Proprioceptive loss and the perception, control and learning of arm movements in humans: evidence from sensory neuronopathy

© 2018 The Author(s) It is uncertain how vision and proprioception contribute to adaptation of voluntary arm movements. In normal participants, adaptation to imposed forces is possible with or without vision, suggesting that proprioception is sufficient; in participants with proprioceptive loss (PL), adaptation is possible with visual feedback, suggesting that proprioception is unnecessary. In experiment 1 adaptation to, and retention of, perturbing forces were evaluated in three chronically deafferented participants. They made rapid reaching movements to move a cursor toward a visual target, and a planar robot arm applied orthogonal velocity-dependent forces. Trial-by-trial error correction was observed in all participants. Such adaptation has been characterized with a dual-rate model: a fast process that learns quickly, but retains poorly and a slow process that learns slowly and retains well. Experiment 2 showed that the PL participants had large individual differences in learning and retention rates compared to normal controls. Experiment 3 tested participants’ perception of applied forces. With visual feedback, the PL participants could report the perturbation’s direction as well as controls; without visual feedback, thresholds were elevated. Experiment 4 showed, in healthy participants, that force direction could be estimated from head motion, at levels close to the no-vision threshold for the PL participants. Our results show that proprioceptive loss influences perception, motor control and adaptation but that proprioception from the moving limb is not essential for adaptation to, or detection of, force fields. The differences in learning and retention seen between the three deafferented participants suggest that they achieve these tasks in idiosyncratic ways after proprioceptive loss, possibly integrating visual and vestibular information with individual cognitive strategies

Comparing production-biomass ratios of benthos and suprabenthos in macrofaunal marine crustaceans

Using available data from the literature, we compared the productionbiomass ratios (P/B) between the suprabenthic (= hyperbenthic) and the benthic (infaunaepifauna) species within the group of the macrofaunal marine crustaceans. This data set consists of 91 P/B estimates (26 for suprabenthos and 65 for infaunaepifauna) for 49 different species. Suprabenthic crustacean P/B was significantly higher than P/B of benthic crustacean (post-hoc Scheffé test; one-way analysis of covariance, ANCOVA; p < 103) and also of other (noncrustacean) benthic invertebrate (p < 104). Predictive multilinear regression (MLR) analysis for macrofaunal marine crustaceans showed P/B to depend significantly on mean annual temperature (T) and mean individual weight (W) (R2 = 0.367). Adding the variable swimming capacity increased goodness-of-fit to R2 = 0.528. The higher P/B of suprabenthic (= swimming) macrofauna in comparison with that of the benthic compartment seems to be related to the most apparent feature of the suprabenthos, its swimming capacity. The high P/Bs reported for suprabenthic species indicate how a nontrivial part of benthic production can be ignored if suprabenthos is not well sampled, therefore biasing the models of energy flow generated for trophic webs

The relationship between reinforcement and explicit control during visuomotor adaptation

The motor system's ability to adapt to environmental changes is essential for maintaining accurate movements. Such adaptation recruits several distinct systems: cerebellar sensory-prediction error learning, success-based reinforcement, and explicit control. Although much work has focused on the relationship between cerebellar learning and explicit control, there is little research regarding how reinforcement and explicit control interact. To address this, participants first learnt a 20° visuomotor displacement. After reaching asymptotic performance, binary, hit-or-miss feedback (BF) was introduced either with or without visual feedback, the latter promoting reinforcement. Subsequently, retention was assessed using no-feedback trials, with half of the participants in each group being instructed to stop aiming off target. Although BF led to an increase in retention of the visuomotor displacement, instructing participants to stop re-aiming nullified this effect, suggesting explicit control is critical to BF-based reinforcement. In a second experiment, we prevented the expression or development of explicit control during BF performance, by either constraining participants to a short preparation time (expression) or by introducing the displacement gradually (development). Both manipulations strongly impaired BF performance, suggesting reinforcement requires both recruitment and expression of an explicit component. These results emphasise the pivotal role explicit control plays in reinforcement-based motor learning.</p