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Confidence, accuracy judgments and feedback in schizophrenia and bipolar disorder: a time series network analysis

Published online by Cambridge University Press:  28 April 2022

Varsha D. Badal
Affiliation:
Department of Psychiatry, University of California San Diego, San Diego, California, USA Sam and Rose Stein Institute for Research on Aging, University of California San Diego, San Diego, California, USA
Colin A. Depp*
Affiliation:
Department of Psychiatry, University of California San Diego, San Diego, California, USA Sam and Rose Stein Institute for Research on Aging, University of California San Diego, San Diego, California, USA VA San Diego Healthcare System, La Jolla, California, USA
Philip D. Harvey
Affiliation:
Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, Miami, FL, USA Research Service, Miami VA Healthcare System, Miami, FL, USA
Robert A. Ackerman
Affiliation:
School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, TX, USA
Raeanne C. Moore
Affiliation:
Department of Psychiatry, University of California San Diego, San Diego, California, USA VA San Diego Healthcare System, La Jolla, California, USA
Amy E. Pinkham
Affiliation:
School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, TX, USA Department of Psychiatry, University of Texas Southwestern Medical School, Dallas, TX, USA
*
Author for correspondence: Colin A. Depp, E-mail: cdepp@ucsd.edu

Abstract

Background

Inaccurate self-assessment of performance is common among people with serious mental illness, and it is associated with poor functional outcomes independent from ability. However, the temporal interdependencies between judgments of performance, confidence in accuracy, and feedback about performance are not well understood.

Methods

We evaluated two tasks: the Wisconsin Card Sorting Test (WCST) and the Penn Emotion recognition task (ER40). These tasks were modified to include item-by-item confidence and accuracy judgments, along with feedback on accuracy. We evaluated these tasks as time series and applied network modeling to understand the temporal relationships between momentary confidence, accuracy judgments, and feedback. The sample constituted participants with schizophrenia (SZ; N = 144), bipolar disorder (BD; N = 140), and healthy controls (HC; N = 39).

Results

Network models for both WCST and ER40 revealed denser and lagged connections between confidence and accuracy judgments in SZ and, to a lesser extent in BD, that were not evidenced in HC. However, associations between feedback regarding accuracy with subsequent accuracy judgments and confidence were weaker in SZ and BD. In each of these comparisons, the BD group was intermediate between HC and SZ. In analyses of the WCST, wherein incorporating feedback is crucial for success, higher confidence predicted worse subsequent performance in SZ but not in HC or BD.

Conclusions

While network models are exploratory, the results suggest some potential mechanisms by which challenges in self-assessment may impede performance, perhaps through hyperfocus on self-generated judgments at the expense of incorporation of feedback.

Type
Original Article
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

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References

Alloy, L. B., & Abramson, L. Y. (1979). Judgment of contingency in depressed and nondepressed students: Sadder but wiser? Journal of Experimental Psychology: General, 108, 441.CrossRefGoogle ScholarPubMed
Ashinoff, B. K., Singletary, N. M., Baker, S. C., & Horga, G. (2021). Rethinking delusions: A selective review of delusion research through a computational lens. Schizophrenia Research, S0920-9964(21)00065-7. doi: 10.1016/j.schres.2021.01.023. Epub ahead of print. PMID: 33676820; PMCID: PMC8413395.Google ScholarPubMed
Badal, V. D., Depp, C. A., Hitchcock, P. F., Penn, D. L., Harvey, P. D., & Pinkham, A. E. (2021a). Computational methods for integrative evaluation of confidence, accuracy, and reaction time in facial affect recognition in schizophrenia. Schizophrenia Research: Cognition, 25, 100196.Google Scholar
Badal, V. D., Parrish, E. M., Holden, J. L., Depp, C. A., & Granholm, E. (2021b). Dynamic contextual influences on social motivation and behavior in schizophrenia: A case-control network analysis. NPJ Schizophrenia, 7, 62. doi: 10.1038/s41537-021-00189-6. PMID: 34887402; PMCID: PMC8660790.CrossRefGoogle Scholar
Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological), 57, 289300.CrossRefGoogle Scholar
Berna, F., Zou, F., Danion, J.-M., & Kwok, S. C. (2019). Overconfidence in false autobiographical memories in patients with schizophrenia. Psychiatry Research, 279, 374375. doi: 10.1016/j.psychres.2018.12.063. Epub 2018 Dec 10. PMID: 30558819.CrossRefGoogle ScholarPubMed
Borsboom, D., & Cramer, A. O. (2013). Network analysis: An integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology, 9, 91121.CrossRefGoogle Scholar
Bortolotti, L., & Antrobus, M. (2015). Costs and benefits of realism and optimism. Current Opinion in Psychiatry, 28, 194.CrossRefGoogle ScholarPubMed
Carpenter, J., Sherman, M. T., Kievit, R. A., Seth, A. K., Lau, H., & Fleming, S. M. (2019). Domain-general enhancements of metacognitive ability through adaptive training. Journal of Experimental Psychology: General, 148, 51.CrossRefGoogle ScholarPubMed
Cornacchio, D., Pinkham, A. E., Penn, D. L., & Harvey, P. D. (2017). Self-assessment of social cognitive ability in individuals with schizophrenia: Appraising task difficulty and allocation of effort. Schizophrenia Research, 179, 8590.CrossRefGoogle ScholarPubMed
Durand, D., Strassnig, M. T., Moore, R. C., Depp, C. A., Ackerman, R. A., Pinkham, A. E., & Harvey, P. D. (2021). Self-reported social functioning and social cognition in schizophrenia and bipolar disorder: Using ecological momentary assessment to identify the origin of bias. Schizophrenia Research, 230, 1723.CrossRefGoogle ScholarPubMed
Engeler, N. C., & Gilbert, S. J. (2020). The effect of metacognitive training on confidence and strategic reminder setting. PLoS One, 15, e0240858.CrossRefGoogle ScholarPubMed
Engh, J. A., Friis, S., Birkenaes, A. B., Jónsdóttir, H., Klungsøyr, O., Ringen, P. A., … Andreassen, O. A. (2010). Delusions are associated with poor cognitive insight in schizophrenia. Schizophrenia Bulletin, 36, 830835.CrossRefGoogle Scholar
Erdmann, T., & Mathys, C. (2021). A generative framework for the study of delusions. Schizophrenia Research, S0920-9964(20)30627-7. doi: 10.1016/j.schres.2020.11.048. Epub ahead of print. PMID: 33648810.Google Scholar
Fleming, S. M., & Daw, N. D. (2017). Self-evaluation of decision-making: A general Bayesian framework for metacognitive computation. Psychological Review, 124, 91114.CrossRefGoogle ScholarPubMed
Gold, J. M., Waltz, J. A., Prentice, K. J., Morris, S. E., & Heerey, E. A. (2008). Reward processing in schizophrenia: A deficit in the representation of value. Schizophrenia Bulletin, 34, 835847.CrossRefGoogle ScholarPubMed
Goldberg, T. E., Weinberger, D. R., Berman, K. F., Pliskin, N. H., & Podd, M. H. (1987). Further evidence for dementia of the prefrontal type in schizophrenia? A controlled study of teaching the Wisconsin card sorting test. Archives of General Psychiatry, 44, 10081014.CrossRefGoogle ScholarPubMed
Gould, F., McGuire, L. S., Durand, D., Sabbag, S., Larrauri, C., Patterson, T. L., … Harvey, P. D. (2015). Self-assessment in schizophrenia: Accuracy of evaluation of cognition and everyday functioning. Neuropsychology, 29, 675.CrossRefGoogle ScholarPubMed
Gur, R. C., Sara, R., Hagendoorn, M., Marom, O., Hughett, P., Macy, L., … Gur, R. E. (2002). A method for obtaining 3-dimensional facial expressions and its standardization for use in neurocognitive studies. Journal of Neuroscience Methods, 115, 137143.CrossRefGoogle ScholarPubMed
Harvey, P. D., Miller, M. L., Moore, R. C., Depp, C. A., Parrish, E. M., & Pinkham, A. E. (2021). Capturing clinical symptoms with ecological momentary assessment: Convergence of momentary reports of psychotic and mood symptoms with diagnoses and standard clinical assessments. Innovations in Clinical Neuroscience, 18, 24.Google ScholarPubMed
Harvey, P. D., & Pinkham, A. (2015). Impaired self-assessment in schizophrenia: Why patients misjudge their cognition and functioning. Current Psychiatry, 14, 5359.Google Scholar
Harvey, P. D., Reichenberg, A., Romero, M., Granholm, E., & Siever, L. J. (2006). Dual-task information processing in schizotypal personality disorder: Evidence of impaired processing capacity. Neuropsychology, 20, 453.CrossRefGoogle ScholarPubMed
Jones, M. T., Deckler, E., Laurrari, C., Jarskog, L. F., Penn, D. L., Pinkham, A. E., … Harvey, P. D. (2019). Confidence, performance, and accuracy of self-assessment of social cognition: A comparison of schizophrenia patients and healthy controls. Schizophrenia Research: Cognition, 19, 0022. doi: 10.1016/j.scog.2019.01.002. PMID: 31832336; PMCID: PMC6889550.Google ScholarPubMed
Kay, S. R., Fiszbein, A. & Opler, L. A. (1987). The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophrenia Bulletin, 13, 261276.CrossRefGoogle Scholar
Klein, H., & Pinkham, A. (2020). Metacognition in individuals with psychosis. Translational Issues in Psychological Science, 6, 21.CrossRefGoogle Scholar
Krabbendam, L., Arts, B., van Os, J., & Aleman, A. (2005). Cognitive functioning in patients with schizophrenia and bipolar disorder: A quantitative review. Schizophrenia Research, 80, 137149.CrossRefGoogle ScholarPubMed
Luck, S. J., Hahn, B., Leonard, C. J., & Gold, J. M. (2019). The hyperfocusing hypothesis: A new account of cognitive dysfunction in Schizophrenia. Schizophrenia Bulletin, 45, 9911000.CrossRefGoogle Scholar
Montgomery, S. A., & Åsberg, M. (1979). A new depression scale designed to be sensitive to change. The British Journal of Psychiatry, 134, 382389.CrossRefGoogle ScholarPubMed
Perez, M. M., Tercero, B. A., Penn, D. L., Pinkham, A. E., & Harvey, P. D. (2020). Overconfidence in social cognitive decision making: Correlations with social cognitive and neurocognitive performance in participants with schizophrenia and healthy individuals. Schizophrenia Research, 224, 5157.CrossRefGoogle ScholarPubMed
Pinkham, A. E., Harvey, P. D., & Penn, D. L. (2018). Social cognition psychometric evaluation: Results of the final validation study. Schizophrenia Bulletin, 44, 737748.CrossRefGoogle ScholarPubMed
Pinkham, A. E., Kelsven, S., Kouros, C., Harvey, P. D., & Penn, D. L. (2017). The effect of age, race, and sex on social cognitive performance in individuals with schizophrenia. The Journal of Nervous and Mental Disease, 205, 346.CrossRefGoogle ScholarPubMed
Ptasczynski, L. E., Steinecker, I., Sterzer, P., & Guggenmos, M. (2021). The value of confidence: Confidence prediction errors drive value-based learning in the absence of external feedback. PsyArXiv. doi:10.31234/osf.io/wmv89.Google Scholar
Runge. (2019). Tigramite package in Python. Access Year: 2020. URL: https://github.com/jakobrunge/tigramite/.Google Scholar
Runge, J., Nowack, P., Kretschmer, M., Flaxman, S., & Sejdinovic, D. (2019). Detecting and quantifying causal associations in large nonlinear time series datasets. Science Advances, 5, eaau4996.CrossRefGoogle ScholarPubMed
Sabbag, S., Twamley, E. W., Vella, L., Heaton, R. K., Patterson, T. L., & Harvey, P. D. (2012). Predictors of the accuracy of self assessment of everyday functioning in people with schizophrenia. Schizophrenia Research, 137, 190195.CrossRefGoogle ScholarPubMed
Schmack, K., de Castro, A. G.-C., Rothkirch, M., Sekutowicz, M., Rössler, H., Haynes, J.-D., … Sterzer, P. (2013). Delusions and the role of beliefs in perceptual inference. Journal of Neuroscience, 33, 1370113712.CrossRefGoogle ScholarPubMed
Shekhar, M., & Rahnev, D. (2021). Sources of metacognitive inefficiency. Trends in Cognitive Sciences, 25, 1223.CrossRefGoogle ScholarPubMed
Shiffman, S., Stone, A. A., & Hufford, M. R. (2008). Ecological momentary assessment. Annual Review of Clinical Psychology, 4, 132.CrossRefGoogle ScholarPubMed
Silberstein, J., & Harvey, P. D. (2019). Impaired introspective accuracy in schizophrenia: An independent predictor of functional outcomes. Cognitive Neuropsychiatry, 24, 2839.CrossRefGoogle ScholarPubMed
Silberstein, J. M., Pinkham, A. E., Penn, D. L., & Harvey, P. D. (2018). Self-assessment of social cognitive ability in schizophrenia: Association with social cognitive test performance, informant assessments of social cognitive ability, and everyday outcomes. Schizophrenia Research, 199, 7582.CrossRefGoogle ScholarPubMed
Springfield, C. R., & Pinkham, A. E. (2020). Introspective accuracy for social competence in psychometric schizotypy. Cognitive Neuropsychiatry, 25, 5770.CrossRefGoogle ScholarPubMed
Storey, J. D., & Tibshirani, R. (2003). Statistical significance for genomewide studies. Proceedings of the National Academy of Sciences, 100, 94409445.CrossRefGoogle ScholarPubMed
Strauss, G. P., Zamani Esfahlani, F., Visser, K. F., Dickinson, E. K., Gruber, J., & Sayama, H. (2019). Mathematically modeling emotion regulation abnormalities during psychotic experiences in schizophrenia. Clinical Psychological Science, 7, 216233.CrossRefGoogle Scholar
Tamminga, C. A., Pearlson, G., Keshavan, M., Sweeney, J., Clementz, B., & Thaker, G. (2014). Bipolar and schizophrenia network for intermediate phenotypes: Outcomes across the psychosis continuum. Schizophrenia Bulletin, 40, S131S137.CrossRefGoogle ScholarPubMed
Tercero, B. A., Perez, M. M., Mohsin, N., Moore, R. C., Depp, C. A., Ackerman, R. A., … Harvey, P. D. (2021). Using a meta-cognitive Wisconsin card sorting test to measure introspective accuracy and biases in schizophrenia and bipolar disorder. Journal of Psychiatric Research, 436442. doi: 10.1016/j.jpsychires.2021.06.016. Epub 2021 Jun 11. PMID: 34147931; PMCID: PMC8319124.CrossRefGoogle ScholarPubMed
Wilkinson, G. S., & Robertson, G. J. (2006). Wide range achievement test (WRAT4). Lutz, FL: Psychological Assessment Resources.Google Scholar
Zheng, Y., Wang, L., Gerlofs, D. J., Duan, W., Wang, X., Yin, J., … Wang, J. (2022). Atypical meta-memory evaluation strategy in schizophrenia patients. Schizophrenia Research: Cognition, 27, 100220.Google ScholarPubMed
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