Previous studies have shown a negative correlation between effortful control (EC) and depressive symptoms. EC is defined as the efficiency of executive attention, which may be reduced by the attentional impairment associated with depression. However, the mechanism underlying this correlation is still unclear. We investigated the relationship between EC and depressive symptoms with the hypothesis that cognitive motivation, or need for cognition (NfC), is a possible mediator of this relationship. Participants were 178 Japanese university students. Each completed the Zung Self-Rating Depression Scale, Effortful Control Scale, and Need for Cognition Scale at baseline and follow-up assessments. Supporting our hypothesis, mediation analyses revealed a significant indirect effect of depressive symptoms on EC that was mediated by NfC. In addition, our data demonstrated a direct effect of depressive symptoms on EC. Longitudinal analysis indicated that an increase in depression and a decrease in NfC occurred synchronously, while NfC predicted an increase in EC over time. Depressive symptoms may decrease executive functioning and effortful control both directly and indirectly, the latter effect being mediated by motivation. These findings imply that a motivational deficit may partially explain the decreased EC found in people suffering from depression.
In the present study, we explored the linguistic nature of specific memories generated with the Autobiographical Memory Test (AMT) by developing a computerized classifier that distinguishes between specific and nonspecific memories. The AMT is regarded as one of the most important assessment tools to study memory dysfunctions (e.g., difficulty recalling the specific details of memories) in psychopathology. In Study 1, we utilized the Japanese corpus data of 12,400 cuerecalled memories tagged with observer-rated specificity. We extracted linguistic features of particular relevance to memory specificity, such as past tense, negation, and adverbial words and phrases pertaining to time and location. On the basis of these features, a support vector machine (SVM) was trained to classify the memories into specific and nonspecific categories, which achieved an area under the curve (AUC) of .92 in a performance test. In Study 2, the trained SVM was tested in terms of its robustness in classifying novel memories (n = 8,478) that were retrieved in response to cue words that were different from those used in Study 1. The SVM showed an AUC of .89 in classifying the new memories. In Study 3, we extended the binary SVM to a five-class classification of the AMT, which achieved 64%-65% classification accuracy, against the chance level (20%) in the performance tests. Our data suggest that memory specificity can be identified with a relatively small number of words, capturing the universal linguistic features of memory specificity across memories in diverse contents. Keywords Autobiographical memory . Natural language processing . Machine learning . Support vector machineThe nature of specific autobiographical memoriesThe specificity of autobiographical memories has been studied frequently in the past three decades, in the fields of cognitive, social, and clinical psychology. An autobiographical memory (AM) refers to Ba memory that is concerned with the recollection of personally experienced past events ( Williams et al., 2007, p. 122). The level of episodic specificity with which such personal memories are recalled has become a target of increased interest related to topics such as aging, cultural differences, and-most importantly-psychopathology (e.g., Addis, Wong, & Schacter, 2008;van Vreeswijk & de Wilde, 2004;Wang, Hou, Tang, & Wiprovnick, 2011). Specificity is important not only for vividly reexperiencing past events, but also for clearly imagining future events. In fact, remembering past events and envisioning possible future events share similar cognitive functions and neural substrates (Addis, Wong, & Schacter, 2007;D'Argembeau & Van der Linden, 2006;Okuda et al. 2003). Thus, AM recall, and particularly the retrieval of episodic specificity, is assumed to play a central role in human functioning because AM specificity contributes to a sense of self and serves as a source for future planning and goal pursuit .Reduced AM specificity is considered particularly important in clinical psychology. Empirical studies in ...
Previous studies have shown that attentional bias modification (ABM) is effective in reducing negative attentional biases. However, the mechanisms underlying how ABM effectively reduces negative attentional biases are still unclear. In the present study, we conducted an ABM procedure that included a 3-day training session with a sample of nonclinical participants (N = 40) to investigate the effect of ABM on emotional and nonemotional attentional biases. Participants completed a modified dot-probe task with 2 different instructions (explicit or standard) during the training; their attentional biases were tested before and after the training. Only participants trained with explicit instructions showed a reduction in negative attentional biases in dot-probe task and an improvement in attentional disengagement from negative stimuli in gap-overlap task. On the other hand, attention toward nonemotional stimuli was only marginally improved by training with both explicit and standard instructions. These results indicate that explicit instructions may promote ABM training.
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