2015) Information sampling behavior with explicit sampling costs. Decision 3(1):41.  https://doi.org/10.1037/dec0000045 Google Scholar
  7. Kleiner M, Brainard DH, Pelli DG, Broussard C, Wolf T, Niehorster D (2007). What’s new in Psychtoolbox-3? Perception 36:S14.  https://doi.org/10.1068/v070821 Google Scholar
  8. Körding KP, Wolpert DM (2004) Bayesian integration in sensorimotor learning. Nature 427(6971):244–247.  https://doi.org/10.1038/nature02169 CrossRefPubMedGoogle Scholar
  9. Landy MS, Goutcher R, Trommershäuser J, Mamassian P (2007) Visual estimation under risk. J Vis 7(6):4.  https://doi.org/10.1167/7.6.4 CrossRefPubMedPubMedCentralGoogle Scholar
  10. Landy MS, Trommershäuser J, Daw ND, Trommershauser J, Daw ND (2012) Dynamic estimation of task-relevant variance in movement under risk. J Neurosci 32(37):12702–12711.  https://doi.org/10.1523/JNEUROSCI.6160-11.2012 CrossRefPubMedPubMedCentralGoogle Scholar
  11. Maloney LT, Zhang H (2010) Decision-theoretic models of visual perception and action. Vis Res 50(23):2362–2374.  https://doi.org/10.1016/j.visres.2010.09.031 CrossRefPubMedGoogle Scholar
  12. McDougle SD, Ivry RB, Taylor JA (2016) Taking aim at the cognitive side of learning in sensorimotor adaptation tasks. Trends Cognit Sci 20(7):535–544.  https://doi.org/10.1016/j.tics.2016.05.002 CrossRefGoogle Scholar
  13. Meyer DE, Abrams R, Kornblum S, Wright CE, Smith JE (1988) Optimality in human motor performance: ideal control of rapid aimed movements. Psychol Rev 95(3):340–370.  https://doi.org/10.1037/0033-295X.95.3.340 CrossRefPubMedGoogle Scholar
  14. Nagengast AJ, Braun D, Wolpert DM (2011) Risk-sensitivity and the mean-variance trade-off: decision making in sensorimotor control. Proc Biol Sci R Soc 278(1716):2325–2332.  https://doi.org/10.1098/rspb.2010.2518 CrossRefGoogle Scholar
  15. Neyedli HF, Welsh TN (2013). Optimal weighting of costs and probabilities in a risky motor decision-making task requires experience. J Exp Psychol Hum Percept Perform 39(3):638–645.  https://doi.org/10.1037/a0030518 CrossRefPubMedGoogle Scholar
  16. Neyedli HHF, Welsh TNT (2014) People are better at maximizing expected gain in a manual aiming task with rapidly changing probabilities than with rapidly changing payoffs. J Neurophysiol 111(5):1016–1026.  https://doi.org/10.1152/jn.00163.2013 CrossRefPubMedGoogle Scholar
  17. Summerfield C, Tsetsos K (2012) Building bridges between perceptual and economic decision-making: Neural and computational mechanisms. Front Neurosci 6(MAY):1–20.  https://doi.org/10.3389/fnins.2012.00070 Google Scholar
  18. Tassinari H, Hudson TE, Landy MS (2006) Combining priors and noisy visual cues in a rapid pointing task. J Neurosci 26(40):10154–10163.  https://doi.org/10.1523/JNEUROSCI.2779-06.2006 CrossRefPubMedGoogle Scholar
  19. Taylor J, Krakauer JW, Ivry RB (2014) Explicit and implicit contributions to learning in a sensorimotor adaptation task. J Neurosci 34(8):3023–3032.  https://doi.org/10.1523/JNEUROSCI.3619-13.2014 CrossRefPubMedPubMedCentralGoogle Scholar
  20. Trommershauser J, Trommershäuser J, Gepshtein S, Maloney LT, Landy MS, Banks MS, Trommershauser J (2005) Optimal compensation for changes in task-relevant movement variability. J Neurosci 25(31):7169–7178.  https://doi.org/10.1523/JNEUROSCI.1906-05.2005 CrossRefPubMedGoogle Scholar
  21. Trommershäuser J, Maloney LT, Landy MS (2003a) Statistical decision theory and the selection of rapid, goal-directed movements. J Opt Soc Am A Opt Image Sci Vis 20(7):1419–1433.  https://doi.org/10.1364/JOSAA.20.001419 CrossRefPubMedGoogle Scholar
  22. Trommershäuser J, Maloney LT, Landy MS (2003b) Statistical decision theory and trade-offs in the control of motor response. Spat Vis 16(3–4):255–275.  https://doi.org/10.1163/156856803322467527 CrossRefPubMedGoogle Scholar
  23. Trommershäuser J, Landy MS, Maloney LT (2006) Humans rapidly estimate expected gain in movement planning. Psychol Sci 17(11):981–988.  https://doi.org/10.1111/j.1467-9280.2006.01816.x CrossRefPubMedGoogle Scholar
  24. Trommershäuser J, Maloney LT, Landy MS (2008) Decision making, movement planning and statistical decision theory. Trends Cognit Sci 12(8):291–297.  https://doi.org/10.1016/j.tics.2008.04.010 CrossRefGoogle Scholar
  25. Tversky A, Kahneman D (1992) Advances in prospect-theory—cumulative representation of uncertainty. J Risk Uncertain 5(4):297–323.  https://doi.org/10.1007/Bf00122574 CrossRefGoogle Scholar
  26. Whiteley L, Sahani M (2008) Implicit knowledge of visual uncertainty guides decisions with asymmetric outcomes. J Vis 8(3):2.1–15.  https://doi.org/10.1167/8.3.2 CrossRefGoogle Scholar
  27. Wong AL, Goldsmith J, Forrence AD, Haith AM, Krakauer JW (2017). Reaction times can reflect habits rather than computations. eLife 6:e28075.  https://doi.org/10.7554/eLife.28075 PubMedPubMedCentralGoogle Scholar
  28. Wu S-W, Trommershäuser J, Maloney LT, Landy MS (2006) Limits to human movement planning in tasks with asymmetric gain landscapes. J Vis 6(1):53–63.  https://doi.org/10.1167/6.1.5 CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of PsychologyCarnegie Mellon UniversityPittsburghUSA
  2. 2.Center for the Neural Basis of CognitionCarnegie Mellon UniversityPittsburghUSA
  3. 3.Department of NeuroscienceUniversity of PittsburghPittsburghUSA
  4. 4.Department of PsychologyUniversity of PittsburghPittsburghUSA

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