This bold and brilliant book asks the ultimate question of the life sciences: How did the human mind acquire its incomparable power? In seeking the answer, Merlin Donald traces the evolution of human culture and cognition from primitive apes to the era of artificial intelligence, and presents an original theory of how the human mind evolved from its presymbolic form. In the emergence of modern human culture, Donald proposes, there were three radical transitions. During the first, our bipedal but still (...) apelike ancestors acquired "mimetic" skill—the ability to represent knowledge through voluntary motor acts—which made Homo erectus successful for over a million years. The second transition—to "mythic" culture —coincided with the development of spoken language. Speech allowed the large-brained Homo sapiens to evolve a complex preliterate culture that survives in many parts of the world today. In the third transition, when humans constructed elaborate symbolic systems ranging from cuneiforms, hieroglyphics, and ideograms to alphabetic languages and mathematics, human biological memory became an inadequate vehicle for storing and processing our collective knowledge. The modern mind is thus a hybrid structure built from vestiges of earlier biological stages as well as new external symbolic memory devices that have radically altered its organization. According to Donald, we are symbol-using creatures, more complex than any that went before us, and we may have not yet witnessed the final modular arrangement of the human mind. (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)

Behavior that solves a problem is distinguished by the fact that it changes another part of the solver's behavior and is strengthened when it does so. Problem solving typically involves the construction of discriminative stimuli. Verbal responses produce especially useful stimuli, because they affect other people. As a culture formulates maxims, laws, grammar, and science, its members behave more effectively without direct or prolonged contact with the contingencies thus formulated. The culture solves problems for its members, and does so by (...) transmitting the verbal discriminative stimuli called rules. Induction, deduction, and the construction of models are ways of producing rules. Behavior that solves a problem may result from direct shaping by contingencies or from rules constructed either by the problem solver or by others. Because different controlling variables are involved, contingency-shaped behavior is never exactly like rule-governed behavior. The distinction must take account of a system which establishes certain contingencies of reinforcement, such as some part of the natural environment, a piece of equipment, or a verbal community; the behavior shaped and maintained by these contingencies; rules, derived from the contingencies, which specify discriminative stimuli, responses, and consequences, and the behavior occasioned by the rules. (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)

We argue that a map is meaningless unless we have a process for using it. Thus, in this paper, we not only offer the world graph as a representation of relationships among situations the animal has encountered and may encounter again, but we also offer algorithms for how the information encoded in the world graph may be used by the animal in determining its behavior. Each node of the graph encodes a recognizable situation in the animal's world, but a given (...) place may well be encoded in a number of different nodes. Nodes not only require algorithms for the recognition of the situation; they store information about drive reduction associated with the encoded situation. We note that the use of graphs as a basis for exploring some search space is well known in artificial intelligence (AI), but we stress the importance of the animal's exploration of its environment for growing the graph, as distinct from the mathematically described potential nodes frequent in AI search spaces. To explore a number of hypotheses about the way information in the world graph is used to guide the animal's movement, we recall a number of classical experiments on maze exploration by animals, and use them to argue for the nonlocal hypothesis (selection of a path does not depend only upon information about the immediate environment of the animal) and the competing nodes hypothesis (more than one drive may enter into the determination of the animal's behavior at any time). An important feature of the model is that it yields exploration and latent learning without the introduction of an exploratory drive. We also note that the performance exhibited by the model appears to be state-dependent when the animal operates under high drive levels. The drive-interaction matrix is offered as a subject for future research.We complement the presentation of the world graph model, its drive dynamics, and how these are constrained by experiments on maze behavior, with a brief analysis of maps in the brain. We distinguish egocentric maps–which we relate to the many visual systems–from allocentric maps. We offer a somewhat unconventional view of short-term and long-term memory. We examine cooperative computation in somatotopically organized networks, relating this to visually guided behavior in the frog, and to the interaction of colliculus and cortex in the control of eye movements. We examine, but do not advocate, the hypothesis that the hippocampus is a cognitive map. We do stress that if it is a cognitive map, it must be seen as a chart of the local neighborhood, rather than the whole atlas; and we note that the cognitive map hypothesis would lead one to expect the region to exhibit activation of place cells before the animal leaves the previous place. (shrink)

Language and speech depend on a relatively well defined neural circuitry, located predominantly in the left hemisphere. In this article, I discuss the origin of the speech circuit in early humans, as an expansion of an auditory-vocal articulatory network that took place after the last common ancestor with the chimpanzee. I will attempt to converge this perspective with aspects of the Mirror System Hypothesis, particularly those related to the emergence of a meaningful grammar in human communication. Basically, the strengthening of (...) auditory-vocal connectivity via the arcuate fasciculus and related tracts generated an expansion of working memory capacity for vocalizations, that was key for learning complex utterances. This process was concomitant with the development of a robust interface with visual working memory, both in the dorsal and ventral streams of auditory and visual processing. This enabled the bidirectional translation of sequential codes into hierarchical visual representations, through the development of a multimodal interface between both systems. (shrink)

Computational modeling of the macaque brain grounds hypotheses on the brain of LCA-m (the last common ancestor of monkey and human). Elaborations thereof provide a brain model for LCA-c (c for chimpanzee). The Mirror System Hypothesis charts further steps via imitation and pantomime to protosign and protolanguage on the path to a "language-ready brain" inHomo sapiens,with the path to speech being indirect. The material poses new challenges for both experimentation and modeling.

This paper is a commentary on the target article by Michael Arbib, “Levels of modeling of mechanisms of visually guided behavior”, in the same issue of the journal, pp. 407–465. -/- I focus on the importance of the inclusion of an ability of a system to entertain, at a given time, multiple instantiations of a given schema (situation template, frame, script, action plan, etc.), and complications introduced into neural/connectionist network systems by such inclusion.

Several themes can be identified in the commentaries. The first is that the climbing fibers may have more than one function; the second is that the climbing fibers provide sensory rather than motor signals. We accept the possibility that climbing fibers may have more than one function consequence(s)’ in the title. Until we know more about the function of the inhibitory input to the inferior olive from the cerebellar nuclei, which are motor structures, we have to keep open the possibility (...) that the climbing fiber signals can be a combination of sensory and motor signals. (shrink)

We ask what cerebellum and basal ganglia arguing that cerebellum tunes motor schemas and their coordination. We argue for a synthesis of models addressing the real-time role and error signaling roles of climbing fibers. bridges between regional and neuro-physiological studies, while relates the neurochemis-try of learning to neural and behavioral levels. [CRÉPEL et al.; HOUK et al.; KANO; LINDEN; SIMPSON et al.; SMITH; THACH; VINCENT].

The present commentary considers the question of what must be learned in different types of motor skills, thereby limiting the question of what should be adjusted in the APG model in order to explain successful learning. It is concluded that an open loop model like the APG might well be able to describe the learning pattern of motor skills in a stable, predictable environment. Recent research on saccadic plasticity, however, illustrates that motor skills performed in an unpredictable environment depend heavily (...) on sensory (mostly visual) feedback, [HOUK et al.]. (shrink)

The primary assumption made in this series of target articles is that the cerebellum is directly involved in motor control. However, in my opinion, there is ample and growing experimental evidence to question this classical view, whether or not learning is involved. I propose, instead, that the cerebellum is involved in the control of data acquisition for many different sensory systems, [CRÉPEL et al., HOUK et al., SMITH, THACH].

The target article on cellular mechanisms of long-term depression appears to have been well received by most authors of the relevant commentaries. This may be due to the fact that this review aimed to give a general account of the topic, rather than just describe previous work of the present author. The present response accordingly only raises questions of major interest for future research.

The adjustable pattern generator (APG) model addresses physiological detail in a manner that renders it eminently testable. However, the problem for which the APG was developed, namely, limb control, may be computationally too complex for this purpose. Instead, it is proposed that recent empirical and theoretical advances in understanding the role of the cerebellum in low-level saccadic control could be used to refine and extend the APG. [HOUK et al.].

This study examines how a Purkinje cell receives its appropriate olivary error signal during the learning of compound movements. We suggest that the Purkinje cell only reinforces those target pyramidal cells which already participate in the movement, subsequently reducing any repeated error signal, such as its own climbing fiber input, [simpson et al.; smith].