Kohonen neural networks and language.

Kohonen neural networks are a type of self-organizing network that recognizes the statistical characteristics of input datasets. The application of this type of neural network to language theory is demonstrated in the present note by showing three brief applications: recognizing word borders, learning the limited phonemes of one's native tongue, and category-specific naming impairments.     

(Physio)logical circuits: the intellectual origins of the McCulloch-Pitts neural networks

This article examines the intellectual and institutional factors that contributed to the collaboration of neuropsychiatrist Warren McCulloch and mathematician Walter Pitts on the logic of neural networks, which culminated in their 1943 publication, "A Logical Calculus of the Ideas Immanent in Nervous Activity." Historians and scientists alike often refer to the McCulloch-Pitts paper as a landmark event in the history of cybernetics, and fundamental to the development of cognitive science and artificial intelligence. This article seeks to bring some historical context to the McCulloch-Pitts collaboration itself, namely, their intellectual and scientific orientations and backgrounds, the key concepts that contributed to their paper, and the institutional context in which their collaboration was made. Although they were almost a generation apart and had dissimilar scientific backgrounds, McCulloch and Pitts had similar intellectual concerns, simultaneously motivated by issues in philosophy, neurology, and mathematics. This article demonstrates how these issues converged and found resonance in their model of neural networks. By examining the intellectual backgrounds of McCulloch and Pitts as individuals, it will be shown that besides being an important event in the history of cybernetics proper, the McCulloch-Pitts collaboration was an important result of early twentieth-century efforts to apply mathematics to neurological phenomena.     

Standard errors and confidence intervals in within-subjects designs: Generalizing Loftus and Masson (1994) and avoiding the biases of alternative accounts

Repeated measures designs are common in experimental psychology. Because of the correlational structure in these designs, the calculation and interpretation of confidence intervals is nontrivial. One solution was provided by Loftus and Masson (Psychonomic Bulletin & Review 1:476-490, 1994). This solution, although widely adopted, has the limitation of implying same-size confidence intervals for all factor levels, and therefore does not allow for the assessment of variance homogeneity assumptions (i.e., the circularity assumption, which is crucial for the repeated measures ANOVA). This limitation and the method's perceived complexity have sometimes led scientists to use a simplified variant, based on a per-subject normalization of the data (Bakeman & McArthur, Behavior Research Methods, Instruments, & Computers 28:584-589, 1996; Cousineau, Tutorials in Quantitative Methods for Psychology 1:42-45, 2005; Morey, Tutorials in Quantitative Methods for Psychology 4:61-64, 2008; Morrison & Weaver, Behavior Research Methods, Instruments, & Computers 27:52-56, 1995). We show that this normalization method leads to biased results and is uninformative with regard to circularity. Instead, we provide a simple, intuitive generalization of the Loftus and Masson method that allows for assessment of the circularity assumption.     

PatPho: A phonological pattern generator for neural networks

Much of the power of neural network modeling for language use and acquisition derives from a reliance on statistical regularities implicit in the phonological properties of words. Researchers have devised several methods for representing the phonology of words, but these methods are often either unable to represent realistically sized lexicons or inadequate in the ways they represent individual words. In this paper, we present a new phonological pattern generator (PatPho) that allows connectionist modelers to derive accurate phonological representations of the English lexicon. PatPho not only generates phonological patterns that can scale up to realistically sized lexicons, but also accurately and parsimoniously captures the similarity structures of the phonology of monosyllabic and multisyllabic words.     

Mathematica: A flexible design environment for neural networks

Several neural networks were developed inMathematica in order to explore the role of “spiky” neurons in neural network memory simulations. UsingMathematica for this task confirmed its value as a powerful tool for neural network development: It exhibited distinct advantages over other environments in programming ease, flexibility of data structures, and the graphical assessment of network performance.     

Prefrontal cortex regulates inhibition and excitation in distributed neural networks.

Prefrontal cortex provides both inhibitory and excitatory input to distributed neural circuits required to support performance in diverse tasks. Neurological patients with prefrontal damage are impaired in their ability to inhibit task-irrelevant information during behavioral tasks requiring performance over a delay. The observed enhancements of primary auditory and somatosensory cortical responses to task-irrelevant distractors suggest that prefrontal damage disrupts inhibitory modulation of inputs to primary sensory cortex, perhaps through abnormalities in a prefrontal-thalamic sensory gating system. Failure to suppress irrelevant sensory information results in increased neural noise, contributing to the deficits in decision making routinely observed in these patients. In addition to a critical role in inhibitory control of sensory flow to primary cortical regions, and tertiary prefrontal cortex also exerts excitatory input to activity in multiple sub-regions of secondary association cortex. Unilateral prefrontal damage results in multi-modal decreases in neural activity in posterior association cortex in the hemisphere ipsilateral to damage. This excitatory modulation is necessary to sustain neural activity during working memory. Thus, prefrontal cortex is able to sculpt behavior through parallel inhibitory and excitatory regulation of neural activity in distributed neural networks.     

Artificial neural networks for analyzing inter-limb coordination: the golf chip shot

Motor control research relies on theories, such as coordination dynamics, adapted from physical sciences to explain the emergence of coordinated movement in biological systems. Historically, many studies of coordination have involved inter-limb coordination of relatively few degrees of freedom. This study looked at the high-dimensional inter-limb coordination used to perform the golf chip shot toward six different target distances. This study also introduces a visualization of high-dimensional coordination relevant within the coordination dynamics theoretical framework. A specific type of Artificial Neural Network (ANN), the Self-Organizing Map (SOM), was used for the analysis. In this study, the trajectory of consecutive best-matching nodes on the output map was used as a collective variable and subsequently fed into a second SOM which was used to create visualization of coordination stability. The SOM trajectories showed changes in coordination between movement patterns used for short chip shots and movement patterns used for long chip shots. The attractor diagrams showed non-linear phase transitions for three out of four players. The methods used in this study may offer a solution for researchers from a coordination dynamics perspective who intend to use data obtained from discrete high-dimensional movements.     

Ecosystemic epistemology: an alternative paradigm for diagnosis.

An alternative paradigm for diagnosis based on cybernetics, ecology, and systems theory is proposed. This paradigm, termed "ecosystemic epistemology," suggests that diagnosis focus on knowing problematic situations in an ecological and systemic way. Theoretical statements concerning this approach are delineated with specific references to major family therapists. This discussion provides a foundation for an ecosystemic epistemology for diagnosis that addresses the following issues: (a) the meaning of system; (b) the symptom's presence in the system; (c) the therapist's presence in the system; (d) the ecological relationship system that emerges in diagnosis; and (e) the relation between ecosystemic epistemology and the process of diagnosis. In effect, this paper attempts to formally describe the underlying epistemology inherent in the work of therapists who approach diagnosis in an ecological and systemic way.