Assessing and explaining individual differences within the adaptive toolbox framework : new methodological and empirical approaches to the recognition heuristic

Individuals do not only show large differences with regard to the judgments and decisions they make, but also with regard to the strategies they use to arrive at their decisions. However, individual differences in decision strategy selection have gained insufficient attention so far. For this reason, I investigate individual differences with respect to the application of the fast-and-frugal heuristics of the adaptive toolbox – a framework that has become increasingly important within the field of decision making. In particular, I address one of the most prominent examples of the adaptive toolbox: the recognition heuristic (RH), that is, a decision strategy for paired comparisons which bases choice solely on recognition while ignoring any additional information. The overarching aim of my thesis is to enhance the understanding of the cognitive and personality traits underlying individual differences in use of the RH. However, so far, there has been a deficiency in the methods relating individual traits to RH-use. For this purpose, I extend a measurement model of the RH to a hierarchical version incorporating individual traits directly into the estimation of RH-use. This methodological advance allows detection of the dispositional determinants of variation in strategy selection regarding the RH in a straightforward and unbiased way. Equipped with the required methods, the first project reported in this thesis investigates temporal and cross-situational stability in use of the RH. By demonstrating these important preconditions, I ensure that it is principally possible to find reliable relations between individual traits and RH-use. Building upon these results, the second project addresses the effect of (fluid and crystallized) intelligence on individual differences in adaptive RH-use. In sum, there is supportive evidence that adaptive application of the RH to the decision context is moderated by fluid but not crystallized intelligence. Extending this line of research, the third project aims at explaining individual differences in RH-use free of any interaction with the situation. In brief, RHuse is found to decrease with need for cognition (i.e., inclination towards cognitively demanding activities) but not to increase with faith in intuition (i.e., trust in feelings). To conclude, by means of the three projects reported herein and with the aid of the newly developed hierarchical measurement model of RH-use, I demonstrate that RH-use represents a person-specific decision making style that is temporally and crosssituationally stable, and that is affected by fluid intelligence and need for cognition

Hierarchical multinomial modeling to explain individual differences in children’s clustering in free recall

The measurement of individual differences in cognitive processes and the advancement of multinomial processing tree (MPT) models were two of William H. Batchelder’s major research interests. Inspired by his work, we investigated developmental differences between 7-year-old children, 10-year-old children, and young adults, in free recall with the pair-clustering model by Batchelder and Riefer (1980, 1986). Specifically, we examined individual differences (in initial levels and in change across multiple study–test trials) in cluster encoding, retrieval, and covariation with three basic cognitive abilities: semantic verbal understanding, short-term memory capacity, information-processing speed. Data from two developmental studies in which 228 participants freely recalled clusterable words in four study–test cycles were used for reanalysis. We combined two model extensions not linked so far (Klauer, 2010; Knapp & Batchelder, 2004). This novel combination of modeling methods made it possible to analyze the relation between individual cognitive abilities and changes in cluster encoding and retrieval across study–test cycles. Inspired by William H. Batchelder, this work illustrates how MPT modeling can contribute to the understanding of cognitive development

The Development of Clustering in Episodic Memory: A Cognitive-Modeling Approach

Supporting Information for article Horn, S., Bayen, U. J., & Michalkiewicz, M. (in press). The development of clustering in episodic memory: A cognitive-modeling approach. Child Development

The Development of Clustering in Episodic Memory: A Cognitive‐Modeling Approach

Younger children's free recall from episodic memory is typically less organized than recall by older children. To investigate if and how repeated learning opportunities help children use organizational strategies that improve recall, the authors analyzed category clustering across four study-test cycles. Seven-year-olds, 10-year-olds, and young adults (N = 150) studied categorically related words for a free-recall task. The cognitive processes underlying recall and clustering were measured with a multinomial model. The modeling revealed that developmental differences emerged particularly in the rate of learning to encode words as categorical clusters. The learning curves showed a common pattern across age groups, indicating developmental invariance. Memory for individual items also contributed to developmental differences and was the only factor driving 7-year-olds' moderate improvements in recall

The limited use of the fluency heuristic : converging evidence across different procedures

In paired comparisons based on which of two objects has the larger criterion value, decision makers could use the subjectively experienced difference in retrieval fluency of the objects as a cue. According to the fluency heuristic (FH) theory, decision makers use fluency—as indexed by recognition speed—as the only cue for pairs of recognized objects, and infer that the object retrieved more speedily has the larger criterion value (ignoring all other cues and information). Model-based analyses, however, have previously revealed that only a small portion of such inferences are indeed based on fluency alone. In the majority of cases, other information enters the decision process. However, due to the specific experimental procedures, the estimates of FH use are potentially biased: Some procedures may have led to an overestimated and others to an underestimated, or even to actually reduced, FH use. In the present article, we discuss and test the impacts of such procedural variations by reanalyzing 21 data sets. The results show noteworthy consistency across the procedural variations revealing low FH use. We discuss potential explanations and implications of this finding

DFG Project Data: Memory-Based Heuristic Decisions

(1) Fast-and-frugal decision making: A cost-benefit approach (Erdfelder & Pohl; 2010-2013). (2) Optimization theory of memory-based heuristic decisions: Model and evaluation (Erdfelder & Pohl; 2013-2016)