title: Dividing attention within and between hemispheres: testing a multiple resources approach to limited-capacity information processing.
abstract: Two experiments tested the limiting case of a multiple resources approach to resource allocation in information processing. In this framework, the left and right hemispheres are assumed to have separate, limited-capacity pools of undifferentiated resources that are not mutually accessible, so that tasks can overlap in their demand for these resources either completely, partially, or not at all. We tested all three degrees of overlap in demand for left hemisphere supplies, using dual-task methodology in which subjects were induced to pay different amounts of attention to each task. Experiment 1 compared complete and partial overlap by combining a verbal memory load with a task in which subjects named nonsense syllables briefly presented to either the left or right visual field (LVF and RVF, respectively). Experiment 2 compared complete versus no overlap by using the same verbal memory load combined with a laterally presented same-different judgment task that did not require a spoken response. Decrements from single-task performance were always more severe when the visual field task stimulus was presented to the RVF. Further, subjects in Experiment 1 were able to trade performance between tasks on both LVF and RVF trials because there was always at least some overlap in left hemisphere demand. In Experiment 2, performance trade-offs were observed on RVF (complete overlap) trials, but not on LVF trials, where no overlap in demand existed. These results contradict a single-capacity model, but they support the idea that the hemispheres' resource supplies are independent and have implications for both cerebral specialization and divided attention issues.
mesh_terms: D001288:Attention; D004193:Discrimination Learning; D004292:Dominance, Cerebral; D006801:Humans; D011939:Mental Recall; D010364:Pattern Recognition, Visual; D010700:Phonetics; D011932:Reading; D012660:Semantics; D012691:Serial Learning; D014796:Visual Perception