Participants attempted to solve a modified version of Wason's selection task. Variables were: sizes of the sets referenced by a specified assertion, form of the assertion, thematic content of the assertion, and the need for sampling or not. In Experiment 1, participants were given enough information to determine the truth or falsity of the specified assertion with certainty; in Experiment 2, they had to rely on sampling and could not determine the assertion's truth or falsity with certainty. Performance was better (...) in Experiment 1 than in Experiment 2, but in both cases much better than what is typically obtained with the conventional selection task. The results support the hypothesis that performance of the selection task is sensitive to the sizes of the sets involved, add credence to the conclusion that framing the task in a thematically meaningful way can facilitate performance, and demonstrate that facilitation does not require deontic reasoning or a well-known convention being involved. The relationship between expressed confidence and level of performance is consistent with other studies showing the former to be a good predictor of the latter. (shrink)

We propose a neural model of multiattribute-decision processes, based on an attractor neural network with dynamic thresholds. The model may be viewed as a generalization of the elimination by aspects model, whereby simultaneous selection of several aspects is allowed. Depending on the amount of synaptic inhibition, various kinds of scanning strategies may be performed, leading in some cases to vacillations among the alternatives. The model predicts that decisions of a longer time duration exhibit a lower violation of the simple scalability (...) law, as opposed to shorter decisions. Furthermore, the model is suggested as a general attribute-based decision module. Accordingly, various decision strategies are manifested depending on the module's parameters. (shrink)