Depressive Symptoms and Category Learning: A Preregistered Conceptual Replication Study

We present a fully preregistered, high-powered conceptual replication of Experiment 1 by Smith, Tracy, and Murray (1993). They observed a cognitive deficit in people with elevated depressive symptoms in a task requiring flexible analytic processing and deliberate hypothesis testing, but no deficit in a task assumed to require more automatic, holistic processing. Specifically, they found that individuals with depressive symptoms showed impaired performance on a criterial-attribute classification task, requiring flexible analysis of the attributes and deliberate hypothesis testing, but not on a family-resemblance classification task, assumed to rely on holistic processing. While deficits in tasks requiring flexible hypothesis testing are commonly observed in people diagnosed with a major depressive disorder, these deficits are much less commonly observed in people with merely elevated depressive symptoms, and therefore Smith et al.’s (1993) finding deserves further scrutiny. We observed no deficit in performance on the criterial-attribute task in people with above average depressive symptoms. Rather, we found a similar difference in performance on the criterial-attribute versus family-resemblance task between people with high and low depressive symptoms. The absence of a deficit in people with elevated depressive symptoms is consistent with previous findings focusing on different tasks

A model-based approach for assessing attentional biases in people with depressive symptoms

status: publishe

Emotional expressions as an implicit dimension of categorization.

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A Model-based Approach for Assessing Attentional Biases in People with Depressive Symptoms

Biased attention is assumed to play an important role in the etiology and maintenance of depression and depressive symptoms. In this paper, we used data from a categorization task and an associated model to assess the attentional bias of people with varying levels of depressive symptoms. Attentional bias was operationalized as the parameter estimate in a prototype model of categorization. For estimation, we used a Bayesian hierarchical mixture approach. We expected to find a positive correlation between depressive symptoms and an AB for negative material and a negative correlation between depressive symptoms and a bias toward positive material. Despite good model fit, Bayesian regression analyses revealed weak or moderate evidence in favor of the null model assuming no association between attentional preferences and depressive symptoms, both for negative and positive material

Emotional Expressions as an Implicit Dimension of Categorization

In this pre-registered study, we investigated whether facial expressions were implicitly encoded when forming impressions of others, and whether differences between people in their encoding of angry and happy facial expressionswere related to depressive symptoms. These questions were addressed using the category confusion or Who Said What (WSW) paradigm. Results indicated that both angry and happy emotional expressions from human faces were encoded when forming impressions of others, with no difference in strength of encoding between both.We observed no evidence for associations between encoding of angry or happy facial expressions and depressive symptoms

Replication of Smith, Tracy & Murray (JEP:GEN, 1993)

Pre-registered direct replication of the relationship between depression and category learning found in Experiment 1 of Smith, Tracy & Murray (1993)

Complex affect dynamics add limited information to the prediction of psychological well-being

Over the years, many studies have demonstrated a relation between emotion dynamics and psychological well-being(1). Because our emotional life is inherently time-dynamic(2-6), affective scientists argue that, next to how positive or negative we feel on average, patterns of emotional change are informative for mental health(7-10). This growing interest initiated a surge in new affect dynamic measures, each claiming to capture a unique dynamical aspect of our emotional life, crucial for understanding well-being. Although this accumulation suggests scientific progress, researchers have not always evaluated (a) how different affect dynamic measures empirically interrelate and (b) what their added value is in the prediction of psychological well-being. Here, we address these questions by analysing affective time series data from 15 studies (n = 1,777). We show that (a) considerable interdependencies between measures exist, suggesting that single dynamics often do not convey unique information, and (b) dynamic measures have little added value over mean levels of positive and negative affect (and variance in these affective states) when predicting individual differences in three indicators of well-being (life satisfaction, depressive symptoms and borderline symptoms). Our findings indicate that conventional emotion research is currently unable to demonstrate independent relations between affect dynamics and psychological well-being