Psychology

We begin this chapter with the bold claim that it provides a neuroscientific explanation of the magic of creativity. Creativity presents a formidable challenge for neuroscience. Neuroscience generally involves studying what happens in the brain when someone engages in a task that involves responding to a stimulus, or retrieving information from memory and using it the right way, or at the right time. If the relevant information is not already encoded in memory, the task generally requires that the individual make systematic use of information that is encoded in memory. But creativity is different. It paradoxically involves studying how someone pulls out of their brain something that was never put into it! Moreover, it must be something both new and useful, or appropriate to the task at hand. The ability to pull out of memory something new and appropriate that was never stored there in the first place is what we refer to as the magic of creativity. Even if we are so fortunate as to determine which areas of the brain are active and how these areas interact during creative thought, we will not have an answer to the question of how the brain comes up with solutions and artworks that are new and appropriate. On the other hand, since the representational capacity of neurons emerges at a level that is higher than that of the individual neurons themselves, the inner workings of neurons is too low a level to explain the magic of creativity. Thus we look to a level that is midway between gross brain regions and neurons. Since creativity generally involves combining concepts from different domains, or seeing old ideas from new perspectives, we focus our efforts on the neural mechanisms underlying the representation of concepts and ideas. Thus we ask questions about the brain at the level that accounts for its representational capacity, i.e. at the level of distributed aggregates of neurons.

Recent experiments show video games have a range of positive cognitive effects, such as improvement in attention, spatial cognition and mental rotation, and also overcoming of cognitive disabilities such as fear of flying. Further, game environments are now being used to generate scientific discoveries, and bring about novel phenomenological effects, such as out-of-body experiences. These advances provide interesting interaction design possibilities for video games. However, since the cognitive mechanisms underlying these experimental effects are unknown, it is difficult to systematically derive novel systems and interaction designs based on these results. We review the emerging cognitive mechanism known as common coding (which proposes a common neural representation connecting execution, perception and imagination of movements), and outline how this mechanism could provide an integrated account of the cognitive effects of video games. We then illustrate, using two ongoing projects, how novel video game interaction designs could be derived by extending common coding theory.

Theories of categorization need to account for ways in which people use their creativity to categorize things, especially in the context of similarity. The current three-phase study is a preliminary attempt to understand how people group concepts together as well as to explore the role of similarity between concepts in creative categorization. Participants were asked to categorize a list of 100 words and the resulting categories were rated for creativity by another set of participants. In the last phase, similarity between words from all the categories was computed using latent semantic analysis. There was a significant correlation between similarity values and creativity ratings for all the categories. In addition, a new set of participants were asked to rate the similarity among the words in the least and most creative categories and there was a significant difference between the mean ratings of the two types of categories. The results indicate that creative categorization is characterized by grouping of dissimilar words. People use creative imagination to construct novel categories by linking apparently dissimilar words.

This interdisciplinary paper hypothesizes that Rembrandt developed new painterly techniquesnovel to the early modern period -in order to engage and direct the gaze of the observer. Though these methods were not based on scientific evidence at the time, we show that they nonetheless are consistent with a contemporary understanding of human vision. Here we propose that artists in the late 'early modern' period developed the technique of textural agencyinvolving selective variation in image detail -to guide the observer's eye and thereby influence the viewing experience. The paper begins by establishing the well-known use of textural agency among modern portrait artists, before considering the possibility that Rembrandt developed these techniques in his late portraits in reaction to his Italian contemporaries. A final section brings the argument full circle, with the presentation of laboratory evidence that Rembrandt's techniques indeed guide the modern viewer's eye in the way we propose.

A painted portrait differs from a photo in that selected regions are often rendered in much sharper detail than other regions. Artists believe these choices guide viewer gaze and influence their appreciation of the portrait, but these claims are difficult to test because increased portrait detail is typically associated with greater meaning, stronger lighting, and a more central location in the composition. In three experiments we monitored viewer gaze and recorded viewer preferences for portraits rendered with a parameterised non-photorealistic technique to mimic the style of Rembrandt (DiPaola, 2009 International Journal of Art and Technology2 82–93). Results showed that viewer gaze was attracted to and held longer by regions of relatively finer detail (experiment 1), and also by textural highlighting (experiment 2), and that artistic appreciation increased when portraits strongly biased gaze (experiment 3). These findings have implications for understanding both human vision scie...

The present study examined the performance of population fit indices used in structural equation modeling. Index performances were evaluated in multiple modeling situations that involved misspecification due to either omitted error covariances or to an incorrectly modeled latent structure. Additional nuisance parameters, including loading size, factor correlation size, model size, and model balance, were manipulated to determine which indices' behaviors were influenced by changes in modeling situations over and above changes in the size and severity of misspecification. The study revealed that certain indices (CFI, NNFI) are more appropriate to use when models involve latent misspecification, while other indices (RMSEA, GFI, SRMR) are more appropriate in situations where models involve misspecification due to omitted error covariances. It was found that the performances of all indices were affected to some extent by additional nuisance parameters.

Much previous theory and evidence in both social and evolutionary psychology has been equivocal and inconsistent regarding whether in-group altruism should predict out-group hostility, and whether this effect should be positive or negative in direction. A "slow" Life History (LH) strategy emphasizes both kin-selected altruism and reciprocal altruism as means of investing heavily in offspring, blood relatives, and mutualistic social relationships with both kith and kin. We therefore investigated whether a slow LH strategy, as a measurable individual-difference variable favoring in-group altruism (positive ethnocentrism), should predict out-group hostility (negative ethnocentrism), and what the direction of the hypothesized effect would be. We found that a multivariate latent variable representing slow LH strategy served as a protective factor against a latent variable representing Negative Ethnocentrism. These results were replicated in the United States of America and in the Republic of Costa Rica using Multisample Structural Equation Model with cross-sample equality constraints.