Motivation, Learning, and Motivated Learning
Motivation, Learning, and Motivated Learning
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动机、学习和动机学习
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发表时间：
1993
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通讯作者：
M. Mangel
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作者：
M. Mangel
正如本卷中的许多论文所说明的那样，生物体的行为可以受到其自身生理状态、环境状态以及生物体关于环境状态的信息的影响。在本章中，我开发了一种功能（即进化）方法，可用于分离和整合生理和环境信息，因为每一种信息都与经验导致的行为变化有关。该方法基于动态的状态变量模型（Mangel和Clark， 1988），该模型明确地将生理学和生态学结合在达尔文的适应度测量框架内，从而响应了Kamil（1983）将行为“优化方法”与其他行为学和心理学方法相结合的呼吁。学习的功能性解释需要对一系列行为的适应性进行评估，以预期繁殖来衡量。用于确定适应度的技术称为随机动态规划。Ward（1987）给出了一个关于栖息地接受度的随机动态规划的简单例子；这个例子实际上是Mangel和Clark（1986）开发的方法的一个特例。
As many of the papers in this volume illustrate, the behavior of an organism can be influenced by its own physiological state, by the state of the environment, and by the information that the organism has about the state of the environment. In this chapter, I develop a functional (i.e., evolutionary) approach that can be used to both separate and integrate physiology and environmental information, since each is connected with changes of behavior as a result of experience. The approach is based on dynamic, state-variable modeling (Mangel and Clark, 1988) which explicitly couples physiology and ecology within the framework of a Darwinian measure of fitness and thus responds to Kamil’s (1983) call to integrate the “optimization approach” to behavior with other methods of ethology and psychology. Functional interpretations of learning require an assessment of the fitness, measured in terms of expected reproduction, of suites of behaviors. The technique used to determine fitness is called stochastic dynamic programming. Ward (1987) gives a simple example of stochastic dynamic programming for habitat acceptance; this example is in fact a special case of the methods developed by Mangel and Clark (1986).