Despite being one of the healthiest developmental periods, morbidity and mortality rates increase dramatically during adolescence, largely due to preventable, risky behaviors. Heightened reward sensitivity, coupled with ineffective cognitive control, has been proposed to underlie adolescents' risk taking. In this study, we test whether reward sensitivity can be redirected to promote safe behavior. Adolescents completed a risk-taking task in the presence of their mother and alone during fMRI. Adolescents demonstrated reduced risk-taking behavior when their mothers were present compared with alone, which was associated with greater recruitment of the ventrolateral prefrontal cortex (VLPFC) when making safe decisions, decreased activation in the ventral striatum following risky decisions and greater functional coupling between the ventral striatum and VLPFC when making safe decisions. Importantly, the very same neural circuitry (i.e. ventral striatum) that has been linked to greater risk-taking can also be redirected toward thoughtful, more deliberative and safe decisions.

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Although models of word meanings based on distributional semantics have proved effective in predicting human judgments of similarity among individual concepts, it is less clear whether or how such models might be extended to account for judgments of similarity among relations between concepts. Here we combine an individual-differences approach with computational modeling to predict human judgments of similarity among word pairs instantiating a variety of abstract semantic relations (e.g., contrast, cause-effect, part-whole). A measure of cognitive capacity predicted individual differences in the ability to discriminate among distinct relations. The human pattern of relational similarity judgments, both at the group level and for individual participants, was best predicted by a model that takes representations of word meanings based on distributional semantics as its inputs and uses them to learn an explicit representation of relations. These findings indicate that although the meanings of abstract semantic relations are not directly coded in the meanings of individual words, important aspects of relational similarity can be derived from distributional semantics.

We report a study examining the role of linguistic context in modulating the influences of individual differences in fluid and crystalized intelligence on comprehension of literary metaphors. Three conditions were compared: no context, metaphor-congruent context, and literal-congruent context. Relative to the baseline no-context condition, the metaphor-congruent context facilitated comprehension of the metaphorical meaning whereas the literal-congruent context impaired it. Measures of fluid and crystalized intelligence both made separable contributions to predicting metaphor comprehension. The metaphor-congruent context selectively increased the contribution of crystalized verbal intelligence. These findings support the hypothesis that a supportive linguistic context encourages use of semantic integration in interpreting metaphors.

Analogical reasoning is an active topic of investigation across education, artificial intelligence (AI), cognitive psychology, and related fields. In all fields of inquiry, explicit analogy problems provide useful tools for investigating the mechanisms underlying analogical reasoning. Such sets have been developed by researchers working in the fields of educational testing, AI, and cognitive psychology. However, these analogy tests have not been systematically made accessible across all the relevant fields. The present paper aims to remedy this situation by presenting a working inventory of verbal analogy problem sets, intended to capture and organize sets from diverse sources.