We note some future areas for work with cognitive models and agents that as Colbert (I am America (and so can you!), 2007) notes, “so can you”. We present three approaches as something like design patterns, so they can be applied to other architectures and tasks. These areas are: (a) Interacting directly with the screen-as-world. It is now possible for models to interact with uninstrumented interfaces both on the machine that the model is running on as well as remote machines. Improved interaction can not only support a broader range of behavior but also make the interaction more accurately model human behavior on tasks that include interaction. Just one implication is that this will force models to have more knowledge about interaction, an area that has been little modeled but essential for all tasks. (b) Providing the cognitive architecture with more representation of the body. In our example, we provide a physiological substrate to implement behavioral moderators’ effects on cognition. Cognitive architectures can now be broader in the measurements they predict and correspond to. This approach provides a more complete and theoretically appropriate way to include new aspects of behavior including stressor effects and emotions in models. And (c) using machine learning techniques, particularly genetic algorithms (GAs), to fit models to data. Because of the model complexity, this is equivalent to performing a multi-variable non-linear stochastic multiple-output regression. Doing this by hand is completely inadequate. While there is a danger of overfitting using a GA, these fits can help provide a better understanding of the model and architecture, including how the architecture changes under moderators such stress. This paper also includes some notes on model maintenance and reporting.

我们注意到与Colbert（我是美国，你也可以！）一起使用认知模型和智能体的一些未来领域。，2007）指出，“你也可以”。我们以设计模式之类的方式介绍了三种方法，因此它们可以应用于其他体系结构和任务。这些领域是：（a）直接与全球屏幕互动。现在，模型可以在运行模型的计算机以及远程计算机上与非仪表接口进行交互。改进的交互不仅可以支持更广泛的行为，而且可以使交互更准确地为包括交互在内的任务模拟人类行为。只是一个暗示就是，这将迫使模型对交互有更多的了解，这个领域很少建模，但对所有任务都是必不可少的。（b）为认知体系提供更多的身体表征。在我们的示例中 我们提供了一种生理底物，以实现行为主持人对认知的影响。认知体系结构现在可以在它们预测和对应的度量范围内更广泛。这种方法提供了一种更完整和理论上合适的方法，以在模型中包括行为的新方面，包括压力源效应和情绪。（c）使用机器学习技术，尤其是遗传算法（GA），以使模型适合数据。由于模型的复杂性，这等效于执行多变量非线性随机多输出回归。手动执行此操作是完全不够的。尽管使用GA可能会导致过拟合的风险，但这些拟合可以帮助您更好地理解模型和体系结构，包括在主持人的压力下如何改变体系结构。