Connectionist models provide a promising alternative to the traditional computational approach that has for several decades dominated cognitive science and artificial intelligence, although the nature of connectionist models and their relation to symbol processing remains controversial. Connectionist models can be characterized by three general computational features: distinct layers of interconnected units, recursive rules for updating the strengths of the connections during learning, and “simple” homogeneous computing elements. Using just these three features one can construct surprisingly elegant and powerful models of (...) memory, perception, motor control, categorization, and reasoning. What makes the connectionist approach unique is not its variety of representational possibilities or its departure from explicit rule-based models, or even its preoccupation with the brain metaphor. Rather, it is that connectionist models can be used to explore systematically the complex interaction between learning and representation, as we try to demonstrate through the analysis of several large networks. (shrink)

This paper is a commentary on the target article by Lokendra Shastri & Venkat Ajjanagadde [S&A]: “From simple associations to systematic reasoning: A connectionist representation of rules, variables and dynamic bindings using temporal synchrony” in same issue of the journal, pp.417–451. -/- It puts S&A's temporal-synchrony binding method in a broader context, comments on notions of pointing and other ways of associating information - in both computers and connectionist systems - and mentions types of reasoning that are a challenge to (...) S&A's system (amongst many other connectionist systems, recognizing that S&A's is much more capable as regards reasoning than most other contemporary systems). (shrink)

Jeannerod's target article describes support, through empirical and neurological findings, for the intriguing idea of motor imagery, a form of representation hypothesized to have levels of functional equivalence with motor preparation, while being consciously accessible. Jeannerod suggests that the subjectively accessible content of motor imagery allows it to be distinguished from motor preparation, which is unconscious. Motor imagery is distinguished from visual imagery in terms of content. Motor images are kinesthetic in nature; they are parametrized by variables such as force (...) and time and they are potentially governed by kinematic rules. Jeannerod acknowledges, however, that motor and visual imagery may not easily be separated, because actions take place in a spatial environment. I agree; in fact, I suggest here that visualization may generally be concomitant with, and may even subjectively dominate, motor imagery. (shrink)

This paper concerns how motor actions are neurally represented and coded. Action planning and motor preparation can be studied using a specific type of representational activity, motor imagery. A close functional equivalence between motor imagery and motor preparation is suggested by the positive effects of imagining movements on motor learning, the similarity between the neural structures involved, and the similar physiological correlates observed in both imaging and preparing. The content of motor representations can be inferred from motor images at a (...) macroscopic level, based on global aspects of the action and the motor rules and constraints which predict the spatial path and kinematics of movements. A more microscopic neural account calls for a representation of object-oriented action. Object attributes are processed in different neural pathways depending on the kind of task the subject is performing. During object-oriented action, a pragmatic representation is activated in which object affordances are transformed into specific motor schemas. Animal as well as human clinical data implicate the posterior parietal and premotor cortical areas in schema instantiation. A mechanism is proposed that is able to encode the desired goal of the action and is applicable to different levels of representational organization. (shrink)

Critics of the computational connectionism of the last decade suggest that it shares undesirable features with earlier empiricist or associationist approaches, and with behaviourist theories of learning. To assess the accuracy of this charge the works of earlier writers are examined for the presence of such features, and brief accounts of those found are given for Herbert Spencer, William James and the learning theorists Thorndike, Pavlov and Hull. The idea that cognition depends on associative connections among large networks of neurons (...) is indeed one with precedents, although the implications of this for psychological issues have been interpreted variously — not all versions of connectionism are alike. (shrink)

Human agents draw a variety of inferences effortlessly, spontaneously, and with remarkable efficiency – as though these inferences were a reflexive response of their cognitive apparatus. Furthermore, these inferences are drawn with reference to a large body of background knowledge. This remarkable human ability seems paradoxical given the complexity of reasoning reported by researchers in artificial intelligence. It also poses a challenge for cognitive science and computational neuroscience: How can a system of simple and slow neuronlike elements represent a large (...) body of systemic knowledge and perform a range of inferences with such speed? We describe a computational model that takes a step toward addressing the cognitive science challenge and resolving the artificial intelligence paradox. We show how a connectionist network can encode millions of facts and rules involving n-ary predicates and variables and perform a class of inferences in a few hundred milliseconds. Efficient reasoning requires the rapid representation and propagation of dynamic bindings. Our model (which we refer to as SHRUTI) achieves this by representing (1) dynamic bindings as the synchronous firing of appropriate nodes, (2) rules as interconnection patterns that direct the propagation of rhythmic activity, and (3) long-term facts as temporal pattern-matching subnetworks. The model is consistent with recent neurophysiological evidence that synchronous activity occurs in the brain and may play a representational role in neural information processing. The model also makes specific psychologically significant predictions about the nature of reflexive reasoning. It identifies constraints on the form of rules that may participate in such reasoning and relates the capacity of the working memory underlying reflexive reasoning to biological parameters such as the lowest frequency at which nodes can sustain synchronous oscillations and the coarseness of synchronization. (shrink)

The question of how the brain can construct a stable representation of the external world despite eye movements is a very old one. If there have been some wrong statements of problems (such as the inverted retinal image), other statements are less naive and have led to analytic solutions possibly adopted by the brain to counteract the spurious effects of eye movements. Following the MacKay (1973) objections to the analytic view of perceptual stability, Bridgeman et al. claim that the idea (...) that signals canceling the effects of saccadic eye movements are needed is also a misconception, as is the claim that stability and position encoding are two distinct problems. It must be remembered, however, that what made the theory of “cancellation” formulated by von Holst and Mittelstaedt (1950) so appealing was the clinical observation of perceptual instability following ocular paralysis. Following the concept of corollary discharge, the theory of efference copy had the advantage of simultaneously solving three problems: the stability of the visual world during the saccade, the same visual stability across saccades, and the visual constancy problem of allowing the subject to know where an object in space is. (shrink)

We identify two aspects of the problem of maintaining perceptual stability despite an observer's eye movements. The first, visual direction constancy, is the (egocentric) stability of apparent positions of objects in the visual world relative to the perceiver. The second, visual position constancy, is the (exocentric) stability of positions of objects relative to each other. We analyze the constancy of visual direction despite saccadic eye movements.Three information sources have been proposed to enable the visual system to achieve stability: the structure (...) of the visual field, proprioceptive inflow, and a copy of neural efference or outflow to the extraocular muscles. None of these sources by itself provides adequate information to achieve visual direction constancy; present evidence indicates that all three are used.Our final question concerns how information processing operations result in a stable world. The three traditionally suggested means have been elimination, translation, or evaluation. All are rejected. From a review of the physiological and psychological evidence we conclude that no subtraction, compensation, or evaluation need take place. The problem for which these solutions were developed turns out to be a false one. We propose a “calibration” solution: correct spatiotopic positions are calculated anew for each fixation. Inflow, outflow, and retinal sources are used in this calculation: saccadic suppression of displacement bridges the errors between these sources and the actual extent of movement. (shrink)

This target article draws together two groups of experimental studies on the control of human movement through peripheral feedback and centrally generated signals of motor commands. First, during natural movement, feedback from muscle, joint, and cutaneous afferents changes; in human subjects these changes have reflex and kinesthetic consequences. Recent psychophysical and microneurographic evidence suggests that joint and even cutaneous afferents may have a proprioceptive role. Second, the role of centrally generated motor commands in the control of normal movements and movements (...) following acute and chronic deafferentation is reviewed. There is increasing evidence that subjects can perceive their motor commands under various conditions, but that this is inadequate for normal movement; deficits in motor performance arise when the reliance on proprioceptive feedback is abolished either experimentally or because of pathology. During natural movement, the CNS appears to have access to functionally useful input from a range of peripheral receptors as well as from internally generated command signals. The unanswered questions that remain suggest a number of avenues for further research. (shrink)

The general problem of visual search can be shown to be computationally intractable in a formal, complexity-theoretic sense, yet visual search is extensively involved in everyday perception, and biological systems manage to perform it remarkably well. Complexity level analysis may resolve this contradiction. Visual search can be reshaped into tractability through approximations and by optimizing the resources devoted to visual processing. Architectural constraints can be derived using the minimum cost principle to rule out a large class of potential solutions. The (...) evidence speaks strongly against bottom-up approaches to vision. In particular, the constraints suggest an attentional mechanism that exploits knowledge of the specific problem being solved. This analysis of visual search performance in terms of attentional influences on visual information processing and complexity satisfaction allows a large body of neurophysiological and psychological evidence to be tied together. (shrink)

Theories of the representation of specific kinetic and spatiotem-poral features of movement range from the explicit assertion that temporal aspects of movement are not represented to the idea that they are represented and that they have neurophysiological correlates. Jeannerod's thesis is that mental and visual images have common mechanisms and that there is a link between the image to move and the mechanisms involved with movement. The target article takes the position that certain parameters are coded in motor representations but (...) that the duration of an action is not one of them. This position is based on the work of Gottlieb et al. and of Decety et al.. Both these studies are worth considering in detail. In Note 1, Jeannerod suggests that: “in time-constrained tasks subjects control the amplitude parameter of force impulses, whereas in spatially constrained tasks the duration of the force impulse is affected by accuracy demands.” This is not exactly correct. Excitation pulse intensity is modulated both in tasks that require spatial and those that require temporal accuracy. Excitation pulse duration is modulated for changes in movement distance and inertial load. If subjects are required to be very accurate spatially, they will move at less than maximum speed for a given distance and this is achieved by lower levels of excitation intensity. (shrink)