背景:几十年的研究已经坚定地确定,认知健康和认知治疗服务是精神病患者的关键需求。然而,许多目前的临床项目没有解决这一需求,尽管个人的认知和社会认知能力在决定其现实世界功能方面发挥着至关重要的作用。早期精神病干预网络早期精神病干预网络中基于实践的初步研究表明,有可能开发和实施描绘个人认知健康概况的工具,并帮助客户和临床医生参与包括认知治疗在内的共同决策和治疗计划。这些发现标志着向个性化认知健康的有希望的转变。
方法:扩展这一早期进展,我们回顾了精神病认知领域/过程中个体差异的概念,作为提供个性化治疗计划的基础.我们提供了使用传统神经心理学措施的研究证据,以及利用逐个试验行为数据来阐明个人采用的不同潜在策略的新兴计算研究的发现。
■我们假设这些计算技术,当与传统的认知评估相结合时,可以丰富我们对治疗需求的个体差异的理解,这反过来可以指导更加个性化的干预措施。
结论:当我们发现临床相关方法将适应不良行为分解为模型参数捕获的单独潜在认知元素时,最终目标是开发和实施方法,使客户及其临床提供者能够利用个人现有的学习能力来改善他们的认知健康和福祉。
BACKGROUND: Decades of research have firmly established that cognitive health and cognitive treatment services are a key need for people living with psychosis. However, many current clinical programs do not address this need, despite the essential role that an individual\'s cognitive and social cognitive capacities play in determining their real-world functioning. Preliminary practice-based research in the Early Psychosis Intervention Network early psychosis intervention network shows that it is possible to develop and implement tools that delineate an individuals\' cognitive health profile and that help engage the client and the clinician in shared decision-making and treatment planning that includes cognitive treatments. These findings signify a promising shift toward personalized cognitive health.
METHODS: Extending upon this early progress, we review the concept of interindividual variability in cognitive domains/processes in psychosis as the basis for offering personalized treatment plans. We present evidence from studies that have used traditional neuropsychological measures as well as findings from emerging computational studies that leverage trial-by-trial behavior data to illuminate the different latent strategies that individuals employ.
UNASSIGNED: We posit that these computational techniques, when combined with traditional cognitive assessments, can enrich our understanding of individual differences in treatment needs, which in turn can guide evermore personalized interventions.
CONCLUSIONS: As we find clinically relevant ways to decompose maladaptive behaviors into separate latent cognitive elements captured by model parameters, the ultimate goal is to develop and implement approaches that empower clients and their clinical providers to leverage individual\'s existing learning capacities to improve their cognitive health and well-being.