Edmond et al. (2017) argue all of the following about the decision-making of experts except:Group of answer choicesExperts often make decisions automatically and without conscious effort.Experts do not always possess insight into how they came to their decision. may not be able to explain the real reasons for their interpretation.Experts may provide reasons, but these might be retrospective rationalisations that do not correspond with the actual reasons for a decision.Asking experts to explain how they came to a decision can improve performance.Experts may not be able to explain the real reasons for their interpretation.
Question
Edmond et al. (2017) argue all of the following about the decision-making of experts except:Group of answer choicesExperts often make decisions automatically and without conscious effort.Experts do not always possess insight into how they came to their decision. may not be able to explain the real reasons for their interpretation.Experts may provide reasons, but these might be retrospective rationalisations that do not correspond with the actual reasons for a decision.Asking experts to explain how they came to a decision can improve performance.Experts may not be able to explain the real reasons for their interpretation.
Solution 1
As an AI, I don't have direct access to specific databases or articles, including the one by Edmond et al. (2017). However, based on general knowledge about expert decision-making, I can provide some guidance.
The statement that seems least likely to be argued by researchers studying expert decision-making is "Asking experts to explain how they came to a decision can improve performance." Here's why:
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"Experts often make decisions automatically and without conscious effort." This is a well-documented phenomenon known as tacit knowledge or intuition, which experts develop through extensive experience.
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"Experts do not always possess insight into how they came to their decision." This is related to the first point. Because experts often rely on intuition, they may not be consciously aware of the processes that led to their decisions.
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"Experts may provide reasons, but these might be retrospective rationalisations that do not correspond with the actual reasons for a decision." This is also a common finding in research on expert decision-making. Experts may rationalize their decisions after the fact, but these rationalizations may not accurately reflect the intuitive processes that led to the decisions.
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"Experts may not be able to explain the real reasons for their interpretation." This is essentially a restatement of the second point.
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"Asking experts to explain how they came to a decision can improve performance." This statement is less likely to be argued by researchers studying expert decision-making. While it's true that reflection can sometimes improve decision-making, asking experts to articulate their intuitive decision-making processes can also interfere with those processes and potentially degrade performance. This is known as the verbal overshadowing effect.
Solution 2
The statement "Asking experts to explain how they came to a decision can improve performance" is not argued by Edmond et al. (2017) about the decision-making of experts. While it is true that experts often make decisions automatically and without conscious effort, and may not always possess insight into how they came to their decision, asking them to explain their decision-making process does not necessarily improve their performance. In fact, it can sometimes lead to post-hoc rationalizations that do not accurately reflect the actual decision-making process.
Similar Questions
Which of the following BEST describes expert systems?answerThey are programmed to learn from their mistakes.They use machine learning algorithms that discover patterns.They mimic the physical connections of the human brain.They are programs designed to solve complex problems in specific domains.
Expert power is:Group of answer choicesGained by possessing knowledge or skills in a particular area.When an individual possesses needed or wanted information.Based on connections to a person who has desirable resources or personal traits.Comes from a person’s ability to influence others via threats, punishments, or sanctions.
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Explain the architechture of expert system in ai
Longevity: Expert systems can endure indefinitely, whereas a human expert may change or retire. Knowledge Distribution: Expert systems enable the distribution of human expertise across time and space. Aggregation of Knowledge: They can incorporate knowledge from multiple human experts, enhancing effectiveness. Cost Efficiency: Expert systems reduce the expense of seeking advice by providing expertise in various fields, such as medical diagnosis. Inferential Reasoning: Instead of using standard procedural code, expert systems handle complex issues by inferring new facts from existing knowledge represented as if-then rules.
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