Part I. Experimental Studies: 2. Experiment in psychology 3. Experiments on perceiving III Experiments on imaging 4-8. Experiments on remembering: (a) The method of description (b) The method of repeated reproduction (c) The method of picture writing (d) The method of serial reproduction (e) The method of serial reproduction picture material 9. Perceiving, recognizing, remembering 10. A theory of remembering 11. Images and their functions 12. Meaning Part II. Remembering as a Study in Social Psychology: 13. Social psychology 14. Social psychology and the matter of recall 15. Social psychology and the manner of recall 16. Conventionalism 17. The notion of a collective unconscious 18. The basis of social recall 19. A summary and some conclusions.

Remembering. A Study in Experimental and Social Psychology, Cambridge (University Press) 1964.

On the basis of computational studies it has been proposed that the central nervous system internally simulates the dynamic behavior of the motor system in planning, control, and learning; the existence and use of such an internal model is still under debate. A sensorimotor integration task was investigated in which participants estimated the location of one of their hands at the end of movements made in the dark and under externally imposed forces. The temporal propagation of errors in this task was analyzed within the theoretical framework of optimal state estimation. These results provide direct support for the existence of an internal model.

http://science.sciencemag.org/content/sci/269/5232/1880.full.pdf

An Internal Model for Sensorimotor Integration

The inability of axons to regenerate after a spinal cord injury in the adult mammalian central nervous system (CNS) can lead to permanent paralysis. At sites of CNS injury, a glial scar develops, containing extracellular matrix molecules including chondroitin sulphate proteoglycans (CSPGs). CSPGs are inhibitory to axon growth in vitro, and regenerating axons stop at CSPG-rich regions in vivo. Removing CSPG glycosaminoglycan (GAG) chains attenuates CSPG inhibitory activity. To test the functional effects of degrading chondroitin sulphate (CS)-GAG after spinal cord injury, we delivered chondroitinase ABC (ChABC) to the lesioned dorsal columns of adult rats. We show that intrathecal treatment with ChABC degraded CS-GAG at the injury site, upregulated a regeneration-associated protein in injured neurons, and promoted regeneration of both ascending sensory projections and descending corticospinal tract axons. ChABC treatment also restored post-synaptic activity below the lesion after electrical stimulation of corticospinal neurons, and promoted functional recovery of locomotor and proprioceptive behaviours. Our results demonstrate that CSPGs are important inhibitory molecules in vivo and suggest that their manipulation will be useful for treatment of human spinal injuries.

Chondroitinase ABC promotes functional recovery after spinal cord injury

When we learn a new motor skill, such as playing an approaching tennis ball, both our sensors and the task possess variability. Our sensors provide imperfect information about the ball's velocity, so we can only estimate it. Combining information from multiple modalities can reduce the error in this estimate. On a longer time scale, not all velocities are a priori equally probable, and over the course of a match there will be a probability distribution of velocities. According to bayesian theory, an optimal estimate results from combining information about the distribution of velocities-the prior-with evidence from sensory feedback. As uncertainty increases, when playing in fog or at dusk, the system should increasingly rely on prior knowledge. To use a bayesian strategy, the brain would need to represent the prior distribution and the level of uncertainty in the sensory feedback. Here we control the statistical variations of a new sensorimotor task and manipulate the uncertainty of the sensory feedback. We show that subjects internally represent both the statistical distribution of the task and their sensory uncertainty, combining them in a manner consistent with a performance-optimizing bayesian process. The central nervous system therefore employs probabilistic models during sensorimotor learning.

Bayesian integration in sensorimotor learning

https://www.thelancet.com/pdfs/journals/lancet/PIIS0140-6736(12)61816-9.pdf

High-performance neuroprosthetic control by an individual with tetraplegia

Subjects were asked to shape the right hand as if to grasp and use a large number of familiar objects. The chosen objects typically are held with a variety of grips, including “precision” and “power” grips. Static hand posture was measured by recording the angular position of 15 joint angles of the fingers and of the thumb. Although subjects adopted distinct hand shapes for the various objects, the joint angles of the digits did not vary independently. Principal components analysis showed that the first two components could account for >80% of the variance, implying a substantial reduction from the 15 degrees of freedom that were recorded. However, even though they were small, higher-order (more than three) principal components did not represent random variability but instead provided additional information about the object. These results suggest that the control of hand posture involves a few postural synergies, regulating the general shape of the hand, coupled with a finer control mechanism providing for small, subtle adjustments. Because the postural synergies did not coincide with grip taxonomies, the results suggest that hand posture may be regulated independently from the control of the contact forces that are used to grasp an object.

https://www.jneurosci.org/content/jneuro/18/23/10105.full.pdf

Postural Hand Synergies for Tool Use

1. The accuracy with which subjects pointed to targets in extrapersonal space was assessed under a variety of experimental conditions. 2. When subjects pointed in the dark to remembered target locations, they made substantial errors. Errors in distance, measured from the shoulder to the target, were sometimes as much as 15 cm. Errors in direction, also measured from the shoulder, were smaller. 3. An analysis of the information transmitted by the location of the subject's finger about the location of the target showed that the information about the target's distance was consistently lower than the information about its direction. 4. The errors in distance persisted when subjects had their arm in view and pointed in the light to remembered target locations. 5. The errors were much smaller when subjects used a pointer to point to the target or when they were asked to reproduce the position of their finger after it had been passively moved to the target. 6. From these findings we conclude that subjects have a reasonably accurate visual representation of target location and are able to effectively use kinesthetically derived information about target location. We therefore suggest that errors in pointing result from errors in the sensorimotor transformation from the visual representation of the target location to the kinematic representation of the arm movement.

http://www.ece.uvic.ca/~bctill/papers/mocap/Soechting_Flanders_1989a.pdf

Sensorimotor representations for pointing to targets in three-dimensional space

We present a model for several early stages of the sensorimotor transformations involved in targeted arm movement. In psychophysical experiments, human subjects pointed to the remembered locations of randomly placed targets in three-dimensional space. They made consistent errors in distance, and from these errors stages in the sensorimotor transformation were deduced. When subjects attempted to move the right index finger to a virtual target they consistently undershot the distance of the more distal targets. Other experiments indicated that the error was in the sensorimotor transformation rather than in the perception of distance. The error was most consistent when evaluated using a spherical coordinate system based at the right shoulder, indicating that the neural representation of target parameters is transformed from a retinocentric representation to a shoulder-centered representation. According to the model, the error in distance results from the neural implementation of a linear approximation in the algorithm to transform shoulder-centered target parameters into a set of arm orientations appropriate for placing the finger on the target. The transformation to final arm orientations places visually derived information into a frame of reference where it can readily be combined with kinesthetically derived information about initial arm orientations. The combination of these representations of initial and final arm orientations could give rise to the representation of movement direction recorded in the motor cortex by Georgopoulos and his colleagues. Later stages, such as the transformation from kinematic (position) to dynamic (force) parameters, or to levels of muscle activation, are beyond the scope of the present model.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0140525X00068813

Early stages in a sensorimotor transformation

1. We define an extrinsic frame of reference to represent the location of a point in extrapersonal space relative to a human subject's shoulder, and we define an intrinsic frame of reference to rep...

/pdf/errors-in-pointing-are-due-to-approximations-in-sensorimotor-1vy75emoj5.pdf

Errors in pointing are due to approximations in sensorimotor transformations

This study was aimed at describing temporal synergies of hand movement and determining the influence of sensory cues on the control of these synergies. Subjects were asked to reach to and grasp various objects under three experimental conditions: (1) memory-guided movements, in which the object was not in view during the movement; (2) virtual object, in which a virtual image of the object was in view but the object was not physically present; and (3) real object, in which the object was in view and physically present. Motion of the arm and of 15 degrees of freedom of the hand was recorded. A principal components analysis was developed to provide a concise description of the spatiotemporal patterns underlying the motion. Vision of the object during the reaching movement had no influence on the kinematics, and the effect of the physical presence of the object became manifest primarily after the fingers had contacted the object. Two principal components accounted for >75% of the variance. For both components, there was a strong positive correlation in the rotations of metacarpophalangeal and proximal interphalangeal joints of the fingers. The first principal component exhibited a pattern of finger extension reversing to flexion, whereas the second principal component became important only in the second half of the reaching movement.

https://www.jneurosci.org/content/jneuro/22/4/1426.full.pdf

Martha Flanders

Papers

Postural Hand Synergies for Tool Use

Sensorimotor representations for pointing to targets in three-dimensional space

Early stages in a sensorimotor transformation

Errors in pointing are due to approximations in sensorimotor transformations

Patterns of Hand Motion during Grasping and the Influence of Sensory Guidance