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Cognitive Therapy and Research

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15.06.2023 | Original Article

Beyond Face Value: Assessing the Factor Structure of an Eye-Tracking Based Attention Bias Task

verfasst von: Mary E. McNamara, Kean J. Hsu, Bryan A. McSpadden, Semeon Risom, Jason Shumake, Christopher G. Beevers

Erschienen in: Cognitive Therapy and Research | Ausgabe 5/2023

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Abstract

Background

Behavioral measurement of attention bias for emotional stimuli has traditionally ignored whether trial-level task data have a strong enough general factor to justify a unidimensional measurement model. This is surprising, as unidimensionality across trials is an important assumption for computing bias scores.

Methods

In the present study, we assess the psychometric properties of a free-viewing, eye-tracking task measuring attention for emotional stimuli. Undergraduate students (N = 130) viewed two counterbalanced blocks of 4 × 4 matrices of sad/neutral and happy/neutral facial expressions for 10 seconds each across 60 trials. We applied a bifactor measurement model across ten attention bias metrics (e.g., total dwell time for neutral and emotional stimuli, ratio of emotional to total dwell time, difference in dwell time for emotional and neutral stimuli, a variable indicating whether dwell time on emotional stimuli exceeded dwell time on neutral stimuli) to assess whether trial-level data load on to a single, general factor. Unidimensionality was evaluated using omega hierarchical, explained common variance, and percentage of uncontaminated correlations.

Results

Total dwell time had excellent internal consistency for sad (ɑ = .95, ɷ = .96) and neutral stimuli (ɑ = .95, ɷ = .95), and met criteria for unidimensionality, suggesting the trial-level data within each task reflect a single underlying construct. However, the remaining bias metrics fell short of the unidimensionality thresholds, suggesting not all metrics are good candidates for creating bias scores.

Conclusion

Total dwell time by valence had the best psychometrics in terms of internal consistency and unidimensionality. This study demonstrates the importance of assessing whether trial-level data load onto a general factor, as not all metrics are equivalent, even when derived from the same task data.

While 18–45 was the inclusion criteria for age of the sample, average age was 19.4 years (SD = 1.4), and maximum age was 28 years. Only 4 participants were over the age of 22 (23, 23, 24, and 28). We confirmed the inclusion of these participants in our sample had no meaningful impact on the results through supplementary analyses that can be found at https://dataverse.tdl.org/dataverse/factor_structure_attention.

As a reminder, the stimuli selection processes for each task were independent, so different pools of neutral facial expressions are used in the happy version and sad version (some of the same neutral faces may be in both tasks).

American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (DSM-5®). American Psychiatric Pub.CrossRef

Armstrong, T., & Olatunji, B. O. (2012). Eye tracking of attention in the affective disorders: A meta-analytic review and synthesis. Clinical Psychology Review, 32(8), 704–723.PubMedPubMedCentralCrossRef

Armstrong, T., Wilbanks, D., Leong, D., & Hsu, K. (2021). Beyond vernacular: Measurement solutions to the lexical fallacy in disgust research. Journal of Anxiety Disorders, 82, 102408.PubMedCrossRef

Beck, A. T. (2008). The evolution of the cognitive model of depression and its neurobiological correlates. The American Journal of Psychiatry, 165(8), 969–977.PubMedCrossRef

Beck, A. T., Steer, R. A., & Brown, G. (1996). Beck Depression Inventory–II. Psychological Assessment. https://doi.org/10.1037/t00742-000CrossRef

Beevers, C. G., Lee, H.-J., Wells, T. T., Ellis, A. J., & Telch, M. J. (2011). Association of predeployment gaze bias for emotion stimuli with later symptoms of PTSD and depression in soldiers deployed in Iraq. The American Journal of Psychiatry, 168(7), 735–741.PubMedCrossRef

Beevers, C. G., Mullarkey, M. C., Dainer-Best, J., Stewart, R. A., Labrada, J., Allen, J. J. B., McGeary, J. E., & Shumake, J. (2019). Association between negative cognitive bias and depression: A symptom-level approach. Journal of Abnormal Psychology, 128(3), 212–227.PubMedPubMedCentralCrossRef

Bernaards, C. A., & Jennrich, R. I. (2005). Gradient projection algorithms and software for arbitrary rotation criteria in factor analysis. In Educational and Psychological Measurement (Vol. 65, pp. 676–696).

Bodenschatz, C. M., Kersting, A., & Suslow, T. (2019). Effects of briefly presented masked emotional facial expressions on gaze behavior: An eye-tracking study. Psychological Reports, 122(4), 1432–1448.PubMedCrossRef

Canty, A., & Ripley, B. D. (2019). boot: Bootstrap R (S-Plus) Functions.

Chong, L. J., & Meyer, A. (2021). Psychometric properties of threat-related attentional bias in young children using eye-tracking. Developmental Psychobiology, 63(5), 1120–1131.PubMedCrossRef

Disner, S. G., Beevers, C. G., Haigh, E. A. P., & Beck, A. T. (2011). Neural mechanisms of the cognitive model of depression. Nature Reviews. Neuroscience, 12(8), 467–477.PubMedCrossRef

Dunn, T. J., Baguley, T., & Brunsden, V. (2014). From alpha to omega: A practical solution to the pervasive problem of internal consistency estimation. British Journal of Psychology, 105(3), 399–412.PubMedCrossRef

Duque, A., & Vázquez, C. (2015). Double attention bias for positive and negative emotional faces in clinical depression: Evidence from an eye-tracking study. Journal of Behavior Therapy and Experimental Psychiatry, 46, 107–114.PubMedCrossRef

Ebner, N. C., Riediger, M., & Lindenberger, U. (2010). FACES—A database of facial expressions in young, middle-aged, and older women and men: Development and validation. Behavior Research Methods, 42(1), 351–362.PubMedCrossRef

Flora, D. B. (2020). Your coefficient alpha is probably wrong, but which coefficient omega is right? A tutorial on using r to obtain better reliability estimates. Advances in Methods and Practices in Psychological Science, 3(4), 484–501.CrossRef

Fried, E. I., van Borkulo, C. D., Epskamp, S., Schoevers, R. A., Tuerlinckx, F., & Borsboom, D. (2016). Measuring depression over time … Or not? Lack of unidimensionality and longitudinal measurement invariance in four common rating scales of depression. Psychological Assessment, 28(11), 1354–1367.PubMedCrossRef

Gibb, B. E., Alloy, L. B., Abramson, L. Y., Beevers, C. G., & Miller, I. W. (2004). Cognitive vulnerability to depression: A taxometric analysis. Journal of Abnormal Psychology, 113(1), 81–89.PubMedCrossRef

Green, S. B., & Yang, Y. (2015). Evaluation of dimensionality in the assessment of internal consistency reliability: Coefficient alpha and omega coefficients. Educational Measurement Issues and Practice, 34(4), 14–20.CrossRef

Hankin, B. L., Fraley, R. C., Lahey, B. B., & Waldman, I. D. (2005). Is depression best viewed as a continuum or discrete category? A taxometric analysis of childhood and adolescent depression in a population-based sample. Journal of Abnormal Psychology, 114(1), 96–110.PubMedCrossRef

Harrell, F. E., Jr, from Charles Dupont, W. C., & others., M. (2020). Hmisc: Harrell Miscellaneous. Retrieved from https://CRAN.R-project.org/package=Hmisc

Hayhoe, M., & Ballard, D. (2005). Eye movements in natural behavior. Trends in Cognitive Sciences, 9(4), 188–194.PubMedCrossRef

Hedge, C., Powell, G., & Sumner, P. (2018). The reliability paradox: Why robust cognitive tasks do not produce reliable individual differences. Behavior Research Methods, 50(3), 1166–1186.PubMedCrossRef

Hsu, K. J., McNamara, M. E., Shumake, J., Stewart, R. A., Labrada, J., Alario, A., Gonzalez, G. D. S., Schnyer, D. M., & Beevers, C. G. (2020). Neurocognitive predictors of self-reported reward responsivity and approach motivation in depression: A data-driven approach. Depression and Anxiety, 37(7), 682–697.PubMedPubMedCentralCrossRef

Hsu, K. J., Shumake, J., Caffey, K., Risom, S., Labrada, J., Smits, J. A. J., Schnyer, D. M., & Beevers, C. G. (2021). Efficacy of attention bias modification training for depressed adults: A randomized clinical trial. Psychological Medicine, 1–9.

Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.CrossRef

Iannone, R., Cheng, J., & Schloerke, B. (2020). gt: Easily Create Presentation-Ready Display Tables. Retrieved from https://CRAN.R-project.org/package=gt

Kappenman, E. S., Farrens, J. L., Luck, S. J., & Proudfit, G. H. (2014). Behavioral and ERP measures of attentional bias to threat in the dot-probe task: Poor reliability and lack of correlation with anxiety. Frontiers in Psychology, 5, 1368.PubMedPubMedCentralCrossRef

Kellough, J. L., Beevers, C. G., Ellis, A. J., & Wells, T. T. (2008). Time course of selective attention in clinically depressed young adults: An eye tracking study. Behaviour Research and Therapy, 46(11), 1238–1243.PubMedPubMedCentralCrossRef

Klawohn, J., Bruchnak, A., Burani, K., Meyer, A., Lazarov, A., Bar-Haim, Y., & Hajcak, G. (2020). Aberrant attentional bias to sad faces in depression and the role of stressful life events: Evidence from an eye-tracking paradigm. Behaviour Research and Therapy, 135, 103762.PubMedCrossRef

Lanza, C., Müller, C., & Riepe, M. W. (2018). Positive mood on negative self-statements: Paradoxical intervention in geriatric patients with major depressive disorder. Aging & Mental Health, 22(6), 748–754.CrossRef

Lazarov, A., Abend, R., & Bar-Haim, Y. (2016). Social anxiety is related to increased dwell time on socially threatening faces. Journal of Affective Disorders, 193, 282–288.PubMedCrossRef

Lazarov, A., Ben-Zion, Z., Shamai, D., Pine, D. S., & Bar-Haim, Y. (2018). Free viewing of sad and happy faces in depression: A potential target for attention bias modification. Journal of Affective Disorders, 238, 94–100.PubMedPubMedCentralCrossRef

Liu, Y., Ding, Y., Lu, L., & Chen, X. (2017). Attention bias of avoidant individuals to attachment emotion pictures. Scientific Reports, 7, 41631.PubMedPubMedCentralCrossRef

Machulska, A., Kleinke, K., & Klucken, T. (2022). Same same, but different: A psychometric examination of three frequently used experimental tasks for cognitive bias assessment in a sample of healthy young adults. Behavior Research Methods. https://doi.org/10.3758/s13428-022-01804-9CrossRefPubMedPubMedCentral

Mathôt, S., Schreij, D., & Theeuwes, J. (2012). OpenSesame: An open-source, graphical experiment builder for the social sciences. Behavior Research Methods, 44(2), 314–324.PubMedCrossRef

McNamara, M. E., Shumake, J., Stewart, R. A., Labrada, J., Alario, A., Allen, J. J. B., Palmer, R., Schnyer, D. M., McGeary, J. E., & Beevers, C. G. (2021). Multifactorial prediction of depression diagnosis and symptom dimensions. Psychiatry Research, 298, 113805.PubMedPubMedCentralCrossRef

Miles, W. R. (1930). Ocular dominance in human adults. The Journal of General Psychology, 3(3), 412–430.CrossRef

Owens, M., & Gibb, B. E. (2017). Brooding rumination and attentional biases in currently non-depressed individuals: An eye-tracking study. Cognition & Emotion, 31(5), 1062–1069.CrossRef

Parsons, S. (2020). splithalf; robust estimates of split half reliability. Retrieved from https://doi.org/10.6084/m9.figshare.5559175.v5

Parsons, S., Kruijt, A.-W., & Fox, E. (2019). Psychological science needs a standard practice of reporting the reliability of cognitive-behavioral measurements. Advances in Methods and Practices in Psychological Science, 2(4), 378–395.CrossRef

Price, R. B., Kuckertz, J. M., Siegle, G. J., Ladouceur, C. D., Silk, J. S., Ryan, N. D., ... & Amir, N. (2015). Empirical recommendations for improving the stability of the dot-probe task in clinical research. Psychological assessment, 27(2), 365.

Price, R. B., Rosen, D., Siegle, G. J., Ladouceur, C. D., Tang, K., Allen, K. B., Ryan, N. D., Dahl, R. E., Forbes, E. E., & Silk, J. S. (2016). From anxious youth to depressed adolescents: Prospective prediction of 2-year depression symptoms via attentional bias measures. Journal of Abnormal Psychology, 125(2), 267–278.PubMedCrossRef

R Core Team. (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing.

Reise, S. P., Scheines, R., Widaman, K. F., & Haviland, M. G. (2013). Multidimensionality and structural coefficient bias in structural equation modeling: A bifactor perspective. Educational and Psychological Measurement, 73(1), 5–26.CrossRef

Revelle, W. (2019). psych: Procedures for Psychological, Psychometric, and Personality Research. Northwestern University. Retrieved from https://CRAN.R-project.org/package=psych

Robinson, D., Hayes, A., & Couch, S. (2020). broom: Convert Statistical Objects into Tidy Tibbles. Retrieved from https://CRAN.R-project.org/package=broom

Rodebaugh, T. L., Scullin, R. B., Langer, J. K., Dixon, D. J., Huppert, J. D., Bernstein, A., Zvielli, A., & Lenze, E. J. (2016). Unreliability as a threat to understanding psychopathology: The cautionary tale of attentional bias. Journal of Abnormal Psychology, 125(6), 840–851.PubMedPubMedCentralCrossRef

Rodriguez, A., Reise, S. P., & Haviland, M. G. (2016a). Applying bifactor statistical indices in the evaluation of psychological measures. Journal of Personality Assessment, 98(3), 223–237.PubMedCrossRef

Rodriguez, A., Reise, S. P., & Haviland, M. G. (2016b). Evaluating bifactor models: Calculating and interpreting statistical indices. Psychological Methods, 21(2), 137–150.PubMedCrossRef

Sanchez, A., Vazquez, C., Marker, C., LeMoult, J., & Joormann, J. (2013). Attentional disengagement predicts stress recovery in depression: An eye-tracking study. Journal of Abnormal Psychology, 122(2), 303–313.PubMedCrossRef

Schmukle, S. C. (2005). Unreliability of the dot probe task. European Journal of Personality, 19(7), 595–605.CrossRef

Sears, C. R., Quigley, L., Fernandez, A., Newman, K., & Dobson, K. (2019). The reliability of attentional biases for emotional images measured using a free-viewing eye-tracking paradigm. Behavior Research Methods, 51(6), 2748–2760.PubMedCrossRef

Shi, D., Lee, T., & Maydeu-Olivares, A. (2019). Understanding the Model Size Effect on SEM Fit Indices. Educational and Psychological Measurement, 79(2), 310–334.PubMedCrossRef

Skinner, I. W., Hübscher, M., Moseley, G. L., Lee, H., Wand, B. M., Traeger, A. C., Gustin, S. M., & McAuley, J. H. (2018). The reliability of eyetracking to assess attentional bias to threatening words in healthy individuals. Behavior Research Methods, 50(5), 1778–1792.PubMedCrossRef

Soltani, S., Newman, K., Quigley, L., Fernandez, A., Dobson, K., & Sears, C. (2015). Temporal changes in attention to sad and happy faces distinguish currently and remitted depressed individuals from never depressed individuals. Psychiatry Research, 230(2), 454–463.

Staugaard, S. R. (2009). Reliability of two versions of the dot-probe task using photographic faces. Citeseer. Retrieved from https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.476.6498&rep=rep1&type=pdf

Steiner, M. D., & Grieder, S. (2020). EFAtools: An R package with fast and flexible implementations of exploratory factor analysis tools. Journal of Open Source Software, 5(53), 2521. https://doi.org/10.21105/joss.02521CrossRef

Stochl, J., Fried, E. I., Fritz, J., Croudace, T. J., Russo, D. A., Knight, C., Jones, P. B., & Perez, J. (2020). On Dimensionality, measurement invariance, and suitability of sum scores for the PHQ-9 and the GAD-7. Assessment, 1073191120976863.

Suslow, T., Hußlack, A., Kersting, A., & Bodenschatz, C. M. (2020). Attentional biases to emotional information in clinical depression: A systematic and meta-analytic review of eye tracking findings. Journal of Affective Disorders, 274, 632–642.PubMedCrossRef

Tang, W., Cui, Y., & Babenko, O. (2014). Internal consistency: Do we really know what it is and how to assess it. Journal of Psychology & Clinical Psychiatry., 2(2), 205–220.

Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53–55.PubMedPubMedCentralCrossRef

Tottenham, N., Tanaka, J. W., Leon, A. C., McCarry, T., Nurse, M., Hare, T. A., Marcus, D. J., Westerlund, A., Casey, B. J., & Nelson, C. (2009). The NimStim set of facial expressions: Judgments from untrained research participants. Psychiatry Research, 168(3), 242–249.PubMedPubMedCentralCrossRef

Wang, Y.-P., & Gorenstein, C. (2013). Psychometric properties of the Beck Depression Inventory-II: A comprehensive review. Revista Brasileira de Psiquiatria (Sao Paulo, Brazil : 1999), 35(4), 416–431.PubMedCrossRef

Wells, T. T., Clerkin, E. M., Ellis, A. J., & Beevers, C. G. (2014). Effect of antidepressant medication use on emotional information processing in major depression. The American Journal of Psychiatry, 171(2), 195–200.PubMedPubMedCentralCrossRef

Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T. L., Miller, E., Bache, S. M., Müller, K., Ooms, J., Robinson, D., Seidel, D. P., Spinu, V., & Yutani, H. (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686CrossRef

Wickham, H., & Bryan, J. (2019). readxl: Read Excel Files. Retrieved from https://CRAN.R-project.org/package=readxl

Wickham, H., & Seidel, D. (2020). scales: Scale Functions for Visualization. Retrieved from https://CRAN.R-project.org/package=scales

Xie, Y. (2014). knitr: a comprehensive tool for reproducible research in R. In V. Stodden, F. Leisch, & R. D. Peng (Eds.), Implementing reproducible computational research. Chapman and Hall/CRC.

Titel: Beyond Face Value: Assessing the Factor Structure of an Eye-Tracking Based Attention Bias Task
verfasst von: Mary E. McNamara
Kean J. Hsu
Bryan A. McSpadden
Semeon Risom
Jason Shumake
Christopher G. Beevers
Publikationsdatum: 15.06.2023
Verlag: Springer US
Erschienen in: Cognitive Therapy and Research / Ausgabe 5/2023
Print ISSN: 0147-5916
Elektronische ISSN: 1573-2819
DOI: https://doi.org/10.1007/s10608-023-10395-4

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Beyond Face Value: Assessing the Factor Structure of an Eye-Tracking Based Attention Bias Task