Parallel distributed processing and neuropsychology: A neural network model of Wisconsin card sorting and verbal fluency

Neural networks can be used as a tool in the explanation of neuropsychological data. Using the Hebbian Learning Rule and other such principles as competition and modifiable interlevel feedback, researchers have successfully modeled a widely used neuropsychological test, the Wisconsin Card Sorting Test. One of these models is reviewed here and extended to a qualitative analysis of how verbal fluency might be modeled, which demonstrates the importance of accounting for the attentional components of both tests. Difficulties remain in programming sequential cognitive processes within a parallel distributed processing (PDP) framework and integrating exceedingly complex neuropsychological tests such as Proverbs. PDP neural network methodology offers neuropsychologists co-validation procedures within narrowly defined areas of reliability and validity.     

Connectionist models of conditioning: A tutorial

Models containing networks of neuron-like units have become increasingly prominent in the study of both cognitive psychology and artificial intelligence. This article describes the basic features of connectionist models and provides an illustrative application to compound-stimulus effects in respondent conditioning. Connectionist models designed specifically for operant conditioning are not yet widely available, but some current learning algorithms for machine learning indicate that such models are feasible. Conversely, designers for machine learning appear to have recognized the value of behavioral principles in producing adaptive behavior in their creations.     

Units of analysis and kinetic structure of behavioral repertoires

It is suggested that molar streams of behavior are constructed of various arrangements of three elementary constituents (elicited, evoked, and emitted response classes). An eight-cell taxonomy is elaborated as a framework for analyzing and synthesizing complex behavioral repertoires based on these functional units. It is proposed that the local force binding functional units into a smoothly articulated kinetic sequence arises from temporally arranged relative response probability relationships. Behavioral integration is thought to reflect the joint influence of the organism's hierarchy of relative response probabilities, fluctuating biological states, and the arrangement of environmental and behavioral events in time.     

EVOLUTION IN THERMODYNAMIC PERSPECTIVE: A HISTORICAL AND PHILOSOPHICAL ANGLE

Abstract. The recently suggested reformulation of Darwinian evolutionary theory, based on the thermodynamics of self-organizing processes, has strong philosophical implications. My claim is that the main philosophical merit of the thermodynamic approach, made especially clear in J.S. Wicken's work, is its insistence on the law-governed, continuous nature of evolution. I attempt to substantiate this claim following a historical analysis of beginning-of-the-century ideas on evolution and matter-life relationship, in particular, the fitness-of-the-environment-for-life theory of the Harvard physiologist L.J. Henderson. In addition, I point to an epistemological common ground underlying the studies of the “thermodynamics school” and other currently active research groups focusing on the emergence and evolution of biological organization.     

An Ecologically Differentiated, Multifactor Model of Adolescent Network Orientation

The paper presents a test of an ecologically differentiated model of social network orientation for adolescents that distinguished between different social network reference groups (family, peers, and nonfamily adults). The model was tested in two consecutive studies. Study 1 describes initial model development (N = 120). Study 2 presents a confirmatory factor analysis with a second sample (N = 430) to replicate the factor structure developed in Study 1. Results supported a three-factor model of network orientation that differentiated between network reference groups. Analyses of concurrent and predictive validity indicated that orientation to network reference groups was differentially related to the perceived quality and frequency of support from members of respective social network groups. Group differences (gender, race) regarding network orientation to different network reference groups were consistent with studies of other social network processes. Implications for the study of the network orientation and the study of social networks more generally are discussed.     

From dialectic to organization: Bogdanov’s contribution to social theory

This paper situates Bogdanov in the context of social theory generally and socialist theory in particular. It outlines briefly the principal characteristics of his mature system, and assesses the strengths and weaknesses of his approach to the fundamental problems of social thought. The paper devotes particular attention to the problem of just how systems develop from less complex to more complex forms of organization, and evaluates Bogdanov’s solution to this problem against the background of populist, social democratic, and Leninist alternatives.     

Single-Stage Prediction Models Do Not Explain the Magnitude of Syntactic Disambiguation Difficulty

The disambiguation of a syntactically ambiguous sentence in favor of a less preferred parse can lead to slower reading at the disambiguation point. This phenomenon, referred to as a garden-path effect, has motivated models in which readers initially maintain only a subset of the possible parses of the sentence, and subsequently require time-consuming reanalysis to reconstruct a discarded parse. A more recent proposal argues that the garden-path effect can be reduced to surprisal arising in a fully parallel parser: words consistent with the initially dispreferred but ultimately correct parse are simply less predictable than those consistent with the incorrect parse. Since predictability has pervasive effects in reading far beyond garden-path sentences, this account, which dispenses with reanalysis mechanisms, is more parsimonious. Crucially, it predicts a linear effect of surprisal: the garden-path effect is expected to be proportional to the difference in word surprisal between the ultimately correct and ultimately incorrect interpretations. To test this prediction, we used recurrent neural network language models to estimate word-by-word surprisal for three temporarily ambiguous constructions. We then estimated the slowdown attributed to each bit of surprisal from human self-paced reading times, and used that quantity to predict syntactic disambiguation difficulty. Surprisal successfully predicted the existence of garden-path effects, but drastically underpredicted their magnitude, and failed to predict their relative severity across constructions. We conclude that a full explanation of syntactic disambiguation difficulty may require recovery mechanisms beyond predictability.     

Cognitive computing models for estimation of reference evapotranspiration: A review

Irrigation practices can be advanced by the aid of cognitive computing models. Repeated droughts, population expansion and the impact of global warming collectively impose rigorous restrictions over irrigation practices. Reference evapotranspiration (ET₀) is a vital factor to predict the crop water requirements based on climate data. There are many techniques available for the prediction of ET₀. An efficient ET₀ prediction model plays an important role in irrigation system to increase water productivity. In the present study, a review has been carried out over cognitive computing models used for the estimation of ET₀. Review exhibits that artificial neural network (ANN) approach outperforms support vector machine (SVM) and genetic programming (GP). Second order neural network (SONN) is the most promising approach among ANN models.