bioRxiv ScienceSearch

Biology subjects

Faisal, A. A.

Publications and source records attributed to Faisal, A. A..

2 recordsLinked to original sources

Does internal metabolic state determine human motor coordination strategy?

Motor coordination requires the orchestration of multiple degrees of freedom in order to perform actions. Humans display characteristic and predictable reaching trajectories even though multiple trajectories are possible. Computational theories of motor control can explain these reaching trajectories by assuming that subjects orchestrate their movements to minimise a cost function, such as end-point variability or movement effort. However, how internal metabolic states influence decision making and sensorimotor control is not well understood. Here we measure human behaviour during a centre out reaching task in two distinct metabolic conditions in the morning - after having had breakfast and not. We find that humans alter their patterns of motor coordination according to their internal metabolic state and that this change in behaviour results in a 20% lower task-related energy expenditure when fasted. We suggest that movements are orchestrated according to different criteria in different metabolic states so that metabolic costs are reduced in low metabolic states. We also predict that motor coordination strategies take the metabolic costs of specific muscle groups into account when planning and executing movements. Thus, metabolic state may alter the computational strategies of decision making between animal-based experiments (in typically low metabolic conditions) and human psychophysics experiments (in typically high metabolic conditions).

neuroscience

Grammars of action in human behavior and evolution

Distinctive human behaviors from tool-making to language are thought to rely on a uniquely evolved capacity for hierarchical action sequencing. Unfortunately, testing of this idea has been hampered by a lack of objective, generalizable methods for measuring the structural complexity of real-world behaviors. Here we present a data-driven approach for quantifying hierarchical structure by extracting action grammars from basic ethograms. We apply this method to the evolutionarily-relevant behavior of stone tool-making by comparing sequences from the experimental replication of {small tilde}2.5 Mya Oldowan vs. more recent {small tilde}0.5 Mya Achuelean tools. Results show that, while using the same \"alphabet\" of elementary actions, Acheulean sequences are structurally more complex. Beyond its specific evolutionary implications, this finding illustrates the broader applicability of our method to investigate the structure of naturalistic human behaviors and cognition. We demonstrate one application by using our complexity measures to re-analyze data from an fMRI study of tool-making action observation.

neuroscience