Coordinated, purposeful movements learned with one effector generalize to another effector, a finding that has important implications for tool use, sports, performing arts, and rehabilitation. This occurs because the motor memory acquired through learning comprises representations that are effector-independent. Despite knowing this for decades, the neural mechanisms and substrates that are causally associated with the encoding of effector-independent motor memories remain poorly understood. Here we exploit intereffector generalization, the behavioral signature of effector-independent representations, to address this crucial gap. We first show in healthy human participants that postlearning generalization across effectors is principally predicted by the level of an implicit mechanism that evolves gradually during learning to produce a temporally stable memory. We then demonstrate that interfering with left but not right posterior parietal cortex (PPC) using high-definition cathodal transcranial direct current stimulation impedes learning mediated by this mechanism, thus potentially preventing the encoding of effector-independent memory components. We confirm this in our final experiment in which we show that disrupting left PPC but not primary motor cortex after learning has been allowed to occur blocks intereffector generalization. Collectively, our results reveal the key mechanism that encodes an effector-independent memory trace and uncover a central role for the PPC in its representation. The encoding of such motor memory components outside primary sensorimotor regions likely underlies a parsimonious neural organization that enables more efficient movement planning in the brain, independent of the effector used to act.

Fixturing is the most commonly used manufacturing constraint in setup planning. The computer-aided fixture design technique is being rapidly developed to reduce the lead-time involved in manufacturing planning. An automated fixture configuration design system has been developed to select modular fixture components automatically and place them in position with satisfactory assembly relationships. In this paper, an automated fixture generation system for prismatic components is presented. Sequential steps for automatic fixture layout planning for machining setups, focusing on determining the most suitable locating and clamping positions in accordance with the 3-2-1 configuration, considering geometrical and dimensional constraints are presented. A software has been developed which takes two-dimensional-manufacturing drawings of the prismatic components as input and generates fixture design automatically. The modularity concept is incorporated in the developed software application and enables locating positions to be as wide apart as possible. The clamping positions are obtained directly opposite to the respective locators as far as possible. The software is tested successfully with numerous examples of prismatic parts involving similar design characteristics.

Learning from motor errors that occur across different limbs is essential for effective tool use, sports training, and rehabilitation. To probe the neural organization of error-driven learning across limbs, we asked whether learning opposing visuomotor mappings with the two arms would interfere. Young right-handers first adapted to opposite visuomotor rotations A and B with different arms and were then reexposed to A 24 h later. We observed that relearning of A was never faster nor were initial errors smaller than prior A learning, which would be expected if there was no interference from B. Rather, errors were greater than or similar to, and learning rate was slower than or comparable to, previous A learning depending on the order in which the arms learned. This indicated robust interference between the motor memories of A and B when they were learned with different arms in close succession. We then proceeded to uncover that the order-dependent asymmetry in performance upon reexposure resulted from asymmetric transfer of learning from the left arm to the right but not vice versa and that the observed interference was retrograde in nature. Such retrograde interference likely occurs because the two arms require the same neural resources for learning, a suggestion consistent with that of our past work showing impaired learning following left inferior parietal damage regardless of the arm used. These results thus point to a common neural basis for formation of new motor memories with different limbs and hold significant implications for how newly formed motor memories interact. NEW & NOTEWORTHY In a series of experiments, we demonstrate robust retrograde interference between competing motor memories developed through error-based learning with different arms. These results provide evidence for shared neural resources for the acquisition of motor memories across different limbs and also suggest that practice with two effectors in close succession may not be a sound approach in either sports or rehabilitation. Such training may not allow newly acquired motor memories to be stabilized.

Humans implicitly adjust their movements when challenged with perturbations that induce sensory prediction errors. Recent work suggests that failure to accomplish task goals could function as a gain on this prediction-error-driven adaptation or could independently trigger additional implicit mechanisms to bring about greater net learning. We aimed to distinguish between these possibilities using a reaching task wherein prediction errors were fixed at zero, but task success was modulated via changes in target location and size. We first observed that task failure caused changes in hand angle that showed classic signatures of implicit learning. Surprisingly however, these adjustments were eliminated when participants were explicitly instructed to ignore task errors. These results fail to support the idea that task errors independently induce implicit learning, and instead endorse the view that they provide a distinct signal to an intentional cognitive process that is responsive to verbal instruction.

The human sensorimotor system is sensitive to both limb-related prediction errors and task-related performance errors. Prediction error signals are believed to drive implicit refinements to motor plans. However, an understanding of the mechanisms that performance errors stimulate has remained unclear largely because their effects have not been probed in isolation from prediction errors. Diverging from past work, we induced performance errors independent of prediction errors by shifting the location of a reach target but keeping the intended and actual kinematic consequences of the motion matched. Our first two experiments revealed that rather than implicit learning, motor adjustments in response to performance errors reflect the use of deliberative, volitional strategies. Our third experiment revealed a potential dissociation of performance-error-driven strategies based on error size. Specifically, behavioral changes following large errors were consistent with goal-directed or model-based control, known to be supported by connections between prefrontal cortex and associative striatum. In contrast, motor changes following smaller performance errors carried signatures of model-free stimulus-response learning, of the kind underpinned by pathways between motor cortical areas and sensorimotor striatum. Across all experiments, we also found remarkably faster re-learning, advocating that such "savings" is associated with retrieval of previously learned strategic error compensation and may not require a history of exposure to limbrelated errors. 3 SIGNIFICANCE STATEMENTHumans adjust their actions if they do not produce desired limb-related sensory consequences or task-related outcomes. We probed whether task-related performance errors induce implicit changes to motor plans at all, or simply trigger the deliberate selection of different actions. We induced performance errors in isolation, and found that they were compensated entirely via intentional, strategic mechanisms consistent with improved action selection. Strategies also appeared to be sensitive to error size, and transitioned from stimulus-response associative behavior to goaldirected control as error magnitude increased. Across all experiments, we also found faster re-learning or "savings", substantiating the view that savings is associated with strategy-use, and does not depend on experience of limb-related prediction errors that bring about implicit adjustments to action plans.