Use-dependent Biases as Optimal Action under Information Bottleneck
<|reference_start|>Use-dependent Biases as Optimal Action under Information Bottleneck: Use-dependent bias is a phenomenon in human sensorimotor behavior whereby movements become biased towards previously repeated actions. Despite being well-documented, the reason why this phenomenon occurs is not yet clearly understood. Here, we propose that use-dependent biases can be understood as a rational strategy for movement under limitations on the capacity to process sensory information to guide motor output. We adopt an information-theoretic approach to characterize sensorimotor information processing and determine how behavior should be optimized given limitations to this capacity. We show that this theory naturally predicts the existence of use-dependent biases. Our framework also generates two further predictions. The first prediction relates to handedness. The dominant hand is associated with enhanced dexterity and reduced movement variability compared to the non-dominant hand, which we propose relates to a greater capacity for information processing in regions that control movement of the dominant hand. Consequently, the dominant hand should exhibit smaller use-dependent biases compared to the non-dominant hand. The second prediction relates to how use-dependent biases are affected by movement speed. When moving faster, it is more challenging to correct for initial movement errors online during the movement. This should exacerbate costs associated with initial directional error and, according to our theory, reduce the extent of use-dependent biases compared to slower movements, and vice versa. We show that these two empirical predictions, the handedness effect and the speed-dependent effect, are confirmed by experimental data.<|reference_end|>
@article{deng2024use-dependent,
    title={Use-dependent Biases as Optimal Action under Information Bottleneck},
    author={Hokin Deng and Adrian Haith},
    journal={arXiv preprint arXiv:2407.17793},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.17793},
    primaryClass={q-bio.NC cs.IT math.IT}
}