Individuals with high trait anxiety tend to be worse at flexibly adapting goal-directed behavior to meet changing demands relative to those with low trait anxiety. Past research on anxiety and cognitive flexibility has used tasks that involve overcoming a recently acquired rule, strategy, or response pattern after an abrupt change in task requirements (e.g., choice X led to positive outcomes but now leads to negative outcomes). An important limitation of this research is that many decision making situations require overcoming a preexisting bias (e.g., deciding whether to withdraw a historically winning investment that has experienced recent losses). In the present study we examined whether anxiety differences in the ability to overcome an acquired response extend to the ability to overcome a preexisting bias, when the bias produces objectively disadvantageous decisions. High anxiety (n = 78) and low anxiety participants (n = 76) completed a commonly used measure of cognitive flexibility, reversal learning, and a novel Framed Gambling Task that assessed the extent to which they could make advantageous decisions when the normatively correct choice was inconsistent with a preexisting framing bias. High anxiety participants showed the expected diminished reversal learning performance and also had poorer ability to make advantageous choices that were inconsistent with the framing bias. Worse performance in the Framed Gambling Task was not driven by poor knowledge of risk contingencies, because high anxiety participants reported the same explicit knowledge as low anxiety participants. Instead, the results suggest high anxiety is associated with general deficits in resolving interference from prepotent responses.

Older adults showed significant improvement over trials in their ability to decrease bias-driven choices, but younger showed greater flexibility. Age-differences in task performance were based on differences in learning and corresponding representations of task-relevant information.

Abstract. In the geosciences, recent attention has been paid to the influence of uncertainty on expert decision making. When making decisions under conditions of uncertainty, people tend to employ heuristics (rules of thumb) based on experience, relying on their prior knowledge and beliefs to intuitively guide choice. Over 50 years of decision making research in cognitive psychology demonstrates that heuristics can lead to less-than-optimal decisions, collectively referred to as biases. For example, a geologist who confidently interprets ambiguous data as representative of a familiar category form their research (e.g., strike slip faults for expert in extensional domains) is exhibiting the availability bias, which occurs when people make judgments based on what is most dominant or accessible in memory. Given the important social and commercial implications of many geoscience decisions, there is a need to develop effective interventions for removing or mitigating decision bias. In this paper, we summarize the key insights from decision making research about how to reduce bias and review the literature on debiasing strategies. First, we define an optimal decision, since improving decision making requires having a standard to work towards. Next, we discuss the cognitive mechanisms underlying decision biases and describe three biases that have been shown to influence geoscientists decision making (availability bias, framing bias, anchoring bias). Finally, we review existing debiasing strategies that have applicability in the geosciences, with special attention given to those strategies that make use of information technology and artificial intelligence (AI). We present two case studies illustrating different applications of intelligent systems for the debiasing of geoscientific decision making, where debiased decision making is an emergent property of the coordinated and integrated processing of human-AI collaborative teams.

Summary Previous research demonstrates that domain experts, like ordinary participant populations, are vulnerable to decision bias. Here, we examine susceptibility to bias amongst expert field scientists. Field scientists operate in less predictable environments than other experts, and feedback on the consequences of their decisions is often unclear or delayed. Thus, field scientists are a population where the findings of scientific research may be particularly vulnerable to bias. In this study, susceptibility to optimism, hindsight, and framing bias was evaluated in a group of expert field geologists using descriptive decision scenarios. Experts showed susceptibility to all three biases, and susceptibility was not influenced by years of science practice. We found no evidence that participants' vulnerability to one bias was related to their vulnerability to another bias. Our findings are broadly consistent with previous research on expertise and decision bias, demonstrating that no expert, regardless their domain experience, is immune to bias.