Semantic priming in schizophrenia: An examination of spreading activation using word pronunciation and multiple SOAs.

Semantic priming in word pronunciation was examined at 5 stimulus onset asynchronies (SOAs) in 75 medicated and 25 unmedicated people with schizophrenia (SCZ) and in 10 depressed and 28 normal controls. At SOAs     

Context, cortex, and dopamine: A connectionist approach to behavior and biology in schizophrenia.

Connectionist models are used to explore the relationship between cognitive deficits and biological abnormalities in schizophrenia. Schizophrenic deficits in tasks that tap attention and language processing are reviewed, as are biological disturbances involving prefrontal cortex and the mesocortical dopamine system. Three computer models are then presented that simulate normal and schizophrenic performance in the Stroop task, the continuous performance test, and a lexical disambiguation task. They demonstrate that a disturbance in the internal representation of contextual information can provide a common explanation for schizophrenic deficits in several attention- and language-related tasks. The models also show that these behavioral deficits may arise from a disturbance in a model parameter (gain) corresponding to the neuromodulatory effects of dopamine, in a model component corresponding to the function of prefrontal cortex. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Dopamine and the mechanisms of cognition: Part I. A neural network model predicting dopamine effects on selective attention

BACKGROUND: Dopamine affects neural information processing, cognition, and behavior; however, the mechanisms through which these three levels of function are affected have remained unspecified. We present a parallel-distributed processing model of dopamine effects on neural ensembles that accounts for effects on human performance in a selective attention task. METHODS: Task performance is stimulated using principles and mechanisms that capture salient aspects of information processing in neural ensembles. Dopamine effects are simulated as a change in gain of neural assemblies in the area of release. RESULTS: The model leads to different predictions as a function of the hypothesized location of dopamine effects. Motor system effects are simulated as a change in gain over the response layer of the model. This induces speeding of reaction times but an impairment of accuracy. Cognitive attentional effects are simulated as a change in gain over the attention layer. This induces a speeding of reaction times and an improvement of accuracy, especially at very fast reaction times and when processing of the stimulus requires selective attention. CONCLUSIONS: A computer simulation using widely accepted principles of processing in neural ensembles can account for reaction time distributions and time-accuracy curves in a selective attention task. The simulation can be used to generate predictions about the effects of dopamine agonists on performance. An empirical study evaluating these predictions is described in a companion paper.     

An anatomically constrained neural network model of fear conditioning.

Conditioning of fear reactions to an auditory conditioned stimulus (CS) paired with a footshock unconditioned stimulus/stimuli (UCS) involves CS transmission to the amygdala from the auditory thalamus, the auditory cortex, or both. This article presents a simple neural network model of this neural system. The model consists of modules of mutually inhibitory nonlinear units representing the different relevant anatomical structures of the thalamo-amygdala and thalamo-cortico-amygdala circuitry. Frequency-specific changes produced by fear conditioning were studied at the behavioral level (stimulus generalization) and the single-unit level (receptive fields). The findings mirror effects observed in conditioning studies of animals. This computational model provides an initial grounding for explorations of how emotional information and behavior are related to anatomical and physiological observations. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Graded State Machines: The Representation of Temporal Contingencies in Simple Recurrent Networks

We explore a network architecture introduced by Elman (1990) for predicting successive elements of a sequence. The network uses the pattern of activation over a set of hidden units from time-step t-1, together with element t, to predict element t + 1. When the network is trained with strings from a particular finite-state grammar, it can learn to be a perfect finite-state recognizer for the grammar. When the net has a minimal number of hidden units, patterns on the hidden units come to correspond to the nodes of the grammar; however, this correspondence is not necessary for the network to act as a perfect finite-state recognizer. Next, we provide a detailed analysis of how the network acquires its internal representations. We show that the network progressively encodes more and more temporal context by means of a probability analysis. Finally, we explore the conditions under which the network can carry information about distant sequential contingencies across intervening elements to distant elements. Such information is maintained with relative ease if it is relevant at each intermediate step; it tends to be lost when intervening elements do not depend on it. At first glance this may suggest that such networks are not relevant to natural language, in which dependencies may span indefinite distances. However, embeddings in natural language are not completely independent of earlier information. The final simulation shows that long distance sequential contingencies can be encoded by the network even if only subtle statistical properties of embedded strings depend on the early information. The network encodes long-distance dependencies by shading internal representations that are responsible for processing common embeddings in otherwise different sequences. This ability to represent simultaneously similarities and differences between several sequences relies on the graded nature of representations used by the network, which contrast with the finite states of traditional automata. For this reason, the network and other similar architectures may be called Graded State Machines.     

Context-processing deficits in schizophrenia: Converging evidence from three theoretically motivated cognitive tasks.

To test the hypothesis that the ability to actively represent and maintain context information is a central function of working memory and that a disturbance in this function contributes to cognitive deficits in schizophrenia, the authors modified 3 tasks—the AX version of the Continuous Performance Test Stroop, and a lexical disambiguation task—and administered them to patients with schizophrenia as well as to depressed and healthy controls. The results suggest an accentuation of deficits in patients with schizophrenia in context-sensitive conditions and cross-task correlations of performance in these conditions. However, the results do not definitively eliminate the possibility of a generalized deficit. The significance of these findings is discussed with regard to the specificity of deficits in schizophrenia and the hypothesis concerning the neural and cognitive mechanisms that underlie these deficits. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Learning artificial grammars with competitive chunking.

When exposed to a regular stimulus field, for instance, that generated by an artificial grammar, subjects unintentionally learn to respond efficiently to the underlying structure (G. A. Miller [1958]; A. S. Reber [see PA, Vol 42:8911]). We explored the hypothesis that the learning process is chunking and that grammatical knowledge is implicitly encoded in a hierarchical network of chunks. We trained subjects on exemplar sentences while inducing them to form specific chunks. Their knowledge was then assessed through judgments of grammaticality. We found that subjects were less sensitive to violations that preserved their chunks than to violations that did not. We derived the theory of competitive chunking (CC) and found that it successfully reproduces, via computer simulations, both Miller's experimental results and our own. In CC, chunks are hierarchical structures strengthened with use by a bottom-up perception process. Strength-mediated competitions determine which chunks are created and which are used by the perception process. (PsycINFO Database Record (c) 2010 APA, all rights reserved)