A Robot Model of Stress-Induced Compulsive Behavior

Stress is one of the potential mechanisms underlying compulsive behavior in obsessive-compulsive spectrum disorders. In this paper, we present a robot model and experiments investigating the interactions between internally- and externally-induced stress and compulsive behavior. Our results show properties of the model with potential implications for understanding how stress can result in the generation and maintenance of compulsive behaviors, and how response-prevention interventions can affect compulsive responses under different conditions.Final Accepted Versio

Modulation of exploratory behavior for adaptation to the context

For autonomous agents (children, animals or robots), exploratory learning is essential as it allows them to take advantage of their past experiences in order to improve their reactions in any situation similar to a situation already experimented. We have already exposed in Blanchard and Canamero (2005) how a robot can learn which situations it should memorize and try to reach, but we expose here architectures allowing the robot to take initiatives and explore new situations by itself. However, exploring is a risky behavior and we propose to moderate this behavior using novelty and context based on observations of animals behaviors. After having implemented and tested these architectures, we present a very interesting emergent behavior which is low-level imitation modulated by context

Robot Models of Mental Disorders

Alongside technological tools to support wellbeing and treatment of mental disorders, models of these disorders can also be invaluable tools to understand, support and improve these conditions. Robots can provide ecologically valid models that take into account embodiment-, interaction-, and context-related elements. Focusing on Obsessive-Compulsive spectrum disorders, in this paper we discuss some of the potential contributions of robot models and relate them to other models used in psychology and psychiatry, particularly animal models. We also present some initial recommendations for their meaningful design and rigorous use.Final Accepted Versio

The Long-Term Efficacy of “Social Buffering” in Artificial Social Agents: Contextual Affective Perception Matters

© 2022 Khan and Cañamero. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). https://creativecommons.org/licenses/by/4.0/In dynamic (social) environments, an affective state of “stress” can be adaptive and promote agent wellbeing, but maladaptive if not appropriately regulated. The presence of (and interactions with) affect-based social support has been hypothesised to provide mechanisms to regulate stress (the “social buffering” hypothesis), though the precise, underlying mechanisms are still unclear. However, the hormone oxytocin has been implicated in mediating these effects in at least two ways: by improving social appraisals and reducing the short-term release of stress hormones (i.e., cortisol), and adapting an agent’s long-term stress tolerance. These effects likely facilitate an agent’s long-term adaptive ability by grounding their physiological and behavioural adaptation in the (affective) social environment, though these effects also appear to be context-dependent. In this paper, we investigate whether two of the hypothesised hormonal mechanisms that underpin the “social buffering” phenomenon affect the long-term wellbeing of (artificial) social agents who share affective social bonds, across numerous social and physical environmental contexts. Building on previous findings, we hypothesise that “social buffering” effects can improve the long-term wellbeing of agents who share affective social bonds in dynamic environments, through regular prosocial interactions with social bond partners. We model some of the effects associated with oxytocin and cortisol that underpin these hypothesised mechanisms in our biologically-inspired, socially-adaptive agent model, and conduct our investigation in a small society of artificial agents whose goal is to survive in challenging environments. Our results find that, while stress can be adaptive and regulated through affective social support, long-term behavioural and physiological adaptation is determined by the contextual perception of affective social bonds, which is influenced by early-stage interactions between affective social bond partners as well as the degree of the physical and social challenges. We also show how these low-level effects associated with oxytocin and cortisol can be used as “biomarkers” of social support and environmental stress. For socially-situated artificial agents, we suggest that these “social buffering” mechanisms can adapt the (adaptive) stress mechanisms, but that the long-term efficacy of this adaptation is related to the temporal dynamics of social interactions and the contextual perception of the affective social and physical environments.Peer reviewe

A Hormone-Driven Epigenetic Mechanism for Adaptation in Autonomous Robots

Different epigenetic mechanisms provide biological organisms with the ability to adjust their physiology and/or morphology and adapt to a wide range of challenges posed by their environments. In particular, one type of epigenetic process, in which hormone concentrations are linked to the regulation of hormone receptors, has been shown to have implications for behavioral development. In this paper, taking inspiration from these biological processes, we investigate whether an epigenetic model based on the concept of hormonal regulation of receptors can provide a similarly robust and general adaptive mechanism for autonomous robots. We have implemented our model using a Koala robot, and tested it in a series of experiments in six different environments with varying challenges to negotiate. Our results, including the emergence of varied behaviors that permit the robot to exploit its current environment, demonstrate the potential of our epigenetic model as a general mechanism for adaptation in autonomous robots.Peer reviewe

Modelling the Social Buffering Hypothesis in an Artificial Life Environment

© 2020 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license: https://creativecommons.org/licenses/by/4.0/.In social species, individuals who form social bonds have been found to live longer, healthier lives. One hypothesised reason for this effect is that social support, mediated by oxytocin, “buffers” responses to stress in a number of ways, and is considered an important process of adaptation that facilitates long-term wellbeing in changing, stressful conditions. Using an artificial life model, we have investigated the role of one hypothesised stress-reducing effect of social support on the survival and social interactions of agents in a small society. We have investigated this effect using different types of social bonds and bond partner combinations across environmentally-challenging conditions. Our results have found that stress reduction through social support benefits the survival of agents with social bonds, and that this effect often extends to the wider society. We have also found that this effect is significantly affected by environmental and social contexts. Our findings suggest that these “social buffering” effects may not be universal, but dependent upon the degree of environmental challenges, the quality of affective relationships and the wider social context

Expression of Grounded Affect: How Much Emotion Can Arousal Convey?

Springer: © 2020 Springer Nature Switzerland AG. This is a post-peer-review, pre-copyedit version of Hickton L., Lewis M., Cañamero L. (2020) Expression of Grounded Affect: How Much Emotion Can Arousal Convey?. In: Mohammad A., Dong X., Russo M. (eds) Towards Autonomous Robotic Systems. TAROS 2020. Lecture Notes in Computer Science, vol 12228. Springer, Cham. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-63486-5_26.In this paper we consider how non-humanoid robots can communicate their affective state via bodily forms of communication (kinesics), and the extent to which this influences how humans respond to them. We propose a simple model of grounded affect and kinesic expression before presenting the qualitative findings of an exploratory study (N=9), during which participants were interviewed after watching expressive and non-expressive hexapod robots perform different ‘scenes’. A summary of these interviews is presented and a number of emerging themes are identified and discussed. Whilst our findings suggest that the expressive robot did not evoke significantly greater empathy or altruistic intent in humans than the control robot, the expressive robot stimulated greater desire for interaction and was also more likely to be attributed with emotion

A Reactive Competitive Emotion Selection System

We present a reactive emotion selection system designed to be used in a robot that needs to respond autonomously to relevant events. A variety of emotion selection models based on “cognitive appraisal” theories exist, but the complexity of the concepts used by most of these models limits their use in robotics. Robots have physical constrains that condition their understanding of the world and limit their capacity to built the complex concepts needed for such models. The system presented in this paper was conceived to respond to “disturbances” detected in the environment through a stream of images, and use this low-level information to update emotion intensities. They are increased when specific patterns, based on Tomkins’ affect theory, are detected or reduced when it is not. This system could also be used as part of (or as first step in the incremental design of) a more cognitively complex emotional system for autonomous robots

An Embodied AI Approach to Individual Differences : Supporting Self-Efficacy in Diabetic Children with an Autonomous Robot

In this paper we discuss how a motivationally autonomous robot, designed using the principles of embodied AI, provides a suitable approach to address individual differences of children interacting with a robot, without having to explicitly modify the system. We do this in the context of two pilot studies using Robin, a robot to support self-confidence in diabetic children.Final Accepted Versio

Does Expression of Grounded Affect in a Hexapod Robot Elicit More Prosocial Responses?

© 2020 The Author(s).We consider how non-humanoid robots can communicate their affective state via bodily forms of communication, and the extent to which this can influence human response. We propose a simple model of grounded affect and kinesic expression and outline two experiments (N=9 and N=180) in which participants were asked to watch expressive and non-expressive hexapod robots perform different ‘scenes’. Our preliminary findings suggest the expressive robot stimulated greater desire for interaction, and was more likely to be attributed with emotion. It also elicited more desire for prosocial behaviour.Final Published versio