The retrospective view of temporal binding (TB), the temporal contraction between one's actions and their effects, proposes that TB is influenced by what happens after the action. However, the role of the interaction between multiple sources of information following the action in the formation of TB has received limited attention. The current study aims to address this gap by investigating the combined influence of social feedback and outcome valence (i.e., positive or negative outcomes) on TB. In Experiment 1, the valenced (...) outcome was followed by either positive or negative social feedback. The results showed that negative social feedback enlarged the difference between TB over negative and positive outcomes relative to positive social feedback. The modulatory effect of social feedback on TB was not observed in the absence of causal beliefs between actions and outcomes (Experiment 2). These results highlight the importance of considering the complex interplay between cognitive, emotional, and social factors in shaping our subjective temporal experience. (shrink)

In this article, we suggest that the study of social interactions and the development of a “sense of agency” in joint action can help determine the content of relevant explanations to be implemented in artificial systems to make them “explainable.” The introduction of automated systems, and more broadly of Artificial Intelligence, into many domains has profoundly changed the nature of human activity, as well as the subjective experience that agents have of their own actions and their consequences – an experience (...) that is commonly referred to as sense of agency. We propose to examine the empirical evidence supporting this impact of automation on individuals’ sense of agency, and hence on measures as diverse as operator performance, system explicability and acceptability. Because of some of its key characteristics, AI occupies a special status in the artificial systems landscape. We suggest that this status prompts us to reconsider human–AI interactions in the light of human–human relations. We approach the study of joint actions in human social interactions to deduce what key features are necessary for the development of a reliable sense of agency in a social context and suggest that such framework can help define what constitutes a good explanation. Finally, we propose possible directions to improve human–AI interactions and, in particular, to restore the sense of agency of human operators, improve their confidence in the decisions made by artificial agents, and increase the acceptability of such agents. (shrink)