Patients' information needs and decision-making processes: what can be learned from genetic counselees?

This field study investigated the information needs and decision-making strategies of 161 genetic counselees interviewed just prior to counseling. Patients were interested mostly in information about the outcomes and consequences of the alternative options at their disposal and about measures to defuse the risks. They wanted mainly information stated with certainty and were less interested in probability information. There was no difference in the search for information between the alternative eventually chosen and the one not chosen. There was a difference, however, among types of decisions and the interest in the various categories of information. These findings were interpreted within the theoretical model of decision making in natural risky situations (O. Huber, 1997). Practical implications for shared medical decision making are presented.     

Perceptual Categorization: Connectionist Modelling and Decision Rules

Although it is currently popular to model human associative learning using connectionist networks, the mechanism by which their output activations are converted to probabilities of response has received relatively little attention. Several possible models of this decision process are considered here, including a simple ratio rule, a simple difference rule, their exponential versions, and a winner-take-all network. Two categorization experiments that attempt to dissociate these models are reported. Analogues of the experiments were presented to a single-layer, feed-forward, delta-rule network. Only the exponential ratio rule and the winner-take-all architecture, acting on the networks' output activations that corresponded to responses available on test, were capable of fully predicting the mean response results. In addition, unlike the exponential ratio rule, the winner-take-all model has the potential to predict latencies. Further studies will be required to determine whether latencies produced under more stringent conditions conform to the model's predictions.