Background: Problems with the voluntary control of behavior, such as those leading to increased antisaccade errors, are accepted as evidence of prefrontal dysfunction in schizophrenia. We previously reported that speeded prosaccade responses, i.e., shorter response latencies for automatic shifts of attention to visual targets, were associated with higher antisaccade error rates in schizophrenia. This suggests that dysregulation of automatic attentional processes may contribute to disturbances in prefrontally mediated control of voluntary behavior.
Methods: Twenty-four antipsychotic-naïve schizophrenia patients and 30 healthy individuals completed three tasks: a no-gap prosaccade task in which subjects shifted gaze toward a peripheral target that appeared coincident with the disappearance of a central fixation target and separate prosaccade and antisaccade tasks in which a temporal gap or overlap of the central target offset and peripheral target onset occurred. Sixteen patients were retested after 6 weeks of antipsychotic treatment.
Results: Patients' prosaccade latencies in the no-gap task were speeded compared with healthy individuals. While patients were not atypical in the degree to which response latencies were speeded or slowed by the gap and overlap manipulations, those patients with diminished attentional engagement on the prosaccade task (i.e., reduced overlap effect) had significantly elevated antisaccade error rates. This effect persisted in patients evaluated after antipsychotic treatment.
Conclusions: This study provides evidence that a reduced ability to engage attention may render patients more distracted by sensory inputs, thereby further compromising impaired executive control during antisaccade tasks. Thus, alterations in attentional and executive control functions can synergistically disrupt voluntary behavioral responses in schizophrenia.
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http://dx.doi.org/10.1016/j.biopsych.2007.11.009 | DOI Listing |
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Large language models (LLMs) have gained significant attention for their capabilities in natural language understanding and generation. However, their widespread adoption potentially raises public mental health concerns, including issues related to inequity, stigma, dependence, medical risks, and security threats. This review aims to offer a perspective within the actor-network framework, exploring the technical architectures, linguistic dynamics, and psychological effects underlying human-LLMs interactions.
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