Beautification of images by generative adversarial networks.

Finding the properties underlying beauty has always been a prominent yet difficult problem. However, new technological developments have often aided scientific progress by expanding the scientists' toolkit. Currently in the spotlight of cognitive neuroscience and vision science are deep neural networks. In this study, we have used a generative adversarial network (GAN) to generate images of increasing aesthetic value. We validated that this network indeed was able to increase the aesthetic value of an image by letting participants decide which of two presented images they considered more beautiful. As our validation was successful, we were justified to use the generated images to extract low- and mid-level features contributing to their aesthetic value. We compared the brightness, contrast, sharpness, saturation, symmetry, colorfulness, and visual complexity levels of "low-aesthetic" images to those of "high-aesthetic" images. We found that all of these features increased for the beautiful images, implying that they may play an important role underlying the aesthetic value of an image. With this study, we have provided further evidence for the potential value GANs may have for research concerning beauty.

Data sharing upon request and statistical consistency errors in psychology: A replication of Wicherts, Bakker and Molenaar (2011).

Sharing research data allows the scientific community to verify and build upon published work. However, data sharing is not common practice yet. The reasons for not sharing data are myriad: Some are practical, others are more fear-related. One particular fear is that a reanalysis may expose errors. For this explanation, it would be interesting to know whether authors that do not share data genuinely made more errors than authors who do share data. (Wicherts, Bakker and Molenaar 2011) examined errors that can be discovered based on the published manuscript only, because it is impossible to reanalyze unavailable data. They found a higher prevalence of such errors in papers for which the data were not shared. However, (Nuijten et al. 2017) did not find support for this finding in three large studies. To shed more light on this relation, we conducted a replication of the study by (Wicherts et al. 2011). Our study consisted of two parts. In the first part, we reproduced the analyses from (Wicherts et al. 2011) to verify the results, and we carried out several alternative analytical approaches to evaluate the robustness of the results against other analytical decisions. In the second part, we used a unique and larger data set that originated from (Vanpaemel et al. 2015) on data sharing upon request for reanalysis, to replicate the findings in (Wicherts et al. 2011). We applied statcheck for the detection of consistency errors in all included papers and manually corrected false positives. Finally, we again assessed the robustness of the replication results against other analytical decisions. Everything taken together, we found no robust empirical evidence for the claim that not sharing research data for reanalysis is associated with consistency errors.

Correction notices in psychology: impactful or inconsequential?

Science is self-correcting, or so the adage goes, but to what extent is that indeed the case? Answering this question requires careful consideration of the various approaches to achieve the collective goal of self-correction. One of the most straightforward mechanisms is individual self-correction: researchers rectifying their own mistakes by publishing a correction notice. Although it offers an efficient route to correcting the scientific record, it has received little to no attention from a metascientific point of view. We aim to fill this void by analysing the content of correction notices published from 2010 until 2018 in the three psychology journals featuring the highest number of corrections over that timespan based on the Scopus database (i.e. Psychological Science with N = 58, Frontiers in Psychology with N = 99 and Journal of Affective Disorders with N = 57). More concretely, we examined which aspects of the original papers were affected (e.g. hypotheses, data-analyses, metadata such as author order, affiliations, funding information etc.) as well as the perceived implications for the papers' main findings. Our exploratory analyses showed that many corrections involved inconsequential errors. Furthermore, authors rarely revised their conclusions, even though several corrections concerned changes to the results. We conclude with a discussion of current policies, and suggest ways to improve upon the present situation by (i) preventing mistakes, and (ii) transparently rectifying those mistakes that do find their way into the literature.

Sound-Symbolism Effects in the Absence of Awareness: A Replication Study.

People have been shown to link particular sounds with particular shapes. For instance, the round-sounding nonword bouba tends to be associated with curved shapes, whereas the sharp-sounding nonword kiki is deemed to be related to angular shapes. People's tendency to associate sounds and shapes has been observed across different languages. In the present study, we reexamined the claim by Hung, Styles, and Hsieh (2017) that such sound-shape mappings can occur before an individual becomes aware of the visual stimuli. More precisely, we replicated their first experiment, in which congruent and incongruent stimuli (e.g., bouba presented in a round shape or an angular shape, respectively) were rendered invisible through continuous flash suppression. The results showed that congruent combinations, on average, broke suppression faster than incongruent combinations, thus providing converging evidence for Hung and colleagues' assertions. Collectively, these findings now provide a solid basis from which to explore the boundary conditions of the effect.