Differences in Social Expectations About Robot Signals and Human Signals.

In our daily lives, we are continually involved in decision-making situations, many of which take place in the context of social interaction. Despite the ubiquity of such situations, there remains a gap in our understanding of how decision-making unfolds in social contexts, and how communicative signals, such as social cues and feedback, impact the choices we make. Interestingly, there is a new social context to which humans are recently increasingly more frequently exposed-social interaction with not only other humans but also artificial agents, such as robots or avatars. Given these new technological developments, it is of great interest to address the question of whether-and in what way-social signals exhibited by non-human agents influence decision-making. The present study aimed to examine whether robot non-verbal communicative behavior has an effect on human decision-making. To this end, we implemented a two-alternative-choice task where participants were to guess which of two presented cups was covering a ball. This game was an adaptation of a "Shell Game." A robot avatar acted as a game partner producing social cues and feedback. We manipulated robot's cues (pointing toward one of the cups) before the participant's decision and the robot's feedback ("thumb up" or no feedback) after the decision. We found that participants were slower (compared to other conditions) when cues were mostly invalid and the robot reacted positively to wins. We argue that this was due to the incongruence of the signals (cue vs. feedback), and thus violation of expectations. In sum, our findings show that incongruence in pre- and post-decision social signals from a robot significantly influences task performance, highlighting the importance of understanding expectations toward social robots for effective human-robot interactions.

Mutual gaze with a robot affects human neural activity and delays decision-making processes.

In most everyday life situations, the brain needs to engage not only in making decisions but also in anticipating and predicting the behavior of others. In such contexts, gaze can be highly informative about others' intentions, goals, and upcoming decisions. Here, we investigated whether a humanoid robot's gaze (mutual or averted) influences the way people strategically reason in a social decision-making context. Specifically, participants played a strategic game with the robot iCub while we measured their behavior and neural activity by means of electroencephalography (EEG). Participants were slower to respond when iCub established mutual gaze before their decision, relative to averted gaze. This was associated with a higher decision threshold in the drift diffusion model and accompanied by more synchronized EEG alpha activity. In addition, we found that participants reasoned about the robot's actions in both conditions. However, those who mostly experienced the averted gaze were more likely to adopt a self-oriented strategy, and their neural activity showed higher sensitivity to outcomes. Together, these findings suggest that robot gaze acts as a strong social signal for humans, modulating response times, decision threshold, neural synchronization, as well as choice strategies and sensitivity to outcomes. This has strong implications for all contexts involving human-robot interaction, from robotics to clinical applications.

Modeling uncertainty-seeking behavior mediated by cholinergic influence on dopamine.

Recent findings suggest that acetylcholine mediates uncertainty-seeking behaviors through its projection to dopamine neurons - another neuromodulatory system known for its major role in reinforcement learning and decision-making. In this paper, we propose a leaky-integrate-and-fire model of this mechanism. It implements a softmax-like selection with an uncertainty bonus by a cholinergic drive to dopaminergic neurons, which in turn influence synaptic currents of downstream neurons. The model is able to reproduce experimental data in two decision-making tasks. It also predicts that: (i) in the absence of cholinergic input, dopaminergic activity would not correlate with uncertainty, and that (ii) the adaptive advantage brought by the implemented uncertainty-seeking mechanism is most useful when sources of reward are not highly uncertain. Moreover, this modeling work allows us to propose novel experiments which might shed new light on the role of acetylcholine in both random and directed exploration. Overall, this study contributes to a more comprehensive understanding of the role of the cholinergic system and, in particular, its involvement in decision-making.

Author Correction: Mice adaptively generate choice variability in a deterministic task.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Mice adaptively generate choice variability in a deterministic task.

Can decisions be made solely by chance? Can variability be intrinsic to the decision-maker or is it inherited from environmental conditions? To investigate these questions, we designed a deterministic setting in which mice are rewarded for non-repetitive choice sequences, and modeled the experiment using reinforcement learning. We found that mice progressively increased their choice variability. Although an optimal strategy based on sequences learning was theoretically possible and would be more rewarding, animals used a pseudo-random selection which ensures high success rate. This was not the case if the animal is exposed to a uniform probabilistic reward delivery. We also show that mice were blind to changes in the temporal structure of reward delivery once they learned to choose at random. Overall, our results demonstrate that a decision-making process can self-generate variability and randomness, even when the rules governing reward delivery are neither stochastic nor volatile.

Emotional metacontrol of attention: Top-down modulation of sensorimotor processes in a robotic visual search task.

Emotions play a significant role in internal regulatory processes. In this paper, we advocate four key ideas. First, novelty detection can be grounded in the sensorimotor experience and allow higher order appraisal. Second, cognitive processes, such as those involved in self-assessment, influence emotional states by eliciting affects like boredom and frustration. Third, emotional processes such as those triggered by self-assessment influence attentional processes. Last, close emotion-cognition interactions implement an efficient feedback loop for the purpose of top-down behavior regulation. The latter is what we call 'Emotional Metacontrol'. We introduce a model based on artificial neural networks. This architecture is used to control a robotic system in a visual search task. The emotional metacontrol intervenes to bias the robot visual attention during active object recognition. Through a behavioral and statistical analysis, we show that this mechanism increases the robot performance and fosters the exploratory behavior to avoid deadlocks.