Talking Nets: A Multi-Agent Connectionist Approach to Communication and Trust between Individuals

A multi-agent connectionist model is proposed that consists of a collection of individual recurrent networks that communicate with each other, and as such is a network of networks. The individual recurrent networks simulate the process of information uptake, integration and memorization within individual agents, while the communication of beliefs and opinions between agents is propagated along connections between the individual networks. A crucial aspect in belief updating based on information from other agents is the trust in the information provided. In the model, trust is determined by the consistency with the receiving agents’ existing beliefs, and results in changes of the connections between individual networks, called trust weights. Thus activation spreading and weight change between individual networks is analogous to standard connectionist processes, although trust weights take a specific function. Specifically, they lead to a selective propagation and thus filtering out of less reliable information, and they implement Grice’s (1975) maxims of quality and quantity in communication. The unique contribution of communicative mechanisms beyond intra-personal processing of individual networks was explored in simulations of key phenomena involving persuasive communication and polarization, lexical acquisition, spreading of stereotypes and rumors, and a lack of sharing unique information in group decisions

Folk Theory of Mind: Conceptual Foundations of Social Cognition

The human ability to represent, conceptualize, and reason about mind and behavior is one of the greatest achievements of human evolution and is made possible by a “folk theory of mind” — a sophisticated conceptual framework that relates different mental states to each other and connects them to behavior. This chapter examines the nature and elements of this framework and its central functions for social cognition. As a conceptual framework, the folk theory of mind operates prior to any particular conscious or unconscious cognition and provides the “framing” or interpretation of that cognition. Central to this framing is the concept of intentionality, which distinguishes intentional action (caused by the agent’s intention and decision) from unintentional behavior (caused by internal or external events without the intervention of the agent’s decision). A second important distinction separates publicly observable from publicly unobservable (i.e., mental) events. Together, the two distinctions define the kinds of events in social interaction that people attend to, wonder about, and try to explain. A special focus of this chapter is the powerful tool of behavior explanation, which relies on the folk theory of mind but is also intimately tied to social demands and to the perceiver’s social goals. A full understanding of social cognition must consider the folk theory of mind as the conceptual underpinning of all (conscious and unconscious) perception and thinking about the social world

Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: NL

Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: N

Affective learning: improving engagement and enhancing learning with affect-aware feedback

This paper describes the design and ecologically valid evaluation of a learner model that lies at the heart of an intelligent learning environment called iTalk2Learn. A core objective of the learner model is to adapt formative feedback based on students’ affective states. Types of adaptation include what type of formative feedback should be provided and how it should be presented. Two Bayesian networks trained with data gathered in a series of Wizard-of-Oz studies are used for the adaptation process. This paper reports results from a quasi-experimental evaluation, in authentic classroom settings, which compared a version of iTalk2Learn that adapted feedback based on students’ affective states as they were talking aloud with the system (the affect condition) with one that provided feedback based only on the students’ performance (the non-affect condition). Our results suggest that affect-aware support contributes to reducing boredom and off-task behavior, and may have an effect on learning. We discuss the internal and ecological validity of the study, in light of pedagogical considerations that informed the design of the two conditions. Overall, the results of the study have implications both for the design of educational technology and for classroom approaches to teaching, because they highlight the important role that affect-aware modelling plays in the adaptive delivery of formative feedback to support learning

The volume and source of cyberabuse influences victim blame and perceptions of attractiveness

Cyberabuse is an escalating problem in society, as opportunities for abuse to occur in online public domains increase. Such acts are often defined by the frequency of abuse, and in many cases multiple individuals play a part in the abuse. Although consequences of such acts are often severe, there is typically little public sympathy/support for victims. To better understand perceptions of victims of abusive online acts, we manipulated the Volume (low, high) and Source (same-source, multi-source) of abusive posts in artificially-manipulated Facebook timelines of four fictitious ‘victims’. One hundred and sixty-four participants [United Kingdom-based; aged 18–59] rated ‘victims’ on measures of direct victim blame (DVB) and perceived social-, physical- and task-attractiveness. Results revealed significant Volume × Source interactions on DVB and social-attractiveness ratings. Few abusive posts authored by a single source yielded higher DVB and lower social-attractiveness ratings. Strong correlations between attractiveness and DVB were observed. We propose that our results could be due to an observer desensitization effect, or that participants interpreted the posts as indicative of friendly ‘teasing’ or ‘banter’ within an established social relationship, helping to explain why victims of online abuse often receive little sympathy or support

Collaborative trails in e-learning environments

This deliverable focuses on collaboration within groups of learners, and hence collaborative trails. We begin by reviewing the theoretical background to collaborative learning and looking at the kinds of support that computers can give to groups of learners working collaboratively, and then look more deeply at some of the issues in designing environments to support collaborative learning trails and at tools and techniques, including collaborative filtering, that can be used for analysing collaborative trails. We then review the state-of-the-art in supporting collaborative learning in three different areas – experimental academic systems, systems using mobile technology (which are also generally academic), and commercially available systems. The final part of the deliverable presents three scenarios that show where technology that supports groups working collaboratively and producing collaborative trails may be heading in the near future