Sternberg S
University of Pennsylvania, Philadelphia, PA 19104-6196, USA.
Acta Psychol (Amst). 2001 Jan;106(1-2):147-246. doi: 10.1016/s0001-6918(00)00045-7.
How can we divide a complex mental process into meaningful parts? In this paper, I explore an approach in which processes are divided into parts that are modular in the sense of being separately modifiable. Evidence for separate modifiability is provided by an instance of selective influence: two factors F and G (usually experimental manipulations) such that part A is influenced by F but invariant with respect to G, while part B is influenced by G but invariant with respect to F. Such evidence also indicates that the modules are functionally distinct. If we have pure measures MA and MB, each of which reflects only one of the parts, we need to show that MA is influenced by F but not G, while MB is influenced by G but not F. If we have only a composite measure MAB of the entire process, we usually also need to confirm a combination rule for how the parts contribute to MAB. I present a taxonomy of separate-modifiability methods, discuss their inferential logic, and describe several examples in each category. The three categories involve measures that are derived pure (based on different transformations of the same data; example: separation of sensory and decision processes by signal detection theory), direct pure (based on different data; example: selective effects of adaptation on spatial-frequency thresholds), and composite (examples: the multiplicative-factor method for the analysis of response rate; the additive-factor method for the analysis of reaction time). Six of the examples concern behavioral measures and functional processes, while four concern brain measures and neural processes. They have been chosen for their interest and importance; their diversity of measures, species, and combination rules; their illustration of different ways of thinking about data; the questions they suggest about possibilities and limitations of the separate-modifiability approach; and the case they make for the fruitfulness of searching for mental modules.
我们如何将一个复杂的心理过程划分为有意义的部分？在本文中，我探讨了一种方法，即把过程划分为在可单独修改意义上具有模块性的部分。选择性影响的一个实例提供了单独可修改性的证据：两个因素F和G（通常是实验操作），使得部分A受F影响但对G不变，而部分B受G影响但对F不变。这样的证据也表明这些模块在功能上是不同的。如果我们有纯测量值MA和MB，每个测量值仅反映其中一个部分，我们需要表明MA受F影响但不受G影响，而MB受G影响但不受F影响。如果我们只有整个过程的综合测量值MAB，我们通常还需要确认各部分如何对MAB起作用的组合规则。我提出了单独可修改性方法的分类法，讨论了它们的推理逻辑，并描述了每个类别中的几个例子。这三个类别涉及通过推导得出的纯测量值（基于相同数据的不同变换；例如：通过信号检测理论分离感觉和决策过程）、直接的纯测量值（基于不同数据；例如：适应对空间频率阈值的选择性影响）以及综合测量值（例如：用于分析反应率的乘法因子法；用于分析反应时间的加法因子法）。其中六个例子涉及行为测量和功能过程，而四个涉及大脑测量和神经过程。选择它们是因为它们有趣且重要；它们在测量、物种和组合规则方面的多样性；它们对思考数据的不同方式的说明；它们对单独可修改性方法的可能性和局限性所提出的问题；以及它们为寻找心理模块的成效所提供的支持。
