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|Title:||Transferability of calibration training between knowledge domains|
|Citation:||Proceedings of the 41st Annual Conference of the Cognitive Science Society (COGSCI, 2019), 2019 / Goel, A.K., Seifert, C.M., Freska, C. (ed./s), vol.41, pp.1362-1367|
|Publisher:||Cognitive Science Society|
|Publisher Place:||Montreal, QB|
|Conference Name:||Annual Conference of the Cognitive Science Society (COGSCI) (24 Jul 2019 - 27 Jul 2019 : Montreal, Canada)|
|Christopher Babadimas, Christopher Boras, Nicholas Rendoulis, Matthew Welsh, Steve Begg|
|Abstract:||Many industry professionals are poorly calibrated, overestimating their ability to make accurate forecasts. Previous research has demonstrated that an individual’s calibration in a specific domain can be improved through calibration training in that domain; however devising a training program for each specific domain within a field is laborious. A more efficient method would be if individuals from different disciplines could undertake the same general training and transfer the skills learnt to their respective, specific domains. This study investigated whether calibration training in a general domain was transferable to the specific domain of petroleum engineering. The results showed that, whilst the feedback training was effective within the general domain, there was only limited transfer to the specific domain. This is argued to be due to recognition failure, where the participants failed to recognise that the skill learnt through training in the general domain could be transferred to the specific domain.|
|Keywords:||calibration; overconfidence; training; skill transfer|
|Rights:||Copyright status unknown|
|Appears in Collections:||Australian School of Petroleum publications|
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