Intertemporal reflection, flexibly thinking forward and backward in time, is vital for one's future planning.Yet, cultivating intertemporal reflection about encountering difficult futures, e.g., developing a progressive cognitive condition like dementia, can be challenging.We assessed people's attitudes towards dementia following conversing with a chatbot presented as either neurotypical or simulating dementia symptoms.While neither the chatbot's presentation nor the framing of participants' future selves impacted attitudes toward dementia, it influenced participants' experiences.When framed as future selves, the chatbot evoked a strong emotional connection, leading to reflection on aging, particularly with the chatbot simulating dementia symptoms.Participants interacting with the chatbot framed as a stranger with simulated symptoms often felt frustrated, especially when they had a task-oriented mindset.Chatbots can be promising tools for prompting reflections on challenging futures, such as dementia, although their effectiveness varies due to the tensions between simulated cognitive decline and expectations for effective communication.

When a human makes a decision, an observer may want to understand the reasons and motivations behind the decision. This understanding is important when IVAs are involved in contextual decision-making or coaching practices. To address this challenge, we propose that an agent’s understanding of its user should include knowledge of the user’s underlying values. Humans prioritise different values – sometimes contradictory – in a manner that depends on the context. We present a method where the agent and user build the required context-sensitive value model together. We use Schwartz’s value theory, which places individuals’ values into ten categories. In a between-subject experiment, with three sessions on different days, we elicit user values by presenting them with moral dilemmas in different contexts on the first day, refine the model by asking users to argue about contradictions on the second day, and let them reflect on the model that they have built together with the system on the third day. We find that users exposed to a value-aware condition are more likely to agree with the robot’s representations of their values post-reflection than those in a baseline. Participants also prioritise different values depending on the context, agreeing with previous findings.

An important aspect of human emotion perception is the use of contextual information to understand others' feelings even in situations where their behavior is not very expressive or has an emotionally ambiguous meaning. For technology to successfully detect affect, it must mimic this human ability when analyzing audiovisual input. Databases upon which machine learning algorithms are trained should capture the context of social interactions as well as the behavior expressed in them. However, there is a lack of consensus about what constitutes relevant context in such databases. In this article, we make two contributions towards overcoming this challenge: (a) we identify two principal sources of context for emotion perceptions based on psychological theory, and (b) we provide an overview of how each of these has been considered in published databases covering social interactions. Our results show that a similar set of contextual features are present across the reviewed databases. Between all the different databases researchers seem to have taken into account a set of contextual features reflecting the sources of context seen in psychological theory. However, within individual databases, these features are not yet systematically varied. This is problematic because it prevents them from being used directly as resources for the modeling of context-sensitive affect detection. Based on our findings, we suggest improvements for the future development of affective databases.