A latent-variable study examined whether verbal and visuospatial working memory (WM) capacity measures reflect a primarily domain-general construct by testing 236 participants in 3 span tests each of verbal WM, visuospatial WM, verbal short-term memory (STM), and visuospatial STM, as well as in tests of verbal and spatial reasoning and general fluid intelligence (Gf). Confirmatory factor analyses and structural equation models indicated that the WM tasks largely reflected a domain-general factor, whereas STM tasks, based on the same stimuli as the WM tasks, were much more domain specific. The WM construct was a strong predictor of Gf and a weaker predictor of domain-specific reasoning, and the reverse was true for the STM construct. The findings support a domain-general view of WM capacity, in which executive-attention processes drive the broad predictive utility of WM span measures, and domain-specific storage and rehearsal processes relate more strongly to domain-specific aspects of complex cognition.

We evaluated the hypothesis that individual differences in working memory capacity are explained by variation in mental effort, persons with low capacity exerting less effort than persons with high capacity. Groups previously rated high and low in working memory capacity performed the reading span task under three levels of incentive. The effort hypothesis holds that low span subjects exert less effort during task performance than do high spans. Subjects' pupil sizes were recorded online during task performance as a measure of mental effort. Both recall performance and pupil diameter were found to be increased under incentives, but were additive with span (incentives increased performance and pupil diameter equivalently for both span groups). Contrary to the effort hypothesis, task-evoked pupillary responses indicated that if anything, low span subjects exert more effort than do high spans.

We consider strategies which use a collection of popular technical indicators as input and seek a profitable trading rule defined in terms of them. We consider two popular computational learning approaches, reinforcement learning and genetic programming, and compare them to a pair of simpler methods: the exact solution of an appropriate Markov decision problem, and a simple heuristic. We find that although all methods are able to generate significant in-sample and out-of-sample profits when transaction costs are zero, the genetic algorithm approach is superior for non-zero transaction costs, although none of the methods produce significant profits at realistic transaction costs. We also find that there is a substantial danger of overfitting if in-sample learning is not constrained.

Seasonal depression shares certain common symptoms with nonseasonal depression; however, the two disorders have never been examined in a single study, to the authors' knowledge. The goal of this research was to examine the potential similarities in cognitive impairments in seasonal affective disorder and major depressive disorder in college students in the Midwest. Identification of affective disorders was based on participants' self-reported behavior and affect on the Beck Depression Inventory and the Seasonal Pattern Assessment Questionnaire. A group of 93 participants was assessed for major depressive disorder and seasonal affective disorder in late autumn and completed the Cognitive Failures Questionnaire for reported difficulties in everyday activities that correspond to problems with perception, attention, and memory retrieval. The results indicated that seasonal affective disorder was highly prevalent (28.0%), substantially more so than major depressive disorder (8.6%). Similar to previous research on major depressive disorder, gender differences were also evident among participants with seasonal affective disorder, with more women qualifying than men. Both affective disorders were associated with higher reports of cognitive failures in comparison to participants with no depressive symptoms. These results reveal that individuals with seasonal affective disorder showed cognitive impairments similar to those with nonseasonal depression.

The purpose of this study was to expand our understanding of the range of negative affect associated with reported problems with everyday functions and activities, measured by the cognitive failures questionnaire (CFQ). Evidence from previous research indicates that individuals meeting criteria for mood disorders, such as major depression or seasonal affective disorder, experience cognitive deficits in memory and attention that can lead to problems with everyday activities reported in the CFQ. The Positive and Negative Affect Scale (PANAS) was used to assess potential correlations with a wider range of negative emotions. Findings for a sample of 129 college students revealed that negative affective experiences were significantly correlated with failures of memory and attention on the CFQ (fear = .41, hostility = .38, sadness = .28, and guilt = .43). Conversely, positive affect was negatively correlated with distractibility (r = −.21). Additional affective scales on the PANAS (e.g., shyness and fatigue) were also associated with higher reports of cognitive failures. The results provide converging evidence of a relationship between negative affective experiences and reported frequency of problems on the cognitive failures questionnaire.

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