The present study examined whether conative factors (e.g., self-efficacy, self-set goal difficulty, and task-specific motivation) are reliable predictors of learning and memory abilities and whether any observed relationships could be explained by two related, yet distinct aspects of attention. Specifically, the present study examined whether the relationship between conative factors and overall learning performance is explained by attentional intensity (the amount of attention allocated to a task) and attentional consistency (the consistency with which attention is allocated to said task). In two studies (N’s > 160), participants completed a paired associate’s (PA) cued recall task while pupil diameter was simultaneously recorded to provide an index of the intensity of attention. Measures of working memory, general episodic long-term memory, task-specific motivation, and memory self-efficacy were also included. Study 2 adopted a similar procedure but embedded thought probes into the encoding phase of each list to provide an index of the consistency of attention. Study 2 also added measures of self-set goal difficulty and effective strategy use. Results suggested that all conative factors were related to intensity and consistency in challenging learning contexts. Furthermore, intensity, consistency, and the variance shared between self-efficacy and self-set goal difficulty (r = 0.86) each explained substantial unique variance in learning when controlling for the influence of other important predictors. Overall, results suggest conative factors are important for understanding individual differences in learning and memory abilities, and part of the reason why these factors are associated with improved learning outcomes is due to intensity and consistency.
Comment: I’ve always believed that conative (non-cognitive) individual difference variables should receive just as much attention as cognitive variables in understanding learning. In fact, in an invited article, I recently proposed the CAMML (cognitive-affective-motivation model of learning) “crossing the rubicon” model of learning that integrates conative (motivation and self-regulated learning), affective (Big 5 personality) and cognitive (CHC) variables in an overarching framework (building on Richard Snow’s concept of aptitude-trait complexes). Click here to download or read the CAMML article. Below are the two key figures for understanding the CAMML model.
