Intuition in chess: a study with world-class players.

Intuition plays a central role in cognition in general and expertise in particular. Dreyfus and Dreyfus's (1986) and Gobet and Chassy's (2008) theories of expert intuition propose that a characteristic feature of expert intuition is the holistic understanding displayed by experts. The ideal way to test this prediction is to use highly expert participants and short presentation times. Chess players (N = 63), ranging from candidate masters to world-class players, had to evaluate chess problems. Evaluating the problems required an understanding of the position as a whole. Results demonstrated an effect of skill (better players had better evaluations), complexity (simpler positions were better evaluated than complex positions) and balance (accuracy diminished when the true evaluations became more extreme). A regression analysis showed that skill accounted for 44% of the variance in evaluation error. These important results support the central role of holistic intuition in expertise.

Building a unified model of the Optional Infinitive Stage: Simulating the cross-linguistic pattern of verb-marking error 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.

The effect of hyperarticulation on speech comprehension under adverse listening conditions.

Comprehension assesses a listener's ability to construe the meaning of an acoustic signal in order to be able to answer questions about its contents, while intelligibility indicates the extent to which a listener can precisely retrieve the acoustic signal. Previous comprehension studies asking listeners for sentence-level information or narrative-level information used native listeners as participants. This is the first study to look at whether clear speech properties (e.g. expanded vowel space) produce a clear speech benefit at the word level for L2 learners for speech produced in naturalistic settings. This study explored whether hyperarticulated speech was more comprehensible than non-hyperarticulated speech for both L1 British English speakers and early and late L2 British English learners in quiet and in noise. Sixteen British English listeners, 16 native Mandarin Chinese listeners as early learners of L2 and 16 native Mandarin Chinese listeners as late learners of L2 rated hyperarticulated samples versus non-hyperarticulated samples in form of words for comprehension under four listening conditions of varying white noise level (quiet or SNR levels of + 16 dB, + 12 dB or + 8 dB) (3 × 2× 4 mixed design). Mean ratings showed all three groups found hyperarticulated speech samples easier to understand than non-hyperarticulated speech at all listening conditions. Results are discussed in terms of other findings (Uther et al., 2012) that suggest that hyperarticulation may generally improve speech processing for all language groups.

Cognitive and academic benefits of music training with children: A multilevel meta-analysis.

Music training has repeatedly been claimed to positively impact children's cognitive skills and academic achievement (literacy and mathematics). This claim relies on the assumption that engaging in intellectually demanding activities fosters particular domain-general cognitive skills, or even general intelligence. The present meta-analytic review (N = 6,984, k = 254, m = 54) shows that this belief is incorrect. Once the quality of study design is controlled for, the overall effect of music training programs is null ([Formula: see text] ≈ 0) and highly consistent across studies (τ2 ≈ 0). Results of Bayesian analyses employing distributional assumptions (informative priors) derived from previous research in cognitive training corroborate these conclusions. Small statistically significant overall effects are obtained only in those studies implementing no random allocation of participants and employing non-active controls ([Formula: see text] ≈ 0.200, p < .001). Interestingly, music training is ineffective regardless of the type of outcome measure (e.g., verbal, non-verbal, speed-related, etc.), participants' age, and duration of training. Furthermore, we note that, beyond meta-analysis of experimental studies, a considerable amount of cross-sectional evidence indicates that engagement in music has no impact on people's non-music cognitive skills or academic achievement. We conclude that researchers' optimism about the benefits of music training is empirically unjustified and stems from misinterpretation of the empirical data and, possibly, confirmation bias.

Does chess instruction improve mathematical problem-solving ability? Two experimental studies with an active control group.

It has been proposed that playing chess enables children to improve their ability in mathematics. These claims have been recently evaluated in a meta-analysis (Sala & Gobet, 2016, Educational Research Review, 18, 46-57), which indicated a significant effect in favor of the groups playing chess. However, the meta-analysis also showed that most of the reviewed studies used a poor experimental design (in particular, they lacked an active control group). We ran two experiments that used a three-group design including both an active and a passive control group, with a focus on mathematical ability. In the first experiment (N = 233), a group of third and fourth graders was taught chess for 25 hours and tested on mathematical problem-solving tasks. Participants also filled in a questionnaire assessing their meta-cognitive ability for mathematics problems. The group playing chess was compared to an active control group (playing checkers) and a passive control group. The three groups showed no statistically significant difference in mathematical problem-solving or metacognitive abilities in the posttest. The second experiment (N = 52) broadly used the same design, but the Oriental game of Go replaced checkers in the active control group. While the chess-treated group and the passive control group slightly outperformed the active control group with mathematical problem solving, the differences were not statistically significant. No differences were found with respect to metacognitive ability. These results suggest that the effects (if any) of chess instruction, when rigorously tested, are modest and that such interventions should not replace the traditional curriculum in mathematics.

Experts' memory superiority for domain-specific random material generalizes across fields of expertise: A meta-analysis.

Experts' remarkable ability to recall meaningful domain-specific material is a classic result in cognitive psychology. Influential explanations for this ability have focused on the acquisition of high-level structures (e.g., schemata) or experts' capability to process information holistically. However, research on chess players suggests that experts maintain some reliable memory advantage over novices when random stimuli (e.g., shuffled chess positions) are presented. This skill effect cannot be explained by theories emphasizing high-level memory structures or holistic processing of stimuli, because random material does not contain large structures nor wholes. By contrast, theories hypothesizing the presence of small memory structures-such as chunks-predict this outcome, because some chunks still occur by chance in the stimuli, even after randomization. The current meta-analysis assessed the correlation between level of expertise and recall of random material in diverse domains. The overall correlation was moderate but statistically significant ([Formula: see text]), and the effect was observed in nearly every study. This outcome suggests that experts partly base their superiority on a vaster amount of small memory structures, in addition to high-level structures or holistic processing.

Becoming an expert: Ontogeny of expertise as an example of neural reuse.

In this commentary, we discuss an important pattern of results in the literature on the neural basis of expertise: (a) decrease of cerebral activation at the beginning of acquisition of expertise and (b) functional cerebral reorganization as a consequence of years of practice. We show how these two results can be integrated with the neural reuse framework.

Why computational models are better than verbal theories: the case of nonword repetition.

Tests of nonword repetition (NWR) have often been used to examine children's phonological knowledge and word learning abilities. However, theories of NWR primarily explain performance either in terms of phonological working memory or long-term knowledge, with little consideration of how these processes interact. One theoretical account that focuses specifically on the interaction between short-term and long-term memory is the chunking hypothesis. Chunking occurs because of repeated exposure to meaningful stimulus items, resulting in the items becoming grouped (or chunked); once chunked, the items can be represented in short-term memory using one chunk rather than one chunk per item. We tested several predictions of the chunking hypothesis by presenting 5-6-year-old children with three tests of NWR that were either high, medium, or low in wordlikeness. The results did not show strong support for the chunking hypothesis, suggesting that chunking fails to fully explain children's NWR behavior. However, simulations using a computational implementation of chunking (namely CLASSIC, or Chunking Lexical And Sub-lexical Sequences In Children) show that, when the linguistic input to 5-6-year-old children is estimated in a reasonable way, the children's data are matched across all three NWR tests. These results have three implications for the field: (a) a chunking account can explain key NWR phenomena in 5-6-year-old children; (b) tests of chunking accounts require a detailed specification both of the chunking mechanism itself and of the input on which the chunking mechanism operates; and (c) verbal theories emphasizing the role of long-term knowledge (such as chunking) are not precise enough to make detailed predictions about experimental data, but computational implementations of the theories can bridge the gap.

The emotional and attitudinal consequences of religious hypocrisy: experimental evidence using a cognitive dissonance paradigm.

We explored the emotional and attitudinal consequences of personal attitude-behavior discrepancies using a religious version of the hypocrisy paradigm. We induced cognitive dissonance in participants (n = 206) by making them feel hypocritical for advocating certain religious behaviors that they had not recently engaged in to their own satisfaction. In Experiment 1, this resulted in higher levels of self-reported guilt and shame compared to the control condition. Experiment 2 further showed that a religious self-affirmation task eliminated the guilt and shame. In Experiment 3, participants boosted their religious attitudes as a result of dissonance, and both religious and non-religious self-affirmation tasks eliminated this effect. The findings provide evidence that dissonance induced through religious hypocrisy can result in guilt and shame as well as an attitude bolstering effect, as opposed to the attitude reconciliation effect that is prevalent in previous dissonance research.

Cognitive Training: A Field in Search of a Phenomenon.

Considerable research has been carried out in the last two decades on the putative benefits of cognitive training on cognitive function and academic achievement. Recent meta-analyses summarizing the extent empirical evidence have resolved the apparent lack of consensus in the field and led to a crystal-clear conclusion: The overall effect of far transfer is null, and there is little to no true variability between the types of cognitive training. Despite these conclusions, the field has maintained an unrealistic optimism about the cognitive and academic benefits of cognitive training, as exemplified by a recent article (Green et al., 2019). We demonstrate that this optimism is due to the field neglecting the results of meta-analyses and largely ignoring the statistical explanation that apparent effects are due to a combination of sampling errors and other artifacts. We discuss recommendations for improving cognitive-training research, focusing on making results publicly available, using computer modeling, and understanding participants' knowledge and strategies. Given that the available empirical evidence on cognitive training and other fields of research suggests that the likelihood of finding reliable and robust far-transfer effects is low, research efforts should be redirected to near transfer or other methods for improving cognition.

Searching for answers: expert pattern recognition and planning.

Does expertise mostly stem from pattern recognition or look-ahead search? van Opheusden et al. contribute to this important debate in cognitive psychology and artificial intelligence (AI) with a multi-method, multi-experiment study and a new model. Using a novel, relatively simple board game, they show that players increase depth of search when improving their skill.

Computational Scientific Discovery in Psychology.

Scientific discovery is a driving force for progress involving creative problem-solving processes to further our understanding of the world. The process of scientific discovery has historically been intensive and time-consuming; however, advances in computational power and algorithms have provided an efficient route to make new discoveries. Complex tools using artificial intelligence (AI) can efficiently analyze data as well as generate new hypotheses and theories. Along with AI becoming increasingly prevalent in our daily lives and the services we access, its application to different scientific domains is becoming more widespread. For example, AI has been used for the early detection of medical conditions, identifying treatments and vaccines (e.g., against COVID-19), and predicting protein structure. The application of AI in psychological science has started to become popular. AI can assist in new discoveries both as a tool that allows more freedom to scientists to generate new theories and by making creative discoveries autonomously. Conversely, psychological concepts such as heuristics have refined and improved artificial systems. With such powerful systems, however, there are key ethical and practical issues to consider. This article addresses the current and future directions of computational scientific discovery generally and its applications in psychological science more specifically.

How chunks, long-term working memory and templates offer a cognitive explanation for neuroimaging data on expertise acquisition: a two-stage framework.

Our review of research on PET and fMRI neuroimaging of experts and expertise acquisition reveals two apparently discordant patterns in working-memory-related tasks. When experts are involved, studies show activations in brain regions typically activated during long-term memory tasks that are not observed with novices, a result that is compatible with functional brain reorganization. By contrast, when involving novices and training programs, studies show a decrease in brain regions typically activated during working memory tasks, with no functional reorganization. We suggest that the latter result is a consequence of practice periods that do not allow important structures to be completely acquired: knowledge structures (i.e., Ericsson and Kintsch's retrieval structures; Gobet and Simon's templates) and in a lesser way, chunks. These structures allow individuals to improve performance on working-memory tasks, by enabling them to use part of long-term memory as working memory, causing a cerebral functional reorganization. Our hypothesis is that the two brain activation patterns observed in the literature are not discordant, but involve the same process of expertise acquisition in two stages: from decreased activation to brain functional reorganization. The dynamic of these two physiological stages depend on the two above-mentioned psychological constructs: chunks and knowledge structures.

Sinuosity and the Affect Grid: a method for adjusting repeated mood scores.

Sinuosity is a measure of how much a travelled pathway deviates from a straight line. In this paper, sinuosity is applied to the measurement of mood. The Affect Grid is a mood scale that requires participants to place a mark on a 9 x 9 grid to indicate their current mood. The grid has two dimensions: pleasure-displeasure (horizontal) and arousal-sleepiness (vertical). In studies where repeated measurements are required, some participants may exaggerate their mood shifts due to faulty interpretation of the scale or a feeling of social obligation to the experimenter. A new equation is proposed, based on the sinuosity measure in hydrology, a measure of the meandering of rivers. The equation takes into account an individual's presumed tendency to exaggerate and meander to correct the score and reduce outliers. The usefulness of the equation is demonstrated by applying it to Affect Grid data from another study.

Magnitude-sensitivity: rethinking decision-making.

Magnitude-sensitivity refers to the result that performance in decision-making, across domains and organisms, is affected by the total value of the possible alternatives. This simple result offers a window into fundamental issues in decision-making and has led to a reconsideration of ecological decision-making, prominent computational models of decision-making, and optimal decision-making. Moreover, magnitude-sensitivity has inspired the design of new robotic systems that exploit natural solutions and apply optimal decision-making policies. In this article, we review the key theoretical and empirical results about magnitude-sensitivity and highlight the importance that this phenomenon has for the understanding of decision-making. Furthermore, we discuss open questions and ideas for future research.

Still no evidence that exergames improve cognitive ability: A commentary on Stanmore et al. (2017).

A recent meta-analysis (Stanmore et al. Neurosci. Biobehav. Rev. 78:34-43, 2017) claimed that exergames exert medium-size positive effects on people's overall cognitive function. The present article critically tests this claim. We argue that the meta-analysis reported inflated effect sizes mainly for three reasons: (a) some effect sizes were miscalculated; (b) there was an excessive amount of true heterogeneity; and (c) no publication-bias-corrected estimates were provided. We have thus recalculated the effect sizes and reanalyzed the data using a more robust approach and more sophisticated techniques. Compared to Stanmore's et al., our models show that: (a) the overall effect sizes are substantially smaller; (b) the amount of true heterogeneity, when any, is much lower; and (c) the publication-bias analyses suggest that the actual effect of exergames on overall cognitive function is slim to null. Therefore, the cognitive benefits of exergames are far from being established.

Explaining quantitative variation in the rate of Optional Infinitive errors across languages: a comparison of MOSAIC and the Variational Learning Model.

In this study, we use corpus analysis and computational modelling techniques to compare two recent accounts of the OI stage: Legate & Yang's (2007) Variational Learning Model and Freudenthal, Pine & Gobet's (2006) Model of Syntax Acquisition in Children. We first assess the extent to which each of these accounts can explain the level of OI errors across five different languages (English, Dutch, German, French and Spanish). We then differentiate between the two accounts by testing their predictions about the relation between children's OI errors and the distribution of infinitival verb forms in the input language. We conclude that, although both accounts fit the cross-linguistic patterning of OI errors reasonably well, only MOSAIC is able to explain why verbs that occur more frequently as infinitives than as finite verb forms in the input also occur more frequently as OI errors than as correct finite verb forms in the children's output.

Working memory training in typically developing children: A multilevel meta-analysis.

Working memory (WM) training in typically developing (TD) children aims to enhance not only performance in memory tasks but also other domain-general cognitive skills, such as fluid intelligence. These benefits are then believed to positively affect academic achievement. Despite the numerous studies carried out, researchers still disagree over the real benefits of WM training. With this meta-analysis (m = 41, k = 393, N = 2,375), we intended to resolve the discrepancies by focusing on the potential sources of within-study and between-study true heterogeneity. Small to medium effects were observed in memory tasks (i.e., near transfer). The size of these effects was proportional to the similarity between the training task and the outcome measure. By contrast, far-transfer measures of cognitive ability (e.g., intelligence) and academic achievement (mathematics and language ability) were essentially unaffected by the training programs, especially when the studies implemented active controls ([Formula: see text] = 0.001, SE = 0.055, p = .982, τ2 = 0.000). Crucially, all the models exhibited a null or low amount of true heterogeneity, which was wholly explained by the type of controls (nonactive vs. active) and by statistical artifacts, in contrast to the claim that this field has produced mixed results. Since the empirical evidence shows the absence of both generalized effects and true heterogeneity, we conclude that there is no reason to keep investing resources in WM training research with TD children.

Why are (the best) women so good at chess? Participation rates and gender differences in intellectual domains.

A popular explanation for the small number of women at the top level of intellectually demanding activities from chess to science appeals to biological differences in the intellectual abilities of men and women. An alternative explanation is that the extreme values in a large sample are likely to be greater than those in a small one. Although the performance of the 100 best German male chess players is better than that of the 100 best German women, we show that 96 per cent of the observed difference would be expected given the much greater number of men who play chess. There is little left for biological or cultural explanations to account for. In science, where there are many more male than female participants, this statistical sampling explanation, rather than differences in intellectual ability, may also be the main reason why women are under-represented at the top end.

How Artificial Intelligence Can Help Us Understand Human Creativity.

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.