1. Distinguishing Self, Other, and Autonomy From Visual Feedback: A Combined Correlation and Acceleration Transfer Analysis
- Author
-
Demirel, Berkay, Moulin-Frier, Clément, Arsiwalla, Xerxes, Verschure, Paul, Sánchez-Fibla, Martí, Universitat Pompeu Fabra [Barcelona] (UPF), Flowing Epigenetic Robots and Systems (Flowers), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institute for Bioengineering of Catalonia [Barcelona] (IBEC), This work was supported by INSOCO-DPI2016-80116-P, socSMC-641321—H2020-FETPROACT-2014, and FI-AGAUR 2020 FI_B 00693. This research was partially funded by the French National Research Agency (https://anr.fr), project ECOCURL, Grant ANR-20-CE23-0006, as well as the Inria Exploratory action ORIGINS (https://www.inria.fr/en/origins)., ANR-20-CE23-0006,ECOCURL,Emergence de la communication par apprentissage par renforcement guidé par la curiosité en environnement multi-agent(2020), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Unité d'Informatique et d'Ingénierie des Systèmes (U2IS), and École Nationale Supérieure de Techniques Avancées (ENSTA Paris)-École Nationale Supérieure de Techniques Avancées (ENSTA Paris)
- Subjects
sensorimotor learning ,[SCCO.COMP]Cognitive science/Computer science ,Human Neuroscience ,[INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE] ,[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] ,attention ,[INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA] ,agency ,developmental psychology ,computational cognition ,autonomy ,Original Research ,theory of mind ,cognitive development - Abstract
In cognitive science, Theory of Mind (ToM) is the mental faculty of assessing intentions and beliefs of others and requires, in part, to distinguish incoming sensorimotor (SM) signals and, accordingly, attribute these to either the self-model, the model of the other, or one pertaining to the external world, including inanimate objects. To gain an understanding of this mechanism, we perform a computational analysis of SM interactions in a dual-arm robotic setup. Our main contribution is that, under the common fate principle, a correlation analysis of the velocities of visual pivots is shown to be sufficient to characterize "the self" (including proximo-distal arm-joint dependencies) and to assess motor to sensory influences, and "the other" by computing clusters in the correlation dependency graph. A correlational analysis, however, is not sufficient to assess the non-symmetric/directed dependencies required to infer autonomy, the ability of entities to move by themselves. We subsequently validate 3 measures that can potentially quantify a metric for autonomy: Granger causality (GC), transfer entropy (TE), as well as a novel “Acceleration Transfer” (AT) measure, which is an instantaneous measure that computes the estimated instantaneous transfer of acceleration between visual features, from which one can compute a directed SM graph. Subsequently, autonomy is characterized by the sink nodes in this directed graph. This study results show that although TE can capture the directional dependencies, a rectified subtraction operation denoted, in this study, as AT is both sufficient and computationally cheaper. This work was supported by INSOCO-DPI2016-80116-P, socSMC-641321—H2020-FETPROACT-2014, and FI-AGAUR 2020 FI_B 00693. This research was partially funded by the French National Research Agency (https://anr.fr), project ECOCURL, Grant ANR-20-CE23-0006, as well as the Inria Exploratory action ORIGINS (https://www.inria.fr/en/origins). This work was supported by INSOCO-DPI2016-80116-P, socSMC-641321—H2020-FETPROACT-2014, and FI-AGAUR 2020 FI_B 00693. This research was partially funded by the French National Research Agency (https://anr.fr), project ECOCURL, Grant ANR-20-CE23-0006, as well as the Inria Exploratory action ORIGINS (https://www.inria.fr/en/origins).
- Published
- 2021
- Full Text
- View/download PDF