2 resultados para Effective Attention Allocation

em Duke University


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Humans make decisions in highly complex physical, economic and social environments. In order to adaptively choose, the human brain has to learn about- and attend to- sensory cues that provide information about the potential outcome of different courses of action. Here I present three event-related potential (ERP) studies, in which I evaluated the role of the interactions between attention and reward learning in economic decision-making. I focused my analyses on three ERP components (Chap. 1): (1) the N2pc, an early lateralized ERP response reflecting the lateralized focus of visual; (2) the feedback-related negativity (FRN), which reflects the process by which the brain extracts utility from feedback; and (3) the P300 (P3), which reflects the amount of attention devoted to feedback-processing. I found that learned stimulus-reward associations can influence the rapid allocation of attention (N2pc) towards outcome-predicting cues, and that differences in this attention allocation process are associated with individual differences in economic decision performance (Chap. 2). Such individual differences were also linked to differences in neural responses reflecting the amount of attention devoted to processing monetary outcomes (P3) (Chap. 3). Finally, the relative amount of attention devoted to processing rewards for oneself versus others (as reflected by the P3) predicted both charitable giving and self-reported engagement in real-life altruistic behaviors across individuals (Chap. 4). Overall, these findings indicate that attention and reward processing interact and can influence each other in the brain. Moreover, they indicate that individual differences in economic choice behavior are associated both with biases in the manner in which attention is drawn towards sensory cues that inform subsequent choices, and with biases in the way that attention is allocated to learn from the outcomes of recent choices.

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This dissertation explores the complex process of organizational change, applying a behavioral lens to understand change in processes, products, and search behaviors. Chapter 1 examines new practice adoption, exploring factors that predict the extent to which routines are adopted “as designed” within the organization. Using medical record data obtained from the hospital’s Electronic Health Record (EHR) system I develop a novel measure of the “gap” between routine “as designed” and routine “as realized.” I link this to a survey administered to the hospital’s professional staff following the adoption of a new EHR system and find that beliefs about the expected impact of the change shape fidelity of the adopted practice to its design. This relationship is more pronounced in care units with experienced professionals and less pronounced when the care unit includes departmental leadership. This research offers new insights into the determinants of routine change in organizations, in particular suggesting the beliefs held by rank-and-file members of an organization are critical in new routine adoption. Chapter 2 explores changes to products, specifically examining culling behaviors in the mobile device industry. Using a panel of quarterly mobile device sales in Germany from 2004-2009, this chapter suggests that the organization’s response to performance feedback is conditional upon the degree to which decisions are centralized. While much of the research on product exit has pointed to economic drivers or prior experience, these central finding of this chapter—that performance below aspirations decreases the rate of phase-out—suggests that firms seek local solutions when doing poorly, which is consistent with behavioral explanations of organizational action. Chapter 3 uses a novel text analysis approach to examine how the allocation of attention within organizational subunits shapes adaptation in the form of search behaviors in Motorola from 1974-1997. It develops a theory that links organizational attention to search, and the results suggest a trade-off between both attentional specialization and coupling on search scope and depth. Specifically, specialized unit attention to a more narrow set of problems increases search scope but reduces search depth; increased attentional coupling also increases search scope at the cost of depth. This novel approach and these findings help clarify extant research on the behavioral outcomes of attention allocation, which have offered mixed results.