Physical self-concept and disturbed eating attitudes and behaviors in French athlete and non-athlete adolescent girls: direct and indirect relations

This study aims to investigate the direct and indirect associations between physical self-subdomains, physical self-worth, global self-worth, and disturbed eating attitudes and behaviors among French non-elite athlete and non-athlete adolescent girls. A sample of adolescent girls including 50 ballet dancers, 41 basketball players, and 47 non-athletes was used in this study. Data obtained from the ballet dancer and basketball player subsamples revealed significant, sample-specific as well as common, direct relations between global and physical self-perceptions and disturbed eating attitudes and behaviors, as well as significant indirect relations (via global self-worth and physical self-worth) between specific physical self-perceptions and disturbed eating attitudes and behaviors. In contrast, no association was found between global and physical self-perceptions in the sample of non-athlete adolescent girls.     

L’imagerie bizarre et la mémoire

One experiment examined free recall memory performance for bizarre and common pictures. Bizarre pictures were designed either deleting some components (SB pictures) either adding some components (AB pictures). A classical bizarreness effect was only obtained for AB pictures. Indeed no facilitative effect of bizarreness was obtained when incomplete fragmented pictures were used. Results were discussed in light of theories interested by the explanation of the bizarreness effect in memory.     

An evaluation of scanpath-comparison and machine-learning classification algorithms used to study the dynamics of analogy making

In recent years, eyetracking has begun to be used to study the dynamics of analogy making. Numerous scanpath-comparison algorithms and machine-learning techniques are available that can be applied to the raw eyetracking data. We show how scanpath-comparison algorithms, combined with multidimensional scaling and a classification algorithm, can be used to resolve an outstanding question in analogy making—namely, whether or not children’s and adults’ strategies in solving analogy problems are different. (They are.) We show which of these scanpath-comparison algorithms is best suited to the kinds of analogy problems that have formed the basis of much analogy-making research over the years. Furthermore, we use machine-learning classification algorithms to examine the item-to-item saccade vectors making up these scanpaths. We show which of these algorithms best predicts, from very early on in a trial, on the basis of the frequency of various item-to-item saccades, whether a child or an adult is doing the problem. This type of analysis can also be used to predict, on the basis of the item-to-item saccade dynamics in the first third of a trial, whether or not a problem will be solved correctly.