Computational Modeling in Pediatric Mental Health.

Computational Modeling in Pediatric Mental Health.
复制标题

儿科心理健康的计算模型。

DOI:
10.1016/j.jaac.2018.12.009
复制
发表时间:
2019
影响因子:
13.3
通讯作者:
Jones,Matt
Jones,Matt
中科院分区:
医学1区
文献类型:
--
作者:
Stoddard,Joel;Jones,Matt

文献摘要

被引文献

相似文献

计算建模最近已成为极大的兴趣,精神卫生临床医生作为一种工具,发现病理生理学的性质和临床评估,预测和治疗。计算精神病学(Computational Psychiatry)是一个术语,用于描述计算模型在行为和心理健康问题中的应用。NIMH对计算建模感兴趣,提出了感兴趣的应用,例如了解精神疾病的神经基础,发现新的治疗方法和预测治疗反应。[1]在这个简短的概述中,我们的目标是触及计算精神病学中出现的主要主题。为了支持一点,计算模型到底是什么?广义地说,它是一组数学表达式,代表自然过程或预测自然状态。计算这些方程所需的工作通常是如此密集,以至于需要计算机。模型可以是一系列复杂的表达式,也可以是一个简单的方程。也许心理学中最著名的是实际发生的事情(结果)和一个人的期望(预测)之间的简单减法。这种差异会影响一个人从某些经历中学到多少东西。它被称为预测误差(通常用符号δ表示),由公式δ=结果-预测表示。许多学习的计算模型都建立在这个基本方程的基础上,来描述预测误差如何影响行为。在这篇综述的后面,我们将讨论这些是如何影响进行暴露疗法的新方法的。
Computational modeling has recently become of great interest to mental health clinicians as a tool for discovering the nature of pathophysiology and for clinical assessment, prediction, and treatment. Computational psychiatry is a term used to describe the application of computational modeling to behavioral and mental health problems. The NIMH has taken an interest in computational modeling, suggesting applications of interest, such as understanding the neural basis of mental illness, discovering new treatments, and predicting treatment response. 1 In this brief overview, our goal is to touch on major themes that have emerged in computational psychiatry.To back up a bit, what exactly is a computational model? Most broadly, it is a set of mathematical expressions that represent a natural process or predict a natural state. The work required to evaluate these equations is usually so intensive that it requires a computer. A model may be a complex series of expressions or a single, simple equation. Perhaps the most famous within psychology is a simple subtraction between what actually occurs (outcome) and what a person expects (prediction). This difference influences how much a person learns from some experiences. It is called prediction error (often represented with the symbol δ) and is represented by the equation δ= Outcome–Prediction. Many computational models of learning build upon this basic equation to describe how prediction error influences behavior. Later in this review, we discuss how these have influenced new methods for conducting exposure therapy.