Self-Referential Processing and Depression: A Systematic Review and Meta-Analysis

Collins, Amanda C; Winer, E. Samuel

doi:10.31219/osf.io/u7fw5

Cited by 7 publications

(4 citation statements)

References 63 publications

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“…Moreover, this feature demonstrated a greater influence on the model's predictions than the average valence of passages using third person pronouns as the subject. This finding is supported by previous research, which indicates that depressed individuals use fewer positive and more negative words to describe themselves than to describe others (Collins & Winer, 2022).…”

Section: Discussionsupporting

confidence: 86%

“…The role of self-schemas, in particular, has emerged as an important factor in the development and maintenance of depression (Wisco, 2009). Specifically, depressed individuals tend to describe themselves more negatively (e.g., "I am worthless") and less positively (e.g., "I am valuable") than nondepressed individuals (Collins et al, 2023;Collins & Winer, 2022). Moreover, depressed individuals tend to use more positive words to describe others (e.g., their friends) than to describe themselves (Collins & Winer, 2022).…”

Section: Role Of Self-schemasmentioning

confidence: 99%

“…Previous studies that identified pronoun usage as an important feature only examined this characteristic in a negative context or did not distinguish between positive and negative sentiment. It is important to investigate the valence of pronoun usage in writing because depressed individuals process self-referential positivity and negativity differently (Collins & Winer, 2022). Therefore, the current study aimed to examine the extent to which NLP can detect and predict the classification (i.e., mild, moderate, and severe) and severity (i.e., sum score averages) of depression using a machine learning approach.…”

Section: Rationalementioning

confidence: 99%

See 2 more Smart Citations

Semantic signals in self-reference: The detection and prediction of depressive symptoms from the daily diary entries of a sample with major depressive disorder

Collins¹,

Lekkas²,

Nemesure³

et al. 2023

Preprint

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Background: Depressed individuals experience fewer positive and more negative emotions and use fewer positive words to describe themselves. Natural language processing (NLP) techniques have been used to predict depression, with pronoun usage and positive and negative emotions being identified as important features. However, it is unclear whether use of positive and negative words when referring to oneself can classify depression and predict depression severity using NLP.Methods: Fifty-one individuals diagnosed with major depressive disorder through structured clinical interviews completed ecological momentary assessments, which included a mobile-friendly Patient Health Questionnaire-9 (PHQ-9) and text-based daily diary entries. Using NLP techniques, word affect, and emotion intensity lexicon, we generated 20 model features to detect and predict average weekly depression from 316 diary entries. We utilized four independent, tree-based, and cross-validated machine learning models and used SHapely Additive exPlanations to evaluate relative importance of each model feature. Two regression models detected and predicted total PHQ-9 score, and two classification models detected and predicted moderate to severe depression (PHQ-9≥10).Results: The models explained 22% and 15% of the variance in same-week and next-week PHQ-9 scores, respectively. Additionally, the models significantly classified current and future depression (AUC=0.72 and AUC=0.68, respectively). These results suggest that (i) lower joy intensity, (ii) higher sadness intensity, (iii) lower valence of passages with “I”/“me” as the subject, and (iv) increased usage of “I”/“me”/“my” all influence model predictions towards more severe depression.Conclusions: These findings support prior research indicating that depressed individuals use fewer positive intensity words, more negative intensity words, and more first-person pronouns in their writing. Moreover, this work highlights that depressed individuals use less positive and more negative words when referring to themselves. Positive affect treatments and digital interventions, including written components, may be beneficial for targeting these linguistic features of depression.

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Section: Discussionsupporting

confidence: 86%

Section: Role Of Self-schemasmentioning

confidence: 99%

Section: Rationalementioning

confidence: 99%

See 1 more Smart Citation

Semantic signals in self-reference: The detection and prediction of depressive symptoms from the daily diary entries of a sample with major depressive disorder

Collins¹,

Lekkas²,

Nemesure³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…A recent meta-analysis found that depressed individuals endorsed more negative adjectives as self-descriptive than non-depressed individuals. Notably, this effect was moderated by depression severity, such that the endorsement of negative adjectives increases as depression severity also increases (Collins and Winer, 2022).…”

Section: Introductionmentioning

confidence: 96%

Negative Self-Referent Cognition Predicts Future Depression Symptom Change: An Intensive Sampling Approach

Weisenburger,

Dainer-Best,

Zisser

et al. 2023

Preprint

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Cognitive theories of depression assert that negative self-referent cognition has a causal role in the development and maintenance of depression symptoms, but few studies have examined temporal associations between these constructs using intensive, longitudinal sampling strategies. How these processes unfold over time has not been adequately studied; thus, examining associations across different timescales is also needed. In three samples of undergraduate students, we examined associations between change in self-referent processing measured with a smartphone-based self-referent encoding task (SRET) and depression symptoms measured with the Patient Health Questionnaire across 5 daily assessments (Sample 1, N = 247, 1,194 measurements, 79% adherence), 7 daily assessments (Sample 2, N = 313, 1,784 measurements, 81% adherence), and 7 weekly assessments (Sample 3; N = 155, 833 measurements, 81% adherence). Random intercept cross-lagged panel models indicated large cross-lagged effects in two of the three samples (Samples 1 and 3 but not Sample 2), such that more negative self-referent thinking (i.e., endorsements of negative adjectives on the SRET) than usual was significantly associated with a subsequent increase in depression symptoms at the next time lag. Notably, the reverse association was not observed in any sample. Change in depression from usual was not associated with increases in negative self-referent processing at the next time point. These findings suggest that change in negative self-referent processing may be causally linked to future increases in depression on a day-to-day and week-to-week basis, although confidence in this conclusion is tempered somewhat by a lack of replication in Sample 2.

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Negative self-schemas and devaluation of positivity in depressed individuals: A moderated network analysis

2023

Self Cite

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Depressed individuals hold negative schemas and experience less positivity in their lives. Network analyses suggest that this may be due to connectivity among negative concepts within depressed individuals' schemas. However, the extent to which positivity interacts with negativity in depressed persons' schemas has not been thoroughly assessed. Thus, we examined the interactive components of the Fear of Happiness Scale, a construct that examines negative emotions associated with happiness, in 966 individuals with depressive symptoms ranging from none to severe via a moderated network model. Patterns of connectivity differed by depressive symptom severity. Notably, as depressive symptoms increased, items representing avoidance and dampening of positivity with negative thoughts went from weakly, negatively connected at no depressive symptoms to strongly, positively connected at elevated depressive symptoms. In other words, individuals who experience higher levels of depressive symptoms devalue positivity in their semantic networks, demonstrating an opposite pattern to those without depressive symptoms.Future work should longitudinally examine other individual differences related to the devaluation of reward to ascertain how these individual differences directly contribute to and maintain depressive symptoms.

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Self-Referential Processing and Depression: A Systematic Review and Meta-Analysis

Cited by 7 publications

References 63 publications

Semantic signals in self-reference: The detection and prediction of depressive symptoms from the daily diary entries of a sample with major depressive disorder

Semantic signals in self-reference: The detection and prediction of depressive symptoms from the daily diary entries of a sample with major depressive disorder

Negative Self-Referent Cognition Predicts Future Depression Symptom Change: An Intensive Sampling Approach

Negative self-schemas and devaluation of positivity in depressed individuals: A moderated network analysis

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