Across cultures and development, humans can extract proportional quantities from nonsymbolic, visual displays. Two competing accounts have been proposed for the contributions of these nonsymbolic skills to fraction understanding. The shared-magnitude account suggests that a cognitive system responsible for these nonsymbolic skills is foundational for symbolic fraction learning, predicting that neural patterns of fraction skills resemble magnitude-driven neural responses to continuous depictions, like unsegmented bars, that emphasize proportional magnitude. However, other depictions of proportions, like discretized, segmented bars, convey both proportional and whole-number information. The shared-interference account proposes that the interference from whole-number information in discretized proportions and symbolic fractions drives the link between these skills and predicts that neural responses to fraction magnitudes would strongly relate to overall activity to discretized depictions, particularly those with misleading whole-number information. We leveraged representational similarity analyses (RSA) to test these predictions. Nineteen young adults (mean age=23.26 years) compared proportions depicted in continuous, discretized, and symbolic formats. Univariate results indicated robust neural distance effects in frontal, parietal, and occipital regions across formats. Region of interest (ROI) analyses of the intraparietal sulcus (IPS) revealed neural rational distance effects for continuous proportions, whereas for discretized and symbolic fractions, only comparisons with whole-number interference exhibited a rational magnitude signal. Critically, RSA demonstrated that the IPS fraction magnitude-related activity showed greater similarity with discretized brain responses than continuous ones, but only in the context of misleading whole-number information. Together, these findings support a combination of the two accounts and suggest that symbolic fraction proficiency, as well as adept discretized proportional reasoning, involves accessing proportional magnitude code despite the presence of misleading whole-number information.
Abreu‐Mendoza et al. (Tue,) studied this question.