13 research outputs found

    Overall harvesting performance.

    No full text
    <p>Average number of harvests per trial for the hand task (left column) and the eye task (right column) for each condition and target type. The bars show the average number of harvests computed from participant means, with error bars representing ±1 SE, and the lines show individual participant means. All bars represent targets of a given size shown for a given value (X-axis), with white, skinny bars representing the smallest target size, light grey, thicker bars representing the medium target size, and thick, dark grey bars representing the largest target size.</p

    Experimental Task and Results

    No full text
    <div><p>(A) Schematic of the apparatus and task. On contact trials (top), in response to an auditory go signal, participants produced a brief force pulse with their right index finger on a force sensor fixed above their left index finger. A similar force pulse was delivered to the left index finger by a torque motor. On no-contact trials (bottom), the force sensor was moved at the start of the trial so that participants made a tapping movement with their right index finger but did not make contact.</p> <p>(B) Mean relative magnitude of the comparison tap to the test tap at the point of perceptual equality as a function of trial type and participant group. Error bars represent ±1 SE.</p></div

    An error-tuned model for sensorimotor learning

    No full text
    <div><p>Current models of sensorimotor control posit that motor commands are generated by combining multiple modules which may consist of internal models, motor primitives or motor synergies. The mechanisms which select modules based on task requirements and modify their output during learning are therefore critical to our understanding of sensorimotor control. Here we develop a novel modular architecture for multi-dimensional tasks in which a set of fixed primitives are each able to compensate for errors in a single direction in the task space. The contribution of the primitives to the motor output is determined by both top-down contextual information and bottom-up error information. We implement this model for a task in which subjects learn to manipulate a dynamic object whose orientation can vary. In the model, visual information regarding the context (the orientation of the object) allows the appropriate primitives to be engaged. This top-down module selection is implemented by a Gaussian function tuned for the visual orientation of the object. Second, each module's contribution adapts across trials in proportion to its ability to decrease the current kinematic error. Specifically, adaptation is implemented by cosine tuning of primitives to the current direction of the error, which we show to be theoretically optimal for reducing error. This error-tuned model makes two novel predictions. First, interference should occur between alternating dynamics only when the kinematic errors associated with each oppose one another. In contrast, dynamics which lead to orthogonal errors should not interfere. Second, kinematic errors alone should be sufficient to engage the appropriate modules, even in the absence of contextual information normally provided by vision. We confirm both these predictions experimentally and show that the model can also account for data from previous experiments. Our results suggest that two interacting processes account for module selection during sensorimotor control and learning.</p></div

    Schematic of the error tuned model (ETM).

    No full text
    <p><b>A</b>. Motor output. The modules each have a preferred direction uniformly covering the possible object orientations (here, 16 modules are shown by the grey peripheral objects). On the n<sup>th</sup> trial the modules each have an adaptive state indicated by the length of the vectors (left panel). In this example, the distribution of adapted states is consistent with recent experience of an object at 270°. On the current trial, the object is changed to an orientation of 0° (blue peripheral object). In this case, the visual contextual tuning gives the greatest weight to modules with preferred directions near 0° (middle panel). The motor contribution of each module (black vectors, right panel) is vector summed to produce the final motor output (green vector). The ideal motor output is shown by the blue vector, leading to an error (magenta vector). <b>B</b>. Motor adaptation is driven by two processes. The top row shows error-independent decay in which visual contextual tuning (middle panel) determines the decay of memory across modules. Here the memory decays most for the current context (0°) and less for more distant contexts. This leads to a set of reduced adaptive states (right panel; original states indicated by solid line). The bottom row shows error-dependent adaptation. The left panel shows the error (magenta) as well as its projection onto each module’s preferred direction (i.e. cosine tuning in which red vectors reflect negative magnitudes). This tuning reflects the extent that changing the adaptive state of a module will reduce the error. These projections are modulated by the visual contextual tuning (middle panel) which is greatest for the current context. This determines how each module updates its adaptive state in response to the error (right panel). The adaptive state on the next trial (n+1; far right panel) is the sum of the decayed states and the state updates, leading to a reduced error on the next trial for the same orientation of the object. Note that this schematic is not drawn to scale and exaggerates some of the changes so that they are visible. The ⊙ symbol represents element-wise multiplication across the modules.</p

    Experiments 1 and 2: Context-dependent adaptation and decay.

    No full text
    <p><b>A</b>. The paradigm for experiments 1 and 2. After an initial exposure block at 180° (yellow background), subjects performed alternating probe blocks presented at one of five orientations between 0° and 180° (green background) followed by re-exposure blocks at 180° (blue background). <b>B</b>. Experiment 1 in which probe blocks consisted of 20 error-clamp trials. The left plot shows the composite trial-series for PD (all trials) and Adaptation (error-clamp probe blocks only). Grey shading shows ±SE across subjects. Each subject experienced the probe blocks in a pseudorandomized order so the trial-series has been rearranged in order of increasing probe orientation (∆0° to ∆180°). The right plots show the corresponding measures averaged over the different probe blocks and over subjects (error-bars show ±SE across subjects). Adaptation is measured from the probe blocks (right top, green background) and re-exposure PD is measured from the re-exposure blocks (right bottom, blue background). Model fits are shown in all panels for the CDM (blue) and ETM (red). Experimental data from [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005883#pcbi.1005883.ref004" target="_blank">4</a>]. <b>C</b>. Experiment 2, plotted as in panel B. In this case, probe blocks consisted of 8 zero-force trials. As in panel B, model fits are shown in all panels. Experimental data from [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005883#pcbi.1005883.ref003" target="_blank">3</a>].</p

    Experiment 3: Opposing dynamics.

    No full text
    <p><b>A.</b> The paradigm consisted of alternating exposure blocks at 180° and 0° followed by two final blocks of zero-force trials (all blocks consist of 24 trials). <b>B.</b> Trial-series averaged across subjects (grey shading shows ±SE across subjects). Performance was stable from the 5th exposure cycle onwards so we omit exposure blocks after this for clarity. The fits of the models are shown in all panels for the CDM (blue) and ETM (red). <b>C.</b> The PD averaged over each of the first four 180° exposure blocks for the experimental data (error-bars are SE across subjects; p-values are for two-tailed paired t-tests as indicated) and CDM and ETM fits. Experimental data from [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005883#pcbi.1005883.ref003" target="_blank">3</a>].</p