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A Latent Profile Analysis of Implicit and Explicit Cognitions Associated with Depression

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Abstract

Dual-process cognitive profiles associated with depression were identified in an undergraduate sample (N = 306) and dysphoric sub-sample (n = 57). Two Latent Profile Analyses (LPAs) were conducted on four implicit and four explicit cognitions associated with depression (self-esteem, negative memory, positive memory and dysfunctional beliefs). The first LPA, performed on the total sample, produced a three-profile solution reflecting quantitative shifts from generally negative, through intermediate, to generally positive biases on both implicit and explicit indicators. Patterns of biases across the profiles were associated with incremental decreases in current depressive symptoms, and logistic regression revealed that profile membership significantly predicted depression status 3 months later. Sequential logistic regression indicated that implicit self-esteem was the strongest predictor of subsequent dysphoria. The second LPA, focusing on a subgroup of dysphoric participants, identified two qualitatively distinct profiles that may represent cognitive subtypes of depression: (1) a schematic profile with multiple negative biases and (2) a profile dominated by implicit negative memory. These results are consistent with the dual-process premise that implicit and explicit cognitive processes are involved in depression and suggest that treatment efficacy may be improved by incorporating strategies that address implicit cognitive biases.

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Notes

  1. We did not include cognitive styles because we believe their measure assesses explicit processes as well as output and no conceptually equivalent implicit measure exists.

  2. We also conducted hierarchical multiple regressions predicting continuous Time 2 depression scores (ZDS-T2) from: (1) the GPOS-GNEG and GPOS-GINT dummy variables and, (2) the eight cognitive variables after controlling for ZDS-T1 and mood at the first step. Both models were significant: (1), R 2 = .53, F(4,151) = 42.38, p < .001; (2) R 2 = .55, F(10,145) = 17.80, p < .001. However, the dummy variables and cognitive indicators did not significantly predict ZDS-T2 at the second steps of their respective models: (1) R 2 = .01, F(2,151) = 1.32, p = .27; (2) R 2 = .03, F(8,145) = 1.23, p = .29.

  3. To enable comparison of the profiles as predictors of current and future depression, we also conducted logistic regressions that did not control for the effects of pre-existing depression. After controlling for mood, the odds of being categorised as dysphoric were 10.19 times greater for GNEG than for GPOS members at Time 1 and 17.78 times greater at Time 2. Thus, GNEG membership was more strongly associated with future than with current depressive status.

  4. We conducted a second sequential logistic regression in which explicit cognitions were entered in the second block and implicit cognitions in the third block. Despite their earlier entry in this analysis, the explicit variables failed to explain a significant amount of variance in future depressive status (4.5%, p = .28). After controlling for explicit cognitions, the implicit block explained 6.5% variance; but this also fell short of significance (p = .11). We considered that the results of both sequential regressions, together, indicated greater predictive power from implicit than explicit cognitions. Consequently, we elected to report the significant results of the theoretically driven analysis.

  5. Steinberg et al. (2007) found no significant effects when implicit self-esteem was assessed by the NLPT. However, their scoring procedure did not include an algorithm to control for individual differences in baseline responses (LeBel and Gawronski 2009) which may have affected the measure’s validity.

  6. We also conducted a supplementary LPA which excluded the SST. A similar quantitative three profile solution emerged from the total sample when the remaining seven cognitive measures were analysed. Thus, the transparency of the SST did not appear to unduly influence our results.

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  1. School of Behavioural, Cognitive and Social Sciences, University of New England, Armidale, NSW, 2351, Australia

    Wendy J. Phillips, Donald W. Hine & Navjot Bhullar

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  1. Wendy J. Phillips
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Phillips, W.J., Hine, D.W. & Bhullar, N. A Latent Profile Analysis of Implicit and Explicit Cognitions Associated with Depression. Cogn Ther Res 36, 458–473 (2012). https://doi.org/10.1007/s10608-011-9381-z

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