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Cognitive training and brain stimulation studies have suggested that human cognition, primarily working memory and attention control processes, can be enhanced. Some authors claim that gains (i.e., post-test minus pretest scores) from such interventions are unevenly distributed among people. The magnification account (expressed by the evangelical "who has will more be given") predicts that the largest gains will be shown by the most cognitively efficient people, who will also be most effective in exploiting interventions. In contrast, the compensation account ("who has will less be given") predicts that such people already perform at ceiling, so interventions will yield the largest gains in the least cognitively efficient people. Evidence for this latter account comes from reported negative correlations between the pretest and the training/stimulation gain. In this paper, with the use of mathematical derivations and simulation methods, we show that such correlations are pure statistical artifacts caused by the widely known methodological error called "regression to the mean". Unfortunately, more advanced methods, such as alternative measures, linear models, and control groups do not guarantee correct assessment of the compensation effect either. The only correct method is to use direct modeling of correlations between latent true measures and gain. As to date no training/stimulation study has correctly used this method to provide evidence in favor of the compensation account, we must conclude that most (if not all) of the evidence should be considered inconclusive.

Cognitive control allows humans to direct and coordinate their thoughts and actions in a flexible way, in order to reach internal goals regardless of interference and distraction. The hallmark test used to examine cognitive control is the Stroop task, which elicits both the weakly learned but goal-relevant and the strongly learned but goal-irrelevant response tendencies, and requires people to follow the former while ignoring the latter. After reviewing the existing computational models of cognitive control in the Stroop task, its novel, integrated utility-based model is proposed. The model uses 3 crucial control mechanisms: response utility reinforcement learning, utility-based conflict evaluation using the Festinger formula for assessing the conflict level, and top-down adaptation of response utility in service of conflict resolution. Their complex, dynamic interaction led to replication of 18 experimental effects, being the largest data set explained to date by 1 Stroop model. The simulations cover the basic congruency effects (including the response latency distributions), performance dynamics and adaptation (including EEG indices of conflict), as well as the effects resulting from manipulations applied to stimulation and responding, which are yielded by the extant Stroop literature.

Fluid intelligence (Gf) is a crucial cognitive ability that involves abstract reasoning in order to solve novel problems. Recent research demonstrated that Gf strongly depends on the individual effectiveness of working memory (WM). We investigated a popular claim that if the storage capacity underlay the WM–Gf correlation, then such a correlation should increase with an increasing number of items or rules (load) in a Gf-test. As often no such link is observed, on that basis the storage-capacity account is rejected, and alternative accounts of Gf (e.g., related to executive control or processing speed) are proposed. Using both analytical inference and numerical simulations, we demonstrated that the load-dependent change in correlation is primarily a function of the amount of floor/ceiling effect for particular items. Thus, the item-wise WM correlation of a Gf-test depends on its overall difficulty, and the difficulty distribution across its items. When the early test items yield huge ceiling, but the late items do not approach floor, that correlation will increase throughout the test. If the early items locate themselves between ceiling and floor, but the late items approach floor, the respective correlation will decrease. For a hallmark Gf-test, the Raven-test, whose items span from ceiling to floor, the quadratic relationship is expected, and it was shown empirically using a large sample and two types of WMC tasks. In consequence, no changes in correlation due to varying WM/Gf load, or lack of them, can yield an argument for or against any theory of WM/Gf. Moreover, as the mathematical properties of the correlation formula make it relatively immune to ceiling/floor effects for overall moderate correlations, only minor changes (if any) in the WM–Gf correlation should be expected for many psychological tests.

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In multi-attribute choice, decision makers use decision strategies to arrive at the final choice. What are the neural mechanisms underlying decision strategy selection? The first goal of this paper is to provide a literature review on the neural underpinnings and cognitive models of decision strategy selection and thus set the stage for a neurocognitive model of this process. The second goal is to outline such a unifying, mechanistic model that can explain the impact of noncognitive factors (e.g., affect, stress) on strategy selection. To this end, we review the evidence for the factors influencing strategy selection, the neural basis of strategy use and the cognitive models of this process. We also present the Bottom-Up Model of Strategy Selection (BUMSS). The model assumes that the use of the rational Weighted Additive strategy and the boundedly rational heuristic Take The Best can be explained by one unifying, neurophysiologically plausible mechanism, based on the interaction of the frontoparietal network, orbitofrontal cortex, anterior cingulate cortex and the brainstem nucleus locus coeruleus. According to BUMSS, there are three processes that form the bottom-up mechanism of decision strategy selection and lead to the final choice: (1) cue weight computation, (2) gain modulation, and (3) weighted additive evaluation of alternatives. We discuss how these processes might be implemented in the brain, and how this knowledge allows us to formulate novel predictions linking strategy use and neural signals.

Persons with intellectual disability are a group at risk of being exposed to overly demanding problem-solving situations, which may produce learned helplessness. The research was based on the informational model of learned helplessness. The consequences of exposure to an unsolvable task and the ability to recognize the symptoms of cognitive exhaustion were tested in 120 students with mild intellectual disability. After the exposure to the unsolvable task, persons in the experimental group obtained lower results than the control group in the escape/avoidance learning task, but a similar result was found in the divergent thinking fluency task. Also, participants in the experimental group had difficulties recognizing the symptoms of the cognitive exhaustion state. After a week’s time, the difference in escape/avoidance learning performance was still observed. The results indicate that exposure to unsolvable tasks may negatively influence the cognitive performance in persons with intellectual disability, although those persons may not identify the cognitive state related to lowered performance.

backgroundAdolescence and young adulthood are frequently characterised by a strong propensity to take risks. Yet, empirical data shows that personality traits, type and features of risk measures, or presence of additional incentives can significantly influence one's risk-taking tendency. Our aim was to investigate young people's risk-taking and point out when and how individual and situational factors may increase or decrease their risk-taking propensity. participants and procedureParticipants were adolescents and emerging adults (N = 173, age range: 13-30). Each completed two behavioural risk measures ("hot" and "cold" decision tasks) in two conditions, with or without financial incentives. Questionnaires assessing self-declared risk-taking, sensation seeking, and impulsivity were also used. Statistical analyses were conducted with gender and age as additional factors. resultsIn "hot" risk tasks all participants risked the same, while the tendency to take risks in "cold" tasks was higher for older participants, especially in the presence of incentives. Males risked more than females, apart from "hot" incentivised tasks where no gender differences were found. Sensation seeking and impulsivity were significant predictors of risk-taking in "hot" incentivised tasks, while performance in "cold" non-incentivised tasks depended on sensation seeking only. conclusionsOur results show that risk-taking is not a unitary phenomenon, and young people are not universal risk-takers. Certain personality traits seem to predispose this group to taking risks, but only in some circumstances (e.g. "hot" decisions). Factors such as task context or additional incentives can not only increase but also decrease risk-taking in young people, resulting in more caution on their behalf.