A machine learning approach to quantifying the specificity of colour–emotion associations and their cultural differences

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 researc

Temporal estimation with two moving objects: overt and covert pursuit

The current study examined temporal estimation in a prediction motion task where participants were cued to overtly pursue one of two moving objects, which could either arrive first, i.e., shortest [time to contact (TTC)] or second (i.e., longest TTC) after a period of occlusion. Participants were instructed to estimate TTC of the first-arriving object only, thus making it necessary to overtly pursue the cued object while at the same time covertly pursuing the other (non-cued) object. A control (baseline) condition was also included in which participants had to estimate TTC of a single, overtly pursued object. Results showed that participants were able to estimate the arrival order of the two objects with very high accuracy irrespective of whether they had overtly or covertly pursued the first-arriving object. However, compared to the single-object baseline, participants’ temporal estimation of the covert object was impaired when it arrived 500 ms before the overtly pursued object. In terms of eye movements, participants exhibited significantly more switches in gaze location during occlusion from the cued to the non-cued object but only when the latter arrived first. Still, comparison of trials with and without a switch in gaze location when the non-cued object arrived first indicated no advantage for temporal estimation. Taken together, our results indicate that overt pursuit is sufficient but not necessary for accurate temporal estimation. Covert pursuit can enable representation of a moving object’s trajectory and thereby accurate temporal estimation providing the object moves close to the overt attentional focus

Spectro-temporal weighting of loudness

Real-world sounds like speech or traffic noise typically exhibit spectro-temporal variability because the energy in different spectral regions evolves differently as a sound unfolds in time. However, it is currently not well understood how the energy in different spectral and temporal portions contributes to loudness. This study investigated how listeners weight different temporal and spectral components of a sound when judging its overall loudness. Spectral weights were measured for the combination of three loudness-matched narrowband noises with different center frequencies. To measure temporal weights, 1,020-ms stimuli were presented, which randomly changed in level every 100 ms. Temporal weights were measured for each narrowband noise separately, and for a broadband noise containing the combination of the three noise bands. Finally, spectro-temporal weights were measured with stimuli where the level of the three narrowband noises randomly and independently changed every 100 ms. The data consistently showed that (i) the first 300 ms of the sounds had a greater influence on overall loudness perception than later temporal portions (primacy effect), and (ii) the lowest noise band contributed significantly more to overall loudness than the higher bands. The temporal weights did not differ between the three frequency bands. Notably, the spectral weights and temporal weights estimated from the conditions with only spectral or only temporal variability were very similar to the corresponding weights estimated in the spectro-temporal condition. The results indicate that the temporal and the spectral weighting of the loudness of a time-varying sound are independent processes. The spectral weights remain constant across time, and the temporal weights do not change across frequency. The results are discussed in the context of current loudness models