This article speaks to the issue raised in my recent post, Physical constraints on computing, process and memory, Part 1 [LeCun], under the somewhat strange notion of the brain has a hyperviscous mesh, which is about timescales of stability in patterns of connectivity, where it is understood that 'information' is registered in those patterns.
Timothy P. Lillicrap · Pablo Moreno‐Briseño · Rosalinda Diaz · Douglas B. Tweed · Nikolaus F. Troje · Juan Fernandez‐Ruiz, Adapting to inversion of the visual field: a new twist on an old problem, Experimental Brain Research 228(3), May 2013, https://doi.org/10.1007/s00221-013-3565-6
Abstract:
While sensorimotor adaptation to prisms that displace the visual field takes minutes, adapting to an inversion of the visual field takes weeks. In spite of a long history of the study, the basis of this profound difference remains poorly understood. Here, we describe the computational issue that underpins this phenomenon and presents experiments designed to explore the mechanisms involved. We show that displacements can be mastered without altering the updated rule used to adjust the motor commands. In contrast, inversions flip the sign of crucial variables called sensitivity derivatives-variables that capture how changes in motor commands affect task error and therefore require an update of the feedback learning rule itself. Models of sensorimotor learning that assume internal estimates of these variables are known and fixed predicted that when the sign of a sensitivity derivative is flipped, adaptations should become increasingly counterproductive. In contrast, models that relearn these derivatives predict that performance should initially worsen, but then improve smoothly and remain stable once the estimate of the new sensitivity derivative has been corrected. Here, we evaluated these predictions by looking at human performance on a set of pointing tasks with vision perturbed by displacing and inverting prisms. Our experimental data corroborate the classic observation that subjects reduce their motor errors under inverted vision. Subjects' accuracy initially worsened and then improved. However, improvement was jagged rather than smooth and performance remained unstable even after 8 days of continually inverted vision, suggesting that subjects improve via an unknown mechanism, perhaps a combination of cognitive and implicit strategies. These results offer a new perspective on classic work with inverted vision.
From the article's introduction:
Visuomotor adaptation to perturbations that displace the visual field, for example, from left to right, is widely studied and well characterized (Harris 1965; Kohler 1963; Kornheiser 1976; Redding and Wallace 1990). Pointing, throwing, and reaching tasks have been used to assess adaptation, and in these tasks, human subjects adapt quickly and smoothly to displacements, typically within minutes (Fernandez-Ruiz et al. 2006; Kitazawa et al. 1997; Martin et al. 1996; Redding et al. 2005; Redding and Wal- lace 1990). When prisms are worn, which displace targets and responses to the right and thus initially produce a left- ward error (Fig. 1b), subjects reduce their errors by correct- ing in the leftward direction on subsequent trials. Doing so, subjects make use of an implicit assumption about how error vectors ought to be used to update motor commands (Fig. 1a). the assumption, which holds in the case of dis- placed vision, is that the relationship between commands and errors (i.e., the sensitivity derivatives) has not been altered.
Comparatively, little is understood about visuomotor adaptation to inversions of the visual field—for example, a perturbation which flips the visual field from left to right about the midline (Fig. 1c). Studies have reported that, although subjects are initially severely impaired by inversions, they were eventually able to reacquire even complex sensorimotor skills, such as riding a bicycle (Harris 1965; Kohler 1963). However, most studies have been qualitative in nature (Rock 1966, 1973; Stratton 1896, 1897) or else have focused on perceptual rather than motor adaptations (Linden et al. 1999; Sekiyama et al. 2000). thus, the reason for the profound difference in the time course of visuomotor adaptation, the manner in which adaptation unfolds, and the mechanisms involved are not well studied.
Gradient vs. cognitive processes:
Superficially, our experimental data agree with the class of gradient-based models which update their feedback learning rule. However, closer examination of our results suggests that adaptation to inversions involves a complex mixture of implicit (i.e., gradient or reinforcement learning) and explicit or “cognitive” processes (e.g., Mazzoni and Krakauer 2006), which is not well modeled by the existing theory.
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