Inferences from memory: Strategy- and exemplar-based judgment models compared

Persson, Magnus; Rieskamp, Jörg

doi:10.1016/j.actpsy.2008.09.010

Cited by 34 publications

(65 citation statements)

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“…Another promising technique involves having children rely on well‐known, self‐generated cue rankings, to encourage 9‐ to 10‐year‐olds to focus on the most relevant pieces of information (see Bereby‐Meyer et al., 2004; Montanelli, 1972). In addition, it may be fruitful to adopt experimental designs that require participants to rely on recalled cues from memory, as opposed to observing cues on a computerized display, to test whether these more demanding situations can lead children to rely on simpler noncompensatory strategies (e.g., Bröder & Schiffer, 2003; Persson & Rieskamp, 2009). Hopefully, such work will help generate interventions with the power to improve children’s real‐world decisions, from food choice to crossing the street.…”

Section: Discussionmentioning

confidence: 99%

When Easy Comes Hard: The Development of Adaptive Strategy Selection

2011

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Can children learn to select the right strategy for a given problem? In one experiment, 9- to 10-year-olds (N = 50), 11- to 12-year-olds (N = 50), and adults (N = 50) made probabilistic inferences. Participants encountered environments favoring either an information-intensive strategy that integrates all available information or an information-frugal strategy that relies only on the most valid pieces of information. Nine- to 10-year-olds but not older children or adults had more difficulties learning to select an information-frugal strategy than an information-intensive strategy. This counterintuitive finding is explained by children's less developed ability to selectively attend to relevant information, an ability that seems to develop during late childhood. The results suggest that whether a strategy can be considered "easy" depends on the development of specific cognitive abilities.

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Section: Discussionmentioning

confidence: 99%

When Easy Comes Hard: The Development of Adaptive Strategy Selection

2011

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“…8 The attention weight varied on a scale from 1 (not important) to 0 (very important). For simplicity and consistency with past research, we assumed identical attention weights for all cues (e.g., Persson & Rieskamp, 2009;von Helversen & Rieskamp, 2008). We set the prior on s to be uniformly distributed in the range of 0 to 1; the TB TTB,WADD model consisting of WADD ε and TTB ε was implemented as before.…”

Section: Testing a Toolbox Against Alternative Modelsmentioning

confidence: 99%

“…For simplicity, we assumed that the attention weights are identical for all cues (see Persson & Rieskamp, 2009;von Helversen & Rieskamp, 2008). Finally, the probability that the first option A from the option pair A and B will be chosen is determined by…”

Section: Appendix a Model Implementation For The Categorization Data mentioning

confidence: 99%

Testing adaptive toolbox models: A Bayesian hierarchical approach.

Scheibehenne

Rieskamp

Wagenmakers

2013

Psychological Review

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103

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Many theories of human cognition postulate that people are equipped with a repertoire of strategies to solve the tasks they face. This theoretical framework of a cognitive toolbox provides a plausible account of intra-and interindividual differences in human behavior. Unfortunately, it is often unclear how to rigorously test the toolbox framework. How can a toolbox model be quantitatively specified? How can the number of toolbox strategies be limited to prevent uncontrolled strategy sprawl? How can a toolbox model be formally tested against alternative theories? The authors show how these challenges can be met by using Bayesian inference techniques. By means of parameter recovery simulations and the analysis of empirical data across a variety of domains (i.e., judgment and decision making, children's cognitive development, function learning, and perceptual categorization), the authors illustrate how Bayesian inference techniques allow toolbox models to be quantitatively specified, strategy sprawl to be contained, and toolbox models to be rigorously tested against competing theories. The authors demonstrate that their approach applies at the individual level but can also be generalized to the group level with hierarchical Bayesian procedures. The suggested Bayesian inference techniques represent a theoretical and methodological advancement for toolbox theories of cognition and behavior.

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“…They supposed that costly retrieval of information from memory triggers noncompensatory decision making. Interestingly, only a few studies have examined decision strategies with memory-based retrieval (Bröder & Gaissmaier, 2007; Bröder, Newell, & Platzer, 2010; Bröder & Schiffer, 2003, 2006; Glöckner & Hodges, 2011; Khader et al, 2011; Persson & Rieskamp, 2009). …”

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Most people do not ignore salient invalid cues in memory-based decisions

Platzer

Bröder

2012

Psychon Bull Rev

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Former experimental studies have shown that decisions from memory tend to rely only on a few cues, following simple noncompensatory heuristics like “take the best.” However, it has also repeatedly been demonstrated that a pictorial, as opposed to a verbal, representation of cue information fosters the inclusion of more cues in compensatory strategies, suggesting a facilitated retrieval of cue patterns. These studies did not properly control for visual salience of cues, however. In the experiment reported here, the cue salience hierarchy established in a pilot study was either congruent or incongruent with the validity order of the cues. Only the latter condition increased compensatory decision making, suggesting that the apparent representational format effect is, rather, a salience effect: Participants automatically retrieve and incorporate salient cues irrespective of their validity. Results are discussed with respect to reaction time data.Electronic supplementary materialThe online version of this article (doi:10.3758/s13423-012-0248-4) contains supplementary material, which is available to authorized users.

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Inferences from memory: Strategy- and exemplar-based judgment models compared

Cited by 34 publications

References 40 publications

When Easy Comes Hard: The Development of Adaptive Strategy Selection

When Easy Comes Hard: The Development of Adaptive Strategy Selection

Testing adaptive toolbox models: A Bayesian hierarchical approach.

Most people do not ignore salient invalid cues in memory-based decisions

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