Differential associations of neurobehavioral traits and cognitive ability to academic achievement in higher education.

BACKGROUND: People vary between each other on several neurobehavioral traits, which may have implications for understanding academic achievement. METHODS: University-level Psychology or Engineering students were assessed for neurobehavioral traits, intelligence, and current psychological distress. Scores were compared with their grade point average (GPA) data. RESULTS: Factors associated with higher GPA differed markedly between groups. For Engineers, intelligence, but not neurobehavioral traits or psychological distress, was a strong correlate of grades. For Psychologists, grades were not correlated with intelligence but they were with the neurobehavioral traits of executive dysfunction, disinhibition, apathy, and positive schizotypy. However, only the latter two were associated independently of psychological distress. Additionally, higher mixed-handedness was associated with higher GPA in the combined sample. CONCLUSIONS: Neurological factors (i.e., neurobehavioral traits and intelligence), are differentially associated with university-level grades, depending on the major studied. However, mixed-handedness may prove to be a better general predictor of academic performance across disciplines.

Modulation of striatum based non-declarative and medial temporal lobe based declarative memory predicts academic achievement at university level.

BACKGROUND: There is a dearth of research on the roles of non-declarative (implicit) learning linked to the striatum and declarative (explicit) learning associated with the medial temporal lobes as predictors of academic attainment. METHODS: Participants were 120 undergraduate students, studying Psychology or Engineering, who completed several long-term memory tests. RESULTS: There was a significant interaction between the groups (Psychology or Engineering) and task type (declarative or non-declarative): Engineers performed better at declarative and psychologists at non-declarative learning. Furthermore, non-declarative but not declarative learning scores were significant correlates of academic achievement (r = 0.326, p < .05). Moreover, competitive modulation (activation of non-declarative learning in conjunction with deactivation of declarative learning) was a significant predictor of future academic achievement in both psychology (r = 0.264, p < .05) and Engineering (r = 0.300, p < .05) groups. CONCLUSIONS: The results confirm that these declarative and non-declarative systems interact competitively and that the extent of this competition may have implications for understanding educational attainment.