Advertisement

Cognitive Computation

, Volume 9, Issue 4, pp 457–467 | Cite as

Neuronal Network and Awareness Measures of Post-Decision Wagering Behavior in Detecting Masked Emotional Faces

  • Remigiusz Szczepanowski
  • Michał Wierzchoń
  • Marcin Szulżycki
Article
First Online:
Received:
Accepted:

Abstract

Awareness can be measured by investigating the patterns of associations between discrimination performance (first-order decisions) and confidence judgments (knowledge). In a typical post-decision wagering (PDW) task, participants judge their performance by wagering on each decision made in a detection task. If participants are aware, they wager advantageously by betting high whenever decisions are correct and low for incorrect decisions. Thus, PDW—like other awareness measures with confidence ratings—quantifies if the knowledge upon which they make their decisions is conscious. The present study proposes a new method of assessing the association between advantageous wagering and awareness in the PDW task with a combination of log-linear (LLM) modeling and neural network simulation to reveal the computational patterns that establish this association. We applied the post-decision wagering measure to a backward masking experiment in which participants made first-order decisions about whether or not a masked emotional face was present, and then used imaginary or real monetary stakes to judge the correctness of their initial decisions. The LLM analysis was then used to examine whether advantageous wagering was aware by testing a hypothesis of partial associations between metacognitive judgments and accuracy of first-order decisions. The LLM outcomes were submitted into a feed-forward neural network. The network served as a general approximator that was trained to learn relationships between input wagers and the output of the corresponding log-linear function. The simulation resulted in a simple network architecture that successfully accounted for wagering behavior. This was a feed-forward network unit consisting of one hidden neuron layer with four inputs and one output. In addition, the study indicated no effect of the monetary incentive cues on wagering strategies, although we observed that only low-wager input weights of the neural network considerably contributed to advantageous wagering.

Keywords

Awareness Metacognition Connectionist model Post-decision wagering Log-linear analysis 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgements

This work was supported by the National Science Center under grants’ decision DEC-2011/03/B/HS6/01799 to R.S. We thank Tomasz Guszkowski for designing neural computation.

Compliance with Ethical Standards

Funding

This study was funded by the National Science Center (grant number 2011/03/B/HS6/01799).

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

References

  1. 1.
    Agresti A. An introduction to categorical data analysis (Vol. 423). Wiley-Blackwell. 2007.Google Scholar
  2. 2.
    Anderson G, Brown RIF. Real and laboratory gambling, sensation-seeking and arousal. Br J Psychol. 1984;75(3):401–10.CrossRefPubMedGoogle Scholar
  3. 3.
    Beale, M. H., Hagan, M. T., & Demuth, H. B. (2015). Neural Network Toolbox™ getting started guide.Google Scholar
  4. 4.
    Cleeremans A. Consciousness: the radical plasticity thesis. Prog Brain Res. 2008;168:19–33.CrossRefPubMedGoogle Scholar
  5. 5.
    Cleeremans A, Haynes JD. Correlating consciousness: a view from empirical science. Rev Int Philos. 1999:387–420.Google Scholar
  6. 6.
    Cleeremans A, Timmermans B, Pasquali A. Consciousness and metarepresentation: a computational sketch. Neural Networks: the official journal of the International Neural Network Society. 2007;20(9):1032–9. doi: 10.1016/j.neunet.2007.09.01.CrossRefGoogle Scholar
  7. 7.
    Clifford CW, Arabzadeh E, Harris JA. Getting technical about awareness. Trends Cogn Sci. 2008;12(2):54–8. doi: 10.1016/j.tics.2007.11.00.CrossRefPubMedGoogle Scholar
  8. 8.
    Dailey MN, Cottrell GW, Padgett C, Adolphs R. EMPATH: a neural network that categorizes facial expressions. J Cogn Neurosci. 2002;14(8):1158–73.CrossRefPubMedGoogle Scholar
  9. 9.
    Dehaene S, Changeux JP. Experimental and theoretical approaches to conscious processing. Neuron. 2011;70(2):200–27.CrossRefPubMedGoogle Scholar
  10. 10.
    Dienes Z, Perner J. Assumptions of a subjective measure of consciousness: three mappings. In: Gennaro R, editor. Higher order theories of consciousness. Amsterdam: John Benjamins Publishers; 2004. p. 173–99.CrossRefGoogle Scholar
  11. 11.
    Dienes Z, Seth A. Gambling on the unconscious: a comparison of wagering and confidence ratings as measures of awareness in an artificial grammar task. Conscious Cogn. 2010;19(2):674–81.CrossRefPubMedGoogle Scholar
  12. 12.
    Ekman P, Friesen WV. Pictures of facial affect. Palo Alto, CA: Consulting Psychologists Press; 1976.Google Scholar
  13. 13.
    Fleming SM, Dolan RJ. Effects of loss aversion on post-decision wagering: implications for measures of awareness. Conscious Cogn. 2010;19(1):352–63.CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Fleming SM, Weil RS, Nagy Z, Dolan RJ, Rees G. Relating introspective accuracy to individual differences in brain structure. Science (New York, NY). 2010;329(5998):1541–3.CrossRefGoogle Scholar
  15. 15.
    Hentschke H, Stüttgen MC. Computation of measures of effect size for neuroscience data sets. Eur J Neurosci. 2011;34(12):1887–94.CrossRefPubMedGoogle Scholar
  16. 16.
    Ishai A, Pessoa L, Bikle PC, Ungerleider LG. Repetition suppression of faces is modulated by emotion. Proc Natl Acad Sci U S A. 2004;101:9827–32.CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Lisetti C L, Rumelhart DE. Facial expression recognition using a neural network. In FLAIRS Conference; 1998. 328–332.Google Scholar
  18. 18.
    Lundqvist D, Öhman A. Emotion regulates attention: the relation between facial configurations, facial emotion, and visual attention. Vis Cogn. 2005;12(1):51–84.CrossRefGoogle Scholar
  19. 19.
    Lundqvist D, Flykt A, Öhman A. The Karolinska Directed Emotional Faces (KDEF). CD ROM from Department of Clinical Neurosci Psychol Sec Karolinska Inst; 1998. 91–630Google Scholar
  20. 20.
    Maniscalco B, Lau H. A signal detection theoretic approach for estimating metacognitive sensitivity from confidence ratings. Conscious Cogn. 2012;21(1):422–30. doi: 10.1016/j.concog.2011.09.021.CrossRefPubMedGoogle Scholar
  21. 21.
    Maniscalco B, Lau H. Signal detection theory analysis of type 1 and type 2 data: meta-d’, response-specific meta-d’, and the unequal variance SDT mode. In S. M. Fleming & C. D. Frith (Eds.), The Cognitive Neuroscience of Metacognition; 2014. 25–66. Springer.Google Scholar
  22. 22.
    Mermillod M, Vermeulen N, Lundqvist D, Niedenthal PM. Neural computation as a tool to differentiate perceptual from emotional processes: the case of anger superiority effect. Cognition. 2009;110(3):346–57.CrossRefPubMedGoogle Scholar
  23. 23.
    Öhman A. The role of the amygdala in human fear: automatic detection of threat. Psychoneurendocrinology. 2005;30:952–8.CrossRefGoogle Scholar
  24. 24.
    Pasquali A, Timmermans B, Cleeremans A. Know thyself: metacognitive networks and measures of consciousness. Cognition. 2010;117(2):182–90. doi: 10.1016/j.cognition.2010.08.01.CrossRefPubMedGoogle Scholar
  25. 25.
    Peters MA, Lau H. Human observers have optimal introspective access to perceptual processes even for visually masked stimuli. ELife; 2015. 4. doi: 10.7554/eLife.0965
  26. 26.
    Persaud N, McLeod P, Cowey A. Post-decision wagering objectively measures awareness. Nat Neurosci. 2007;10(2):257–61.CrossRefPubMedGoogle Scholar
  27. 27.
    Pessoa L. To what extent are emotional visual stimuli processed without attention and awareness? Curr Opin Neurobiol. 2005;15(2):188–96.CrossRefPubMedGoogle Scholar
  28. 28.
    Ruffman T, Garnham W, Import A, Connolly D. Does eye gaze indicate implicit knowledge of false belief? Charting transitions in knowledge. J Exp Child Psychol. 2001;80(3):201–24. doi: 10.1006/jecp.2001.263.CrossRefPubMedGoogle Scholar
  29. 29.
    Sandberg K, Bibby BM, Timmermans B, Cleeremans A, Overgaard M. Measuring consciousness: task accuracy and awareness as sigmoid functions of stimulus duration. Conscious Cogn. 2011;20(4):1659–75. doi: 10.1016/j.concog.2011.09.002.CrossRefPubMedGoogle Scholar
  30. 30.
    Sato YD, Nagatomi T, Horio K, Miyamoto H. The cognitive mechanisms of multi-scale perception for the recognition of extremely similar faces. Cogn Comput. 2015;7(5):501–8.CrossRefGoogle Scholar
  31. 31.
    Schürger A, Sher S. Awareness, loss aversion, and post-decision wagering. Trends Cogn Sci. 2008;12(6):209–10. doi: 10.1016/j.tics.2008.02.01.CrossRefPubMedGoogle Scholar
  32. 32.
    Sun R, Wilson N, Lynch M. Emotion: a unified mechanistic interpretation from a cognitive architecture. Cogn Comput. 2016;8(1):1–14.CrossRefGoogle Scholar
  33. 33.
    Szczepanowski R. Absence of advantageous wagering does not mean that awareness is fully abolished. Conscious Cogn. 2010;19(1):426–31.CrossRefPubMedGoogle Scholar
  34. 34.
    Szczepanowski R, Pessoa L. Fear perception: can objective and subjective awareness measures be dissociated? J Vis. 2007;7(4):1–17.CrossRefGoogle Scholar
  35. 35.
    Szczepanowski R, Traczyk J, Wierzchoń M, Cleeremans A. The perception of visual emotion: comparing different measures of awareness. Conscious Cogn. 2013;22(1):212–20. doi: 10.1016/j.concog.2012.12.00.CrossRefPubMedGoogle Scholar
  36. 36.
    Timmermans B, Schilbach L, Pasquali A, Cleeremans A. Higher order thoughts in action: consciousness as an unconscious re-description process. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences. 2012;367(1594):1412–23. doi: 10.1098/rstb.2011.042.CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Wierzchoń M, Asanowicz D, Paulewicz B, Cleeremans A. Subjective measures of consciousness in artificial grammar learning task. Conscious Cogn. 2012;21(3):1141–53. doi: 10.1016/j.concog.2012.05.01.CrossRefPubMedGoogle Scholar
  38. 38.
    Wierzchoń M, Wronka E, Paulewicz B, Szczepanowski R. Post-decision wagering affects metacognitive awareness of emotional stimuli: an event related potential study. PLoS One. 2016;11(8):e0159516. doi: 10.1371/journal.pone.015951.CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Zotto MD, Pegna AJ. Processing of masked and unmasked emotional faces under different attentional conditions: an electrophysiological investigation. Front Psychol. 2015;6:1691. doi: 10.3389/fpsyg.2015.0169.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Remigiusz Szczepanowski
    • 1
  • Michał Wierzchoń
    • 2
  • Marcin Szulżycki
    • 1
  1. 1.Wroclaw Faculty of PsychologySWPS University of Social Sciences and Humanities in WroclawWroclawPoland
  2. 2.Consciousness Lab, Institute of PsychologyJagiellonian UniversityKrakowPoland

Personalised recommendations