The influence of information redundancy on probabilistic inferences

Information redundancy affects the accuracy of inference strategies. A simulation study illustrates that under high-information redundancy simple heuristics that rely on only the most important information are as accurate as strategies that integrate all available information, whereas under low redundancy integrating information becomes advantageous. Assuming that people exercise adaptive strategy selection, it is predicted that their inferences will more often be captured by simple heuristics that focus on part of the available information insituations ofhigh-information redundancy, especially when information search is costly. This prediction is confirmed in two experiments. The participants' task was to repeatedly infer which of two alternatives, described by several cues, had a higher criterion value. In the first experiment, simple heuristics predicted the inference process better under high-information redundancy than under low-information redundancy. In the second experiment, this result could be generalized to an inference situation in which participants had no prior opportunity to learn about the strategies' accuracies through outcome feedback. The results demonstrate that people are able to respond adaptively to different decision environments under various learning opportunities.     

A test of the diffusion model explanation for the worst performance rule using preregistration and blinding

People with higher IQ scores also tend to perform better on elementary cognitive-perceptual tasks, such as deciding quickly whether an arrow points to the left or the right Jensen (2006). The worst performance rule (WPR) finesses this relation by stating that the association between IQ and elementary-task performance is most pronounced when this performance is summarized by people’s slowest responses. Previous research has shown that the WPR can be accounted for in the Ratcliff diffusion model by assuming that the same ability parameter—drift rate—mediates performance in both elementary tasks and higher-level cognitive tasks. Here we aim to test four qualitative predictions concerning the WPR and its diffusion model explanation in terms of drift rate. In the first stage, the diffusion model was fit to data from 916 participants completing a perceptual two-choice task; crucially, the fitting happened after randomly shuffling the key variable, i.e., each participant’s score on a working memory capacity test. In the second stage, after all modeling decisions were made, the key variable was unshuffled and the adequacy of the predictions was evaluated by means of confirmatory Bayesian hypothesis tests. By temporarily withholding the mapping of the key predictor, we retain flexibility for proper modeling of the data (e.g., outlier exclusion) while preventing biases from unduly influencing the results. Our results provide evidence against the WPR and suggest that it may be less robust and less ubiquitous than is commonly believed.     

SSL: a theory of how people learn to select strategies

The assumption that people possess a repertoire of strategies to solve the inference problems they face has been raised repeatedly. However, a computational model specifying how people select strategies from their repertoire is still lacking. The proposed strategy selection learning (SSL) theory predicts a strategy selection process on the basis of reinforcement learning. The theory assumes that individuals develop subjective expectations for the strategies they have and select strategies proportional to their expectations, which are then updated on the basis of subsequent experience. The learning assumption was supported in 4 experimental studies. Participants substantially improved their inferences through feedback. In all 4 studies, the best-performing strategy from the participants' repertoires most accurately predicted the inferences after sufficient learning opportunities. When testing SSL against 3 models representing extensions of SSL and against an exemplar model assuming a memory-based inference process, the authors found that SSL predicted the inferences most accurately.     

Positive and negative recency effects in retirement savings decisions

Retirement savings decisions can be influenced by the fund composition of the retirement savings plan. In 2 experiments, strong composition effects were observed, with a larger percentage of resources being invested in stock funds when more stock than bond funds were offered. Although participants changed their allocations repeatedly, the opportunity to learn did not alter the composition effects. Learning processes led to positive and negative recency effects as well, providing evidence that allocations were strongly influenced by the recent performance of the different allocation options. Two learning models were tested to explain these learning processes. The first, a local adaptation learning model, assumes that people change their behavior on the basis of recent experience, whereas the second, a reinforcement learning model, assumes that decisions are made on the basis of the totality of accumulated experience. The local adaptation model was more accurate in predicting allocation decisions, in explaining positive and negative recency effects, and in showing why composition effects are not overcome by learning.     

The Quality of Response Time Data Inference: A Blinded,Collaborative Assessment of the Validity of Cognitive Models

Psychonomic Bulletin & Review - Most data analyses rely on models. To complement statistical models, psychologists have developed cognitive models, which translate observed variables into...