Automatic integration of numerical formats examined with frequency-tagged EEG

Abstract

How humans integrate and abstract numerical information across different formats is one of the most debated questions in human cognition. We addressed the neuronal signatures of the numerical integration using an EEG technique tagged at the frequency of visual stimulation. In an oddball design, participants were stimulated with standard sequences of numbers (< 5) depicted in single (digits, dots, number words) or mixed notation (dots—digits, number words—dots, digits—number words), presented at 10 Hz. Periodically, a deviant stimulus (> 5) was inserted at 1.25 Hz. We observed significant oddball amplitudes for all single notations, showing for the first time using this EEG technique, that the magnitude information is spontaneously and unintentionally abstracted, irrespectively of the numerical format. Significant amplitudes were also observed for digits—number words and number words—dots, but not for digits—dots, suggesting an automatic integration across some numerical formats. These results imply that direct and indirect neuro-cognitive links exist across the different numerical formats.

A shared numerical magnitude representation evidenced by the distance effect in frequency-tagging EEG

“…Significant responses (Z > 1.64, see “ Methods ”) were found on 7 consecutive harmonics for general visual responses (from 6 to 42 Hz) and on 9 harmonics for discrimination responses (from 1.2 to 10.8 Hz, but excluding the 6-Hz frequency). In line with previous FPVS studies on numerical representation 31 , 32 , 38 , we expected posterior general and discrimination responses, although more visual activation was anticipated for the response to the general visual stimulation than for the discrimination process. This was indeed confirmed by a ranking of the electrodes based on their response amplitude (see “ Methods ”) revealing that channels P8, PO8, PO10, O2 elicited the highest general visual responses while channels P8, P10, PO8 and PO10 showed the highest discrimination responses within the right hemisphere.…”

“…Numerosities smaller than 3, moreover, have almost identical non-symbolic and iconic representations, while these two formats are clearly distinct for larger numerosities. Additionally, as small symbolic numerosities are more frequently encountered in daily life, it cannot be excluded that the responses obtained 32 actually reflect a difference of complexity in the processing of small versus large numerosities, rather than the processing of magnitude per se. A second limitation is that the representation codes were only compared two by two, which can supposedly be explained by the fact that the symbolic format contains two different codes (i.e., digits and number words) while the non-symbolic format is only represented by dots.…”

“…This would also give the possibility to assess numerical magnitude processing at a higher level of abstraction, as it would require participants to generalize magnitude information across formats. Accordingly, a recent study 32 used the same magnitude contrast (small/large 31 ) but with mixed codes (i.e., dots and digits, number words and dots, digits and number words). Results suggest an automatic integration of magnitude across number words and dots, digits and number words, but not dots and digits.…”

Weaker semantic priming effects with number words in the second language of math learning.

“…The acquisition of number words promotes precise numerical representation as sustained by developmental studies (Negen & Sarnecka, 2009, 2012; Wynn, 1992) and cross-linguistic studies on languages with restricted number words (Frank et al, 2008; Pica et al, 2004; Pitt et al, 2022; Spaepen et al, 2013). Number words’ and Indo-Arabic digits’ semantic representations are associated with common numerical features (e.g., magnitude, order, or parity, Koechlin et al, 1999; Marinova et al, 2021). Moreover, the development of number semantic representations predicts later mathematic performances both when considering number words (Desoete et al, 2012; Lê & Noël, 2021; Major et al, 2017; van Marle et al, 2014) and Indo-Arabic digits (Göbel et al, 2014; Schneider et al, 2017).…”

Linguistic and attentional factors – not statistical regularities – contribute to word-selective neural responses with FPVS-oddball paradigms

“…In recent years, a frequency-tagging electroencephalographic (EEG) approach based on 'oddball', or 'oddball-like' design, has been successfully applied to explore visual recognition in the nonverbal (objects, faces, or numerosities; e.g., Guillaume et al, 2018Guillaume et al, , 2020Liu-Shuang et al, 2014a;Marinova et al, 2021;Marlair et al, 2021;Nurdal et al, 2021;Retter et al, 2020;Stothart et al, 2017) and verbal domains (letters, words, word semantic categories; e.g., (Lochy et al, 2015;Volfart et al, 2021). This fast periodic visual stimulation (FPVS) oddball approach is typically based on variable exemplars of a frequent stimulus category (or "base" category) presented at a rapid rate (e.g., 10 Hz), interrupted at a slower periodic rate (usually 1/5, e.g., 2 Hz) by a contrastive stimulus category (or "deviant" category) (Rossion et al, 2018(Rossion et al, , 2020.…”