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Recent work has derived the optimal policy for two-alternative value-based decisions, in which decision-makers compare the subjective expected reward of two alternatives. Under specific task assumptions --- such as linear utility, linear cost of time and constant processing noise --- the optimal policy is implemented by a diffusion process in which parallel decision thresholds collapse over time as a function of prior knowledge about average reward across trials. This policy predicts that the decision dynamics of each trial are dominated by the difference in value between alternatives and are insensitive to the magnitude of the alternatives (i.e., their summed values). This prediction clashes with empirical evidence showing magnitude-sensitivity even in the case of equal alternatives, and with ecologically plausible accounts of decision making. Previous work has shown that relaxing assumptions about linear utility or linear time cost can give rise to optimal magnitude-sensitive policies. Here we question the assumption of constant processing noise, in favour of input-dependent noise. The neurally plausible assumption of input-dependent noise during evidence accumulation has received strong support from previous experimental and modelling work. We show that including input-dependent noise in the evidence accumulation process results in a magnitude-sensitive optimal policy for value-based decision-making, even in the case of a linear utility function and a linear cost of time, for both single (i.e., isolated) choices and sequences of choices in which decision-makers maximise reward rate. Compared to explanations that rely on non-linear utility functions and/or non-linear cost of time, our proposed account of magnitude-sensitive optimal decision-making provides a parsimonious explanation that bridges the gap between various task assumptions and between various types of decision making.

Recent years have been marked by important developments in artificial intelligence (AI). These developments have highlighted serious limitations in human rationality and shown that computers can be highly creative. There are also important positive outcomes for psychologists studying creativity. It is now possible to design entirely new classes of experiments that are more promising than the simple tasks typically used for studying creativity in psychology. In addition, given the current and future AI algorithms for developing new data structures and programs, novel theories of creativity are on the horizon. Thus, AI opens up entire new avenues for studying human creativity in psychology.

Theory building in science requires replication and integration of findings regarding a particular research question. Second-order meta-analysis (i.e., a meta-analysis of meta-analyses) offers a powerful tool for achieving this aim, and we use this technique to illuminate the controversial field of cognitive training. Recent replication attempts and large meta-analytic investigations have shown that the benefits of cognitive-training programs hardly go beyond the trained task and similar tasks. However, it is yet to be established whether the effects differ across cognitive-training programs and populations (children, adults, and older adults). We addressed this issue by using second-order meta-analysis. In Models 1 (k = 99) and 2 (k = 119), we investigated the impact of working-memory training on near-transfer (i.e., memory) and far-transfer (e.g., reasoning, speed, and language) measures, respectively, and whether it is mediated by the type of population. Model 3 (k = 233) extended Model 2 by adding six meta-analyses assessing the far-transfer effects of other cognitive-training programs (video-games, music, chess, and exergames). Model 1 showed that working-memory training does induce near transfer, and that the size of this effect is moderated by the type of population. By contrast, Models 2 and 3 highlighted that far-transfer effects are small or null. Crucially, when placebo effects and publication bias were controlled for, the overall effect size and true variance equaled zero. That is, no impact on far-transfer measures was observed regardless of the type of population and cognitive-training program. The lack of generalization of skills acquired by training is thus an invariant of human cognition.

Engagement in leisure activities has been claimed to be highly beneficial in the elderly. Practicing such activities is supposed to help older adults to preserve cognitive function, physical function, and mental health, and thus to contribute to successful aging. We used structural equation modeling (SEM) to analyze the impact of leisure activities on these constructs in a large sample of Japanese older adults (N = 809; age range 72-74). The model exhibited an excellent fit (CFI = 1); engaging in leisure activities was positively associated with all the three successful aging indicators. These findings corroborate previous research carried out in Western countries and extend its validity to the population of Eastern older adults. Albeit correlational in nature, these results suggest that active engagement in leisure activities can help older adults to maintain cognitive, physical, and mental health. Future research will clarify whether there is a causal relationship between engagement in leisure activities and successful aging.

The relationship between handedness and mathematical ability is still highly controversial. While some researchers have claimed that left-handers are gifted in mathematics and strong right-handers perform the worst in mathematical tasks, others have more recently proposed that mixed-handers are the most disadvantaged group. However, the studies in the field differ with regard to the ages and the gender of the participants, and the type of mathematical ability assessed. To disentangle these discrepancies, we conducted five studies in several Italian schools (total participants: N = 2,314), involving students of different ages (six to seventeen) and a range of mathematical tasks (e.g., arithmetic and reasoning). The results show that (a) linear and quadratic functions are insufficient for capturing the link between handedness and mathematical ability; (b) the percentage of variance in mathematics scores explained by handedness was larger than in previous studies (between 3 and 10% vs. 1%), and (c) the effect of handedness on mathematical ability depended on age, type of mathematical tasks, and gender. In accordance with previous research, handedness does represent a correlate of achievement in mathematics, but the shape of this relationship is more complicated than has been argued so far.

This study extended an existing crosslinguistic model of verb-marking errors in children's early multiword speech (MOSAIC) by adding a novel mechanism that defaults to the most frequent form of the verb where this accounts for a high proportion of forms in the input. Our simulations showed that the resulting model not only provides a better explanation of the data on typically developing children but also captures the crosslinguistic pattern of verb-marking error in children with developmental language disorder, including the tendency of English-speaking children to show higher rates of optional-infinitive errors and the tendency of Dutch-, German-, and Spanish-speaking children to show higher rates of agreement errors. The new version of MOSAIC thus provides a unified crosslinguistic model of the pattern of verb-marking errors in typically developing children and children with developmental language disorder.

Verb-marking errors are a characteristic feature of the speech of typically-developing (TD) children and are particularly prevalent in the speech of children with Developmental Language Disorder (DLD). However, both the pattern of verb-marking error in TD children and the pattern of verb-marking deficit in DLD vary across languages and interact with the semantic and syntactic properties of the language being learned. In this paper, we review work using a computational model called MOSAIC. We show how this work allows us to understand several features of the cross-linguistic data that are otherwise difficult to explain. We also show how discrepancies between the developmental data and the quantitative predictions generated by MOSAIC can be used to identify weaknesses in our current understanding and lead to further theory development; and how the resulting model (MOSAIC+) helps us understand differences in the cross-linguistic patterning of verb-marking errors in TD children and children with DLD.

Considerable research has been carried out on visual search, with single or multiple targets. However, most studies have used artificial stimuli with low ecological validity. In addition, little is known about the effects of target complexity and expertise in visual search. Here, we investigate visual search in three conditions of complexity (detecting a king, detecting a check, and detecting a checkmate) with chess players of two levels of expertise (novices and club players). Results show that the influence of target complexity depends on level of structure of the visual display. Different functional relationships were found between artificial (random chess positions) and ecologically valid (game positions) stimuli: With artificial, but not with ecologically valid stimuli, a "pop out" effect was present when a target was visually more complex than distractors but could be captured by a memory chunk. This suggests that caution should be exercised when generalising from experiments using artificial stimuli with low ecological validity to real-life stimuli.

The respective roles of knowledge and search have received considerable attention in the literature on expertise. However, most of the evidence on knowledge has been indirect--e.g., by inferring the presence of chunks in long-term memory from performance in memory recall tasks. Here we provide direct estimates of the amount of monochrestic (single use) and rote knowledge held by chess players of varying skill levels. From a large chess database, we analyzed 76,562 games played in 2008 by individuals ranging from Class B players (average players) to Masters to measure the extent to which players deviate from previously known initial sequences of moves ("openings"). Substantial differences were found in the number of moves known by players of different skill levels, with more expert players knowing more moves. Combined with assumptions independently made about the branching factor in master games, we estimate that masters have memorized about 100,000 opening moves. Our results support the hypothesis that monochrestic knowledge is essential for reaching high levels of expertise in chess. They provide a direct, quantitative estimate of the number of opening moves that players have to know to reach master level.