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Dissociated Neural Processing for Decisions in Managers and Non-Managers

Overview of attention for article published in PLOS ONE, August 2012
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Title
Dissociated Neural Processing for Decisions in Managers and Non-Managers
Published in
PLOS ONE, August 2012
DOI 10.1371/journal.pone.0043537
Pubmed ID
Authors

Svenja Caspers, Stefan Heim, Marc G. Lucas, Egon Stephan, Lorenz Fischer, Katrin Amunts, Karl Zilles

Abstract

Functional neuroimaging studies of decision-making so far mainly focused on decisions under uncertainty or negotiation with other persons. Dual process theory assumes that, in such situations, decision making relies on either a rapid intuitive, automated or a slower rational processing system. However, it still remains elusive how personality factors or professional requirements might modulate the decision process and the underlying neural mechanisms. Since decision making is a key task of managers, we hypothesized that managers, facing higher pressure for frequent and rapid decisions than non-managers, prefer the heuristic, automated decision strategy in contrast to non-managers. Such different strategies may, in turn, rely on different neural systems. We tested managers and non-managers in a functional magnetic resonance imaging study using a forced-choice paradigm on word-pairs. Managers showed subcortical activation in the head of the caudate nucleus, and reduced hemodynamic response within the cortex. In contrast, non-managers revealed the opposite pattern. With the head of the caudate nucleus being an initiating component for process automation, these results supported the initial hypothesis, hinting at automation during decisions in managers. More generally, the findings reveal how different professional requirements might modulate cognitive decision processing.

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Geographical breakdown

Country Count As %
Germany 2 3%
United States 1 2%
France 1 2%
Unknown 55 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 27%
Researcher 10 17%
Student > Doctoral Student 6 10%
Professor 4 7%
Student > Master 4 7%
Other 11 19%
Unknown 8 14%
Readers by discipline Count As %
Psychology 18 31%
Neuroscience 11 19%
Medicine and Dentistry 7 12%
Business, Management and Accounting 4 7%
Computer Science 3 5%
Other 6 10%
Unknown 10 17%