Value-Based Decision Making: An Interactive Activation Perspective.
Prominent theories of value-based decision making have assumed that choices are made via the maximization of some objective function (e.g., expected value) and that the process of decision making is serial and unfolds across modular subprocesses (e.g., perception, valuation, and action selection). H...
| Publicado en: | Psychological Review Vol. 127; no. 2; pp. 153 - 186 |
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| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
American Psychological Association
Mar2020
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=141825666&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 141825666 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 0033295X PYV jtl: Psychological Review issn: 0033295X maglogo: N pubinfo: dt: Mar2020 vid: 127 iid: 2 pid: 34 pub: American Psychological Association artinfo: ui: 141825666 10.1037/rev0000164 ppf: 153 ppct: 33 formats: tig: atl: Value-Based Decision Making: An Interactive Activation Perspective. aug: au: Suri, Gaurav Gross, James J. McClelland, James L. affil: Department of Psychology, San Francisco State University Department of Psychology, Stanford University su: Decision making Decision theory Expected returns sug: subj: Decision making Decision theory Expected returns keyword: computer simulation interactive activation neural networks parallel distributed processing value-based decision making computer simulation interactive activation neural networks parallel distributed processing value-based decision making ab: Prominent theories of value-based decision making have assumed that choices are made via the maximization of some objective function (e.g., expected value) and that the process of decision making is serial and unfolds across modular subprocesses (e.g., perception, valuation, and action selection). However, the influence of a large number of contextual variables that are not related to expected value in any direct way and the ubiquitous reciprocity among variables thought to belong to different subprocesses suggest that these assumptions may not always hold. Here, we propose an interactive activation framework for value-based decision making that does not assume that objective function maximization is the only consideration affecting choice or that processing is modular or serial. Our framework holds that processing takes place via the interactive propagation of activation in a set of simple, interconnected processing elements. We use our framework to simulate a broad range of well-known empirical phenomena—primarily focusing on decision contexts that feature nonoptimal decision making and/or interactive (i.e., not serial or modular) processing. Our approach is constrained at Marr's (1982) algorithmic and implementational levels rather than focusing strictly on considerations of optimality at the computational theory level. It invites consideration of the possibility that choice is emergent and that its computation is distributed. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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