What is the airspeed velocity of an unladen swallow? modeling numerical judgments of realistic stimuli

TL;DR

This paper shows how to extract cues from complex natural stimuli using similarity ratings and apply them in cognitive models for numerical judgments.

Contribution

A method to extract cues from naturalistic stimuli using MDS and apply them in hierarchical Bayesian models for judgment tasks.

Findings

MDS analysis recovers predefined cues from artificial stimuli as effectively as original cue values.

The method replicates previous findings using complex naturalistic stimuli in multiple-cue judgment tasks.

This approach bridges the gap between artificial and naturalistic stimuli in cognitive modeling.

Abstract

Research on processes of multiple-cue judgments usually uses artificial stimuli with predefined cue structures, such as artificial bugs with four binary features like back color, belly color, gland size, and spot shape. One reason for using artifical stimuli is that the cognitive models used in this area need known cues and cue values. This limitation makes it difficult to apply the models to research questions with complex naturalistic stimuli with unknown cue structure. In two studies, building on early categorization research, we demonstrate how cues and cue values of complex naturalistic stimuli can be extracted from pairwise similarity ratings with a multidimensional scaling analysis. These extracted cues can then be used in a state-of-the-art hierarchical Bayesian model of numerical judgments. In the first study, we show that predefined cue structures of artificial stimuli are well recovered by an MDS analysis of similarity judgments and that using these MDS-based attributes as cues in a cognitive model of judgment data from an existing experiment leads to the same inferences as when the original cue values were used. In the second study, we use the same procedure to replicate previous findings from multiple-cue judgment literature using complex naturalistic stimuli.

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