Elementary arithmetic is highly prevalent in our daily lives. However, despite decades of research, we are only beginning to understand how the brain solves simple calculations. Here, we applied machine learning techniques to magnetoencephalography (MEG) signals in an effort to decompose the successive processing stages and mental transformations underlying elementary arithmetic. Adults subjects verified single-digit addition and subtraction problems such as 3 + 2 = 9 in which each successive symbol was presented sequentially. MEG signals revealed a cascade of partially overlapping brain states. While the first operand could be transiently decoded above chance level, primarily based on its visual properties, the decoding of the second operand was more accurate and lasted longer. Representational similarity analyses suggested that this decoding rested on both visual and magnitude codes. We were also able to decode the operation type (additions vs. subtraction) during practically the entire trial after the presentation of the operation sign. At the decision stage, MEG indicated a fast and highly overlapping temporal dynamics for (1) identifying the proposed result, (2) judging whether it was correct or incorrect, and (3) pressing the response button. Surprisingly, however, the internally computed result could not be decoded. Our results provide a first comprehensive picture of the unfolding processing stages underlying arithmetic calculations at a single-trial level, and suggest that externally and internally generated neural codes may have different neural substrates.

Decoding the processing stages of mental arithmetic with magnetoencephalography / Pinheiro-Chagas, Pedro; Piazza, Manuela; Dehaene, Stanislas. - In: CORTEX. - ISSN 0010-9452. - ELETTRONICO. - 2019:114(2019), pp. 124-139. [10.1016/j.cortex.2018.07.018]

Decoding the processing stages of mental arithmetic with magnetoencephalography

Pinheiro-Chagas, Pedro;Piazza, Manuela;
2019

Abstract

Elementary arithmetic is highly prevalent in our daily lives. However, despite decades of research, we are only beginning to understand how the brain solves simple calculations. Here, we applied machine learning techniques to magnetoencephalography (MEG) signals in an effort to decompose the successive processing stages and mental transformations underlying elementary arithmetic. Adults subjects verified single-digit addition and subtraction problems such as 3 + 2 = 9 in which each successive symbol was presented sequentially. MEG signals revealed a cascade of partially overlapping brain states. While the first operand could be transiently decoded above chance level, primarily based on its visual properties, the decoding of the second operand was more accurate and lasted longer. Representational similarity analyses suggested that this decoding rested on both visual and magnitude codes. We were also able to decode the operation type (additions vs. subtraction) during practically the entire trial after the presentation of the operation sign. At the decision stage, MEG indicated a fast and highly overlapping temporal dynamics for (1) identifying the proposed result, (2) judging whether it was correct or incorrect, and (3) pressing the response button. Surprisingly, however, the internally computed result could not be decoded. Our results provide a first comprehensive picture of the unfolding processing stages underlying arithmetic calculations at a single-trial level, and suggest that externally and internally generated neural codes may have different neural substrates.
114
Pinheiro-Chagas, Pedro; Piazza, Manuela; Dehaene, Stanislas
Decoding the processing stages of mental arithmetic with magnetoencephalography / Pinheiro-Chagas, Pedro; Piazza, Manuela; Dehaene, Stanislas. - In: CORTEX. - ISSN 0010-9452. - ELETTRONICO. - 2019:114(2019), pp. 124-139. [10.1016/j.cortex.2018.07.018]
File in questo prodotto:
File Dimensione Formato  
2019_Chagas_Decoding the processing stages of mental Arithmetics_Cortex2019.pdf

embargo fino al 01/12/2020

Tipologia: Post-print referato (Refereed author’s manuscript)
Licenza: Creative commons
Dimensione 4.01 MB
Formato Adobe PDF
4.01 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11572/212624
Citazioni
  • ???jsp.display-item.citation.pmc??? 8
  • Scopus 9
  • ???jsp.display-item.citation.isi??? 9
social impact