Building and Understanding the Minimal Self.

Two research movements—cognitive science and cognitive robotics—pursue different agendas in studying the minimal self. Cognitive science creates abstract models that predict human experimental data, while cognitive robotics builds embodied learning machines that develop a self from scratch, like human infants. Although both approaches can produce causal mechanistic models, building a minimal self in a robot is not the same as understanding the human minimal self. Conclusions drawn from robotic models about humans, or vice versa, require caution. Incorporating constraints from multiple levels of analysis is essential for models that predict, generate, and causally explain real-world behavior.

Study at a glance

Abstract

Within the methodologically diverse interdisciplinary research on the minimal self, we identify two movements with seemingly disparate research agendas - cognitive science and cognitive (developmental) robotics. Cognitive science, on the one hand, devises rather abstract models which can predict and explain human experimental data related to the minimal self. Incorporating the established models of cognitive science and ideas from artificial intelligence, cognitive robotics, on the other hand, aims to build embodied learning machines capable of developing a self "from scratch" similar to human infants. The epistemic promise of the latter approach is that, at some point, robotic models can serve as a testbed for directly investigating the mechanisms that lead to the emergence of the minimal self. While both approaches can be productive for creating causal mechanistic models of the minimal self, we argue that building a minimal self is different from understanding the human minimal self. Thus, one should be cautious when drawing conclusions about the human minimal self based on robotic model implementations and vice versa. We further point out that incorporating constraints arising from different levels of analysis will be crucial for creating models that can predict, generate, and causally explain behavior in the real world.