Monday, May 27, 2013

Training our ability to make decisions on uncertain outcomes.

When making decisions, we often retrieve a limited set of items from memory. These retrieved items provide evidence for competing options. For example, a dark cloud may elicit memories of heavy rains, leading one to pack an umbrella instead of sunglasses. Likewise, when viewing an X-ray, a radiologist may retrieve memories of similar X-rays from other patients. Whether or not these other patients have a tumor may provide evidence for or against the presence of a tumor in the current patient. Giguèrea and Love do an interesting study showing how people's ability to make accurate predictions of probabilistic outcomes can be improved if they are trained on an idealized version of a the distribution. They say it in their abstract as clearly as I can:
Some decisions, such as predicting the winner of a baseball game, are challenging in part because outcomes are probabilistic. When making such decisions, one view is that humans stochastically and selectively retrieve a small set of relevant memories that provides evidence for competing options. We show that optimal performance at test is impossible when retrieving information in this fashion, no matter how extensive training is, because limited retrieval introduces noise into the decision process that cannot be overcome. One implication is that people should be more accurate in predicting future events when trained on idealized rather than on the actual distributions of items. In other words, we predict the best way to convey information to people is to present it in a distorted, idealized form. Idealization of training distributions is predicted to reduce the harmful noise induced by immutable bottlenecks in people’s memory retrieval processes. In contrast, machine learning systems that selectively weight (i.e., retrieve) all training examples at test should not benefit from idealization. These conjectures are strongly supported by several studies and supporting analyses. Unlike machine systems, people’s test performance on a target distribution is higher when they are trained on an idealized version of the distribution rather than on the actual target distribution. Optimal machine classifiers modified to selectively and stochastically sample from memory match the pattern of human performance. These results suggest firm limits on human rationality and have broad implications for how to train humans tasked with important classification decisions, such as radiologists, baggage screeners, intelligence analysts, and gamblers.
Here are some clips from their text:
For probabilistic problems, such as determining whether a tumor is cancerous, whether it will rain, or whether a passenger is a security threat, selectively sampling memory at the time of decision makes it impossible for the learner to overcome uncertainty in the training domain. From a signal-detection perspective, selective sampling from memory results in noisy and inconsistent placement of the criterion across decision trials. Even with a perfect memory for all past experiences, a learner who selectively samples from memory will perform suboptimally on ambiguous category structures

Figure (A) Categories A (red curve) and B (green curve) are probabilistic, overlapping distributions. After experiencing many training items (denoted by the red A and green B letters), an optimal classifier places the decision criterion (dotted line) to maximize accuracy, and will classify all new test items to left of the criterion as A and all items to the right of the criterion as B. (B) Thus, the optimal classifier will always judge item S8 to be an A. In contrast, a model that stochastically and nonexhaustively samples similar items from memory may retrieve the three circled items and classify S8 as a B, which is not the most likely category. This sampling model will never achieve optimal performance when trained on ambiguous category structures. (C) Idealizing the category structures during training such that all items to the left of the criterion are labeled as A and to the right as B (underlined items are idealized) leads to optimal performance for both the optimal classifier and the selective sampling model.

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