Several canonical experimental paradigms (serial reaction task, mxn task, etc.) have been proposed to study the typicalbehavioural phenomenon in sequential key-press tasks. However, not much work has been done on studying motor se-quencing in grid-navigation tasks. In this work, using empirical examinations, we systematically show grid-navigationtask as an instance of skill learning paradigm. The participants performed Grid-Sailing Task (GST), which required nav-igating (by executing sequential key-presses) a 5x5 grid from start to goal position while using a particular key-mappingamong the 3 cursor movement directions and the 3 keyboard buttons. We employ two different experiments to argue forthe learning of cognitive strategies as well as motor sequences. By rejecting the motor adaptation argument and validatingthe law of practice, we characterize GST as a skill learning task. We further argue for advantages of GST as a general,canonical task over others for use in skill learning studies.

Several canonical experimental paradigms (serial reaction task, mxn task, etc.) have been proposed to study the typi-cal behavioural phenomena in a sequential motor key-press task. The repeated execution of visuomotor sequences insuch paradigms lead to overall performance improvement such that the inter-response intervals in between certain sub-sequences decreases as compared to that across other sub-sequences. This efficient and hierarchical cluster organisation iscalled motor chunking. We provide empirical evidence for motor chunking in grid-navigation sequencing tasks. The par-ticipants performed Grid-Sailing Task (GST) [Fermin et. al., 2010] that required navigating a 10x10 grid from start to goalposition while using a particular key-mapping between the 3 cursor movement directions and the 3 keyboard buttons. Thisstudy confirms the emergence of subject-specific, unique temporal patterns related to chunking after substantial practice.

There have been numerous attempts in explaining the general learning behaviours using model-based and model-freemethods. While the model-based control is flexible yet computationally expensive in planning, the model-free control isquick but inflexible. Multiple arbitration schemes have been suggested to achieve the data efficiency and computationalefficiency of model-based and model-free control schemes, respectively. In this context, we propose a quantitative ’value-of-information’ based arbitration between both the controllers in order to establish a general computational frameworkfor skill learning. The interacting model-based and model-free reinforcement learning processes are arbitrated using anuncertainty-based value-of-information estimation. We further show that our algorithm performs better than Q-learning aswell as Q-learning with experience replay.