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Besides allowing us to perceive our surroundings, eye movements are also a window into our mind and a rich source of information on who we are, how we feel, and what we do. Here we show that eye movements during an everyday task predict aspects of our personality. We tracked eye movements of 42 participants while they ran an errand on a university campus and subsequently assessed their personality traits using well-established questionnaires. Using a state-of-the-art machine learning method and a rich set of features encoding different eye movement characteristics, we were able to reliably predict four of the Big Five personality traits (neuroticism, extraversion, agreeableness, conscientiousness) as well as perceptual curiosity only from eye movements. Further analysis revealed new relations between previously neglected eye movement characteristics and personality. Our findings demonstrate a considerable influence of personality on everyday eye movement control, thereby complementing earlier studies in laboratory settings. Improving automatic recognition and interpretation of human social signals is an important endeavor, enabling innovative design of human–computer systems capable of sensing spontaneous natural user behavior to facilitate efficient interaction and personalization.

Null hypothesis significance testing uses the seemingly arbitrary probability of .05 as a means of objectively determining whether a tested effect is reliable. Within recent psychological articles, research has found an overrepresentation of p values around this cut-off. The present study examined whether this overrepresentation is a product of recent pressure to publish or whether it has existed throughout psychological research. Articles published in 1965 and 2005 from two prominent psychology journals were examined. Like previous research, the frequency of p values at and just below .05 was greater than expected compared to p frequencies in other ranges. While this overrepresentation was found for values published in both 1965 and 2005, it was much greater in 2005. Additionally, p values close to but over .05 were more likely to be rounded down to, or incorrectly reported as, significant in 2005 than in 1965. Modern statistical software and an increased pressure to publish may explain this pattern. The problem may be alleviated by reduced reliance on p values and increased reporting of confidence intervals and effect sizes.

Cognitive rehabilitation for attention deficits following stroke.

There is some evidence that human subjects preferentially select small numbers when asked to sample numbers from large intervals "at random". A retrospective analysis of single digit frequencies in 16 independent experiments with the Mental Dice Task (generation of digits 1-6 during 1 min) confirmed the occurrence of small-number biases (SNBs) in 488 healthy subjects. A subset of these experiments suggested a spatial nature of this bias in the sense of a "leftward" shift along the number line. First, individual SNBs were correlated with leftward deviations in a number line bisection task (but unrelated to the bisection of physical lines). Second, in 20 men, the magnitude of SNBs significantly correlated with leftward attentional biases in the judgment of chimeric faces. Finally, cognitive activation of the right hemisphere enhanced SNBs in 20 different men, while left hemisphere activation reduced them. Together, these findings provide support for a spatial component in random number generation. Specifically, they allow an interpretation of SNBs in terms of "pseudoneglect in number space." We recommend the use of random digit generation for future explorations of spatial-attentional asymmetries in numerical processing and discuss methodological issues relevant to prospective designs.