In response to recommendations to redefine statistical significance to p ≤ .005, we propose that researchers should transparently report and justify all choices they make when designing a study, including the alpha level.

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Westerners habitually think in analytical ways, whereas East Asians tend to favor holistic styles of thinking. We replicated this difference but showed that it disappeared after control deprivation (Experiment 1). Brief experiences of control deprivation, which stimulate increased desire for control, caused Chinese participants to shift toward Western-style analytical thinking in multiple ways (Experiments 2-5). Western Caucasian participants also increased their use of analytical thinking after control deprivation (Experiment 6). Manipulations that required Chinese participants to think in Western, analytical ways caused their sense of personal control to increase (Experiments 7-9). Prolonged experiences of control deprivation, which past work suggested foster an attitude more akin to learned helplessness than striving for control, had the opposite effect of causing Chinese participants to shift back toward a strongly holistic style of thinking (Experiments 10-12). Taken together, the results support the reality of cultural differences in cognition but also the cross-cultural similarity of using analytical thinking when seeking to enhance personal control.

Emotional facial expressions are thought to have evolved because they play a crucial role in species' survival. From infancy, humans develop dedicated neural circuits [1] to exhibit and recognize a variety of facial expressions [2]. But there is increasing evidence that culture specifies when and how certain emotions can be expressed - social norms - and that the mature perceptual mechanisms used to transmit and decode the visual information from emotional signals differ between Western and Eastern adults [3-5]. Specifically, the mouth is more informative for transmitting emotional signals in Westerners and the eye region for Easterners [4], generating culture-specific fixation biases towards these features [5]. During development, it is recognized that cultural differences can be observed at the level of emotional reactivity and regulation [6], and to the culturally dominant modes of attention [7]. Nonetheless, to our knowledge no study has explored whether culture shapes the processing of facial emotional signals early in development. The data we report here show that, by 7 months, infants from both cultures visually discriminate facial expressions of emotion by relying on culturally distinct fixation strategies, resembling those used by the adults from the environment in which they develop [5].

A major challenge in modern eye movement research is to statistically map where observers are looking, by isolating the significant differences between groups and conditions. As compared to the signals from contemporary neuroscience measures, such as magneto/electroencephalography and functional magnetic resonance imaging, eye movement data are sparser, with much larger variations in space across trials and participants. As a result, the implementation of a conventional linear modeling approach on two-dimensional fixation distributions often returns unstable estimations and underpowered results, leaving this statistical problem unresolved (Liversedge, Gilchrist, & Everling, 2011). Here, we present a new version of the iMap toolbox (Caldara & Miellet, 2011) that tackles this issue by implementing a statistical framework comparable to those developed in state-ofthe-art neuroimaging data-processing toolboxes. iMap4 uses univariate, pixel-wise linear mixed models on smoothed fixation data, with the flexibility of coding for multiple betweenand within-subjects comparisons and performing all possible linear contrasts for the fixed effects (main effects, interactions, etc.). Importantly, we also introduced novel nonparametric tests based on resampling, to assess statistical significance.Finally, we validated this approach by using both experimental and Monte Carlo simulation data. iMap4 is a freely available MATLAB open source toolbox for the statistical fixation mapping of eye movement data, with a user-friendly interface providing straightforward, easy-to-interpret statistical graphical outputs. iMap4 matches the standards of robust statistical neuroimaging methods and represents an important step in the data-driven processing of eye movement fixation data, an important field of vision sciences.Keywords Eye movement analysis . Statistical mapping . Linear mixed models Human beings constantly move the eyes to sample visual information of interest from the environment. Eye fixations deliver inputs with the highest resolution to the human visual cortex from the fovea, as well as blurry, low-spatial-frequency information from peripheral vision (Rayner, 1998). Thus, isolating statistically where and how long fixations are deployed to process visual information is of particular interest to behavioral researchers, psychologists, and neuroscientists. Moreover, fixation mapping has a wide range of practical applications in determining marketing strategies and the understanding of consumer behaviour (Duchowski, 2002).Conventional eye movement data analyses rely on the estimation of probabilities of occurrence of fixations and saccades (or their characteristics, such as duration or length) within predefined regions of interest (ROIs), which are at best defined a priori-but often also defined a posteriori, on the basis of data exploration, which inflates the Type I error rate.