Abstract
The pre-decisional process of hypothesis generation is a ubiquitous cognitive faculty that we continually employ in an effort to understand our environment and thereby support appropriate judgments and decisions. Although we are beginning to understand the fundamental processes underlying hypothesis generation, little is known about how various temporal dynamics, inherent in real world generation tasks, influence the retrieval of hypotheses from long-term memory. This paper presents two experiments investigating three data acquisition dynamics in a simulated medical diagnosis task. The results indicate that the mere serial order of data, data consistency (with previously generated hypotheses), and mode of responding influence the hypothesis generation process. An extension of the HyGene computational model endowed with dynamic data acquisition processes is forwarded and explored to provide an account of the present data.
Highlights
Hypothesis generation is a pre-decisional process by which we formulate explanations and beliefs regarding the occurrences we observe in our environment
This finding clearly demonstrates that not all available data contribute to the hypothesis generation process and that the serial position of a datum can be an important factor governing the weight allocated to it in the generation process. These results are consistent with the notion that the data weightings utilized in the generation process are governed by the amount of working memory activation possessed by each datum
Such a discrete utilization would likely result in a more gradual recency effect than seen in the data
Summary
Hypothesis generation is a pre-decisional process by which we formulate explanations and beliefs regarding the occurrences we observe in our environment. A physician observes a pattern of symptoms presented by a patient (i.e., data) and uses this information to generate likely diagnoses (i.e., hypotheses) in an effort to explain the patient’s presenting symptoms Given these examples, the importance of developing a full understanding of the processes underlying hypothesis generation is clear, as the consequences of impoverished or inaccurate hypothesis generation can be injurious. This, in turn, dictates that individual pieces of data are acquired in some relative temporal relation to one another These constraints, individual data acquisition over time and the relative ordering of data, are likely to have significant consequences for hypothesis generation processes. In the to-be-presented experiments, we presented the symptoms sequentially and manipulated the symptom’s sequence structures in the“decision making phase.” As the data acquisition unfolds over time, the results of these experiments provide insight into the dynamic data acquisition and hypothesis generation processes operating over time that are important for computational models
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