Abstract
Analogical reasoning refers to the process of drawing inferences on the basis of the relational similarity between two domains. Although this complex cognitive ability has been the focus of inquiry for many years, most models rely on measures that cannot capture individuals' thought processes moment by moment. In the present study, we used participants' eye movements to investigate reasoning strategies in real time while solving visual propositional analogy problems (A:B::C:D). We included both a semantic and a perceptual lure on every trial to determine how these types of distracting information influence reasoning strategies. Participants spent more time fixating the analogy terms and the target relative to the other response choices, and made more saccades between the A and B items than between any other items. Participants' eyes were initially drawn to perceptual lures when looking at response choices, but they nonetheless performed the task accurately. We used participants' gaze sequences to classify each trial as representing one of three classic analogy problem solving strategies and related strategy usage to analogical reasoning performance. A project-first strategy, in which participants first extrapolate the relation between the AB pair and then generalize that relation for the C item, was both the most commonly used strategy as well as the optimal strategy for solving visual analogy problems. These findings provide new insight into the role of strategic processing in analogical problem solving.
Highlights
Analogical reasoning—the process of generating inferences based on relational correspondences between two domains—is ubiquitous in most forms of thought (Hofstadter and Sander, 2013)
Our analyses focused on fixations on and between seven critical areas of interest (AOIs): the A, B, and C items, as well as the Target, Semantic lure, Perceptual lure, and Unrelated lure items
We measured the proportion of time that participants fixated each AOI
Summary
Analogical reasoning—the process of generating inferences based on relational correspondences between two domains—is ubiquitous in most forms of thought (Hofstadter and Sander, 2013). Project-first models (e.g., Sternberg, 1977; Hummel and Holyoak, 1997; Doumas et al, 2008) stem from the psychometric tradition of using propositional analogies (i.e., A:B::C:?—see Figure 1) to study fluid intelligence. In these models, analogies are solved by first generating a rule that relates the A and B terms, mapping the A and C terms, and applying a similar rule
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