A salesperson wishes to visit a number of cities before returning home using the shortest possible route, whilst only visiting each city once. This optimization problem, called the Travelling Salesman Problem, is difficult to solve using exhaustive algorithms due to the exponential growth in the number of possible solutions. Interestingly, when presented in Euclidean space (ETSP), humans quickly find good solutions. Past studies, however, are in disagreement whether human solutions are impacted by the participant’s ability to process figural effects in the graph geometry. In this study, we used principal component analysis to combine two correlated [r = 0.37, p < 0.01] self-assessed personality measures, i.e., a participant’s sense of direction and a participant’s level of conscientiousness, onto a single impulsiveness/cautiousness dimension. We then showed, using simple linear regression, that this new dimension is a significant predictor [R2 = 0.12, p < 0.01] of the number of edge crossings that occur in human ETSP solutions, a key metric of graph optimality. Our study provides evidence to suggest that human solutions to the ETSP are significantly affected by individual differences, including personality and cognitive traits.
Capturing affective response to valent stimuli using eye tracking is of interest not only to academic research but also to commercial equipment developers (e.g. car dashboards). In order to investigate whether a low-cost eye tracker can effectively detect participants’ physiological response to negatively valent stimuli, 44 participants aged 19–24 (mean = 24.7, SD = 5.8) were recruited to complete the visual backward masking paradigm in a repeated-measure experimental design. Saccadic duration and pupil sizes were recorded using a lower-end 60-Hz tracker. Data was analysed using a mix of parametric and non-parametric tests. Our results suggest that valence in the form of fearful vs neutral faces has a significant main effect on both saccadic duration [V = 931, P < 0.001, d = 0.96] and pupil size [t(43) = 29.81, P < 0.001, d = 3.91)]. Our findings were further supported by Bayes factor analysis, which showed that saccadic duration data was 24 times more likely to occur, and pupil size measurement data was 89 times more likely, under the alternative hypothesis, showing that differences in valence had a main effect. The combined evidence produced by our Bayesian analysis, the large effect sizes of our frequentist analysis and the significant effect on two separate measurements lead us to suggest that, under the right conditions, low-cost eye trackers can successfully detect changes in saccadic duration and pupil sizes as a result of physiological responses to threat-relevant visual stimuli.
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