Cognitive Models of Choice: Comparing Decision Field Theory to the Proportional Difference Model

Scheibehenne, Benjamin; Rieskamp, Jörg; González‐Vallejo, Claudia

doi:10.1111/j.1551-6709.2009.01034.x

Cited by 26 publications

(21 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, however, some authors have highlighted the possible contribution of mechanisms involving more automatic information processing in risky choice, such as decision field theory (Johnson and Busemeyer, 2005; Rieskamp, 2008) and parallel constraint satisfaction (Glöckner and Herbold, 2011). In a model comparison investigation based on people's risky choices, for instance, Scheibehenne et al (2009) found supporting evidence for decision field theory. Despite these encouraging results, it is currently unclear how these models can give rise to several classical empirical regularities such as the fourfold pattern, the common ratio effect, or the common consequence effect—all of which have been critical in the evolution of models of risky choice.…”

Section: Discussionmentioning

confidence: 94%

Testing process predictions of models of risky choice: a quantitative model comparison approach

et al. 2013

View full text Add to dashboard Cite

This article presents a quantitative model comparison contrasting the process predictions of two prominent views on risky choice. One view assumes a trade-off between probabilities and outcomes (or non-linear functions thereof) and the separate evaluation of risky options (expectation models). Another view assumes that risky choice is based on comparative evaluation, limited search, aspiration levels, and the forgoing of trade-offs (heuristic models). We derived quantitative process predictions for a generic expectation model and for a specific heuristic model, namely the priority heuristic (Brandstätter et al., 2006), and tested them in two experiments. The focus was on two key features of the cognitive process: acquisition frequencies (i.e., how frequently individual reasons are looked up) and direction of search (i.e., gamble-wise vs. reason-wise). In Experiment 1, the priority heuristic predicted direction of search better than the expectation model (although neither model predicted the acquisition process perfectly); acquisition frequencies, however, were inconsistent with both models. Additional analyses revealed that these frequencies were primarily a function of what Rubinstein (1988) called “similarity.” In Experiment 2, the quantitative model comparison approach showed that people seemed to rely more on the priority heuristic in difficult problems, but to make more trade-offs in easy problems. This finding suggests that risky choice may be based on a mental toolbox of strategies.

show abstract

Section: Discussionmentioning

confidence: 94%

Testing process predictions of models of risky choice: a quantitative model comparison approach

et al. 2013

View full text Add to dashboard Cite

show abstract

“…Within this category, sequential sampling models represent a particularly promising approach (e.g., Busemeyer & Townsend, 1993;Scheibehenne, Rieskamp, & Gonzalez-Vallejo, 2009;Usher & McClelland, 2004). These models have a long tradition in psychology, explaining, for instance, memory and perception processes (e.g., Ratcliff, 1978;Townsend & Ashby, 1983;Vickers, 1970).…”

mentioning

confidence: 99%

Rigorously testing multialternative decision field theory against random utility models.

Berkowitsch¹,

Scheibehenne

Rieskamp

2014

Journal of Experimental Psychology: General

Self Cite

103

161

View full text Add to dashboard Cite

Cognitive models of decision making aim to explain the process underlying observed choices. Here, we test a sequential sampling model of decision making, multialternative decision field theory (MDFT; Roe, Busemeyer, & Townsend, 2001), on empirical grounds and compare it against 2 established random utility models of choice: the probit and the logit model. Using a within-subject experimental design, participants in 2 studies repeatedly choose among sets of options (consumer products) described on several attributes. The results of Study 1 showed that all models predicted participants' choices equally well. In Study 2, in which the choice sets were explicitly designed to distinguish the models, MDFT had an advantage in predicting the observed choices. Study 2 further revealed the occurrence of multiple context effects within single participants, indicating an interdependent evaluation of choice options and correlations between different context effects. In sum, the results indicate that sequential sampling models can provide relevant insights into the cognitive process underlying preferential choices and thus can lead to better choice predictions.

show abstract

“…Therefore, the decision is then based on the attributes for which one of the options has a clear advantage. González-Vallejo's (2002) proportional difference model proposes that options are compared attribute-wise and for each attribute the proportional differences between options are accumulated to favor one or the other option (see also Scheibehenne, Rieskamp, & González-Vallejo, 2009). Analogically, Payne, Bettman, and Johnson (1988) describe a number of decision strategies, including lexicographic rules, that assume a comparison of the options' outcomes with each other (see also Rieskamp & Hoffrage, 1999, 2008.…”

Section: Theories That Take Covariance Into Accountmentioning

confidence: 99%

How outcome dependencies affect decisions under risk.

Andraszewicz¹,

Rieskamp²,

Scheibehenne³

2015

Decision

Self Cite

View full text Add to dashboard Cite

Many economic theories of decision making assume that people evaluate options independently of other available options. However, recent cognitive theories such as decision field theory suggest that people's evaluations rely on a relative comparison of the options' potential consequences such that the subjective value of an option critically depends on the context in which it is presented. To test this prediction, we examined pairwise choices between monetary gambles and varied the degree to which the gambles' outcomes covaried with one another. When people evaluate options by comparing their outcomes, a high covariance between these outcomes should make a decision easier, as suggested by decision field theory. In line with this prediction, the observed choice proportions in 2 experiments (N ϭ 39 and 24, respectively) depended on the magnitude of the covariance. We call this effect the covariance effect. Our findings are in line with the theoretic predictions and show that the discriminability ratio in decision field theory can reflect the choice difficulty. These results confirm that interdependent evaluations of options play an important role in human decision making under risk and show that covariance is an important aspect of the choice context.

show abstract

Cognitive Models of Choice: Comparing Decision Field Theory to the Proportional Difference Model

Cited by 26 publications

References 68 publications

Testing process predictions of models of risky choice: a quantitative model comparison approach

Testing process predictions of models of risky choice: a quantitative model comparison approach

Rigorously testing multialternative decision field theory against random utility models.

How outcome dependencies affect decisions under risk.

Contact Info

Product

Resources

About