### abstract ###
the goal of obtaining information to improve classification accuracy can strongly conflict with the goal of obtaining information for improving payoffs
two environments with such a conflict were identified through computer optimization
three subsequent experiments investigated people's search behavior in these environments
experiments  NUMBER  and  NUMBER  used a multiple-cue probabilistic category-learning task to convey environmental probabilities
in a subsequent search task subjects could query only a single feature before making a classification decision
the crucial manipulation concerned the search-task reward structure
the payoffs corresponded either to accuracy  with equal rewards associated with the two categories  or to an asymmetric payoff function  with different rewards associated with each category
in experiment  NUMBER   in which learning-task feedback corresponded to the true category  people later preferentially searched the accuracy-maximizing feature  whether or not this would improve monetary rewards
in experiment  NUMBER   an asymmetric reward structure was used during learning
subjects searched the reward-maximizing feature when asymmetric payoffs were preserved in the search task
however  if search-task payoffs corresponded to accuracy  subjects preferentially searched a feature that was suboptimal for reward and accuracy alike
importantly  this feature would have been most useful  under the learning-task payoff structure
experiment  NUMBER  found that  if words and numbers are used to convey environmental probabilities  neither reward nor accuracy consistently predicts search
these findings emphasize the necessity of taking into account people's goals and search-and-decision processes during learning  thereby challenging current models of information search
### introduction ###
when diagnosing and treating a patient  when choosing a job candidate or a mate  and in many other situations  one must make decisions without having all the relevant information
are there widely applicable strategies for identifying useful queries
what governs people's information search
in information-acquisition situations where no particular benefits and costs apply  statistical optimal experimental design oed models provide one framework for evaluating the value of alternative queries  CITATION
a variety of experiments suggest that such models can also provide a reasonable description of human information-search behavior  CITATION   in situations with no explicit external payoffs
but in many situations-for instance  when deciding whether something is safe to eat  whether a suspicious suitcase contains a bomb  or whether a patient should be sent to the cardiac care unit-strong asymmetries in consequences for particular correct or incorrect decisions apply
these asymmetries have implications for classification decisions
for instance  a potential cardiac patient should be sent to the cardiac care unit if the risk of heart attack is greater than some threshold
the threshold should clearly be less than a  NUMBER  percent  chance of heart attack  which would be the threshold for maximizing overall classification accuracy
are there implications  for information search  of varying benefits and costs for different kinds of correct and incorrect decisions
intuitively  it may seem that the best strategy is to conduct queries that allow determination of the true state of nature as accurately as possible  and to take payoffs into account only in the actual classification decision
that intuition is distinctly wrong
as our theoretical analyses and simulations illustrate  situation-specific costs and benefits must be considered when determining which information to acquire which test to conduct  which question to ask  which query to make  and not only when making classification decisions
in other words  many highly informative tests are useless  given the applicable situation-specific reward structures
to what extent does people's information-search behavior appropriately reflect the costs or benefits associated with different correct or mistaken decisions
we address this in three experiments  using a probabilistic multiple-cue category learning and information-search paradigm
importantly  we identify and investigate situations in which the goal of obtaining information that helps to maximize the number of correct classification decisions should in principle lead to different search behavior than is appropriate to maximize reward  given the particular environment's reward structure
the environmental reward structure consists of the payoffs for each kind of correct classification decision  and the costs of each kind of incorrect classification decision
we examine whether people's information-search behavior appropriately reflects situation-specific reward structures  or whether people may use probability gain  CITATION  to guide their search decisions  even when it is not adaptive to do so
