Causal Conditionals, Tendency Causal Claims and Statistical Relevance

Indicative conditionals and tendency causal claims are closely related to each other (e.g., Frosch and Byrne 2012), but despite these connections, they are usually studied separately. A unifiying framework could consist in their dependence on probabilistic factors such as statistical relevance, but theoretical research along these lines (e.g., Eells 1991; Douven 2008, 2016) needs to be strengthened by more empirical results. This paper closes that gap and presents empirical results on how judgments on tendency causal claims and indicative conditionals are driven by probabilistic factors, and how these factors (in particular statistical relevance) differ in their predictive power for both causal and conditional claims

Moderate contrast in the evaluation of paintings is liked more but remembered less than high contrast

Many visual aspects of paintings, as well as exposure to art and cultural norms, contribute to the aesthetic evaluation of paintings. The current study looked at heightened visual contrast as an important factor in the appreciation of paintings. Participants evaluated abstract digitized paintings that were manipulated in contrast for an appreciation task and were later presented with these paintings in a memory task. The results indicated that for art appreciation, a moderate increase in contrast resulted in the highest appreciation for paintings whereas recognition memory was better for paintings with a higher increase in contrast. These results replicate earlier findings with regard to the role of contrast in aesthetic perception and extend these findings by demonstrating a surprising different effect of contrast manipulation for recognition memory. Confidence with which memory decisions were made was in line with art appreciation decisions not memory performance

Implicit emotion regulation in the context of viewing artworks

Presenting affective pictures as a work of art could change perceivers’ judgment and strength in emotional reactions. Aesthetic theory states that perceivers of art emotionally distance themselves, allowing them to appreciate works of art depicting gruesome events. To examine whether implicit emotion regulation is induced by an art context, we assessed whether presenting pleasant and unpleasant IAPS pictures as either “works of art comprising paintings, digital renderings, and photographs of staged scenes” or “photographs depicting real events” modulated perceivers’ Late Positive Potentials (LPP) and likability ratings. In line with previous research and aesthetic theory, participants evaluated the IAPS pictures as more likable when they were presented as works of art than when they were presented as photographs. Moreover, participants’ late LPP amplitudes (600 – 900 m

Intuitions About the Reference of Proper Names:A Meta-Analysis

The finding that intuitions about the reference of proper names vary cross-culturally (Machery et al.Cognition92: 1-12.2004) was one of the early milestones in experimental philosophy. Many follow-up studies investigated the scope and magnitude of such cross-cultural effects, but our paper provides the first systematic meta-analysis of studies replicating (Machery et al.Cognition92: 1-12.2004). In the light of our results, we assess the existence and significance of cross-cultural effects for intuitions about the reference of proper names

Causal Conditionals, Tendency Causal Claims and Statistical Relevance

Indicative conditionals and tendency causal claims are closely related (e.g., Frosch and Byrne, 2012), but despite these connections, they are usually studied separately. A unifying framework could consist in their dependence on probabilistic factors such as high conditional probability and statistical relevance (e.g., Adams, 1975; Eells, 1991; Douven, 2008, 2015). This paper presents a comparative empirical study on differences between judgments on tendency causal claims and indicative conditionals, how these judgments are driven by probabilistic factors, and how these factors differ in their predictive power for both causal and conditional claims

Causal Conditionals, Tendency Causal Claims and Statistical Relevance

Indicative conditionals and tendency causal claims are closely related to each other (e.g., Frosch and Byrne 2012), but despite these connections, they are usually studied separately. A unifiying framework could consist in their dependence on probabilistic factors such as statistical relevance, but theoretical research along these lines (e.g., Eells 1991; Douven 2008, 2016) needs to be strengthened by more empirical results. This paper closes that gap and presents empirical results on how judgments on tendency causal claims and indicative conditionals are driven by probabilistic factors, and how these factors (in particular statistical relevance) differ in their predictive power for both causal and conditional claims

Seven steps toward more transparency in statistical practice

Item does not contain fulltextWe argue that statistical practice in the social and behavioural sciences benefits from transparency, a fair acknowledgement of uncertainty and openness to alternative interpretations. Here, to promote such a practice, we recommend seven concrete statistical procedures: (1) visualizing data; (2) quantifying inferential uncertainty; (3) assessing data preprocessing choices; (4) reporting multiple models; (5) involving multiple analysts; (6) interpreting results modestly; and (7) sharing data and code. We discuss their benefits and limitations, and provide guidelines for adoption. Each of the seven procedures finds inspiration in Merton’s ethos of science as reflected in the norms of communalism, universalism, disinterestedness and organized scepticism. We believe that these ethical considerations - as well as their statistical consequences - establish common ground among data analysts, despite continuing disagreements about the foundations of statistical inference.8 p

Perspectives on Scientific Error

Theoretical arguments and empirical investigations indicate that a high proportion of published findings do not replicate and are likely false. The current position paper provides a broad perspective on scientific error, which may lead to replication failures. This broad perspective focuses on reform history and on opportunities for future reform. We organize our perspective along four main themes: institutional reform, methodological reform, statistical reform and publishing reform. For each theme, we illustrate potential errors by narrating the story of a fictional researcher during the research cycle. We discuss future opportunities for reform. The resulting agenda provides a resource to usher in an era that is marked by a research culture that is less error-prone and a scientific publication landscape with fewer spurious findings

Multiple perspectives on inference for two simple statistical scenarios

When data analysts operate within different statistical frameworks (e.g., frequentist versus Bayesian, emphasis on estimation versus emphasis on testing), how does this impact the qualitative conclusions that are drawn for real data? To study this question empirically we selected from the literature two simple scenarios—involving a comparison of two proportions and a Pearson correlation—and asked four teams of statisticians to provide a concise analysis and a qualitative interpretation of the outcome. The results showed considerable overall agreement; nevertheless, this agreement did not appear to diminish the intensity of the subsequent debate over which statistical framework is more appropriate to address the questions at hand