An Empirical and Computational Investigation of Skill Learning in Internally-guided Sequencing

Abstract

Sequence learning plays a central role in the acquisition of many daily life motor skills such as typing or playing the piano. Several canonical experimental paradigms such as the serial reaction time task, discrete sequence production task and m × n task have been proposed to study the typical behavioral phenomenon in sequencing tasks. Such paradigms are externally-specified, where the environment or the task paradigm extrinsically provides the sequence of stimuli that guides the motor actions. Such paradigms differ from a class of more realistic motor tasks that are internally-guided, where the sequence of motor actions is self-generated or internally-specified. Most previous studies on discrete sequencing have employed externally-specified paradigms and therefore, the cognitive mechanisms underlying skill learning in internally-guided sequencing paradigms remain largely unexplored. This thesis presents an empirical and computational investigation of skill learning in internally- guided sequencing. We employ the Grid-Sailing Task (GST) as a canonical paradigm to study internally- guided sequence learning. The GST requires navigating by executing sequential keypresses, a n × n grid from start to goal (SG) position while using a particular key-mapping (KM) among the three cursor- movement directions and the three keyboard buttons. In the first study, we investigate the learning processes involved in internally-guided sequencing. The participants performed two behavioral experiments – Single-SG and Mixed-SG condition. The partici- pants first completed the Single-SG condition, which required performing GST on a single SG position repeatedly. By showing performance-related improvements in various behavioral measures such as the execution time and reward score, we show that motor learning contributes to the trajectory-specific learning in GST with the repeated execution of the same keypress sequences. The Mixed-SG condition involved performing GST using the same KM (from Single-SG condition) on two novel SG positions presented in a random, inter-mixed manner. Since the participants utilize the previously learned KM, we anticipate a transfer of learning from the Single-SG condition. The acquisition and transfer of a KM-specific internal model facilitate efficient trajectory planning on novel SG conditions. The acquisi- tion of such a KM-specific internal model amounts to trajectory-independent cognitive learning in GST. We provide evidence for the role of cognitive learning in GST by showing transfer-related performance improvements in the Mixed-SG condition. In a subsequent study, we probe the involvement of a particular motor learning process called motor chunking. Motor chunking is a phenomenon which enables efficient execution of the motor sequences by chaining several elementary actions into sub-sequences called motor chunks. The participants per-formed GST on a 10 × 10 grid, executing the same trajectory repeatedly throughout the experiment. We provide empirical evidence for motor chunking by showing the emergence of subject-specific, unique temporal patterns in response times. Our findings show spontaneous chunking without pre-specified or externally guided structures while replicating the earlier results with a less constrained, internally guided sequencing paradigm. In another study, we employ an inter-manual transfer task to examine the stage-wise transitions in motor sequence learning. The participants performed GST on inter-leaved normal and transfer blocks. The dominant hand was used on the normal block and the non-dominant hand was used on the transfer block. The length of the first normal block varied across days. We found increasing differences in execution time between the normal and transfer blocks across days as the effector-dependent learning consolidated. Our findings confirm a switch from the effector-independent cognitive learning phase to the effector-dependent motor learning phase after substantial practice. We then situate internally-guided sequencing in a dual-process account of skill learning and propose computational analogues for the goal-directed and the habitual controller. We propose two hybrid rein- forcement learning frameworks that integrate model-based and model-free mechanisms to account for the dual learning processes. Using simulations and model-fitting experiments, we compare the proposed hybrid frameworks, namely, value-of-information based arbitration and weighted-hybrid arbitration. We show that weighted-hybrid arbitration describes the empirical data better than other models. Our pro- posed framework gives a computational account of the learning in internally-guided sequencing.