Structuring effect of tools conceptualized through initial goal fixedness for work activity

Analysis of work activities in nuclear industry has highlighted a new psycho-cognitive phenomenon: the structuring effect of tools (SET) sometimes leading to unexpected operating deviations; the subject is unable to perform a task concerning object A using or adapting a tool designed and presented to perform the same task concerning object B when object A is expected by the subject. Conditions to isolate and identify the SET were determined and reproduced in experiments for further analysis. Students and seven professional categories of adults (N = 77) were involved in three experimental conditions (control group, group with prior warning, group with final control) while individually performing a task with similar characteristics compared to real operating conditions and under moderate time-pressure. The results were: (1) highest performance with prior warning and (2) demonstration that academic and professional training favor the SET. After discussing different cognitive processes potentially related to the SET, we described (3) the psycho-cognitive process underlying the SET: Initial Goal Fixedness (IGF), a combination of the anchoring of the initial goal of the activity with a focus on the features of the initial goal favored by an Einstellung effect. This suggested coping with the negative effect of the SET by impeding the IGF rather than trying to increase the subjects’ awareness at the expense of their health. Extensions to other high-risk industries were discussed

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The information inequality for function spaces given a singular information matrix

In this work we extend the scope of the classical Cram´er-Rao lower bound, or information inequality, from Euclidean to function spaces. In other words we derive a tight lower bound on the autocovariance function of a function estimator. We do this in the context of system identification. Two key elements of system identification are experiment design and model selection. The novel information inequality on function spaces is important for model selection because it allows the user to compare estimators using different model structures. We provide a consistent treatment of the case where the Fisher information matrix is singular. This makes it possible to take into account that in optimal experiment design one tries to mask those parts of the system non-identifiable, which are irrelevant for the application