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SummaryThe origin of talent and expertise is currently the subject of intense debate, with explanations ranging from purely biological to purely environmental. This report shows that the population of expert chess players in the northern hemisphere shows a seasonal pattern, with an excess of births in late winter and early spring. This effect remains when taking into account the distribution of births in the population at large, using statistics from the European Union member countries. A similar pattern has been found with schizophrenia, and the possible link between these two phenomena is discussed.

Abstract The nonword repetition (NWR) test has been shown to be a good predictor of children's vocabulary size. NWR performance has been explained using phonological working memory, which is seen as a critical component in the learning of new words. However, no detailed specification of the link between phonological working memory and long‐term memory (LTM) has been proposed. In this paper, we present a computational model of children's vocabulary acquisition (EPAM‐VOC) that specifies how phonological working memory and LTM interact. The model learns phoneme sequences, which are stored in LTM and mediate how much information can be held in working memory. The model's behaviour is compared with that of children in a new study of NWR, conducted in order to ensure the same nonword stimuli and methodology across ages. EPAM‐VOC shows a pattern of results similar to that of children: performance is better for shorter nonwords and for wordlike nonwords, and performance improves with age. EPAM‐VOC also simulates the superior performance for single consonant nonwords over clustered consonant nonwords found in previous NWR studies. EPAM‐VOC provides a simple and elegant computational account of some of the key processes involved in the learning of new words: it specifies how phonological working memory and LTM interact; makes testable predictions; and suggests that developmental changes in NWR performance may reflect differences in the amount of information that has been encoded in LTM rather than developmental changes in working memory capacity.

This volume offers selected papers exploring issues arising from scientific discovery in the social sciences. It features a range of disciplines including behavioural sciences, computer science, finance, and statistics with an emphasis on philosophy. The first of the three parts examines methods of social scientific discovery. Chapters investigate the nature of causal analysis, philosophical issues around scale development in behavioural science research, imagination in social scientific practice, and relationships between paradigms of inquiry and scientific fraud. The next part considers the practice of social science discovery. Chapters discuss the lack of genuine scientific discovery in finance where hypotheses concern the cheapness of securities, the logic of scientific discovery in macroeconomics, and the nature of that what discovery with the Solidarity movement as a case study. The final part covers formalising theories in social science. Chapters analyse the abstract model theory of institutions as a way of representing the structure of scientific theories, the semi-automatic generation of cognitive science theories, and computational process models in the social sciences. The volume offers a unique perspective on scientific discovery in the social sciences. It will engage scholars and students with a multidisciplinary interest in the philosophy of science and social science.

Chess involves the capacity to reason iteratively about potential intentional choices of an opponent and therefore involves high levels of explicit theory of mind [ToM] (i.e. ability to infer mental states of others) alongside clear, strategic rule-based decision-making. Functional magnetic resonance imaging was used on 12 healthy male novice chess players to identify cortical regions associated with chess, ToM and empathising. The bloodoxygen- level-dependent (BOLD) response for chess and empathising tasks was extracted from each ToM region. Results showed neural overlap between ToM, chess and empathising tasks in righthemisphere temporo-parietal junction (TPJ) [BA40], left-hemisphere superior temporal gyrus [BA22] and posterior cingulate gyrus [BA23/31]. TPJ is suggested to underlie the capacity to reason iteratively about another's internal state in a range of tasks. Areas activated by ToM and empathy included righthemisphere orbitofrontal cortex and bilateral middle temporal gyrus: areas that become active when there is need to inhibit one's own experience when considering the internal state of another and for visual evaluation of action rationality. Results support previous findings, that ToM recruits a neural network with each region sub-serving a supporting role depending on the nature of the task itself. In contrast, a network of cortical regions primarily located within right- and left-hemisphere medial-frontal and parietal cortex, outside the internal representational network was selectively recruited during the chess task. We hypothesize that in our cohort of novice chess players the strategy was to employ an iterative thinking pattern which in part involved mentalizing processes and recruited core ToM related regions.

AbstractTests 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.