It has been shown that prior knowledge and information are organized according to categories, and that also background knowledge plays an important role in classification. The purpose of this study is first, to investigate the relationship between background knowledge and text classification, and second, to incorporate this relationship in a computational model. Our behavioral results demonstrate that participants with access to background knowledge (experts), overall performed significantly better than those without access to this knowledge (novices). More importantly, we show that experts rely more on relational features than surface features, an aspect that bagof- words methods fail to capture. We then propose a computational model for text classification which incorporates background knowledge. This model is built upon vector-based representation methods and achieves significantly more accurate results over other models that were tested.

Recent advances in technology have paved the way for human-agent interactions to become ubiquitous in our daily lives, anddecades worth of research on virtual agents have enhancedthese interactions. However, for the most part, the effect of dif-ferent types of agents on the human brain is unknown, and theneuroscience of human-agent interactions is rarely studied. Inthis study, we examine the underlying neural systems involvedin processing and responding to different types of negotiatingagents. More specifically, we show that different brain patternsare observed for various types of virtual agents; consequently,we can decode the strategy and emotional display of the agentbased on the counterpart’s brain activity. Using fMRI data, weanalyzed participants’ brain activity during negotiations withagents who show three different emotional expressions and usetwo different types of negotiation strategies. We demonstratethat, using Multi-Voxel Pattern Analysis, we can reliably de-code agents’ emotional expressions based on the activity in theleft dorsal anterior insula, and also agents’ strategies based onthe activity in the frontal pole.

The importance of human-agent negotiation, and the role ofemotion in such negotiations, have been emphasized in human-agent interaction research. Thus far, studies have focused onbehavioral effects, rather than examining the neural underpin-nings of different behaviors shown in human-agent interac-tions. Here, we used a multi-round negotiation platform, in-stead of the more common single-shot negotiation, and wereable to find distinct brain patterns in emotion-related regionsof the brain during different types of offers. Using multi-voxelpattern analysis to analyze brain imaging data acquired duringfunctional MRI scanning, we show that it is possible to pre-dict whether the negotiator concedes, does not change, or asksfor more during the negotiation. Most importantly, we demon-strate that left dorsal anterior insula, which is known to be anemotion-related brain region, shows a different pattern of ac-tivity for each of the three offer types.