Neural correlates of media multitasking influencing switching but not sustained attention among college students: Evidence from a hierarchical Bayesian perspective

Media multitasking, the concurrent engagement with multiple media tasks, has become increasingly prevalent among students, raising concerns about its impact on attentional control. Within the framework of self-regulated learning (MASRL theory), this study establishes the relationship between person-level media multitasking experience and monitoring and control, along with regulation of effort at the task-person level. Using ERPs measurements combined with Bayesian computational modeling, we compared behavioral and neurocognitive differences between heavy media multitaskers (HMMs) and light media multitaskers (LMMs) during sustained attention and attention-switching tasks. Results revealed that HMMs exhibited faster response times in the attention-switching task (but not sustained attention task) compared to LMMs. ERP analysis showed enhanced neural responses in HMMs during the attention-switching task, suggesting a processing advantage in cognitive flexibility. Bayesian modeling of attentional parameter α further demonstrated that HMMs more efficiently allocated attentional resources during task-switching. These findings challenge the notion that media multitasking uniformly impairs attention, proposing instead that it may foster adaptable cognitive switching strategies. This perspective highlights the potential to rethink educational approaches given the demonstrated cognitive flexibility of HMMs, suggesting that instruction could be enhanced by incorporating structured opportunities for productive task-switching alongside traditional sustained attention activities. Digital learning environments might be optimized by adapting to students' individual attentional profiles, while targeted metacognitive interventions could help students harness the benefits of media multitasking while mitigating its potential drawbacks. By bridging cognitive neuroscience with pedagogical practice, this research provides a neurocognitive foundation for developing more effective learning strategies in technology-rich educational settings.