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Author(s): Grossberg, S. |
Year: 2000
Citation: Biological Psychiatry, 48, 81-98
Abstract: An outstanding problem in psychiatry concerns how to link discoveries about the pharmacological, neurophysiological, and neuroanatomical substrates of mental disorders to the abnormal behaviors that they control. A related problem concerns how to understand abnormal behaviors on a continuum with normal behaviors. During the past few decades, neural models have been developed of how normal cognitive and emotional processes learn from the environment, focus attention and act upon motivationally important events, and cope with unexpected events. When arousal or volitional signals in these models are suitably altered, they give rise to symptoms that strikingly resemble negative and positive symptoms of schizophrenia, including flat affect, impoverishment of will, attentional problems, loss of a theory of mind, thought derailment, hallucinations, and delusions. This article models how emotional centers of the brain, such as the amygdala, interact with sensory and prefrontal cortices (notably ventral, or orbital, prefrontal cortex) to generate affective states, attend to motivationally salient sensory events, and elicit motivated behaviors. Closing this feedback loop between cognitive and emotional centers is predicted to generate a cognitive?emotional resonance that can support conscious awareness. When such emotional centers become depressed, negative symptoms of schizophrenia emerge in the model. Such emotional centers are modeled as opponent affective processes, such as fear and relief, whose response amplitude and sensitivity are calibrated by an arousal level and chemical transmitters that slowly inactivate, or habituate, in an activity-dependent way. These opponent processes exhibit an Inverted-U, whereby behavior becomes depressed if the arousal level is chosen too large or too small. The negative symptoms are owing to the way in which the depressed opponent process interacts with other circuits throughout the brain.
Topics:
Biological Learning,
Models:
Other,
