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Potential Contributions of Clinical Mathematical Psychology to Robust Modeling in Cognitive Science

机译：临床数学的潜在贡献建模在认知心理学强劲科学

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摘要

Potential contributions of clinical mathematical psychology to robust modeling in cognitive science are described. Potential contributions include model generalization testing, through evaluating model performance with extreme individual differences provided by clinical samples. Solution-oriented model support, in the form of end-use vindication, is available by exploiting measurement models for clinical assessment of symptom-significant cognitive functioning and monitoring cognitive aspects of treatment regimens, notably CNS-directed pharmacotherapy. Provision can be made for formal, transparent anchoring of cognition in clinical cognitive neuroimaging to counter "reverse logic" (circularity) in clinical neuroimaging research. Analytical modeling also can provide a quantitative nexus for integrating levels of neuroimaging (e.g., functional magnetic resonance imaging and functional magnetic resonance spectroscopy). Mixture models, treating model parameters as being randomly distributed across participants, defensibly can extend model support to occurrences of "data over-dispersion". Model support, furthermore, is available according to theoretical significance of parameter mixing distribution hyper-parameters. Parameter mixing distributions furthermore can serve as stabilizing Bayesian priors, to help address clinically imposed small N model testing. Although emanating from clinical mathematical psychology, appropriation of several recommended practices, to quantitative cognitive modeling generally, is deemed advisable.
机译：临床数学的潜在贡献建模在认知心理学强劲科学描述。包括模型泛化测试通过评估模型的性能与极端临床提供的个体差异样本。形式的最终用途证明,是可用的利用临床测量模型评估symptom-significant认知功能和监控的认知方面治疗方案,特别是CNS-directed药物治疗。正式的、透明的锚定的认知临床认知神经影像计数器“反逻辑”(圆)在临床神经影像学研究。可以提供一个定量关系整合水平的神经影像(如功能性磁磁共振成像和机能性共振光谱)。模型参数是随机分布的在参与者,戍可以扩展模型支持“数据over-dispersion”的出现。模型支持,此外,是可用的根据理论的意义hyper-parameters参数混合分布。此外可以混合参数分布作为稳定贝叶斯先验,帮助解决临床实施小N模型试验。尽管来自临床的数学心理学,拨款的若干建议实践、定量认知建模一般来说,被认为是可取的。

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