Number representation is influenced by numerical processing level: an ERP study

Abstract

The same numerical magnitude can be manifested in different physical notations. However, how the numbers with distinct notations are mentally represented is still unclear. Here, we hypothesized that how the number is mentally represented is influenced by the numerical processing level of the tasks. If the task only needed a low-level processing, the representation would be dependent on the surface forms of the numbers, exhibiting a numerical notation-dependent effect. By contrast, if the task required a deeper magnitude processing, the processing would utilize an abstract numerical format whose effects are notation independent. To test this hypothesis, we manipulated the notation type and the numerical processing level of the tasks. An ERP component N270 was taken to index the mismatch between the internal representation and the probed number. The results showed that N270 was enhanced when the magnitude was mismatched between two numbers. More importantly, under the task requiring a low-level processing (e.g., magnitude comparison), compared with the same notations, the latency of N270 difference wave was delayed by different notations, exhibiting a notation-dependent effect. However, in the task involving a deeper processing (e.g., magnitude addition), the N270 latencies were earlier for probes having distinct notations (Mandarin or Arabic number) than for probes having the same notations as in the addition operation (Mahjong). Moreover, no difference was found on N270 latencies between the two distinct notations but with similar degree of familiarity. Taken together, these results support our hypothesis that the numerical processing level affects the number representation.

A Facilitatory Effect of Perceptual Incongruity on Target-Source Matching in Pictorial Metaphors of Chinese Advertising: EEG Evidence

“…Many neurons in subregions of the frontal and parietal cortex show elevated responses during tasks requiring the active storage of feature-or stimulus-specific visual information (e.g., Bisley & Pasternak, 2000;Mendoza-Halliday et al, 2014;Miller & Tippett, 1996). These results indicate that frontal delta and theta activities observed in the delayed matching task are not the neural index of external behavioral conflicts, but rather reflect the endogenous detection of mismatch processing (Liang et al, 2012).…”

“…Delta oscillatory responses are usually increased in the frontal and central locations during cognitive load Güntekin & Başar, 2016;. Moreover, theta oscillations (4-8 Hz) have been consistently documented to play a major role in visual working memory functions (Liang et al, 2017;Liang et al, 2012;Sauseng et al, 2010). Generally, theta power increases in response to semantic or syntactic violations and is mainly distributed in frontal areas of the brain (Bastiaansen et al, 2002;Davidson & Indefrey, 2007;Hald et al, 2006).…”

How do incorrect results change the processing of arithmetic information? Evidence from a divided visual field experiment

“…The influence of notation consistency on proportion of correct responses also differed for correct and incorrect equations. This means that not only is there a numerical notation-dependent effect for low-level processing tasks (Liang et al, 2012), but that this effect can be modified by high level features. For correct equations, the proportion of correct responses increased with notation consistency, while for incorrect equations it remained unchanged.…”

“…Consequently, how numbers are mentally represented has been hypothesized to be influenced by the processing level of the task (Liang et al, 2012). Those tasks for which a low-level processing is sufficient have exhibited a numerical notation-dependent effect, whereas tasks that rely on a deeper magnitude processing utilize an abstract numerical representation.…”

“…Moreover, consistent with Zbrodoff and Logan (2000), the present study hypothesized that under relevant and demanding mathematical conditions incorrect equations would take longer to resolve but that accuracy rates would also be greater. It was anticipated that mathematical accuracy resolution would be further influenced by notation consistency (Liang et al, 2012), in a way that is dependent on hemispheric processing. Such an interaction would be expected if greater complexity is indeed required to tease apart the hemispheric functional asymmetry of arithmetic processing.…”