Search

The importance of target luminance in evaluating the relative efficiency of structured versus unstructured search in a large and visually homogeneous field is discussed.
Differences in detection times appear to be due to the apparent luminance of the target. Detection is a result of a trade-off between the physical luminance of the target and the degree of spatial uncertainty associated with a particular search pattern. In a structured visual field, with a systematic search pattern, the spatial uncertainty of the target is conceived as being lower than with an unstructured field and search pattern.
Evidence from studies on visual search and on the effect of spatial uncertainty on visual performance are presented. Some of the industrial implications of the theory, particularly in the field of inspection, are also described. [ABSTRACT FROM AUTHOR]

Our paper challenges the conventional wisdom that the flat maximum inflicts the 'curse of insensitivity' on the modelling of judgement and decision processes. In particular, we argue that this widely demonstrated failure on the part of conventional statistical methods to differentiate between competing models has a useful role to play in the development of accessible and economical applied systems, since it allows a low cost choice between systems which vary in their cognitive demands on the user and in their ease of development and implementation. To illustrate our thesis, we take two recent applications of linear scoring models used for credit. scoring and for the prediction of sudden infant death. The paper discusses the nature and determinants of the flat maximum as well as its role in applied cognition. Other sections mention certain unanswered questions about the development of linear, scoring models and briefly describe competing formulations for prediction. [ABSTRACT FROM AUTHOR]

This paper is intended as a non-technical introduction to a growing aspect of what has been termed 'intellectual technology'. The particular area chosen is the use of simple linear additive models for judgement and decision making purposes. Such models are said to either outperform, or perform at least as well as, the human judges on which they are based, hence they are said to 'bootstrap' such human inputs. Although the paper will provide a fairly comprehensive list of recent applications of such models, from postgraduate selection to judgements of marital happiness, the work will concentrate on the topic of Credit Scoring as an exemplar - that is, the assignment of credit by means of a simple additive rule. The paper will also present a simple system, due to Dawes, of classifying such models according to the form and source of their weights. The paper further discusses the reasons for bootstrapping and that other major phenomenon of such models - that is, the one can rarely distinguish between the prescriptions of such models, however the weights have been arrived at. It is argued that this 'principle of the flat maximum' allows us to develop a technology of judgement. The paper continues with a brief historical survey of the reactions of human experts to such models and their superiority, and suggestions for a better mix of expert and model on human engineering lines. Finally, after a brief comparison between expert systems and linear additive models, the paper concludes with a brief survey of possible future developments. A short Appendix describes two applications of such models.