This thesis aimed to develop a paradigm for the study of anomaly handling and to investigate the factors that influence success in detecting and classifying anomalies. A simulated anomaly-handling environment was created to mimic an intelligence analysis task in a security setting. A series of experiments was designed to test hypotheses concerning sources of difficulty in detecting potential anomalies and making decisions about appropriate classifications of potentially anomalous events. Results across all experiments showed that complex problems, representing anomalies, were more difficult to solve than simple problems, and that this poor performance was consistent with the use of suboptimal strategies based on recognition of perceptual characteristics rather than inferences drawn from available data. Performance on complex problems was reduced still further when participants were exposed to trials that established a mental set. However, performance was improved when participants were given feedback on the correctness of their responses to each trial, which eliminated the negative effects of exposure to mental set. Another factor that impacted on successful decision-making was the cost of making errors. When participants were faced with a penalty for making incorrect decisions, solution rates improved compared with when performance was not related to reward. This has consequences for anomaly handling industries where the consequence of failure of often high. Unexpectedly, a number of the results indicated that there are situations where mental set may confer a benefit to decision making in a task of anomaly categorisation. Given the dominance of recognition-based strategies, it appears that mental set can refine the detection of perceptually relevant patterns, which can signal sudden changes in pattern that can lead to a switch from recognition-based to inferential task solution strategies. Overall, the merits for the use of simulated environments in critical decision making areas are discussed, and the contributory factors towards successful anomaly handling are analysed.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:742159 |
| Date | January 2018 |
| Creators | Leadley, Robert |
| Publisher | University of Sussex |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://sro.sussex.ac.uk/id/eprint/75902/ |
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