Abstract

Individual differences are a crucial aspect of field studies because of the consequences they can have on performance. However, in Cognitive Ergonomics, individual differences have mainly been interpreted as expertise effects. As can be noted from the literature, this limitation has led to difficulties in explaining differences between experts. Using a case study which identifies significant variations between expert performances [Jouglet, D., Piechowiak, S., Vanderhaegen, F., 2003. A shared workspace to support man-machine reasoning: application to cooperative distant diagnosis. Cognition, Technology & Work 5, 127–139], we attempt to go beyond the traditional approach based on expertise levels. Instead, we refer to the notion of cognitive styles. First, we consider methodological issues raised by a posteriori identification of cognitive styles within this diagnosis task. Second, we present the results of our analysis showing that individual differences are related to a particular dimension of cognitive style in which a balance between task requirements and cognitive resources is managed. Finally, we draw conclusions on the importance of cognitive styles in Cognitive Ergonomics.

Keywords: Individual differences; Cognitive style; Expertise; Diagnosis; Strategy

Article Outline

1. Introduction

2. Some aspects of diagnosis activities

2.1. The concept of diagnosis

2.2. Dimensions of diagnosis strategies

2.2.1. Primitives related to representation

2.2.1.1. Type of representation of the device

2.2.1.2. Type of knowledge of the device

2.2.1.3. Representation complexity

2.2.2. Primitive related to processing: complexity of hypothesis processing

3. Explanations of individual differences in diagnosis

3.1. The role of expertise on diagnosis strategies

3.2. The role of cognitive styles in diagnosis strategies

3.2.1. Holism and serialism

3.2.2. Field dependence/independence and task/resource commitment

4. A case study: troubleshooting diagnosis

5. Method

5.1. Participants and scenarios

5.2. Data analysis

5.3. Hypotheses summary

6. Results

6.1. General analysis of the dimensions

6.1.1. Representation type is mainly functional

6.1.2. Representation complexity is mainly structural

6.1.3. Hypothesis testing is mainly done using successive scanning

6.2. Analysis of representation and processing

6.2.1. Type of representation and representation complexity

6.2.2. Hypothesis scanning

6.2.3. Cognitive workload

7. Discussion

7.1. Relevance of task/resource commitment to explaining results

7.2. Cognitive styles and cognitive ergonomics

8. Conclusion

Acknowledgements

References