by Keyword: Decision-making

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Amil, Adrián F., Puigbó, J.-Y., Verschure, P., (2020). Cholinergic control of chaos and evidence sensitivity in a neocortical model of perceptual decision-making Biomimetic and Biohybrid Systems 9th International Conference, Living Machines 2020 (Lecture Notes in Computer Science) , Springer International Publishing (Freiburg, Germany) 12413, 92-96

Perceptual decision-making in the brain is commonly modeled as a competition among tuned cortical populations receiving stimulation according to their perceptual evidence. However, the contribution of evidence on the decision-making process changes through time. In this regard, the mechanisms controlling the sensitivity to perceptual evidence remain unknown. Here we explore this issue by using a biologically constrained model of the neocortex performing a dual-choice perceptual discrimination task. We combine mutual and global GABAergic inhibition, which are differentially regulated by acetylcholine (ACh), a neuromodulator linked to enhanced stimulus discriminability. We find that, while mutual inhibition determines the phase-space separation between two stable attractors representing each stimulus, global inhibition controls the formation of a chaotic attractor in-between the two, effectively protecting the weakest stimulus. Hence, under low ACh levels, where global inhibition dominates, the decision-making process is chaotic and less determined by the difference between perceptual evidences. On the contrary, under high ACh levels, where mutual inhibition dominates, the network becomes very sensitive to small differences between stimuli. Our results are in line with the putative role of ACh in enhanced stimulus discriminability and suggest that ACh levels control the sensitivity to sensory inputs by regulating the amount of chaos.

Keywords: Acetylcholine, Cortical model, Decision-making, Chaos

Freire, Ismael T., Urikh, D., Arsiwalla, X. D., Verschure, P., (2020). Machine morality: From harm-avoidance to human-robot cooperation Biomimetic and Biohybrid Systems 9th International Conference, Living Machines 2020 (Lecture Notes in Computer Science) , Springer International Publishing (Freiburg, Germany) 12413, 116-127

We present a new computational framework for modeling moral decision-making in artificial agents based on the notion of ‘Machine Morality as Cooperation’. This framework integrates recent advances from cross-disciplinary moral decision-making literature into a single architecture. We build upon previous work outlining cognitive elements that an artificial agent would need for exhibiting latent morality, and we extend it by providing a computational realization of the cognitive architecture of such an agent. Our work has implications for cognitive and social robotics. Recent studies in human neuroimaging have pointed to three different decision-making processes, Pavlovian, model-free and model-based, that are defined by distinct neural substrates in the brain. Here, we describe how computational models of these three cognitive processes can be implemented in a single cognitive architecture by using the distributed and hierarchical organization proposed by the DAC theoretical framework. Moreover, we propose that a pro-social drive to cooperate exists at the Pavlovian level that can also bias the rest of the decision system, thus extending current state-of-the-art descriptive models based on harm-aversion.

Keywords: Morality, Moral decision-making, Computational models, Cognitive architectures, Cognitive robotics, Human-robot interaction

Santos-Pata, D., Verschure, P., (2018). Human vicarious trial and error is predictive of spatial navigation performance Frontiers in Behavioral Neuroscience 12, Article 237

When learning new environments, rats often pause at decision points and look back and forth over their possible trajectories as if they were imagining the future outcome of their actions, a behavior termed “Vicarious trial and error” (VTE). As the animal learns the environmental configuration, rats change from deliberative to habitual behavior, and VTE tends to disappear, suggesting a functional relevance in the early stages of learning. Despite the extensive research on spatial navigation, learning and VTE in the rat model, fewer studies have focused on humans. Here, we tested whether head-scanning behaviors that humans typically exhibit during spatial navigation are as predictive of spatial learning as in the rat. Subjects performed a goal-oriented virtual navigation task in a symmetric environment. Spatial learning was assessed through the analysis of trajectories, timings, and head orientations, under habitual and deliberative spatial navigation conditions. As expected, we found that trajectory length and duration decreased with the trial number, implying that subjects learned the spatial configuration of the environment over trials. Interestingly, IdPhi (a standard metric of VTE) also decreased with the trial number, suggesting that humans benefit from the same head-orientation scanning behavior as rats at spatial decision-points. Moreover, IdPhi captured exclusively at the first decision-point of each trial, was correlated with trial trajectory duration and length. Our findings demonstrate that in VTE is a signature of the stage of spatial learning in humans, and can be used to predict performance in navigation tasks with high accuracy.

Keywords: Deliberation, Habitual, Hippocampus, Navigation, Spatial decision-making

Vouloutsi, Vasiliki, Halloy, José, Mura, Anna, Mangan, Michael, Lepora, Nathan, Prescott, T. J., Verschure, P., (2018). Biomimetic and Biohybrid Systems 7th International Conference, Living Machines 2018, Paris, France, July 17–20, 2018, Proceedings , Springer International Publishing (Lausanne, Switzerland) 10928, 1-551

This book constitutes the proceedings of the 7th International Conference on Biomimetic and Biohybrid Systems, Living Machines 2018, held in Paris, France, in July 2018. The 40 full and 18 short papers presented in this volume were carefully reviewed and selected from 60 submissions. The theme of the conference targeted at the intersection of research on novel life-like technologies inspired by the scientific investigation of biological systems, biomimetics, and research that seeks to interface biological and artificial systems to create biohybrid systems.

Keywords: Artificial neural network, Bio-actuators, Bio-robotics, Biohybrid systems, Biomimetics, Bipedal robots, Earthoworm-like robots, Robotics, Decision-making, Tactile sensing, Soft robots, Locomotion, Insects, Sensors, Actuators, Robots, Artificial intelligence, Neural networks, Motion planning, Learning algorithms