Abstract
Reasoning, our ability to solve novel problems, has been shown to improve as a result of learning experiences. However, the underlying mechanisms of change in this high-level cognitive ability are unclear. We hypothesized that possible mechanisms include improvements in the encoding, maintenance, and/or integration of relations among mental representations – i.e., relational thinking. Here, we developed several eye gaze metrics to pinpoint learning mechanisms that underpin improved reasoning performance. We collected behavioral and eyetracking data from young adults who participated in a Law School Admission Test preparation course involving word-based reasoning problems or reading comprehension. The Reasoning group improved more than the Comprehension group on a composite measure of four visuospatial reasoning assessments. Both groups improved similarly on an eyetracking paradigm involving transitive inference problems, exhibiting faster response times while maintaining high accuracy levels; nevertheless, the Reasoning group exhibited a larger change than the Comprehension group on an ocular metric of relational thinking. Across the full sample, individual differences in response time reductions were associated with increased efficiency of relational thinking. Accounting for changes in visual search and a more specific measure of relational integration improved the prediction accuracy of the model, but changes in these two processes alone did not adequately explain behavioral improvements. These findings provide evidence of transfer of learning across different kinds of reasoning problems after completing a brief but intensive course. More broadly, the high temporal precision and rich derivable parameters of eyetracking make it a powerful approach for probing learning mechanisms.
Highlights
Reasoning, the ability to solve novel problems, relies on multiple cognitive processes including relational thinking[1,2,3] as well as working memory and cognitive control
Rather than measuring visual search as the number of fixations a participant made before looking at the irrelevant scales ever again, we identified the point in the trial at which the probability of looking at an irrelevant scale dipped below chance and the probability of looking at a relevant scale rose above chance
When considering whether the changes in relational thinking in the Reasoning group were greater than in the Comprehension group, we find that the data are 3.66 times more likely under the model including the Group×Time term compared to the model testing only the main effects
Summary
The ability to solve novel problems, relies on multiple cognitive processes including relational thinking[1,2,3] as well as working memory and cognitive control (e.g., refs. 4–7). The ability to solve novel problems, relies on multiple cognitive processes including relational thinking[1,2,3] as well as working memory and cognitive control Relational thinking is an essential component, as it allows us to form relational representations from mere percepts.[8] Solving reasoning problems, such as those involving transitive inference, relies heavily on processes supported by relational thinking, including the ability to encode, maintain, and integrate mental relations.[1,3] Together, these processes allow us to identify patterns and solve novel problems and are fundamental for human learning We leveraged the high temporal precision and rich derivable parameters of eyetracking to index cognitive processes that may support improvements in reasoning over time
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