Decision Making on Spatially Continuous Scales.
A new diffusion model of decision making in continuous space is presented and tested. The model is a sequential sampling model in which both spatially continuously distributed evidence and noise are accumulated up to a decision criterion (a 1 dimensional [1D] line or a 2 dimensional [2D] plane). The...
| Published in: | Psychological Review Vol. 125; no. 6; pp. 888 - 936 |
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| Format: | Article |
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American Psychological Association
Nov2018
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=133445276&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 133445276 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: Nov2018 vid: 125 iid: 6 pid: 34 pub: American Psychological Association artinfo: ui: 133445276 10.1037/rev0000117 ppf: 888 ppct: 48 formats: tig: atl: Decision Making on Spatially Continuous Scales. aug: au: Ratcliff, Roger affil: The Ohio State University su: Decision making Identification Gaussian processes Random noise theory Evidence sug: subj: Decision making Identification Gaussian processes Random noise theory Evidence keyword: diffusion model distributed representations Gaussian process noise response time spatially continuous scale diffusion model distributed representations Gaussian process noise response time spatially continuous scale ab: A new diffusion model of decision making in continuous space is presented and tested. The model is a sequential sampling model in which both spatially continuously distributed evidence and noise are accumulated up to a decision criterion (a 1 dimensional [1D] line or a 2 dimensional [2D] plane). There are two major advances represented in this research. The first is to use spatially continuously distributed Gaussian noise in the decision process (Gaussian process or Gaussian random field noise) which allows the model to represent truly spatially continuous processes. The second is a series of experiments that collect data from a variety of tasks and response modes to provide the basis for testing the model. The model accounts for the distributions of responses over position and response time distributions for the choices. The model applies to tasks in which the stimulus and the response coincide (moving eyes or fingers to brightened areas in a field of pixels) and ones in which they do not (color, motion, and direction identification). The model also applies to tasks in which the response is made with eye movements, finger movements, or mouse movements. This modeling offers a wide potential scope of applications including application to any device or scale in which responses are made on a ID continuous scale or in a 2D spatial field. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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