Posttraumatic stress disorder (PTSD) is a common and disabling condition. Recent estimates of the lifetime prevalence range between 6% and 15%, making this condition possibly more common than major depressive disorder (Breslau et al., 1998; Kessler et al., 1995). In addition, many patients who have been the victims of directed violence, such as rape or assault, continue to meet PTSD criteria 10 years after the incident (Breslau et al., 1998).

Background: Eye movement desensitization and reprocessing (EMDR) is becoming a recognized and accepted form of psychotherapy for posttraumatic stress disorder (PTSD). Yet, its mechanism of action remains unclear and much controversy exists about whether eye movements or other forms of bilateral kinesthetic stimulation contribute to its clinical effects beyond the exposure elements of the procedure. Methods: Twenty-one patients with single-event PTSD (average Impact of Event Scale score: 49.5) received three consecutive sessions of EMDR with three different types of auditory and kinesthetic stimulation (tones and vibrations): intermittent alternating right-left (as commonly used with the standard EMDR protocol), intermittent simultaneous bilateral, and continuous bilateral. Therapists were blinded to the type of stimulation they delivered, and stimulation type assignment was randomized and counterbalanced. Results: All three stimulation types resulted in clinically significant reductions of subjective units of distress (SUD). Yet, alternating stimulation resulted in faster reductions of SUD when only sessions starting with a new target memory were considered. Conclusions: There are clinically significant effects of the EMDR procedure that appear to be independent of the nature of the kinesthetic stimulation used. However, alternating stimulation may confer an additional benefit to the EMDR procedure that deserves attention in future studies.

Somatization is the experience of physical symptoms in response to emotional distress. It is common, costly, and frustrating to both the patient and physician. Successful treatment of somatization requires the physician to pursue a positive diagnosis rather than rely on a diagnosis of exclusion. Treatment consists of giving an acceptable explanation of the symptoms to the patient, avoiding unwarranted interventions, and arranging brief but regular office visits for which the patient does not need to develop a new symptom to receive medical attention.

Using a pharmacological probe, procaine hydrochloride, the authors elicited consistent and selective activation of anterior limbic and paralimbic structures in normal human volunteers as documented by H215O positron emission tomography. This activation was associated with a range of emotional, somatic, and visceral experiences, often similar to those experienced during the aura of temporal lobe epilepsy. Several subjects also experienced panic attacks. This study confirms that selective anterior limbic/paralimbic activity in normal human volunteers evokes many emotional phenomena as well as common "ill-defined" symptoms observed in clinical conditions. The present combination of procaine challenge and neuroimaging provides a noninvasive procedure to probe the contribution of different anterior limbic and paralimbic structures to normal human emotions and to neuropsychiatric disorders.

Recent discoveries about the neural system and cellular mechanisms in pathways mediating classical fear conditioning have provided a foundation for pursuing concurrent connectionist models of this form of emotional learning. The models described are constrained by the known anatomy underlying the behavior being simulated. To date, implementations capture salient features of fear learning, both at the level of behavior and at the level of single cells, and additionally make use of generic biophysical constraints to mimic fundamental excitatory and inhibitory transmission properties. Owing to the modular nature of the systems model, biophysical modeling can be carried out in a single region, in this case the amygdala. Future directions include application of the biophysical model to questions about temporal summation in the two sensory input paths to amygdala, and modeling of an attentional interrupt signal that will extend the emotional processing model to interactions with cognitive systems.

Fear conditioning involves the temporal association of a “neutral” conditioned stimulus (CS), such as a tone or flashing light, with a noxious unconditioned stimulus (US), such as a footshock. After CS-US pairings, the CS acquires the capacity to elicit fear responses.

In his book`Mechanisms of Implicit Learning' (1993) Axel Cleeremans describes how nite state grammars can be modeled successfully with connectionist networks namely with Simple Recurrent Networks (SRNs) developed by Jeerey El-man. However, SRNs cannot be used for modeling arbitrary nite state grammars. In this paper I describe the limitations of this approach.

In their commentary, Jobe et al. point out that the primary action of neuroleptics is to decrease dopamine activity. They note that this fact, along with the therapeutic effect of these medications in schizophrenia, would seem to be inconsistent with the hypothesis that schizophrenia is associated with a decrease of dopamine activity in the prefrontal cortex. In this reply, we clarify our position on the relationship between disturbances of dopamine activity in schizophrenia and the action of neuroleptic medications. In particular, we review a more detailed discussion of this issue that we provided in an earlier report, in which we proposed many of the same ideas described by Jobe et al., that may reconcile the pharmacological and behavioral effects of neuroleptics with our theory concerning disturbances of dopamine in schizophrenia. Chief among these is the possibility that neuroleptics may help correct an imbalance in dopaminergic activity between cortical and subcortical systems.

Functional magnetic resonance imaging (fMRI) was used to examine the pattern of activity of the prefrontal cortex during performance of subjects in a nonspatial working memory task. Subjects observed sequences of letters and responded whenever a letter repeated with exactly one nonidentical letter intervening. In a comparison task, subjects monitored similar sequences of letters for any occurrence of a single, prespecified target letter. Functional scanning was performed using a newly developed spiral scan image acquisition technique that provides high‐resolution, multislice scanning at approximately five times the rate usually possible on conventional equipment (an average of one image per second). Using these methods, activation of the middle and inferior frontal gyri was reliably observed within individual subjects during performance of the working memory task relative to the comparison task. Effect sizes (2–4%) closely approximated those that have been observed within primary sensory and motor cortices using similar fMRI techniques. Furthermore, activation increased and decreased with a time course that was highly consistent with the task manipulations. These findings corroborate the results of positron emission tomography studies, which suggest that the prefrontal cortex is engaged by tasks that rely on working memory. Furthermore, they demonstrate the applicability of newly developed fMRI techniques using conventional scanners to study the associative cortex in individual subjects. © 1994 Wiley‐Liss, Inc.

Accumulating data from neurophysiology and neuropsychology have suggested two information processing roles for prefrontal cortex (PFC): 1) short-term active memory; and 2) inhibition. We present a new behavioral task and a computational model which were developed in parallel. The task was developed to probe both of these prefrontal functions simultaneously, and produces a rich set of behavioral data that act as constraints on the model. The model is implemented in continuous-time, thus providing a natural framework in which to study the temporal dynamics of processing in the task. We show how the model can be used to examine the behavioral consequences of neuromodulation in PFC. Specifically, we use the model to make novel and testable predictions regarding the behavioral performance of schizophrenics, who are hypothesized to suffer from reduced dopaminergic tone in this brain area.

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, embed dings 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.

At the level of individual neurons, catecholamine release increases the responsivity of cells to excitatory and inhibitory inputs. We present a model of catecholamine effects in a network of neural-like elements. We argue that changes in the responsivity of individual elements do not affect their ability to detect a signal and ignore noise. However, the same changes in cell responsivity in a network of such elements do improve the signal detection performance of the network as a whole. We show how this result can be used in a computer simulation of behavior to account for the effect of CNS stimulants on the signal detection performance of human subjects.

We explore a network architecture introduced by Elman (1988) 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 network has a minimal number of hidden units, patterns on the hidden units come to correspond to the nodes of the grammar, although this correspondence is not necessary for the network to act as a perfect finite-state recognizer. We explore the conditions under which the network can carry information about distant sequential contingencies across intervening 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.