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The empirical evidence that human color categorization exhibits some universal patterns beyond superficial discrepancies across different cultures is a major breakthrough in cognitive science. As observed in the World Color Survey (WCS), indeed, any two groups of individuals develop quite different categorization patterns, but some universal properties can be identified by a statistical analysis over a large number of populations. Here, we reproduce the WCS in a numerical model in which different populations develop independently their own categorization systems by playing elementary language games. We find that a simple perceptual constraint shared by all humans, namely the human Just Noticeable Difference (JND), is sufficient to trigger the emergence of universal patterns that unconstrained cultural interaction fails to produce. We test the results of our experiment against real data by performing the same statistical analysis proposed to quantify the universal tendencies shown in the WCS [Kay P & Regier T. (2003) Proc. Natl. Acad. Sci. USA 100: 9085-9089], and obtain an excellent quantitative agreement. This work confirms that synthetic modeling has nowadays reached the maturity to contribute significantly to the ongoing debate in cognitive science.computational cognitive science | natural categorization | multiagent modeling | complex systems | statistical physics T he finding that color naming patterns present some conserved features across cultures (1) is a milestone in the debate on the existence of universals in human categorization (2). The data collected in the World Color Survey (WCS) (3), extending the pioneering work by Berlin and Kay (1), provide empirical evidence in favor of the fact that categorization is not simply a matter of conventions, but rather depends on the physiological and cognitive features of the categorizing subjects, in contrast with previous theories according to which categories are arbitrarily defined by different cultures (4). Over the years, the existence of universals in color categorization has become generally accepted (2, 5-7), though it has been the subject of strong controversy, and a debate is still ongoing (8-12). Recently, however, a set of statistical tests have proved quantitatively that the WCS data do in fact contain clear signatures of universal tendencies in color naming, both across industrialized and nonindustrialized languages (13). In any case, the WCS maintains a central role and its data, as a fundamental (and almost unique) experimental repository, are still under constant scrutiny, as shown by the continuous flow of publications related to them (see, for instance, (13-18)).Color categorization represents a case study in a wide debate on the origins, meanings, and properties of categorization systems (6, 7). In recent years, mathematical and computational models have been designed to explore the roles of various hypotheses concerning these issues, checking their implications in simplified, yet hopefully transparent, synthetic experiments (19). Generally ...

Gough and Tunmer’s (1986) simple view of reading (SVR) proposed that reading comprehension (RC) is a function of language comprehension (LC) and word recognition/decoding. Braze et al. (2007) presented data suggesting an extension of the SVR in which knowledge of vocabulary (V) affected RC over and above the effects of LC. Tunmer and Chapman (2012) found a similar independent contribution of V to RC when the data were analyzed by hierarchical regression. However, additional analysis by factor analysis and structural equation modeling indicated that the effect of V on RC was, in fact, completely captured by LC itself and there was no need to posit a separate direct effect of V on RC. In the present study, we present new data from young adults with sub-optimal reading skill (N = 286). Latent variable and regression analyses support Gough and Tunmer’s original proposal and the conclusions of Tunmer and Chapman that V can be considered a component of LC and not an independent contributor to RC.

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Subjective well-being includes ‘affect’ and ‘satisfaction with life’ (SWL). This study proposes a unified approach to construct a profile of subjective well-being based on social media language in Facebook status updates. We apply sentiment analysis to generate users’ affect scores, and train a random forest model to predict SWL using affect scores and other language features of the status updates. Results show that: the computer-selected features resemble the key predictors of SWL as identified in early studies; the machine-predicted SWL is moderately correlated with the self-reported SWL (r = 0.36, p < 0.01), indicating that language-based assessment can constitute valid SWL measures; the machine-assessed affect scores resemble those reported in a previous experimental study; and the machine-predicted subjective well-being profile can also reflect other psychological traits like depression (r = 0.24, p < 0.01). This study provides important insights for psychological prediction using multiple, machine-assessed components and longitudinal or dense psychological assessment using social media language.