Accommodating Response Time Alters Predicted ROC Functions for Discrete-State and Dual-Process Models

2014 ◽  
Author(s):  
Jeffrey Starns
Keyword(s):  
Response Time ◽  
Process Models ◽  
Dual Process ◽  
Discrete State ◽  
Dual Process Models

Re-examining dominance of categories in impression formation: A test of dual-process models.

2018 ◽  
Vol 115 (1) ◽  
pp. 1-30 ◽  
Author(s):  
Brian M. Monroe ◽  
Bryan L. Koenig ◽  
Kum Seong Wan ◽  
Tei Laine ◽  
Swati Gupta ◽  
...  
Keyword(s):  
Impression Formation ◽  
Process Models ◽  
Dual Process ◽  
Dual Process Models

scholarly journals A mismatch with dual process models of addiction rooted in psychology

2008 ◽  
Vol 31 (4) ◽  
pp. 460-460
Author(s):  
Reinout W. Wiers ◽  
Remco Havermans ◽  
Roland Deutsch ◽  
Alan W. Stacy
Keyword(s):  
Process Models ◽  
Dual Process ◽  
Planning System ◽  
Dual Process Models ◽  
Lack Of Fit ◽  
Psychological Studies

AbstractThe model of addiction proposed by Redish et al. shows a lack of fit with recent data and models in psychological studies of addiction. In these dual process models, relatively automatic appetitive processes are distinguished from explicit goal-directed expectancies and motives, whereas these are all grouped together in the planning system in the Redish et al. model. Implications are discussed.

Is Biased Processing of Strong Attitudes Peripheral? An Extension of the Dual Process Models of Attitude Change

1999 ◽  
Vol 85 (2) ◽  
pp. 589-605 ◽  
Author(s):  
Eun-Yeong Na
Keyword(s):  
Attitude Change ◽  
Resistance To Change ◽  
Process Models ◽  
Dual Process ◽  
Attitude Strength ◽  
Extended Model ◽  
Main Hypothesis ◽  
Dual Process Models ◽  
Boomerang Effect ◽  
Biased Processing

It was suggested that the dual process models of attitude change should be extended to include the biased processing of strong attitudes. The main hypothesis of the extended model is that too much involvement intrinsic in strong attitudes may hinder objective processing, resulting in resistance to change even under strong message. Both attitude change and cognitive response measures in a 3 (attitude strength) x 2 (message quality) factorial design experiment supported the extended model. Only the holders of moderate attitudes showed greater attitude change when given a strong, rather than a weak, message. When given a strong message, holders of strong attitudes showed a boomerang effect by generating relatively greater counter-arguments (implying a central but biased processing with high motivation) in contrast with holders of weak attitudes who generated indifferent appeals and greater change in attitude regardless of the quality of the argument (implying a peripheral processing with low motivation).

scholarly journals Single- and Dual-Process Models of Biased Contingency Detection

2016 ◽  
Vol 63 (1) ◽  
pp. 3-19 ◽  
Author(s):  
Miguel A. Vadillo ◽  
Fernando Blanco ◽  
Ion Yarritu ◽  
Helena Matute
Keyword(s):  
Process Models ◽  
Dual Process ◽  
Contingency Learning ◽  
Contingency Detection ◽  
Dual Process Models ◽  
Dependent Variables ◽  
Single Process

Abstract. Decades of research in causal and contingency learning show that people’s estimations of the degree of contingency between two events are easily biased by the relative probabilities of those two events. If two events co-occur frequently, then people tend to overestimate the strength of the contingency between them. Traditionally, these biases have been explained in terms of relatively simple single-process models of learning and reasoning. However, more recently some authors have found that these biases do not appear in all dependent variables and have proposed dual-process models to explain these dissociations between variables. In the present paper we review the evidence for dissociations supporting dual-process models and we point out important shortcomings of this literature. Some dissociations seem to be difficult to replicate or poorly generalizable and others can be attributed to methodological artifacts. Overall, we conclude that support for dual-process models of biased contingency detection is scarce and inconclusive.

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