Quantifying social performance: A review with implications for further work.

Human social performance has been a focus of theory and investigation for more than a century. Attempts to quantify social performance have focused on self-report and non-social performance measures grounded in intelligence-based theories. An expertise framework, when applied to individual differences in social interaction performance, offers novel insights and methods of quantification that could address limitations of prior approaches. The purposes of this review are 3-fold. First, to define the central concepts related to individual differences in social performance, with a particular focus on the intelligence-based framework that has dominated the field. Second, to make an argument for a revised conceptualization of individual differences in social-emotional performance as a social expertise. In support of this second aim, the putative components of a social-emotional expertise and the potential means for their assessment will be outlined. To end, the implications of an expertise-based conceptual framework for the application of computational modeling approaches in this area will be discussed. Taken together, expertise theory and computational modeling methods have the potential to advance quantitative assessment of social interaction performance.

Searching for Semantic Knowledge: A Vector Space Semantic Analysis of the Feature Generation Task.

A recent neuropsychological study found that amnesic patients with hippocampal damage (HP) and severe declarative memory impairment produce markedly fewer responses than healthy comparison (CO) participants in a semantic feature generation task (Klooster and Duff, 2015), consistent with the idea that hippocampal damage is associated with semantic cognitive deficits. Participants were presented with a target word and asked to produce as many features of that word as possible (e.g., for target word "book," "read words on a page"). Here, we use the response sequences collected by Klooster and Duff (2015) to develop a vector space model of semantic search. We use this model to characterize the dynamics of semantic feature generation and consider the role of the hippocampus in this search process. Both HP and CO groups tended to initiate the search process with features close in semantic space to the target word, with a gradual decline in similarity to the target word over the first several responses. Adjacent features in the response sequence showed stronger similarity to each other than to non-adjacent features, suggesting that the search process follows a local trajectory in semantic space. Overall, HP patients generated features that were closer in semantic space to the representation of the target word, as compared to the features generated by the CO group, which ranged more widely in semantic space. These results are consistent with a model in which a compound retrieval cue (containing a representation of the target word and a representation of the previous response) is used to probe semantic memory. The model suggests that the HP group's search process is restricted from ranging as far in semantic space from the target word, relative to the CO group. These results place strong constraints on the structure of models of semantic memory search, and on the role of hippocampus in probing semantic memory.