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Open Access Publications from the University of California

Scholarly Works (3 results)

  • Thesis
  • Peer Reviewed
UC San Diego Electronic Theses and Dissertations (2019)

The striatal direct and indirect pathways exhibit overlapping yet distinct functions in movement planning and control. However, it remains unclear how those distinct functions arise. We investigated an input structure to the striatum, the secondary motor cortex (M2), to find whether this input can potentially drive the functional heterogeneity of direct and indirect pathways in skilled and innate movements. Here, we used monosynaptic retrograde viral labeling, two photon imaging and perturbation experiments to investigate the activity correlation between direct/indirect medium spiny neurons (MSNs) in the dorsal lateral striatum (DLS) and their target cell-type-specific projecting neurons in M2. We found that activity patterns of projecting neurons in M2 do not appear to be overlapping with the activity patterns observed in MSNs in DLS. In addition, perturbations in parafascicular nucleus (PF) led to the abolishment of MSN activities in DLS, while M2 perturbations only decreased MSN activities. Therefore, we proposed that the functional heterogeneity of direct and indirect MSNs in movement control is not likely to be differentiated in M2, and there is a possibility that this heterogeneity can be differentiated in PF.

    Cover page: Target-Cell-Type Specificity of Corticostriatal Pathways in Movement Control
    • Article
    • Peer Reviewed
    Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 46 (2024)
    Planning in complex environments is crucial in everyday life, yet the underlying cognitive abilities remain unclear. We investigated this through an online experiment (n=476) where participants completed nine cognitive tasks: Raven's Matrices, Mental Rotation, Corsi Block Task, Change-Detection Task, Pattern Recognition Task, Wisconsin Card Sorting Task, a complex two-player game called Four-in-a-Row, and two simpler planning tasks. We found moderate correlations across most metrics, aligning with existing literature on cognitive interconnectivity. Notably, performance in the Four-in-a-Row game significantly correlated with all other tasks, implying a shared cognitive basis for planning, regardless of task complexity. Additionally, latent variable analysis revealed distinct factors underlying planning in different state spaces, with working memory capacity playing a crucial role in navigating larger spaces. These findings shed light on the cognitive architecture of complex planning.
      Cover page: The Cognitive Components of Complex PlanningCreative Commons 'BY' version 4.0 license
      • Article
      • Peer Reviewed
      Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 44 (2022)
      Deep reinforcement learning agents such as AlphaZero have achieved superhuman strength in complex combinatorial games. By contrast, the cognitive science of planning has mostly focused on simple tasks for experimental and computational tractability. Using a board game that strikes a balance between complexity and tractability, we find that AlphaZero agents improve in value function quality and planning depth through learning, similar to human in previous modeling work. In addition, these metrics reflect causal contributions to AlphaZero's playing strength. Yet the strongest contributor is the policy quality. The decrease in policy entropy also drives the increase in planning depth. The contribution of planning depth to performance is lessened in late training. These results contribute to a joint understanding of machine and human planning, providing an interpretable way of understanding the learning and strength of AlphaZero, while generating novel hypothesis on human planning.
        Cover page: Comparing Machine and Human Learning in a Planning Task of Intermediate ComplexityCreative Commons 'BY' version 4.0 license
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