Decision heuristics in contexts exploiting intrinsic skill

Neil Dundon, Jaron Colas, Neil Garrett, Viktoriya Babenko, Elizabeth Rizor, Dengxian Yang, Mairtin MacNamara, Linda Petzold, Scott Grafton

Research output: Working paperPreprint


Heuristics can inform human decision making in complex environments through a reduction of computational requirements and a robustness to overparameterisation. However, tasks capturing the efficiency of reduced decision dimensionality typically ignore action proficiency in determining rewards. The value of movement parameterisation in sensorimotor control questions whether heuristics preserve efficiency when actions are non-trivial. We developed a novel selection-execution task requiring joint optimisation of action selection and spatio-temporal skill. Optimal choices could be determined by either a spatio-temporal forward simulation or a simpler spatial heuristic. Sequential-sampling models of action-selection response times parsimoniously distinguished human participants who adopted either strategy. Heuristics preserved broad decisional advantages over forward simulations. In addition, heuristics aligned with greater action proficiency, though predominantly through the core feature (spatial) shaping their decision policy. We accordingly reveal evidence that the dimensionality of information guiding action selection might be yoked to the granularity of plasticity in the motor system.
Original languageEnglish
Publication statusPublished - 5 Apr 2022

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