Slow but flexible or fast but rigid? Discrete and continuous processes compared

Abstract

AbstractA tradeoff always exists when dealing with multi-step tasks. High-level cognitive processes can find the best sequence of actions to achieve goals in uncertain environments, but they are slow and require significant computational demand. Contrarily, lower-level processing allows reacting to environmental stimuli rapidly, but with limited capacity to determine optimal actions. Through reiteration of the same task, biological organisms find the optimal tradeoff: from primitive movements, composite actions gradually emerge by creating task-specific neural structures. The two frameworks of a recent theory called “active inference” can capture high-level and low-level processes of human behavior, but how task specialization may occur in there terms is still unclear. Here, we compare two hierarchical strategies on a pick-and-place task: a discrete-continuous model with planning capabilities and a continuous-only model with fixed transitions. We analyze several consequences of defining movements in intrinsic and extrinsic domains. Finally, we propose how discrete actions might be encoded into continuous representations, comparing them with different motor learning phases and laying the foundations for further studies on bio-inspired task adaptation.