A cross-culture, cross-gender comparison of perspective taking mechanisms

Being able to judge another person's visuo-spatial perspective is an essential social skill, hence we investigated the generalizability of the involved mechanisms across cultures and genders. Developmental, cross-species, and our own previous research suggest that two different forms of perspective taking can be distinguished, which are subserved by two distinct mechanisms. The simpler form relies on inferring another's line-of-sight, whereas the more complex form depends on embodied transformation into the other's orientation in form of a simulated body rotation. Our current results suggest that, in principle, the same basic mechanisms are employed by males and females in both, East-Asian (EA; Chinese) and Western culture. However, we also confirmed the hypothesis that Westerners show an egocentric bias, whereas EAs reveal an other-oriented bias. Furthermore, Westerners were slower overall than EAs and showed stronger gender differences in speed and depth of embodied processing. Our findings substantiate differences and communalities in social cognition mechanisms across genders and two cultures and suggest that cultural evolution or transmission should take gender as a modulating variable into account.

Modulating membership grades to gain consensus for fuzzy set uncertainty values in a clinical decision support system

This paper deals with a very important issue in any knowledge engineering discipline: the accurate representation and modelling of real life data and its processing by human experts. The work is applied to the GRiST Mental Health Risk Screening Tool for assessing risks associated with mental-health problems. The complexity of risk data and the wide variations in clinicians' expert opinions make it difficult to elicit representations of uncertainty that are an accurate and meaningful consensus. It requires integrating each expert's estimation of a continuous distribution of uncertainty across a range of values. This paper describes an algorithm that generates a consensual distribution at the same time as measuring the consistency of inputs. Hence it provides a measure of the confidence in the particular data item's risk contribution at the input stage and can help give an indication of the quality of subsequent risk predictions. © 2010 IEEE.