By Stephen J. Payne, Andrew Howes
This lecture describes a theoretical framework for the behavioural sciences that holds excessive promise for theory-driven examine and layout in Human-Computer interplay. The framework is designed to take on the adaptive, ecological, and bounded nature of human behaviour. it's designed to aid scientists and practitioners cause approximately why humans decide to behave as they do and to give an explanation for which techniques humans opt for in keeping with software, ecology, and cognitive details processing mechanisms. A key concept is that folks decide on techniques as a way to maximise application given constraints. The framework is illustrated with a few examples together with pointing, multitasking, skim-reading, on-line deciding to buy, sign Detection conception and analysis, and the effect of popularity on paying for judgements. Importantly, those examples span from perceptual/motor coordination, via cognition to social interplay. eventually, the lecture discusses the demanding concept that humans search to discover optimum thoughts and in addition discusses the consequences for behavioral research in HCI. desk of Contents: advent: A Framework for Cognitive technological know-how study on HCI / heritage / sign Detection idea and Collaborative analysis / Discretionary activity Interleaving / move making plans / Multimodal interplay and textual content access / E-commerce / shopping a number of records and browse studying / Adaptively dispensing Cognition / E-commerce suggestions / dialogue
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Additional info for Adaptive Interaction: A Utility Maximization Approach to Understanding Human Interaction with Technology
4 COLLABORATIVE DIAGNOSIS Although much research on SDT has focussed on understanding the performance of individuals engaged in diagnosis tasks, the theory can be extended to collaborative diagnosis, for which it leads to some very interesting and applicable results and research questions. We contend that this theory has considerable potential for new insights for the design of collaborative technologies. Perhaps the most general question that can be asked—although mathematically quite complex—is how to derive optimal performance from a team of observers who have varying discrimination capabilities and access to only partially overlapping evidence of varying diagnostic value.
In order to explain the movement duration we need to optimise a utility function that is sensitive to misses and errors caused by spatial variation. Here we assume that time is the utility currency and that the range of muscle forces that can be applied constitutes the strategy space. Further, we assume that the person chooses a strategy S so as to minimise the sum of the expected movement duration T and the average time cost of errors and misses E. S = arg min(TS + ES ) S The value of ES can be calculated given assumptions about (1) the probability of error and (2) the costs of error.
If the two members vary greatly in discrimination, the confidenceweighed judgment may be worse than the more discriminating member’s. This model would do at least as well as the best member, whatever the discrepancy of the members’ discrimination capabilities. By manipulating environmental noise separately for each individual, these models were tested. The participants agreed/joint responses were well predicted by the confidence model. The models briefly noted above, and the empirical finding, have direct practical implications, as sketched below.