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The Neural Underpinnings of Mental Attention Capacity

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dc.contributor.advisorStevens, Dale
dc.creatorGuevara, Mylann Ashley
dc.date.accessioned2019-03-05T14:50:47Z
dc.date.available2019-03-05T14:50:47Z
dc.date.copyright2018-10-25
dc.date.issued2019-03-05
dc.date.updated2019-03-05T14:50:47Z
dc.degree.disciplinePsychology(Functional Area: Brain, Behaviour & Cognitive Sciences)
dc.degree.levelMaster's
dc.degree.nameMA - Master of Arts
dc.description.abstractWorking memory refers to the online maintenance and manipulation of information to solve problems; a function that is constrained by a limited mental attention capacity (M-capacity). The Theory of Constructive Operators (TCO) describes M-capacity as an operator that can boost task relevant information in the brain while task irrelevant information is inhibited in order to facilitate successful task performance. The current investigation aimed to study the neural underpinnings of M-capacity in the brain, specifically looking at how neural activation is modulated as a function of cognitive load and the domain of stimuli; with regard for how this relationship is affected by critical factors predicted by the TCO. A dynamic relationship between brain activity and cognitive load was found, where different neural networks became engaged and disengaged depending on cognitive load, and this relationship interacted with the domain of stimuli in the bilateral fusiform gyri and right middle occipital gyrus. These observations justify a number of future investigations to further understand how the modulation of neural networks in the brain relates to M-capacity generally, and individual differences in M-capacity.
dc.identifier.urihttp://hdl.handle.net/10315/35856
dc.language.isoen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectCognitive psychology
dc.subject.keywordsM-capacity
dc.subject.keywordsNeural Networks
dc.subject.keywordsWorking memory
dc.titleThe Neural Underpinnings of Mental Attention Capacity
dc.typeElectronic Thesis or Dissertation

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