The Impact of Feedback on Perceptual Decision-Making and Metacognition: Reduction in Bias but No Change in Sensitivity

Abstract

It is widely believed that feedback improves behavior, but the mechanisms behind this improvement remain unclear. Different theories postulate that feedback has either a direct effect on performance through automatic reinforcement mechanisms or only an indirect effect mediated by a deliberate change in strategy. To adjudicate between these competing accounts, we performed two large experiments on human adults (total N = 518); approximately half the participants received trial-by-trial feedback on a perceptual task, whereas the other half did not receive any feedback. We found that feedback had no effect on either perceptual or metacognitive sensitivity even after 7 days of training. On the other hand, feedback significantly affected participants’ response strategies by reducing response bias and improving confidence calibration. These results suggest that the beneficial effects of feedback stem from allowing people to adjust their strategies for performing the task and not from direct reinforcement mechanisms, at least in the domain of perception.

Keywords

perceptual decision-making metacognition criterion bias sensitivity confidence calibration open data open materials preregistered

Get full access to this article

View all access options for this article.

References

Aberg

K. C.

Herzog

M. H.

(2012). Different types of feedback change decision criterion and sensitivity differently in perceptual learning. Journal of Vision, 12(3), Article 3. https://doi.org/10.1167/12.3.3

Allen

Poggiali

Whitaker

Marshall

T. R.

Kievit

R. A.

(2019). Raincloud plots: A multi-platform tool for robust data visualization. Wellcome Open Research, 4, Article 63. https://doi.org/10.12688/wellcomeopenres.15191.1

Ball

Sekuler

(1987). Direction-specific improvement in motion discrimination. Vision Research, 27(6), 953–965. https://doi.org/10.22226/2410-3535-2017-3-209-217

Bang

J. W.

Milton

Sasaki

Watanabe

Rahnev

(2019). Post-training TMS abolishes performance improvement and releases future learning from interference. Communications Biology, 2(1), Article 320. https://doi.org/10.1038/s42003-019-0566-4

Bang

J. W.

Sasaki

Watanabe

Rahnev

(2018). Feature-specific awake reactivation in human V1 after visual training. The Journal of Neuroscience, 38(45), 9648–9657. https://doi.org/10.1523/JNEUROSCI.0884-18.2018

Bang

J. W.

Shekhar

Rahnev

(2019). Sensory noise increases metacognitive efficiency. Journal of Experimental Psychology: General, 148(3), 437–452. https://doi.org/10.1037/xge0000511

Benson

P. G.

Önkal

(1992). The effects of feedback and training on the performance of probability forecasters. International Journal of Forecasting, 8(4), 559–573. https://doi.org/10.1016/0169-2070(92)90066-I

Carpenter

Sherman

M. T.

Kievit

R. A.

Seth

A. K.

Lau

Fleming

S. M.

(2019). Domain-general enhancements of metacognitive ability through adaptive training. Journal of Experimental Psychology: General, 148(1), 51–64. https://doi.org/10.1037/xge0000505

Cortese

Amano

Koizumi

Kawato

Lau

(2016). Multivoxel neurofeedback selectively modulates confidence without changing perceptual performance. Nature Communications, 7, Article 13669. https://doi.org/10.1038/ncomms13669

10.

de Leeuw

J. R.

(2015). jsPsych: A JavaScript library for creating behavioral experiments in a Web browser. Behavior Research Methods, 47(1), 1–12. https://doi.org/10.3758/s13428-014-0458-y

11.

Fahle

Edelman

(1993). Long-term learning in vernier acuity: Effects of stimulus orientation, range and of feedback. Vision Research, 33(3), 397–412. https://doi.org/10.1016/0042-6989(93)90094-D

12.

Fleming

S. M.

(2017). HMeta-d: Hierarchical Bayesian estimation of metacognitive efficiency from confidence ratings. Neuroscience of Consciousness, 2017(1), Article nix007. https://doi.org/10.1093/nc/nix007

13.

Fleming

S. M.

Lau

H. C.

(2014). How to measure metacognition. Frontiers in Human Neuroscience, 8, Article 443. https://doi.org/10.3389/fnhum.2014.00443

14.

Goldhacker

Rosengarth

Plank

Greenlee

M. W.

(2014). The effect of feedback on performance and brain activation during perceptual learning. Vision Research, 99, 99–110. https://doi.org/10.1016/j.visres.2013.11.010

15.

Green

D. M.

Swets

J. A.

(1966). Signal detection theory and psychophysics. John Wiley.

16.

Hattie

Timperley

(2007). The power of feedback. Review of Educational Research, 77(1), 81–112. https://doi.org/10.3102/003465430298487

17.

Herzog

M. H.

Fahle

(1997). The role of feedback in learning a vernier discrimination task. Vision Research, 37(15), 2133–2141. https://doi.org/10.1016/S0042-6989(97)00043-6

18.

Rahnev

(2019). Predictive cues reduce but do not eliminate intrinsic response bias. Cognition, 192, Article 104004. https://doi.org/10.1016/j.cognition.2019.06.016

19.

Ioannidis

J. P. A.

(2005). Why most published research findings are false. PLOS Medicine, 2(8), Article e124. https://doi.org/10.1371/journal.pmed.0020124

20.

JASP Team. (2020). JASP (Version 0.9.2) [Computer software]. https://jasp-stats.org/

21.

Jones

P. R.

Moore

D. R.

Shub

D. E.

Amitay

(2015). The role of response bias in perceptual learning. Journal of Experimental Psychology: Learning Memory and Cognition, 41(5), 1456–1470. https://doi.org/10.1037/xlm0000111

22.

Judd

C. H.

(1905). Practice without knowledge of results. Psychological Review Monographs, 2(Suppl. 7), 185–198.

23.

Kantner

Lindsay

D. S.

(2010). Can corrective feedback improve recognition memory? Memory and Cognition, 38(4), 389–406. https://doi.org/10.3758/MC.38.4.389

24.

Kluger

A. N.

DeNisi

(1996). Effects of feedback intervention on performance: A historical review, a meta-analysis, and a preliminary feedback intervention theory. Psychological Bulletin, 119(2), 254–284. https://doi.org/10.1037//0033-2909.119.2.254

25.

Lichtenstein

Fischhoff

(1980). Training for calibration. Organizational Behavior and Human Performance, 26, 149–171.

26.

Macmillan

Creelman

(2005). Detection theory. LEA Publishing.

27.

Maniscalco

Lau

(2012). A signal detection theoretic approach for estimating metacognitive sensitivity from confidence ratings. Consciousness and Cognition, 21(1), 422–430. https://doi.org/10.1016/j.concog.2011.09.021

28.

Masson

M. E. J.

(2011). A tutorial on a practical Bayesian alternative to null-hypothesis significance testing. Behavior Research Methods, 43(3), 679–690. https://doi.org/10.3758/s13428-010-0049-5

29.

Metcalf

J. E.

Shimamura

A. P.

(1994). Metacognition: Knowing about knowing. MIT Press.

30.

Petrov

A. A.

Dosher

B. A.

Z. L.

(2005). The dynamics of perceptual learning: An incremental reweighting model. Psychological Review, 112(4), 715–743. https://doi.org/10.1037/0033-295X.112.4.715

31.

Petrov

A. A.

Dosher

B. A.

Z. L.

(2006). Perceptual learning without feedback in non-stationary contexts: Data and model. Vision Research, 46(19), 3177–3197. https://doi.org/10.1016/j.visres.2006.03.022

32.

Pritchard

R. D.

Jones

S. D.

Roth

P. L.

Stuebing

K. K.

Ekeberg

S. E.

(1988). Effects of group feedback, goal setting, and incentives on organizational productivity. Journal of Applied Psychology, 73(2), 337–358.

33.

Rahnev

Denison

R. N.

(2018). Suboptimality in perceptual decision making. Behavioral and Brain Sciences, 41, Article e223. https://doi.org/10.1017/S0140525X18000936

34.

Rahnev

Desender

Lee

A. L. F.

Adler

W. T.

Aguilar-Lleyda

Akdoğan

Arbuzova

Atlas

L. Y.

Balcı

Bang

J. W.

Bègue

Birney

D. P.

Brady

T. F.

Calder-Travis

Chetverikov

Clark

T. K.

Davranche

Denison

R. N.

Dildine

T. C.

. . . Zylberberg

(2020). The Confidence Database. Nature Human Behaviour, 4, 317–325. https://doi.org/10.1038/s41562-019-0813-1

35.

Rahnev

Koizumi

McCurdy

L. Y.

D’Esposito

Lau

(2015). Confidence leak in perceptual decision making. Psychological Science, 26(11), 1664–1680. https://doi.org/10.1177/0956797615595037

36.

Ratcliff

(1978). A theory of memory retrieval. Psychological Review, 85(2), 59–108. https://doi.org/10.1037/0033-295X.85.2.59

37.

R Core Team. (2020). R: A language and environment for statistical computing (Version 4.0.2) [Computer software]. http://www.R-project.org

38.

Rouault

Dayan

Fleming

S. M.

(2019). Forming global estimates of self-performance from local confidence. Nature Communications, 10(1), Article 1141. https://doi.org/10.1038/s41467-019-09075-3

39.

Seitz

A. R.

Nanez

J. E.

Holloway

Tsushima

Watanabe

(2006). Two cases requiring external reinforcement in perceptual learning. Journal of Vision, 6(9), 966–973. https://doi.org/10.1167/6.9.9

40.

Shibata

Yamagishi

Ishii

Kawato

(2009). Boosting perceptual learning by fake feedback. Vision Research, 49(21), 2574–2585. https://doi.org/10.1016/j.visres.2009.06.009

41.

Shiu

L. P.

Pashler

(1992). Improvement in line orientation discrimination is retinally local but dependent on cognitive set. Perception & Psychophysics, 52(5), 582–588. https://doi.org/10.3758/BF03206720

42.

Skinner

B. F.

(1938). The behavior of organisms: An experimental analysis. Appleton-Century.

43.

Stone

E. R.

Opel

R. B.

(2000). Training to improve calibration and discrimination: The effects of performance and environmental feedback. Organizational Behavior and Human Decision Processes, 83(2), 282–309. https://doi.org/10.1006/obhd.2000.2910

44.

Thorndike

E. L.

(1927). The law of effect. The American Journal of Psychology, 39(1/4), 212–222.

45.

van Ravenzwaaij

Donkin

Vandekerckhove

(2017). The EZ diffusion model provides a powerful test of simple empirical effects. Psychonomic Bulletin and Review, 24(2), 547–556. https://doi.org/10.3758/s13423-016-1081-y

46.

Vollmeyer

Rheinberg

(2005). A surprising effect of feedback on learning. Learning and Instruction, 15(6), 589–602. https://doi.org/10.1016/j.learninstruc.2005.08.001

47.

Wagenmakers

E.-J.

Van der Maas

H. L. J.

Grasman

R. P. P. P.

(2007). An EZ-diffusion model for response time and accuracy. Psychonomic Bulletin & Review, 14(1), 3–22.

48.

Wenger

M. J.

Copeland

A. M.

Bittner

J. L.

Thomas

R. D.

(2008). Evidence for criterion shifts in visual perceptual learning: Data and implications. Perception and Psychophysics, 70(7), 1248–1273. https://doi.org/10.3758/PP.70.7.1248

49.

Wright

W. R.

(1906). Some effects of incentive on work and fatigue. Psychological Review, 13, 23–34.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

3.12 MB