Don't take another bite: how sociocultural norms for appearance affect women's eating behavior

Four studies tested the impact of exposure to thin images on women's eating behavior. In Study 1, women who were exposed to commercials containing thin models ate less in a taste test than women exposed to neutral commercials. The next two studies revealed that the impact of the thin images could be reduced by challenging the sociocultural norms for appearance. In Study 2, including images of relatively heavier women who have been successful in life (an indirect challenge to the norm) attenuated the impact of the thin images on women's eating behavior. Study 3 demonstrated that convincing women that their peers do not endorse the sociocultural norms also reduced the impact of the thin images. In Study 4, we found that exposure to thin images led to activation of an association between heaviness and rejection and that the more this association was activated, the less participants ate.     

Modeling the symptoms of psychopathology: A pluralistic approach

Our understanding of mental disorders has traditionally focused on syndromes and symptom clusters rather than on the nature of the symptom and signs themselves. Using a core symptom of depression, anhedonia, as an extended example, this paper illustrates how the development of multiple models of symptoms, at various scales of analysis, may advance the explanation and classification of mental disorders. We begin by outlining the Phenomena Detection Method (PDM), which links different phases of the inquiry process to provide a methodology for conceptualizing the symptoms of psychopathology and for constructing multi-level models of the pathological processes that comprise them. Next, we apply the PDM to anhedonia, building a compositional explanation of this core symptom by way of multiple models across four scales (levels): molecular, neural, cognitive, and phenomenological. Finally, we evaluate our approach in comparison to existing strategies for understanding mental disorders.     

Fuzzy rough sets: Survey and proposal of an enhanced knowledge representation model based on automatic noisy sample detection

Fuzzy Rough Set (FRS) theory, which has been emerged thanks to unifying Rough Set and Fuzzy Set ones, is a powerful mathematical tool for handling and processing real data of imprecise, incomplete, inconsistent and uncertain nature. It has drawn attention of many researchers, scientists and industrials in various domains over the last three decades. However, different studies have showed that its classical knowledge representation model has a main weakness linked to its sensitivity to data noise which decreases both its effectiveness and application scope. In this paper, we survey the current FRS paradigms developed to deal with this issue and propose a new FRS model based on the Automatic Noisy Sample Detection (ANSD-FRS) able to cope with noise influence in classification tasks. Besides, we study the principal properties of this new model and reformulate the most applied FRS concepts relying on its operators. Numerous experiments have been conducted to analyze the ANSD-FRS behavior compared to the commonly used FRS models reputed as the most noise-resistant paradigms. These experiment results have proved the performance and robustness of the ANSD-FRS in comparison with those renowned models.     

Using spotted hyena optimizer for training feedforward neural networks

Spotted hyena optimizer (SHO) is a novel metaheuristic optimization algorithm based on the behavior of spotted hyena and their collaborative behavior in nature. In this paper, we design a spotted hyena optimizer for training feedforward neural network (FNN), which is regarded as a challenging task since it is easy to fall into local optima. Our objective is to apply metaheuristic optimization algorithm to tackle this problem better than the mathematical and deterministic methods. In order to confirm that using SHO to train FNN is more effective, five classification datasets and three function-approximations are applied to benchmark the performance of the proposed method. The experimental results show that the proposed SHO algorithm for optimization FNN has the best comprehensive performance and has more outstanding performance than other the state-of-the-art metaheuristic algorithms in terms of the performance measures.     

The Editing Density of Moving Images Influences Viewers’ Time Perception: The Mediating Role of Eye Movements

The present study examined whether cinematographic editing density affects viewers’ perception of time. As a second aim, based on embodied models that conceive time perception as strictly connected to the movement, we tested the hypothesis that the editing density of moving images also affects viewers’ eye movements and that these later mediate the effect of editing density on viewers’ temporal judgments. Seventy participants watched nine video clips edited by manipulating the number of cuts (slow- and fast-paced editing against a master shot, unedited condition). For each editing density, multiple video clips were created, representing three different kinds of routine actions. The participants’ eye movements were recorded while watching the video, and the participants were asked to report duration judgments and subjective passage of time judgments after watching each clip. The results showed that participants subjectively perceived that time flew more while watching fast-paced edited videos than slow-paced or unedited videos; by contrast, concerning duration judgments, participants overestimated the duration of fast-paced videos compared to the master-shot videos. Both the slow- and the fast-paced editing generated shorter fixations than the master shot, and the fast-paced editing led to shorter fixations than the slow-paced editing. Finally, compared to the unedited condition, editing led to an overestimation of durations through increased eye mobility. These findings suggest that the editing density of moving images by increasing the number of cuts effectively altered viewers’ experience of time and add further evidence to prior research showing that performed eye movement is associated with temporal judgments.     

Associations between abstainer,moderate and heavy drinker prototypes and drinking behaviour in young adults

Objective: Previous research has focused mostly on abstainer and/or general drinker prototypes. The present studies examined an abstainer, moderate drinker and heavy drinker prototype in relation to drinking behaviour.

Design: Two studies among young adults aged 18–25 (paper-and-pencil, cross-sectional, N?=?140; online, prospective, N?=?451) assessed prototype favourability and participants’ perceived similarity to the prototypes. Participants were also categorised into abstainers, moderate, and heavy drinkers.

Results: Similarity and favourability had similar sequences in both studies: the moderate drinker and abstainer prototypes were evaluated most favourable and felt similar to; the heavy drinker prototype was rated the least favourable and felt similar to. Importantly, heavy drinking participants felt most similar to the moderate drinker prototype and rated the heavy drinker least desirable. The results suggest a need for research to include other prototypes, such as the moderate drinker, besides the abstainer and heavy drinker.

Conclusion: The studies provide insights into the contribution of alternative prototypes (i.e. moderate drinker) into the relationship between prototype perceptions and drinking behaviour. The results suggest tailoring prototype-based interventions according to drinking behaviour. Importantly, realistic perceived similarity to the more favourable moderate drinker prototype may therefore need to be encouraged only after appropriate reductions in heavy drinkers’ alcohol consumption.     

Validation of the Comprehensive ICF Core Set for rheumatoid arthritis: The perspective of psychologists

The ‘Comprehensive ICF Core Set for rheumatoid arthritis (RA)’ is an application of the International Classification of Functioning, Disability and Health (ICF) and represents the typical spectrum of problems in functioning of patients with RA. The objective of this study was to validate this ICF Core Set from the perspective of psychologists. Psychologists experienced in RA treatment were asked about the problems of RA patients, treated by psychologists, in a three-round survey using the Delphi technique. Responses were linked to the ICF. Twenty psychologists in five countries gave a total of 303 responses that were linked to 65 different ICF categories. Fifteen responses were linked to the not yet developed ICF component personal factors and nine were not covered by ICF. Overall, 66% of the ICF categories linked to the responses of the psychologists were represented by the Comprehensive ICF Core Set for RA. Several responses that were not covered need to be investigated further.     

The long-term effect of relational information in classification learning

This study examines the long-term effect of mutual information in the learning of Shepardian classifications. Mutual information is a measure of the complexity of the relationship between features because it quantifies how the features relate to each other. For instance, in various categorisation models, Type VI concepts—originally studied by Shepard, Hovland, and Jenkins (1961)—are unanimously judged to be the most complex kind of 3-D Boolean concepts. This has been largely confirmed by empirical data. Yet, it is apparently inconsistent with the fact that this concept entails the greatest amount of mutual information of all the 3-D Boolean concepts. The present study was aimed at verifying whether individuals can use relational information, in the long run, to devise easier strategies for category learning. Subject performance was measured repeatedly for 1 hour on either successive Type VI concepts (using different features between problems) or successive Type IV concepts. The results showed that shortly after the second problem, Type VI concepts became easier to learn than Type IV ones. The gap between the mean per-problem error rates of the two concepts continued to increase as the number of problems increased. Two other experiments tended to confirm this trend. The discussion brings up the idea of combining different metrics in categorisation models in order to include every possible way for subjects to simplify the categorisation process.     

Truth table task: Working memory load,latencies, and perceived relevance

The aim of the present study is to uncover the relation between cognitive ability and the answer patterns yielded by the truth table task. According to the Classical Mental Models Theory, people with high working memory capacity answer according to two-valued or “logical” answer patterns. The Suppositional Theory and the Revised Mental Models Theory predict that the answer patterns given by the most intelligent ones are three-valued or “defective”. Correlations are examined, and in three experiments it is tested with a dual task paradigm whether a differential working memory load alters participants' answer patterns. A positive correlation is observed between cognitive ability and three-valued answer patterns, but no effect of the working memory load manipulation is revealed. With an inspection of the classification times we shed light on the processes underlying truth table judgements. We conclude that the Revised Mental Models Theory provides the best account for our results.