To understand the endogenous components of the event-related brain potential (ERP), we must use data about the components' antecedent conditions to form hypotheses about the information-processing function of the underlying brain activity. These hypotheses, in turn, generate testable predictions about the consequences of the component. We review the application of this approach to the analysis of the P300 component. The amplitude of the P300 is controlled multiplicatively by the subjective probability and the task relevance of the eliciting events, whereas its (...) latency depends on the duration of stimulus evaluation. These and other factors suggest that the P300 is a manifestation of activity occurring whenever one's model of the environment must be revised. Tests of three predictions based on this “context updating” model are reviewed. Verleger's critique is based on a misconstrual of the model as well as a partial and misleading reading of the relevant literature. (shrink)

P3 is the most prominent of the electrical potentials of the human electroencephalogram that are sensitive to psychological variables. According to the most influential current hypothesis about its psychological significance [E. Donchin's], the “context updating” hypothesis, P3 reflects the updating of working memory. This hypothesis cannot account for relevant portions of the available evidence and it entails some basic contradictions. A more general formulation of this hypothesis is that P3 reflects the updating of expectancies. This version implies that P3-evoking stimuli (...) are initially unexpected but later become expected. This contradiction cannot be resolved within this formulation. The alternative “context closure” hypothesis retains the concept of “strategic information processing” emphasized by the context updating hypothesis. P3s are evoked by events that are awaited when subjects deal with repetitive, highly structured tasks; P3s arise from subjects' combining successive stimuli into larger units. The tasks in which P3s are elicited can accordingly be classified in terms of their respective formal sequences of stimuli. P3 may be a physiological indicator of excess activation being released from perceptual control areas. (shrink)

The paper is a commentary on the target article by Christine A. Skarda & Walter J. Freeman, “How brains make chaos in order to make sense of the world”, in the same issue of the journal, pp.161–195. -/- I confine my comments largely to some philosophical claims that Skarda & Freeman make and to the relationship of their model to connectionism. Some of the comments hinge on what symbols are and how they might sit in neural systems.

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)

The appropriate methodology for psychological research depends on whether one is studying mental algorithms or their implementation. Mental algorithms are abstract specifications of the steps taken by procedures that run in the mind. Implementational issues concern the speed and reliability of these procedures. The algorithmic level can be explored only by studying across-task variation. This contrasts with psychology's dominant methodology of looking for within-task generalities, which is appropriate only for studying implementational issues.The implementation-algorithm distinction is related to a number of (...) other “levels” considered in cognitive science. Its realization in Anderson's ACT theory of cognition is discussed. Research at the algorithmic level is more promising because it is hard to make further fundamental scientific progress at the implementational level with the methodologies available. Protocol data, which are appropriate only for algorithm-level theories, provide a richer source than data at the implementational level. Research at the algorithmic level will also yield more insight into fundamental properties of human knowledge because it is the level at which significant learning transitions are defined.The best way to study the algorithmic level is to look for differential learning outcomes in pedagogical experiments that manipulate instructional experience. This provides control and prediction in realistically complex learning situations. The intelligent tutoring paradigm provides a particularly fruitful way to implement such experiments.The implications of this analysis for the issue of modularity of mind, the status of language, research on human/computer interaction, and connectionist models are also examined. (shrink)

Neuroscience is a multidisciplinary effort to understand the structures and functions of the brain and brain-mind relations. This effort results in an increasing amount of data, generated by sophisticated technologies. However, these data enhance our descriptive knowledge, rather than improve our understanding of brain functions. This is caused by methodological gaps both within and between subdisciplines constituting neuroscience, and the atomistic approach that limits the study of macro- and mesoscopic issues. Whole-brain measurement technologies do not resolve these issues, but rather (...) aggravate them by the complexity problem. The present article is devoted to methodological and epistemic problems that obstruct the development of human neuroscience. We neither discuss ontological questions nor review data, except when it is necessary to demonstrate a methodological issue. As regards intradisciplinary methodological problems, we concentrate on those within neurobiology and psychology. As regards interdisciplinary problems, we suggest that core disciplines of neuroscience can be integrated using systemic concepts that also entail human-environment relations. We emphasize the necessity of a meta-discussion that should entail a closer cooperation with philosophy as a discipline of systematic reflection. The atomistic reduction should be complemented by the explicit consideration of the embodiedness of the brain and the embeddedness of humans. The discussion is aimed at the development of an explicit methodology of integrative human neuroscience, which will not only link different fields and levels, but also help in understanding clinical phenomena. (shrink)