People form impressions of others in relation to how trustworthy they are, and let these judgments affect their subsequent behaviour. There is some evidence that people can accurately predict who cooperates in an experimental setting. However, it is unclear what kind of cues lead to correct predictions. This study reports findings of a Prisoner's Dilemma game between pairs of strangers (N = 40) and pairs of friends (N = 40). It was found that the personality construct of Machiavellianism in the partner predicted perceptions of trustworthiness. People rated high-Machs as less likely to cooperate, but only in pairs of strangers. In addition, duration of eye gaze of a stranger had a positive correlation with predictions of cooperation, but smiling strangers were rated as less likely cooperators. Machiavellianism in strangers was not related to non-verbal communication. It is possible that we have evolved the capacity to detect how Machiavellian a stranger is, but the exact mechanism is still unknown.
Theory of Mind (ToM), the capacity to read mental states of other people, is a tool that allows individuals to function efficiently during social interactions. Although ToM has been studied extensively in clinical populations, few studies have focused on ToM capacity in the general population. In this study, the relationship between ToM and the personality construct Machiavellianism are investigated. In evolutionary terms, a link between exploitative strategy and ToM would be expected — in order to be a successful manipulator, an individual should have a good understanding of the emotions and intentions of others. We found that Machiavellianism was, in fact, negatively correlated with both hot and cold measures of Theory of Mind. We suggest that high-Machs have deficits in empathizing ability, which allows them to exploit others. The findings also question the idea of Machiavellian Intelligence as a driving force behind evolution of social intelligence in humans.
Plant communities in extensive landscapes are often mapped remotely using detectable patterns based on vegetation structure and canopy species with a high relative cover. A plot-based classification which includes species with low relative canopy cover and ignores vegetation structure, may result in plant communities not easily reconcilable with the landscape patterns represented in mapping. In our study, we investigate the effects on classification outcomes if we (1) remove rare species based on canopy cover, and (2) incorporate vegetation structure by weighting species’ cover by different measures of vegetation height. Using a dataset of 101 plots of savanna vegetation in north-eastern Australia we investigated first, the effect of removing rare species using four cover thresholds (1, 5, 8 and 10% contribution to total cover) and second, weighting species by four height measures including actual height as well as continuous and categorical transformations. Using agglomerative hierarchical clustering we produced a classification for each dataset and compared them for differences in: patterns of plot similarity, clustering, species richness and evenness, and characteristic species. We estimated the ability of each classification to predict species cover using generalised linear models. We found removing rare species at any cover threshold produced characteristic species appearing to correspond to landscape scale changes and better predicted species cover in grasslands and shrublands. However, in woodlands it made no difference. Using actual height of vegetation layer maintained vegetation structure, emphasised canopy and then sub-canopy species in clustering, and predicted species cover best of the height-measures tested. Thus, removing rare species and weighting species by height are useful techniques for identifying plant communities from plot-based classifications which are conceptually consistent with those in landscape scale mapping. This increases the confidence of end-users in both the classifications and the maps, thus enhancing their use in land management decisions.