Information search in judgment tasks: A regression model and some preliminary findings

Improving New Users’ Query Performance

As the volume of data in organizational databases grows, organizations are seeking to use this data to improve organizational success. To this end, users are being asked to query these databases to provide information to help answer questions posed by key management personnel. Users who have had extensive experience with an organization’s data can often detect the presence of errors in their queries when query results do not correspond to their ex ante expectations. New users, however, are less familiar with the data they will be querying. Having no, or limited, ex ante expectations for query results, new users may be unaware that the result produced by their query is incorrect. Unwarranted confidence in the correctness of their queries predisposes these users to stop looking for query errors even when their queries still contain errors. This behavior, premature stopping of query revision, prompts investigating whether new users’ query performance would improve if they were not only provided with, but used, readily available information to form ex ante expectations. Our results demonstrated a threshold effect in new users heeding information for forming ex ante expectations. That is, the mere availability of information for forming ex ante expectations made no difference in query performance. When admonishing users to heed ex ante information, however, there was an associated increase in the accuracy of their queries. These results suggest that users unfamiliar with a particular database might make fewer query errors if they not only received readily available information but were then prompted to use the information to form ex ante expectations for query results.

SSL: a theory of how people learn to select strategies

The assumption that people possess a repertoire of strategies to solve the inference problems they face has been raised repeatedly. However, a computational model specifying how people select strategies from their repertoire is still lacking. The proposed strategy selection learning (SSL) theory predicts a strategy selection process on the basis of reinforcement learning. The theory assumes that individuals develop subjective expectations for the strategies they have and select strategies proportional to their expectations, which are then updated on the basis of subsequent experience. The learning assumption was supported in 4 experimental studies. Participants substantially improved their inferences through feedback. In all 4 studies, the best-performing strategy from the participants' repertoires most accurately predicted the inferences after sufficient learning opportunities. When testing SSL against 3 models representing extensions of SSL and against an exemplar model assuming a memory-based inference process, the authors found that SSL predicted the inferences most accurately.

Collecting information: optimizing outcomes, screening options, or facilitating discrimination?

Collection of information prior to a decision may be integrated into a compensatory choice process; if it is, the information packet that is collected should be the one that produces the highest net gain. Alternatively, information may be collected in order to screen out options that fail to meet minimum standards; if this is the case, people should not choose options on which they have not collected available information. We tested these and other predictions from the two approaches in four experiments. Participants were given specific information about three attributes of each choice option but only probabilistic information about a fourth one. They rated attractiveness of options, decided whether to collect specific information about the fourth attribute of each one, rated options again, and then selected one of them. Data were consistent with neither of the above approaches. Instead they suggested that people collect information in order to facilitate their ability to discriminate between the attractiveness of options.