A Methodological Framework for Stimuli Control: Insights From Numerical Cognition

Abstract

The stimuli presented in cognitive experiments have a crucial role in the ability to isolate the underlying mechanism from other interweaved mechanisms. New ideas aimed at unveiling cognitive mechanisms are often realized through introducing new stimuli. This, in turn, raises challenges in reconciling results to literature. We demonstrate this challenge in the field of numerical cognition. Stimuli used in this field are designed to present quantity in a non symbolic manner. Physical properties, such as surface area and density, inherently correlate with quantity, masking the mechanism underlying numerical perception. Different generation methods (GMs) are used to control these physical properties. However, the way a GM controls physical properties affects numerical judgments in different ways, compromising comparability and the pursuit of cumulative science. Here, using a novel data-driven approach, we provide a methodological review of non symbolic stimuli GMs developed since 2000. Our results reveal that the field thrives and that a wide variety of GMs are tackling new methodological and theoretical ideas. However, the field lacks a common language and means to integrate new ideas into the literature. These shortcomings impair the interpretability, comparison, replication, and reanalysis of previous studies that have considered new ideas. We present guidelines for GMs relevant also to other fields and tasks involving perceptual decisions, including (a) defining controls explicitly and consistently, (b) justifying controls and discussing their implications, (c) considering stimuli statistical features, and (d) providing complete stimuli set, matching responses, and generation code. We hope these guidelines will promote the integration of findings and increase findings’ explanatory power.