Biomedical subjects

Philip N Sabes

Publications and source records attributed to Philip N Sabes.

4 recordsLinked to original sources

PubMed2006

Modeling sensorimotor learning with linear dynamical systems.

Recent studies have employed simple linear dynamical systems to model trial-by-trial dynamics in various sensorimotor learning tasks. Here we explore the theoretical and practical considerations that arise when employing the general class of linear dynamical systems (LDS) as a model for sensorimotor learning. In this framework, the state of the system is a set of parameters that define the current sensorimotor transformation-the function that maps sensory inputs to motor outputs. The class of LDS models provides a first-order approximation for any Markovian (state-dependent) learning rule that specifies the changes in the sensorimotor transformation that result from sensory feedback on each movement. We show that modeling the trial-by-trial dynamics of learning provides a substantially enhanced picture of the process of adaptation compared to measurements of the steady state of adaptation derived from more traditional blocked-exposure experiments. Specifically, these models can be used to quantify sensory and performance biases, the extent to which learned changes in the sensorimotor transformation decay over time, and the portion of motor variability due to either learning or performance variability. We show that previous attempts to fit such models with linear regression have not generally yielded consistent parameter estimates. Instead, we present an expectation-maximization algorithm for fitting LDS models to experimental data and describe the difficulties inherent in estimating the parameters associated with feedback-driven learning. Finally, we demonstrate the application of these methods in a simple sensorimotor learning experiment: adaptation to shifted visual feedback during reaching.

Algorithms↗

PubMed2005

Flexible strategies for sensory integration during motor planning.

When planning target-directed reaching movements, human subjects combine visual and proprioceptive feedback to form two estimates of the arm's position: one to plan the reach direction, and another to convert that direction into a motor command. These position estimates are based on the same sensory signals but rely on different combinations of visual and proprioceptive input, suggesting that the brain weights sensory inputs differently depending on the computation being performed. Here we show that the relative weighting of vision and proprioception depends both on the sensory modality of the target and on the information content of the visual feedback, and that these factors affect the two stages of planning independently. The observed diversity of weightings demonstrates the flexibility of sensory integration and suggests a unifying principle by which the brain chooses sensory inputs so as to minimize errors arising from the transformation of sensory signals between coordinate frames.

Adolescent↗

PubMed2003

Multisensory integration during motor planning.

When planning goal-directed reaches, subjects must estimate the position of the arm by integrating visual and proprioceptive signals from the sensory periphery. These integrated position estimates are required at two stages of motor planning: first to determine the desired movement vector, and second to transform the movement vector into a joint-based motor command. We quantified the contributions of each sensory modality to the position estimate formed at each planning stage. Subjects made reaches in a virtual reality environment in which vision and proprioception were dissociated by shifting the location of visual feedback. The relative weighting of vision and proprioception at each stage was then determined using computational models of feedforward motor control. We found that the position estimate used for movement vector planning relies mostly on visual input, whereas the estimate used to compute the joint-based motor command relies more on proprioceptive signals. This suggests that when estimating the position of the arm, the brain selects different combinations of sensory input based on the computation in which the resulting estimate will be used.

Adult↗

PubMed2002

Parietal representation of object-based saccades.

When monkeys make saccadic eye movements to simple visual targets, neurons in the lateral intraparietal area (LIP) display a retinotopic, or eye-centered, coding of the target location. However natural saccadic eye movements are often directed at objects or parts of objects in the visual scene. In this paper we investigate whether LIP represents saccadic eye movements differently when the target is specified as part of a visually displayed object. Monkeys were trained to perform an object-based saccade task that required them to make saccades to previously cued parts of an abstract object after the object reappeared in a new orientation. We recorded single neurons in area LIP of two macaque monkeys and analyzed their activity in the object-based saccade task, as well as two control tasks: a standard memory saccade task and a fixation task with passive object viewing. The majority of LIP neurons that were tuned in the memory saccade task were also tuned in the object-based saccade task. Using a hierarchical generalized linear model analysis, we compared the effects of three different spatial variables on the firing rate: the retinotopic location of the target, the object-fixed location of the target, and the orientation of the object in space. There was no evidence of an explicit object-fixed representation in the activity in LIP during either of the object-based tasks. In other words, no cells had receptive fields that rotated with the object. While some cells showed a modulation of activity due to the location of the target on the object, these variations were small compared to the retinotopic effects. For most cells, firing rates were best accounted for by either the retinotopic direction of the movement, the orientation of the object, or both spatial variables. The preferred direction of these retinotopic and object orientation effects were found to be invariant across tasks. On average, the object orientation effects were consistent with the retinotopic coding of potential target locations on the object. This interpretation is supported by the fact that the magnitude of these two effects were roughly equal in the early portions of the trial, but around the time of the motor response, the retinotopic effects dominated. We conclude that LIP uses the same retinotopic coding of saccade target whether the target is specified as an absolute point in space or as a location on a moving object.

Animals↗