Time underlies many interesting human behaviors. Thus, the question of how to represent time in connectionist models is very important. One approach is to represent time implicitly by its effects on processing rather than explicitly (as in a spatial representation). The current report develops a proposal along these lines first described by Jordan (1986) which involves the use of recurrent links in order to provide networks with a dynamic memory. In this approach, hidden unit patterns are fed back to themselves: (...) the internal representations which develop thus reflect task demands in the context of prior internal states. A set of simulations is reported which range from relatively simple problems (temporal version of XOR) to discovering syntactic/semantic features for words. The networks are able to learn interesting internal representations which incorporate task demands with memory demands: indeed, in this approach the notion of memory is inextricably bound up with task processing. These representations reveal a rich structure, which allows them to be highly context‐dependent, while also expressing generalizations across classes of items. These representations suggest a method for representing lexical categories and the type/token distinction. (shrink)

Voluntary acts are preceded by electrophysiological (RPs). With spontaneous acts involving no preplanning, the main negative RP shift begins at about200 ms. Control experiments, in which a skin stimulus was timed (S), helped evaluate each subject's error in reporting the clock times for awareness of any perceived event.

What psychological and philosophical significance should we attach to recent efforts at computer simulations of human cognitive capacities? In answering this question, I find it useful to distinguish what I will call "strong" AI from "weak" or "cautious" AI. According to weak AI, the principal value of the computer in the study of the mind is that it gives us a very powerful tool. For example, it enables us to formulate and test hypotheses in a more rigorous and precise fashion. (...) But according to strong AI, the computer is not merely a tool in the study of the mind; rather, the appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to. (shrink)

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 to dynamic parameters, or to levels of muscle activation, are beyond the scope of the present model. (shrink)

Much of the progress in the fields constituting cognitive science has been based upon the use of explicit information processing models, almost exclusively patterned after conventional serial computers. An extension of these ideas to massively parallel, connectionist models appears to offer a number of advantages. After a preliminary discussion, this paper introduces a general connectionist model and considers how it might be used in cognitive science. Among the issues addressed are: stability and noise‐sensitivity, distributed decision‐making, time and sequence problems, and (...) the representation of complex concepts. (shrink)

A set of hypotheses is formulated for a connectionist approach to cognitive modeling. These hypotheses are shown to be incompatible with the hypotheses underlying traditional cognitive models. The connectionist models considered are massively parallel numerical computational systems that are a kind of continuous dynamical system. The numerical variables in the system correspond semantically to fine-grained features below the level of the concepts consciously used to describe the task domain. The level of analysis is intermediate between those of symbolic cognitive models (...) and neural models. The explanations of behavior provided are like those traditional in the physical sciences, unlike the explanations provided by symbolic models. (shrink)

A theory is presented to explain how accurate, single-joint movements are controlled. The theory applies to movements across different distances, with different inertial loads, toward targets of different widths over a wide range of experimentally manipulated velocities. The theory is based on three propositions. (1) Movements are planned according to “strategies” of which there are at least two: a speed-insensitive (SI) and a speed-sensitive (SS) one. (2) These strategies can be equated with sets of rules for performing diverse movement tasks. (...) The choice between SI and SS depends on whether movement speed and/or movement time (and hence appropriate muscle forces) must be constrained to meet task requirements. (3) The electromyogram can be interpreted as a low-pass filtered version of the controlling signal to the motoneuron pools. This controlling signal can be modelled as a rectangular excitation pulse in which modulation occurs in either pulse amplitude or pulse width. Movements to different distances and with loads are controlled by the SI strategy, which modulates pulse width. Movements in which speed must be explicitly regulated are controlled by the SS strategy, which modulates pulse amplitude. The distinction between the two movement strategies reconciles many apparent conflicts in the motor control literature. (shrink)

Intermediate constructs are required as bridges between complex behaviors and realistic models of neural circuitry. For cognitive scientists in general, schemas are the appropriate functional units; brain theorists can work with neural layers as units intermediate between structures subserving schemas and small neural circuits.After an account of different levels of analysis, we describe visuomotor coordination in terms of perceptual schemas and motor schemas. The interest of schemas to cognitive science in general is illustrated with the example of perceptual schemas in (...) high-level vision and motor schemas in the control of dextrous hands.Rana computatrix, the computational frog, is introduced to show how one constructs an evolving set of model families to mediate flexible cooperation between theory and experiment. Rana computatrix may be able to do for the study of the organizational principles of neural circuitry what Aplysia has done for the study of subcellular mechanisms of learning. Approach, avoidance, and detour behavior in frogs and toads are analyzed in terms of interacting schemas. Facilitation and prey recognition are implemented as tectal-pretectal interactions, with the tectum modeled by an array of tectal columns. We show how layered neural computation enters into models of stereopsis and how depth schemas may involve the interaction of accommodation and binocular cues in anurans. (shrink)

Marr (1982) may have been one of the rst vision researchers to insist that in modeling vision it is important to separate the location of visual features from their type. He argued that in early stages of visual processing there must be “place tokens” that enable subsequent stages of the visual system to treat locations independent of what specic feature type was at that location. Thus, in certain respects a collinear array of diverse features could still be perceived as a (...) line, and under certain conditions could function as such in perceptual phenomena like the Poggendorf illusion. (shrink)

Purely parallel neural networks can model object recognition in brief displays – the same conditions under which illusory conjunctions have been demonstrated empirically. Correcting errors of illusory conjunction is the “tag-assignment” problem for a purely parallel processor: the problem of assigning a spatial tag to nonspatial features, feature combinations, and objects. This problem must be solved to model human object recognition over a longer time scale. Our model simulates both the parallel processes that may underlie illusory conjunctions and the serial (...) processes that may solve the tag-assignment problem in normal perception. One component of the model extracts pooled features and another provides attentional tags that correct illusory conjunctions. Our approach addresses two questions: How can objects be identified from simultaneously attended features in a parallel, distributed representation? How can the spatial selectional requirements of such an attentional process be met by a separation of pathways for spatial and nonspatial processing? Our analysis of these questions yields a neurally plausible simulation of tag assignment based on synchronizing feature processing activity in a spatial focus of attention. (shrink)

Studies have shown that in the mammalian neuromuscular system stretch reflexes are localized within individual muscles. Neuromuscular compartmentalization, the partitioning of sensory output from muscles, and the partitioning of segmental pathways to motor nuclei have also been demonstrated. This evidence indicates that individual motor nuclei and the muscles they innervate are not homogeneous functional units. An analysis of the functional significance of reflex localization and partitioning suggests that segmental control mechanisms are based on subdivisions of motor nuclei–muscle complexes. A partitioned (...) organization of segmental control mechanisms could utilize (1) the potential functional diversity of muscle fiber types, (2) the variety of mechanical actions of individual muscles arising from their distributed origins and insertions, and (3) diverse architectural features such as intramuscular variations in pinnation and complex in-series and in-parallel arrangements of muscle fibers. The differentiated activity observed in some muscles during natural movements also calls for localized segmental control mechanisms. Partitioning may also play a role in mechanical interactions between contracting motor units and in increasing the stability of neuromuscular systems. The functional advantages of reflex localization and partitioning suggest they are probably common features of segmental systems, whose organization reflects the structure and function of their associated neuromuscular systems. (shrink)

We propose a new dynamical mechanism for information processing in mind and brain. We emphasize that a hermeneutic process is one of the key processes manifesting the functions of the brain and that it can be formulated as an itinerant motion in ultrahigh dimensional dynamical systems, which may give a new realm of the dynamic information processing. Our discussions are based on the notion of chaotic information processing and the observations of biological chaos.

Posterior parietal cortex has traditionally been considered to be a sensory association area in which higher-order processing and intermodal integration of incoming sensory information occurs. In this paper, evidence from clinical reports and from lesion and behavioral-electrophysiological experiments using monkeys is reviewed and discussed in relation to the overall functional organization of posterior parietal association cortex, and particularly with respect to a proposed posterior parietal mechanism concerned with the initiation and control of certain classes of eye and limb movements. Preliminary (...) data from studies of the effects of posterior parietal lesions on oculomotor control in monkeys are reported.The behavioral effects of lesions of posterior parietal cortex in monkeys have been found to be similar to those which follow analogous damage of the minor hemisphere in humans, while behavioral-electrophysiological experiments have disclosed classes of neurons in this area which have functional properties closely related to the behavioral acts that are disrupted by lesions of the area. On the basis of current data from these areas of study, it is proposed that the sensory association model of posterior parietal function is inadequate to account for the complexities of the present evidence. Instead, it now appears that many diverse neural mechanisms are locatedin partin parietal cortex, that some of these mechanisms are involved in sensory processing and perceptual functions, but that others participate in motor control, and that still others are involved in attentional, motivational, or emotional processes. It is further proposed that the elementary units of these various neural mechanisms are distributed within posterior parietal cortex according to the columnar hypothesis of Mountcastle. (shrink)