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Previous literature on lie detection abilities bears an interesting paradox. On the group level, people detect others’ lies at guessing level. However, when asked to evaluate their own abilities, people report being able to detect lies (i.e., self-reported lie detection). Understanding this paradox is important because decisions which rely on credibility assessment and deception detection can have serious implications (e.g., trust in others, legal issues). In two online studies, we tested whether individual differences account for variance in self-reported lie detection abilities. We assessed personality traits (Big-Six personality traits, Dark Triad), empathy, emotional intelligence, cultural values, trust level, social desirability, and belief in one’s own lie detection abilities. In both studies, mean self-reported lie detection abilities were above chance level. Then, lower out-group trust and higher social desirability levels predicted higher self-reported lie detection abilities. These results suggest that social trust and norms shape our beliefs about our own lie detection abilities.

Two studies testing whether novel imaging solutions, allow for extremely high-resolution digital reproductions of artworks, heightened aesthetic appreciation and enhanced recognition of paintings and artefacts.

The colour category PURPLE is strangely heterogeneous, potentially due to the use of different cognates. We asked French speakers from Algeria, France, and Switzerland (n = 274) to produce up to three free associations with violet (basic term), pourpre, and lilas (non-basic terms). We counted 2,075 associations. We developed a coding scheme that i) covers nine major themes, and ii) shows high inter-rater reliability. Overall, the themes colour terms and natural elements and objects were most prominent showing that participants provided closely related associations. Finally, violet triggered more diverse semantic associations than pourpre or lilas. This was true for all countries. It seems that the basic term PURPLE carries more diverse associations and connotations than the non-basic terms.

The link between colour and emotion and its possible similarity across cultures are questions that have not been fully resolved. Online, 711 participants from China, Germany, Greece and the UK associated 12 colour terms with 20 discrete emotion terms in their native languages. We propose a machine learning approach to quantify (a) the consistency and specificity of colour–emotion associations and (b) the degree to which they are country-specific, on the basis of the accuracy of a statistical classifier in (a) decoding the colour term evaluated on a given trial from the 20 ratings of colour–emotion associations and (b) predicting the country of origin from the 240 individual colour–emotion associations, respectively. The classifier accuracies were significantly above chance level, demonstrating that emotion associations are to some extent colour-specific and that colour–emotion associations are to some extent country-specific. A second measure of country-specificity, the in-group advantage of the colour-decoding accuracy, was detectable but relatively small (6.1%), indicating that colour– emotion associations are both universal and culture-specific. Our results show that machine learning is a promising tool when analysing complex datasets from emotion research.

The subject of the research is the analysis of associative relations between twenty emotional concepts (interest, amusement, pride, joy, pleasure, satisfaction, admiration, love, relief, compassion, sadness, guilt, sorrow, shame, disappointment, fear, disdain, disgust, hatred and anger) and twelve basic names of colurs in the Russian language (red, orange, yellow, green, light blue, blue, violet, brown, pink, grey, black and white). The research is aimed at 1) discovering chromatic and achromatic meanings of emotions; 2)discovering syntagmatic and paradigmatic relations between color associations; 3) conduction of a linguocultural expertise of national specifics of discovered associative relations. The research is based on the on-line experiment that involved 103 Russian speakers (63 females and 40 males, the average age of resondents is 36.5, min age is 19 and max age is 78, sd=16.7). To analyze emotions, the researcher has applied the Geneva Emotion Wheel Inventory (GEW version 3.0) as a tool for self-evaluation of emotions and attitudes to a particular event. The results of the experiment have proved that Russian culture correlates the lightness of color to the valency of emotions. Positive emotions are associated with light colors and negative emotions are associated with darker colors. The closest emotional relations were demonstrated by yellow and orange, light blue and green, red and pink, black and grey. These pairs are often found as components of color images, too.

Popular opinions link colours and emotions. Yet, affective connotations to colours are heterogeneous (e.g. red represents anger and love) partly because they relate to different contexts. Despite insufficient evidence, colours are used in applied settings (health, marketing, etc.) for their supposed effects on cognitive and affective functioning. Summarizing the literature, we invite for systematic research to investigate when and how colours link with affective phenomena. We need to i) distinguish between situations in which colours are physically shown or linguistically treated, ii) specify types of affective processes (e.g. emotion, mood, preference), and iii) investigate cross-cultural differences. Having these needs in mind, we initiated an international online survey on semantic colour-emotion associations. We outline theoretical considerations and present the survey’s design.