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When people are given quantified information (e.g., 'there is a 60% chance of rain'), the format of quantifiers (i.e., numerical: 'a 60% chance' vs. verbal: 'it is likely') might affect their decisions. Previous studies with indirect cues of judgements and decisions (e.g., response times, decision outcomes) give inconsistent findings that could support either a more intuitive process for verbal than numerical quantifiers or a greater focus on the context (e.g., rain) for verbal than numerical quantifiers. We used two pre-registered eye-tracking experiments (n(1) = 148, n(2) = 133) to investigate decision-making processes with verbal and numerical quantifiers. Participants evaluated multiple verbally or numerically quantified nutrition labels (Experiment 1) and weather forecasts (Experiment 2) with different context valence (positive or negative), and quantities ('low', 'medium', or 'high' in Experiment 1 and 'possible', 'likely', or 'very likely' in Experiment 2) presented in a fully within-subjects design. Participants looked longer at verbal than numerical quantifiers, and longer at the contextual information with verbal quantifiers. Quantifier format also affected judgements and decisions: in Experiment 1, participants judged positive labels to be better in the verbal compared to the equivalent numerical condition (and to be worse for negative labels). In Experiment 2, participants decided on rain protection more for a verbal forecast of rain than the equivalent numerical forecast. The results fit the explanation that verbal quantifiers put more focus on the informational context than do numerical quantifiers, rather than prompting more intuitive decisions. [ABSTRACT FROM AUTHOR]

Most decision-making models describing individual differences in heuristics and biases tasks build on the assumption that reasoners produce a first incorrect answer in a quick, automatic way which they may or may not override later and that the advantage of high capacity reasoners arises from this late correction mechanism. To investigate this assumption, we developed a mouse-tracking analysis technique to capture individuals' first answers and subsequent thinking dynamics. Across two denominator neglect task experiments, we observed that individuals initially move the mouse cursor towards the correct answer option in a substantial number of cases suggesting that reasonersmay not always produce an incorrect answer first. Furthermore, we observed that, compared to low capacity reasoners, high capacity individuals revise their first answer more frequently if it is incorrect and make fewer changes if it is correct. However, we did not find evidence that high capacity individuals produce correct initial answers more frequently. Consistent with the predictions of previous decision-making models, these results suggest that in the denominator neglect task the capacity-normativity relationship arises after the initial response is formulated. The present work demonstrates how the analysis of mouse trajectories can be utilized to investigate individual differences in decision-making and help us better apprehend the dynamics of thinking behind decision biases. [ABSTRACT FROM AUTHOR]

Most decision-making models describing individual differences in heuristics and biases tasks build on the assumption that reasoners produce a first incorrect answer in a quick, automatic way which they may or may not override later and that the advantage of high capacity reasoners arises from this late correction mechanism. To investigate this assumption, we developed a mouse-tracking analysis technique to capture individuals' first answers and subsequent thinking dynamics. Across two denominator neglect task experiments, we observed that individuals initially move the mouse cursor towards the correct answer option in a substantial number of cases suggesting that reasonersmay not always produce an incorrect answer first. Furthermore, we observed that, compared to low capacity reasoners, high capacity individuals revise their first answer more frequently if it is incorrect and make fewer changes if it is correct. However, we did not find evidence that high capacity individuals produce correct initial answers more frequently. Consistent with the predictions of previous decision-making models, these results suggest that in the denominator neglect task the capacity-normativity relationship arises after the initial response is formulated. The present work demonstrates how the analysis of mouse trajectories can be utilized to investigate individual differences in decision-making and help us better apprehend the dynamics of thinking behind decision biases. [ABSTRACT FROM AUTHOR]