Less is More? Instructions Modulate the Way We Interact with Continuous Features in Non-Symbolic Dot-Array Comparison Tasks

Avitan, Aviv; Galili, Hanit; Henik, Avishai

doi:10.2139/ssrn.4065685

Cited by 1 publication

(1 citation statement)

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“…For example, changing the task from a numerical task in which the participants' goal is to choose the more numerous array, to a physical task in which the participants' goal is to choose the larger array, changes the effect of physical properties on performance (Katzin et al, 2019;Leibovich et al, 2015;Leibovich-Raveh et al, 2018;Salti et al, 2017;Tomlinson et al, 2020). Avitan et al (2022) found that changing the task goal to choose a smaller magnitude (quantity or area) instead of the larger magnitude modulated participants' performance and interacted with task type (numerical or physical). Leibovich-Raveh et al (2018) manipulated participants' emphasis on accuracy or speed during a numerical comparison task and discovered that the effect of different physical properties was dependent on participants' emphasis on accuracy or speed.…”

Section: The Multifaced Control Of Non-symbolic Numerical Stimulimentioning

confidence: 99%

A Methodological Framework for Stimuli Control: Insights from Numerical Cognition

Shilat¹,

Henik²,

Galili³

et al. 2023

Preprint

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Non-symbolic numerical stimuli play a crucial role in numerical cognition. Physical properties such as surface area, density, and item circumference are inherently correlated with quantity. The correlations between physical properties and quantity mask the mechanism underlying numerical perception. Non-symbolic stimuli are generated using different generation methods (GMs) aimed at controlling physical properties. The way a GM controls physical properties affects numerical judgments. Here, using a novel data-driven approach, we provide a methodological review of non-symbolic stimuli GMs developed since 2000. Annotators tagged the GMs’ control over physical properties. Next, GMs were qualitatively analyzed for different property controls, terminology, and definitions. The tagging and qualitative analysis provided data suitable for quantitative analysis of GMs. We found that the field thrives with a wide variety of GMs aimed at tackling new methodological and theoretical ideas. However, the field lacks a common language and a method to incorporate new ideas in the existing literature. Furthermore, these shortcomings impair the comparison, replication, and reanalysis of previous studies in light of new ideas. We present guidelines for GMs that will hopefully contribute to the field. First, researchers should define controlled properties explicitly and consistently. Second, researchers should provide the code package used to generate stimuli. Third, researchers should also provide the actual stimuli, coupled with the behavioral and neuronal responses. This last guideline would enable researchers to reanalyze previously obtained data, enabling incorporating new ideas in the context of prior research.

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