The human mind is endowed with a remarkable ability to support novel cognitive skills, such as reading, writing, map-based navigation, mathematical reasoning, and scientific logic. Recently, humanity has invented another powerful cognitive tool: computer programming. The ability to flexibly instruct programmable machines has led to a rapid technological transformation of communities across the world (Ensmenger, 2012); however, little is known about the cognitive and neural systems that underlie computer programming skills.

Here, we investigate which neural systems support one critical aspect of computer programming: computer code comprehension. By code comprehension, we refer to a set of cognitive processes that allow programmers to interpret individual program tokens (such as keywords, variables, and function names), combine them to extract the meaning of program statements, and, finally, combine the statements into a mental representation of the entire program. It is important to note that code comprehension may be cognitively and neurally separable from cognitive operations required to process program content, that is, the actual operations described by code. For instance, to predict the output of the program that sums the first three elements of an array, the programmer should identify the relevant elements and then mentally perform the summation. Most of the time, processing program content recruits a range of cognitive processes known as computational thinking (Wing, 2006; Wing, 2011), which include algorithm identification, pattern generalization/abstraction, and recursive reasoning (e.g., Kao, 2010). These cognitive operations are notably different from code comprehension per se and may not require programming knowledge at all (Guzdial, 2008). Thus, research studies where people read computer programs should account for the fact that interpreting a computer program involves two separate cognitive phenomena: processing computer code that comprises the program (i.e., code comprehension) and mentally simulating the procedures described in the program (i.e., processing problem content).

Given that code comprehension is a novel cognitive tool, typically acquired in late childhood or in adulthood, we expect it to draw on preexisting cognitive systems. However, the question of which cognitive processes support code comprehension is nontrivial. Unlike some cognitive inventions that are primarily linked to a single cognitive domain (e.g., reading/writing building on spoken language), code comprehension plausibly bears parallels to multiple distinct cognitive systems. First, it may rely on domain-general executive resources, including working memory and cognitive control (Bergersen and Gustafsson, 2011; Nakagawa et al., 2014; Nakamura et al., 2003). In addition, it may draw on the cognitive systems associated with math and logic (McNamara, 1967; Papert, 1972), in line with the traditional construal of coding as problem-solving (Dalbey and Linn, 1985; Ormerod, 1990; Pea and Kurland, 1984; Pennington and Grabowski, 1990). Finally, code comprehension may rely on the system that supports comprehension of natural languages (Fedorenko et al., 2019; Murnane, 1993; Papert, 1993). Like natural language, computer code makes heavy use of hierarchical structures (e.g., loops, conditionals, and recursive statements), and, like language, it can convey an unlimited amount of meaningful information (e.g., describing objects or action sequences). These similarities could, in principle, make the language circuits well suited for processing computer code.

Neuroimaging research is well positioned to disentangle the relationship between code comprehension and other cognitive domains. Many cognitive processes are known to evoke activity in specific brain regions/networks: thus, observing activity for the task of interest in a particular region or network with a known function can indicate which cognitive processes are likely engaged in that task (Mather et al., 2013). Prior research (Assem et al., 2020; Duncan, 2010; Duncan, 2013; Duncan and Owen, 2000) has shown that executive processes – such as attention, working memory, and cognitive control – recruit a set of bilateral frontal and parietal brain regions collectively known as the multiple demand (MD) system. If code comprehension primarily relies on domain-general executive processes, we expect to observe code-evoked responses within the MD system, distributed across both hemispheres. Math and logic also evoke responses within the MD system (Fedorenko et al., 2013), although this activity tends to be left-lateralized (Amalric and Dehaene, 2016; Amalric and Dehaene, 2019; Goel and Dolan, 2001; Micheloyannis et al., 2005; Monti et al., 2007; Monti et al., 2009; Pinel and Dehaene, 2010; Prabhakaran et al., 1997; Reverberi et al., 2009). If code comprehension draws on the same mechanisms as math and logic, we expect to observe left-lateralized activity within the MD system. Finally, comprehension of natural language recruits a set of left frontal and temporal brain regions known as the language system (e.g., Fedorenko and Thompson-Schill, 2014). These regions respond robustly to linguistic input, both visual and auditory (Deniz et al., 2019; Fedorenko et al., 2010; Nakai et al., 2020; Regev et al., 2013; Scott et al., 2017). However, they show little or no response to tasks in non-linguistic domains, such as executive functions, math, logic, music, action observation, and non-linguistic communicative signals, like gestures (Fedorenko et al., 2011; Jouravlev et al., 2019; Monti et al., 2009; Monti et al., 2012; Pritchett et al., 2018; see Fedorenko and Blank, 2020, for a review). If code comprehension relies on the same circuits that map form to meaning in natural language, we expect to see activity within the language system.

Evidence from prior neuroimaging investigations of code comprehension is inconclusive. Existing studies have provided some evidence for left-lateralized activity in regions that roughly correspond to the language system (Siegmund et al., 2014; Siegmund et al., 2017), as well as some evidence for the engagement of frontal and parietal regions resembling the MD system (Floyd et al., 2017; Huang et al., 2019; Siegmund et al., 2014; Siegmund et al., 2017). However, none of these prior studies sought to explicitly distinguish code comprehension from other programming-related processes, and none of them provide quantitative evaluations of putative shared responses to code and other tasks, such as working memory, math, or language (cf. Liu et al., 2020; see Discussion).

Here, we use functional magnetic resonance imaging (fMRI) to evaluate the role of the MD system and the language system in computer code comprehension. Three design features that were lacking in earlier neuroimaging studies of programming allow us to evaluate the relative contributions of these two candidate systems. First, we contrast neural responses evoked by code problems with those evoked by content-matched sentence problems (Figure 1A); this comparison allows us to disentangle activity evoked by code comprehension from activity evoked by the underlying program content (which is matched across code and sentence problems).

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Second, we use independent ‘localizer’ tasks (Brett et al., 2002; Fedorenko et al., 2010; Saxe et al., 2006) to identify our networks of interest: a working memory task to localize the MD system and a passive reading task to localize the language system (Figure 1B). The functional localization approach obviates the reliance on the much-criticized ‘reverse inference’ reasoning (Poldrack, 2006; Poldrack, 2011), whereby functions are inferred from coarse macro-anatomical landmarks. Instead, we can directly interpret code-evoked activity within functionally defined regions of interest (Mather et al., 2013). In addition, localization of the MD and language networks is performed in individual participants, which is important given substantial variability in their precise locations across individuals (Fedorenko and Blank, 2020; Shashidhara et al., 2019b) and leads to higher sensitivity and functional resolution (Nieto-Castañón and Fedorenko, 2012).

Third, to draw general conclusions about code comprehension, we investigate two very different programming languages: Python, a popular general-purpose programming language, and ScratchJr, an introductory visual programming language for creating animations, designed for young children (Bers and Resnick, 2015). In the Python experiment, we further examine two problem types (math problems and string manipulation) and three basic types of program structure (sequential statements, for loops, and if statements). Comprehension of both Python and ScratchJr code requires retrieving the meaning of program tokens and combining them into statements, despite the fact that the visual features of the tokens in the two languages are very different (text vs. images). If a brain system is involved in code comprehension, we expect its response to generalize across programming languages and problem types, similar to how distinct natural languages in bilinguals and multilinguals draw on the same language regions (Kroll et al., 2015).

Taken together, these design features of our study allow us to draw precise and generalizable conclusions about the neural basis of code comprehension.

Participants performed a program comprehension task inside an MRI scanner. In each trial, participants, all proficient in the target programming language, read either a code problem or a content-matched sentence problem (Figure 1A) and were asked to predict the output. In Experiment 1 (24 participants, 15 women), code problems were written in Python, a general-purpose text-based programming language (Sanner, 1999). In Experiment 2 (19 participants, 12 women), code problems were written in ScratchJr, an introductory graphical programming language developed for children aged 5–7 (Bers, 2018). Both experiments were conducted with adults to facilitate result comparison. Good behavioral performance confirmed that participants were proficient in the relevant programming language and engaged with the task (Python: 99.6% response rate, 85% accuracy on code problems; ScratchJr: 98.6% response rate, 79% accuracy on code problems; see Figure 2—figure supplement 1 for detailed behavioral results). Participants additionally performed two functional localizer tasks: a hard vs. easy spatial working memory task, used to define the MD system, and a sentence vs. nonword reading task, used to define the language system (Figure 1B; see Materials and methods for details).

We then contrasted neural activity in the MD and language systems during code problem comprehension with activity during (a) sentence problem comprehension and (b) the nonword reading condition from the language localizer task. Sentence problem comprehension requires simulating the same operations as code problem comprehension (mathematical operations or string manipulation for Python, video simulation for ScratchJr), so contrasting code problems with sentence problems allows us to isolate neural responses evoked by code comprehension from responses evoked by processing problem content. Nonword reading elicits weak responses in both the language system and the MD system (in the language system, this response likely reflects low-level perceptual and/or phonological processing; in the MD system, it likely reflects the basic task demands associated with maintaining attention or reading pronounceable letter strings). Because the nonword response is much weaker than responses to the localizer conditions of interest (Fedorenko et al., 2010; Mineroff et al., 2018), nonword reading can serve as a control condition for both the MD and language systems, providing a more stringent baseline than simple fixation. Given the abundant evidence that the MD system and the language system are each strongly functionally interconnected (Blank et al., 2014; Mineroff et al., 2018; Paunov et al., 2019), we perform the key analyses at the system level.

MD system exhibits robust and generalizable bilateral responses during code comprehension

We found strong bilateral responses to code problems within the MD system in both Experiments 1 and 2 (Figures 2 and 3). These responses were stronger than responses to both the sentence problem condition (Python: β = 1.03, p<0.001, ScratchJr: β = 1.38, p<0.001) and the control nonword reading condition (Python: β = 2.17, p<0.001; ScratchJr: β = 1.23, p<0.001). The fact that code problems drove the MD system more strongly than content-matched sentence problems (despite the fact that sentence problems generally took longer to respond to; see Figure 2—figure supplement 1) demonstrates that the MD system responds to code comprehension specifically rather than simply being activated by the underlying problem content.

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To further test the generalizability of MD responses, we capitalized on the fact that our Python stimuli systematically varied along two dimensions: (1) problem type (math problems vs. string manipulation) and (2) problem structure (sequential statements, for loops, if statements). Strong responses were observed in the MD system (Figure 4A and B) regardless of problem type (β = 3.02, p<0.001; no difference between problem types) and problem structure (β = 3.14, p<0.001; sequential problems evoked a slightly weaker response, β = −0.20, p=0.002). This analysis demonstrates that the responses were not driven by one particular type of problem or by mental operations related to the processing of a particular code structure.

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We also tested whether MD responses to code showed a hemispheric bias similar to what is typically seen for math and logic problems (Goel and Dolan, 2001; Micheloyannis et al., 2005; Monti et al., 2007; Monti et al., 2009; Pinel and Dehaene, 2010; Prabhakaran et al., 1997; Reverberi et al., 2009). Neither Python nor ScratchJr problems showed a left-hemisphere bias for code comprehension. For Python, the size of the code problems > sentence problems effect did not interact with hemisphere (β = 0.11, p=0.46), even though the magnitude of responses to code problems as compared to nonword reading was stronger in the left hemisphere (β = 0.63, p<0.001). These results show that neural activity evoked by Python code comprehension was bilaterally distributed but that activity evoked by the underlying problem content was left-lateralized. For ScratchJr, the size of the code problems > sentence problems effect interacted with hemisphere, with stronger responses in the right hemisphere (β = 0.57, p=0.001), perhaps reflecting the bias of the right hemisphere toward visuo-spatial processing (Corballis, 2003; Hugdahl, 2011; Sheremata et al., 2010).

Follow-up analyses of activity in individual regions within the MD system demonstrated that 17 of the 20 MD fROIs (all except the fROIs located in left medial frontal cortex and in the left and right insula) responded significantly more strongly to Python code problems than to sentence problems (see Supplementary file 1 for all fROI statistics). Responses to ScratchJr were significantly stronger than responses to sentence problems in 6 of the 10 left hemisphere MD fROIs (the effect was not significant in the fROIs in superior frontal gyrus, the dorsal part of the precentral gyrus, the medial frontal cortex, and the insula) and in 8 of the 10 right hemisphere MD fROIs (the effect was not significant in the fROIs in the medial frontal cortex and the insula; see Supplementary file 1 for all fROI statistics). These analyses demonstrate that code processing is broadly distributed across the MD system rather than being localized to a particular region or to a small subset of regions.

Overall, we show that MD responses to code are strong, do not exclusively reflect responses to problem content, generalize across programming languages and problem types, and are observed across most MD fROIs.

The ability to interpret computer code is a remarkable cognitive skill that bears parallels to diverse cognitive domains, including general executive functions, math, logic, and language. The fact that coding can be learned in adulthood suggests that it may rely on existing cognitive systems. Here, we tested the role of two candidate neural systems in computer code comprehension: the domain-general MD system (Duncan, 2010), which has been linked to diverse executive demands and implicated in math and logic (e.g., Amalric and Dehaene, 2019; Goel, 2007; Monti et al., 2007; Monti et al., 2009), and the language-selective system (Fedorenko et al., 2011), which has been linked to lexical and combinatorial linguistic processes (e.g., Bautista and Wilson, 2016; Fedorenko et al., 2010; Fedorenko et al., 2012; Fedorenko et al., 2020; Keller et al., 2001; Mollica et al., 2020). We found robust bilateral responses to code problems within the MD system, a pattern that held across two very different programming languages (Python and ScratchJr), types of problems (math and string manipulation), and problem structure (sequential statements, for loops, and if statements). In contrast, responses in the language system were substantially lower than those elicited by the content-matched sentence problems and exceeded responses to the control condition (nonwords reading) only for one of the two programming languages tested.

Our work uniquely contributes to the study of computer programming in the mind and brain by addressing two core issues that made it difficult to interpret results from prior studies. First, we disentangle responses evoked by code comprehension from responses to problem content (which is often not code-specific) by contrasting code problems with content-matched sentence problems. Our findings suggest that earlier reports of left-lateralized code-evoked activity (Siegmund et al., 2014) may reflect processing program content rather than code comprehension per se. This distinction should also be considered when interpreting results of other studies of programming effects on brain activity, such as debugging (Castelhano et al., 2019), variable tracking (Ikutani and Uwano, 2014; Nakagawa et al., 2014), use of semantic cues or program layout (Fakhoury et al., 2018; Siegmund et al., 2017), program generation (Krueger et al., 2020), and programming expertise (Ikutani et al., 2020).

Second, we analyze responses in brain areas that are functionally localized in individual participants, allowing for straightforward interpretation of the observed responses (Mather et al., 2013; Saxe et al., 2006). This approach stands in contrast to the traditional approach, whereby neural responses are averaged across participants on a voxel-by-voxel basis, and the resulting activation clusters are interpreted via ‘reverse inference’ from anatomy (e.g., Poldrack, 2006; Poldrack, 2011). Functional localization is particularly important when analyzing responses in frontal, temporal, and parietal association cortex, which is known to be functionally heterogeneous and variable across individuals (Blank et al., 2017; Braga et al., 2019; Fedorenko and Kanwisher, 2009; Frost and Goebel, 2012; Shashidhara et al., 2019b; Tahmasebi et al., 2012; Vázquez-Rodríguez et al., 2019).

The results of our work align well with the results of another recent study on program comprehension (Liu et al., 2020). Liu et al. investigated the neural correlates of program comprehension by contrasting Python code problems with fake code. The code problem condition was similar to ours, whereas the fake code condition involved viewing scrambled code, followed by a visual recognition task. The code problems > fake code contrast is broader than ours: it includes both code comprehension (interpreting Python code) and the processing of problem content (manipulating characters in a string). Our results show that the MD system is involved in both processes, but Python code comprehension is bilateral, whereas the processing of problem content is left-lateralized. We would therefore expect the code problems > fake code contrast to activate the MD system, engaging the left hemisphere more strongly than the right due to the demands of problem content processing. This is precisely what Liu et al. found. Further, similar to us, Liu et al. conclude that it is the MD regions, not the language regions, that are primarily involved in program comprehension.

Materials used for the programming tasks, fROI responses in individual participants (used for generating Figures 2-4), behavioral data, and analysis code files are available on the paper's website https://github.com/ALFA-group/neural-program-comprehension (copy archived at https://archive.softwareheritage.org/swh:1:rev:616e893d05038da620bdf9f2964bd3befba75dc5/). Whole brain activation maps are available at https://osf.io/9jfn5/.

1. 1. Ivanova AA
   2. Srikant S
   3. Sueoka Y
   4. Kean HH
   5. Dhamala R
   6. O'Reilly U-M
   7. Bers MU
   8. Fedorenko E

   (2020) Open Science Framework

   ID 10.17605/OSF.IO/9JFN5. Comprehension of computer code relies primarily on domain-general executive resources.

   https://osf.io/9jfn5/

1. Preprint

   1. Allamanis M
   2. Barr ET
   3. Devanbu P
   4. Sutton C

   (2018) A survey of machine learning for big code and naturalness

   arXiv.

   http://arxiv.org/abs/1709.06182

   • Google Scholar
2. 1. Amalric M
   2. Dehaene S

   (2016) Origins of the brain networks for advanced mathematics in expert mathematicians

   PNAS 113:4909–4917.

   https://doi.org/10.1073/pnas.1603205113

   • PubMed
   • Google Scholar
3. 1. Amalric M
   2. Dehaene S

   (2019) A distinct cortical network for mathematical knowledge in the human brain

   NeuroImage 189:19–31.

   https://doi.org/10.1016/j.neuroimage.2019.01.001

   • PubMed
   • Google Scholar
4. 1. Anderson AJ
   2. Lalor EC
   3. Lin F
   4. Binder JR
   5. Fernandino L
   6. Humphries CJ
   7. Conant LL
   8. Raizada RDS
   9. Grimm S
   10. Wang X

   (2019) Multiple regions of a cortical network commonly encode the meaning of words in multiple grammatical positions of read sentences

   Cerebral Cortex 29:2396–2411.

   https://doi.org/10.1093/cercor/bhy110

   • PubMed
   • Google Scholar
5. 1. Apperly IA
   2. Samson D
   3. Carroll N
   4. Hussain S
   5. Humphreys G

   (2006) Intact first- and second-order false belief reasoning in a patient with severely impaired grammar

   Social Neuroscience 1:334–348.

   https://doi.org/10.1080/17470910601038693

   • PubMed
   • Google Scholar
6. 1. Ardila A
   2. Rosselli M

   (2002) Acalculia and dyscalculia

   Neuropsychology Review 12:179–231.

   https://doi.org/10.1023/a:1021343508573

   • PubMed
   • Google Scholar
7. 1. Assem M
   2. Glasser MF
   3. Van Essen DC
   4. Duncan J

   (2020) A Domain-General cognitive core defined in multimodally parcellated human cortex

   Cerebral Cortex 30:4361–4380.

   https://doi.org/10.1093/cercor/bhaa023

   • PubMed
   • Google Scholar
8. 1. Baldassano C
   2. Hasson U
   3. Norman KA

   (2018) Representation of Real-World event schemas during narrative perception

   The Journal of Neuroscience 38:9689–9699.

   https://doi.org/10.1523/JNEUROSCI.0251-18.2018

   • PubMed
   • Google Scholar
9. 1. Bates D
   2. Mächler M
   3. Bolker B
   4. Walker S

   (2015) Fitting linear Mixed-Effects models using lme4

   Journal of Statistical Software 67:1–48.

   https://doi.org/10.18637/jss.v067.i01

   • Google Scholar
10. 1. Bautista A
    2. Wilson SM

    (2016) Neural responses to grammatically and lexically degraded speech

    Language, Cognition and Neuroscience 31:567–574.

    https://doi.org/10.1080/23273798.2015.1123281

    • Google Scholar
11. 1. Bergersen GR
    2. Gustafsson J-E

    (2011) Programming skill, knowledge, and working memory among professional software developers from an investment theory perspective

    Journal of Individual Differences 32:201–209.

    https://doi.org/10.1027/1614-0001/a000052

    • Google Scholar
12. Conference

    1. Bers MU

    (2018) Coding, playgrounds and literacy in early childhood education: the development of KIBO robotics and ScratchJr

    IEEE Global Engineering Education Conference (EDUCON). pp. 2094–2102.

    https://doi.org/10.1109/EDUCON.2018.8363498

    • Google Scholar
13. 1. Bers MU

    (2019) Coding as another language: a pedagogical approach for teaching computer science in early childhood

    Journal of Computers in Education 6:499–528.

    https://doi.org/10.1007/s40692-019-00147-3

    • Google Scholar
14. Book

    1. Bers MU
    2. Resnick M

    (2015)

    The Official ScratchJr Book: Help Your Kids Learn to Code (1)

    No Starch Press.

    • Google Scholar
15. 1. Berwick RC
    2. Friederici AD
    3. Chomsky N
    4. Bolhuis JJ

    (2013) Evolution, brain, and the nature of language

    Trends in Cognitive Sciences 17:89–98.

    https://doi.org/10.1016/j.tics.2012.12.002

    • PubMed
    • Google Scholar
16. 1. Binder JR
    2. Desai RH
    3. Graves WW
    4. Conant LL

    (2009) Where is the semantic system? A critical review and meta-analysis of 120 functional neuroimaging studies

    Cerebral Cortex 19:2767–2796.

    https://doi.org/10.1093/cercor/bhp055

    • PubMed
    • Google Scholar
17. 1. Blank I
    2. Kanwisher N
    3. Fedorenko E

    (2014) A functional dissociation between language and multiple-demand systems revealed in patterns of BOLD signal fluctuations

    Journal of Neurophysiology 112:1105–1118.

    https://doi.org/10.1152/jn.00884.2013

    • PubMed
    • Google Scholar
18. 1. Blank IA
    2. Kiran S
    3. Fedorenko E

    (2017) Can neuroimaging help aphasia researchers? addressing generalizability, variability, and interpretability

    Cognitive Neuropsychology 34:377–393.

    https://doi.org/10.1080/02643294.2017.1402756

    • PubMed
    • Google Scholar
19. 1. Blank IA
    2. Fedorenko E

    (2017) Domain-General brain regions do not track linguistic input as closely as Language-Selective regions

    The Journal of Neuroscience 37:9999–10011.

    https://doi.org/10.1523/JNEUROSCI.3642-16.2017

    • PubMed
    • Google Scholar
20. 1. Blank IA
    2. Fedorenko E

    (2020) No evidence for differences among language regions in their temporal receptive windows

    NeuroImage 219:116925.

    https://doi.org/10.1016/j.neuroimage.2020.116925

    • PubMed
    • Google Scholar
21. 1. Braga RM
    2. Van Dijk KRA
    3. Polimeni JR
    4. Eldaief MC
    5. Buckner RL

    (2019) Parallel distributed networks resolved at high resolution reveal close juxtaposition of distinct regions

    Journal of Neurophysiology 121:1513–1534.

    https://doi.org/10.1152/jn.00808.2018

    • PubMed
    • Google Scholar
22. 1. Braga RM
    2. DiNicola LM
    3. Becker HC
    4. Buckner RL

    (2020) Situating the left-lateralized language network in the broader organization of multiple specialized large-scale distributed networks

    Journal of Neurophysiology 124:1415–1448.

    https://doi.org/10.1152/jn.00753.2019

    • PubMed
    • Google Scholar
23. 1. Brett M
    2. Johnsrude IS
    3. Owen AM

    (2002) The problem of functional localization in the human brain

    Nature Reviews Neuroscience 3:243–249.

    https://doi.org/10.1038/nrn756

    • PubMed
    • Google Scholar
24. 1. Buse RPL
    2. Weimer WR

    (2010) Learning a metric for code readability

    IEEE Transactions on Software Engineering 36:546–558.

    https://doi.org/10.1109/TSE.2009.70

    • Google Scholar
25. 1. Cappelletti M
    2. Butterworth B
    3. Kopelman M

    (2001) Spared numerical abilities in a case of semantic dementia

    Neuropsychologia 39:1224–1239.

    https://doi.org/10.1016/S0028-3932(01)00035-5

    • PubMed
    • Google Scholar
26. 1. Castelhano J
    2. Duarte IC
    3. Ferreira C
    4. Duraes J
    5. Madeira H
    6. Castelo-Branco M

    (2019) The role of the insula in intuitive expert bug detection in computer code: an fMRI study

    Brain Imaging and Behavior 13:623–637.

    https://doi.org/10.1007/s11682-018-9885-1

    • PubMed
    • Google Scholar
27. 1. Cetron JS
    2. Connolly AC
    3. Diamond SG
    4. May VV
    5. Haxby JV
    6. Kraemer DJM

    (2019) Decoding individual differences in STEM learning from functional MRI data

    Nature Communications 10:1–10.

    https://doi.org/10.1038/s41467-019-10053-y

    • PubMed
    • Google Scholar
28. Conference

    1. Chen X
    2. Affourtit J
    3. Norman-Haignere S
    4. Jouravlev O
    5. Malik-Moraleda S
    6. Kean HH
    7. Regev T
    8. McDermott J
    9. Fedorenko E

    (2020)

    The fronto-temporal language system does not support the processing of music

    Society for Neurobiology of Language.

    • Google Scholar
29. 1. Chklovskii DB
    2. Koulakov AA

    (2004) MAPS IN THE BRAIN: what can we learn from them?

    Annual Review of Neuroscience 27:369–392.

    https://doi.org/10.1146/annurev.neuro.27.070203.144226

    • Google Scholar
30. 1. Cohen L
    2. Dehaene S
    3. Chochon F
    4. Lehéricy S
    5. Naccache L

    (2000) Language and calculation within the parietal lobe: a combined cognitive, anatomical and fMRI study

    Neuropsychologia 38:1426–1440.

    https://doi.org/10.1016/S0028-3932(00)00038-5

    • PubMed
    • Google Scholar
31. 1. Corballis PM

    (2003) Visuospatial processing and the right-hemisphere interpreter

    Brain and Cognition 53:171–176.

    https://doi.org/10.1016/S0278-2626(03)00103-9

    • PubMed
    • Google Scholar
32. 1. Crittenden BM
    2. Mitchell DJ
    3. Duncan J

    (2015) Recruitment of the default mode network during a demanding act of executive control

    eLife 4:e06481.

    https://doi.org/10.7554/eLife.06481

    • PubMed
    • Google Scholar
33. 1. Dalbey J
    2. Linn MC

    (1985) The demands and requirements of computer programming: a literature review

    Journal of Educational Computing Research 1:253–274.

    https://doi.org/10.2190/BC76-8479-YM0X-7FUA

    • Google Scholar
34. 1. Deniz F
    2. Nunez-Elizalde AO
    3. Huth AG
    4. Gallant JL

    (2019) The representation of semantic information across human cerebral cortex during listening versus reading is invariant to stimulus modality

    The Journal of Neuroscience 39:7722–7736.

    https://doi.org/10.1523/JNEUROSCI.0675-19.2019

    • PubMed
    • Google Scholar
35. 1. Diachek E
    2. Blank I
    3. Siegelman M
    4. Affourtit J
    5. Fedorenko E

    (2020) The Domain-General multiple demand (MD) Network does not support core aspects of language comprehension: a Large-Scale fMRI investigation

    The Journal of Neuroscience 40:4536–4550.

    https://doi.org/10.1523/JNEUROSCI.2036-19.2020

    • PubMed
    • Google Scholar
36. 1. Dronkers NF
    2. Ludy CA
    3. Redfern BB

    (1998) Pragmatics in the absence of verbal language: descriptions of a severe aphasic and a language-deprived adult

    Journal of Neurolinguistics 11:179–190.

    https://doi.org/10.1016/S0911-6044(98)00012-8

    • Google Scholar
37. 1. Duncan J

    (2010) The multiple-demand (MD) system of the primate brain: mental programs for intelligent behaviour

    Trends in Cognitive Sciences 14:172–179.

    https://doi.org/10.1016/j.tics.2010.01.004

    • PubMed
    • Google Scholar
38. 1. Duncan J

    (2013) The structure of cognition: attentional episodes in mind and brain

    Neuron 80:35–50.

    https://doi.org/10.1016/j.neuron.2013.09.015

    • PubMed
    • Google Scholar
39. 1. Duncan J
    2. Owen AM

    (2000) Common regions of the human frontal lobe recruited by diverse cognitive demands

    Trends in Neurosciences 23:475–483.

    https://doi.org/10.1016/S0166-2236(00)01633-7

    • PubMed
    • Google Scholar
40. 1. Ensmenger N

    (2012) The digital construction of technology: rethinking the history of computers in society

    Technology and Culture 53:753–776.

    https://doi.org/10.1353/tech.2012.0126

    • Google Scholar
41. Conference

    1. Fakhoury S
    2. Ma Y
    3. Arnaoudova V
    4. Adesope O

    (2018) The effect of poor source code lexicon and readability on developers’ Cognitive Load

    Proceedings of the 26th Conference on Program Comprehension. pp. 286–296.

    https://doi.org/10.1145/3196321.3196347

    • Google Scholar
42. 1. Fedorenko E
    2. Hsieh PJ
    3. Nieto-Castañón A
    4. Whitfield-Gabrieli S
    5. Kanwisher N

    (2010) New method for fMRI investigations of language: defining ROIs functionally in individual subjects

    Journal of Neurophysiology 104:1177–1194.

    https://doi.org/10.1152/jn.00032.2010

    • PubMed
    • Google Scholar
43. 1. Fedorenko E
    2. Behr MK
    3. Kanwisher N

    (2011) Functional specificity for high-level linguistic processing in the human brain

    PNAS 108:16428–16433.

    https://doi.org/10.1073/pnas.1112937108

    • PubMed
    • Google Scholar
44. 1. Fedorenko E
    2. Nieto-Castañon A
    3. Kanwisher N

    (2012) Lexical and syntactic representations in the brain: an fMRI investigation with multi-voxel pattern analyses

    Neuropsychologia 50:499–513.

    https://doi.org/10.1016/j.neuropsychologia.2011.09.014

    • PubMed
    • Google Scholar
45. 1. Fedorenko E
    2. Duncan J
    3. Kanwisher N

    (2013) Broad domain generality in focal regions of frontal and parietal cortex

    PNAS 110:16616–16621.

    https://doi.org/10.1073/pnas.1315235110

    • PubMed
    • Google Scholar
46. 1. Fedorenko E
    2. Ivanova A
    3. Dhamala R
    4. Bers MU

    (2019) The language of programming: a cognitive perspective

    Trends in Cognitive Sciences 23:525–528.

    https://doi.org/10.1016/j.tics.2019.04.010

    • PubMed
    • Google Scholar
47. Preprint

    1. Fedorenko E
    2. Blank I
    3. Siegelman M
    4. Mineroff Z

    (2020) Lack of selectivity for syntax relative to word meanings throughout the language network

    bioRxiv.

    https://doi.org/10.1101/477851

    • Google Scholar
48. 1. Fedorenko E
    2. Blank IA

    (2020) Broca's Area Is Not a Natural Kind

    Trends in Cognitive Sciences 24:270–284.

    https://doi.org/10.1016/j.tics.2020.01.001

    • PubMed
    • Google Scholar
49. 1. Fedorenko E
    2. Kanwisher N

    (2009) Neuroimaging of language: why Hasn't a clearer picture emerged?

    Language and Linguistics Compass 3:839–865.

    https://doi.org/10.1111/j.1749-818X.2009.00143.x

    • Google Scholar
50. 1. Fedorenko E
    2. Thompson-Schill SL

    (2014) Reworking the language network

    Trends in Cognitive Sciences 18:120–126.

    https://doi.org/10.1016/j.tics.2013.12.006

    • PubMed
    • Google Scholar
51. 1. Ferstl EC
    2. Neumann J
    3. Bogler C
    4. von Cramon DY

    (2008) The extended language network: a meta-analysis of neuroimaging studies on text comprehension

    Human Brain Mapping 29:581–593.

    https://doi.org/10.1002/hbm.20422

    • PubMed
    • Google Scholar
52. 1. Fischer J
    2. Mikhael JG
    3. Tenenbaum JB
    4. Kanwisher N

    (2016) Functional neuroanatomy of intuitive physical inference

    PNAS 113:E5072–E5081.

    https://doi.org/10.1073/pnas.1610344113

    • PubMed
    • Google Scholar
53. 1. Fitch WT
    2. Hauser MD
    3. Chomsky N

    (2005) The evolution of the language faculty: clarifications and implications

    Cognition 97:179–210.

    https://doi.org/10.1016/j.cognition.2005.02.005

    • PubMed
    • Google Scholar
54. 1. Fitch WT
    2. Martins MD

    (2014) Hierarchical processing in music, language, and action: lashley revisited

    Annals of the New York Academy of Sciences 1316:87–104.

    https://doi.org/10.1111/nyas.12406

    • Google Scholar
55. Conference

    1. Floyd B
    2. Santander T
    3. Weimer W

    (2017) Decoding the representation of code in the brain: an fMRI study of code review and expertise

    2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE). pp. 175–186.

    https://doi.org/10.1109/ICSE.2017.24

    • Google Scholar
56. 1. Freedman DJ
    2. Riesenhuber M
    3. Poggio T
    4. Miller EK

    (2001) Categorical representation of visual stimuli in the primate prefrontal cortex

    Science 291:312–316.

    https://doi.org/10.1126/science.291.5502.312

    • PubMed
    • Google Scholar
57. 1. Frost MA
    2. Goebel R

    (2012) Measuring structural-functional correspondence: spatial variability of specialised brain regions after macro-anatomical alignment

    NeuroImage 59:1369–1381.

    https://doi.org/10.1016/j.neuroimage.2011.08.035

    • PubMed
    • Google Scholar
58. 1. Goel V

    (2007) Anatomy of deductive reasoning

    Trends in Cognitive Sciences 11:435–441.

    https://doi.org/10.1016/j.tics.2007.09.003

    • PubMed
    • Google Scholar
59. 1. Goel V
    2. Dolan RJ

    (2001) Functional neuroanatomy of three-term relational reasoning

    Neuropsychologia 39:901–909.

    https://doi.org/10.1016/S0028-3932(01)00024-0

    • PubMed
    • Google Scholar
60. 1. Guzdial M

    (2008) EducationPaving the way for computational thinking

    Communications of the ACM 51:25–27.

    https://doi.org/10.1145/1378704.1378713

    • Google Scholar
61. 1. Hassenfeld Z
    2. Govind M
    3. E De Ruiter L
    4. Umashi Bers M

    (2020) If you can program, you can write: learning introductory programming across literacy levels

    Journal of Information Technology Education: Research 19:065–085.

    https://doi.org/10.28945/4509

    • Google Scholar
62. 1. Hassenfeld ZR
    2. Bers MU

    (2020) Debugging the writing process: lessons from a comparison of students’ Coding and Writing Practices

    The Reading Teacher 73:735–746.

    https://doi.org/10.1002/trtr.1885

    • Google Scholar
63. 1. Hasson U
    2. Chen J
    3. Honey CJ

    (2015) Hierarchical process memory: memory as an integral component of information processing

    Trends in Cognitive Sciences 19:304–313.

    https://doi.org/10.1016/j.tics.2015.04.006

    • PubMed
    • Google Scholar
64. 1. Hauser MD
    2. Chomsky N
    3. Fitch WT

    (2002) The faculty of language: what is it, who has it, and how did it evolve?

    Science 298:1569–1579.

    https://doi.org/10.1126/science.298.5598.1569

    • PubMed
    • Google Scholar
65. Conference

    1. Hermans F
    2. Aldewereld M

    (2017)

    Programming is writing is programming

    Companion to the First International Conference on the Art, Science and Engineering of Programming. pp. 1–8.

    • Google Scholar
66. 1. Holmes AP
    2. Friston KJ

    (1998) Generalisability, random effects & population inference

    NeuroImage 7:S754.

    https://doi.org/10.1016/S1053-8119(18)31587-8

    • Google Scholar
67. Conference

    1. Huang Y
    2. Liu X
    3. Krueger R
    4. Santander T
    5. Hu X
    6. Leach K
    7. Weimer W

    (2019) Distilling neural representations of data structure manipulation using fMRI and fNIRS

    2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE). pp. 396–407.

    https://doi.org/10.1109/icse.2019.00053

    • Google Scholar
68. 1. Hugdahl K

    (2011) Hemispheric asymmetry: contributions from brain imaging

    Wiley Interdisciplinary Reviews: Cognitive Science 2:461–478.

    https://doi.org/10.1002/wcs.122

    • PubMed
    • Google Scholar
69. 1. Hugdahl K
    2. Raichle ME
    3. Mitra A
    4. Specht K

    (2015) On the existence of a generalized non-specific task-dependent network

    Frontiers in Human Neuroscience 9:430.

    https://doi.org/10.3389/fnhum.2015.00430

    • PubMed
    • Google Scholar
70. Preprint

    1. Ikutani Y
    2. Kubo T
    3. Nishida S
    4. Hata H
    5. Matsumoto K
    6. Ikeda K
    7. Nishimoto S

    (2020) Expert programmers have fine-tuned cortical representations of source code

    bioRxiv.

    https://doi.org/10.1101/2020.01.28.923953

    • Google Scholar
71. Conference

    1. Ikutani Y
    2. Uwano H

    (2014) Brain activity measurement during program comprehension with NIRS

    15th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD). pp. 1–6.

    https://doi.org/10.1109/SNPD.2014.6888727

    • Google Scholar
72. Preprint

    1. Ivanova AA
    2. Mineroff Z
    3. Zimmerer V
    4. Kanwisher N
    5. Varley R
    6. Fedorenko E

    (2019) The language network is recruited but not required for non-verbal semantic processing

    bioRxiv.

    https://doi.org/10.1101/696484

    • Google Scholar
73. Software

    1. Ivanova A
    2. Srikant S

    (2020) The Neuroscience of Program Comprehension, version swh:1:rev:616e893d05038da620bdf9f2964bd3befba75dc5

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:a7cde799c41db00358ac86bba057cf6d39a38a34;origin=https://github.com/ALFA-group/neural-program-comprehension;visit=swh:1:snp:ad67e98e649825b3b845a2050da8d86d000134cc;anchor=swh:1:rev:616e893d05038da620bdf9f2964bd3befba75dc5/
74. 1. Jackendoff R

    (2009) Parallels and nonparallels between language and music

    Music Perception 26:195–204.

    https://doi.org/10.1525/mp.2009.26.3.195

    • Google Scholar
75. 1. Jacoby N
    2. Bruneau E
    3. Koster-Hale J
    4. Saxe R

    (2016) Localizing pain matrix and theory of mind networks with both verbal and non-verbal stimuli

    NeuroImage 126:39–48.

    https://doi.org/10.1016/j.neuroimage.2015.11.025

    • PubMed
    • Google Scholar
76. 1. Jacoby N
    2. Fedorenko E

    (2020) Discourse-level comprehension engages medial frontal theory of mind brain regions even for expository texts

    Language, Cognition and Neuroscience 35:780–796.

    https://doi.org/10.1080/23273798.2018.1525494

    • PubMed
    • Google Scholar
77. 1. Jouravlev O
    2. Zheng D
    3. Balewski Z
    4. Le Arnz Pongos A
    5. Levan Z
    6. Goldin-Meadow S
    7. Fedorenko E

    (2019) Speech-accompanying gestures are not processed by the language-processing mechanisms

    Neuropsychologia 132:107132.

    https://doi.org/10.1016/j.neuropsychologia.2019.107132

    • PubMed
    • Google Scholar
78. 1. Julian JB
    2. Fedorenko E
    3. Webster J
    4. Kanwisher N

    (2012) An algorithmic method for functionally defining regions of interest in the ventral visual pathway

    NeuroImage 60:2357–2364.

    https://doi.org/10.1016/j.neuroimage.2012.02.055

    • PubMed
    • Google Scholar
79. 1. Kahn HJ
    2. Whitaker HA

    (1991) Acalculia: an historical review of localization

    Brain and Cognition 17:102–115.

    https://doi.org/10.1016/0278-2626(91)90071-F

    • PubMed
    • Google Scholar
80. 1. Kanwisher N

    (2010) Functional specificity in the human brain: a window into the functional architecture of the mind

    PNAS 107:11163–11170.

    https://doi.org/10.1073/pnas.1005062107

    • PubMed
    • Google Scholar
81. Website

    1. Kao E

    (2010) Exploring computational thinking

    Google Research Blog. Accessed April 13, 2020.

    https://ai.googleblog.com/2010/10/exploring-computational-thinking.html
82. 1. Keller TA
    2. Carpenter PA
    3. Just MA

    (2001) The neural bases of sentence comprehension: a fMRI examination of syntactic and lexical processing

    Cerebral Cortex 11:223–237.

    https://doi.org/10.1093/cercor/11.3.223

    • PubMed
    • Google Scholar
83. Book

    1. Klare GR

    (1963)

    Measurement of Readability

    Iowa State University Press.

    • Google Scholar
84. 1. Kroll JF
    2. Dussias PE
    3. Bice K
    4. Perrotti L

    (2015) Bilingualism, mind, and brain

    Annual Review of Linguistics 1:377–394.

    https://doi.org/10.1146/annurev-linguist-030514-124937

    • PubMed
    • Google Scholar
85. Conference

    1. Krueger R
    2. Huang Y
    3. Liu X
    4. Santander T
    5. Weimer W
    6. Leach K

    (2020) Neurological divide: an fMRI study of prose and code writing

    2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE). pp. 678–690.

    https://doi.org/10.1145/3377811.3380348

    • Google Scholar
86. 1. Kuznetsova A
    2. Brockhoff PB
    3. Christensen RHB

    (2017) lmerTest Package: Tests in linear mixed effects models

    Journal of Statistical Software 82:1–26.

    https://doi.org/10.18637/jss.v082.i13

    • Google Scholar
87. 1. Lemer C
    2. Dehaene S
    3. Spelke E
    4. Cohen L

    (2003) Approximate quantities and exact number words: dissociable systems

    Neuropsychologia 41:1942–1958.

    https://doi.org/10.1016/S0028-3932(03)00123-4

    • PubMed
    • Google Scholar
88. Book

    1. Lerdahl F
    2. Jackendoff RS

    (1996)

    A Generative Theory of Tonal Music

    MIT Press.

    • Google Scholar
89. 1. Lerner Y
    2. Honey CJ
    3. Silbert LJ
    4. Hasson U

    (2011) Topographic mapping of a hierarchy of temporal receptive windows using a narrated story

    Journal of Neuroscience 31:2906–2915.

    https://doi.org/10.1523/JNEUROSCI.3684-10.2011

    • PubMed
    • Google Scholar
90. 1. Liu Y
    2. Kim J
    3. Wilson C
    4. Bedny M

    (2020) Computer code comprehension shares neural resources with formal logical inference in the fronto-parietal network

    eLife 9:e59340.

    https://doi.org/10.7554/eLife.59340

    • Google Scholar
91. 1. Mahowald K
    2. Fedorenko E

    (2016) Reliable individual-level neural markers of high-level language processing: a necessary precursor for relating neural variability to behavioral and genetic variability

    NeuroImage 139:74–93.

    https://doi.org/10.1016/j.neuroimage.2016.05.073

    • PubMed
    • Google Scholar
92. 1. Mather M
    2. Cacioppo JT
    3. Kanwisher N

    (2013) How fMRI can inform cognitive theories

    Perspectives on Psychological Science 8:108–113.

    https://doi.org/10.1177/1745691612469037

    • PubMed
    • Google Scholar
93. Conference

    1. McNamara WJ

    (1967)

    The selection of computer personnel: past, present, future

    Proceedings of the Fifth SIGCPR Conference on Computer Personnel Research. pp. 52–56.

    • Google Scholar
94. 1. Michalka SW
    2. Kong L
    3. Rosen ML
    4. Shinn-Cunningham BG
    5. Somers DC

    (2015) Short-Term memory for space and time flexibly recruit complementary Sensory-Biased frontal lobe attention networks

    Neuron 87:882–892.

    https://doi.org/10.1016/j.neuron.2015.07.028

    • PubMed
    • Google Scholar
95. 1. Micheloyannis S
    2. Sakkalis V
    3. Vourkas M
    4. Stam CJ
    5. Simos PG

    (2005) Neural networks involved in mathematical thinking: evidence from linear and non-linear analysis of electroencephalographic activity

    Neuroscience Letters 373:212–217.

    https://doi.org/10.1016/j.neulet.2004.10.005

    • PubMed
    • Google Scholar
96. 1. Miller EK
    2. Erickson CA
    3. Desimone R

    (1996) Neural mechanisms of visual working memory in prefrontal cortex of the macaque

    The Journal of Neuroscience 16:5154–5167.

    https://doi.org/10.1523/JNEUROSCI.16-16-05154.1996

    • PubMed
    • Google Scholar
97. 1. Miller EK
    2. Cohen JD

    (2001) An integrative theory of prefrontal cortex function

    Annual Review of Neuroscience 24:167–202.

    https://doi.org/10.1146/annurev.neuro.24.1.167

    • PubMed
    • Google Scholar
98. 1. Mineroff Z
    2. Blank IA
    3. Mahowald K
    4. Fedorenko E

    (2018) A robust dissociation among the language, multiple demand, and default mode networks: evidence from inter-region correlations in effect size

    Neuropsychologia 119:501–511.

    https://doi.org/10.1016/j.neuropsychologia.2018.09.011

    • PubMed
    • Google Scholar
99. 1. Mitchell DJ
    2. Bell AH
    3. Buckley MJ
    4. Mitchell AS
    5. Sallet J
    6. Duncan J

    (2016) A putative Multiple-Demand system in the macaque brain

    The Journal of Neuroscience 36:8574–8585.

    https://doi.org/10.1523/JNEUROSCI.0810-16.2016

    • PubMed
    • Google Scholar
100. 1. Mollica F
     2. Siegelman M
     3. Diachek E
     4. Piantadosi ST
     5. Mineroff Z
     6. Futrell R
     7. Kean H
     8. Qian P
     9. Fedorenko E

     (2020) Composition is the core driver of the Language-selective network

     Neurobiology of Language 1:104–134.

     https://doi.org/10.1162/nol_a_00005

     • Google Scholar
101. 1. Monti MM
     2. Osherson DN
     3. Martinez MJ
     4. Parsons LM

     (2007) Functional neuroanatomy of deductive inference: a language-independent distributed network

     NeuroImage 37:1005–1016.

     https://doi.org/10.1016/j.neuroimage.2007.04.069

     • PubMed
     • Google Scholar
102. 1. Monti MM
     2. Parsons LM
     3. Osherson DN

     (2009) The boundaries of language and thought in deductive inference

     PNAS 106:12554–12559.

     https://doi.org/10.1073/pnas.0902422106

     • PubMed
     • Google Scholar
103. 1. Monti MM
     2. Parsons LM
     3. Osherson DN

     (2012) Thought beyond language: neural dissociation of algebra and natural language

     Psychological Science 23:914–922.

     https://doi.org/10.1177/0956797612437427

     • PubMed
     • Google Scholar
104. 1. Mukamel R
     2. Fried I

     (2012) Human intracranial recordings and cognitive neuroscience

     Annual Review of Psychology 63:511–537.

     https://doi.org/10.1146/annurev-psych-120709-145401

     • PubMed
     • Google Scholar
105. 1. Murnane JS

     (1993) The psychology of computer languages for introductory programming courses

     New Ideas in Psychology 11:213–228.

     https://doi.org/10.1016/0732-118X(93)90035-C

     • Google Scholar
106. Conference

     1. Nakagawa T
     2. Kamei Y
     3. Uwano H
     4. Monden A
     5. Matsumoto K
     6. German DM

     (2014) Quantifying programmers’ Mental Workload During Program Comprehension Based on Cerebral Blood Flow Measurement: A Controlled Experiment

     Companion Proceedings of the 36th International Conference on Software Engineering. pp. 448–451.

     https://doi.org/10.1145/2591062.2591098

     • Google Scholar
107. Preprint

     1. Nakai T
     2. Yamaguchi HQ
     3. Nishimoto S

     (2020) Convergence of modality invariance and attention selectivity in the cortical semantic circuit

     bioRxiv.

     https://doi.org/10.1101/2020.06.19.160960v1

     • Google Scholar
108. Conference

     1. Nakamura M
     2. Monden A
     3. Itoh T
     4. Matsumoto K
     5. Kanzaki Y
     6. Satoh H

     (2003) Queue-based cost evaluation of mental simulation process in program comprehension

     5th International Workshop on Enterprise Networking and Computing in Healthcare Industry (IEEE Cat. No.03EX717). pp. 351–360.

     https://doi.org/10.1109/METRIC.2003.1232480

     • Google Scholar
109. 1. Nieto-Castañón A
     2. Fedorenko E

     (2012) Subject-specific functional localizers increase sensitivity and functional resolution of multi-subject analyses

     NeuroImage 63:1646–1669.

     https://doi.org/10.1016/j.neuroimage.2012.06.065

     • PubMed
     • Google Scholar
110. Book

     1. Ormerod T

     (1990)

     Human Cognition and Programming

     In: Ormerod T, editors. Psychology of Programming. academic press. pp. 63–82.

     • Google Scholar
111. 1. Papert S

     (1972) Teaching children to be mathematicians versus teaching about mathematics

     International Journal of Mathematical Education in Science and Technology 3:249–262.

     https://doi.org/10.1080/0020739700030306

     • Google Scholar
112. Book

     1. Papert SA

     (1993)

     Mindstorms: Children, Computers, and Powerful Ideas (2)

     New York: Basic Books, Inc.

     • Google Scholar
113. 1. Parvizi J
     2. Kastner S

     (2018) Promises and limitations of human intracranial electroencephalography

     Nature Neuroscience 21:474–483.

     https://doi.org/10.1038/s41593-018-0108-2

     • Google Scholar
114. 1. Paunov AM
     2. Blank IA
     3. Fedorenko E

     (2019) Functionally distinct language and theory of mind networks are synchronized at rest and during language comprehension

     Journal of Neurophysiology 121:1244–1265.

     https://doi.org/10.1152/jn.00619.2018

     • PubMed
     • Google Scholar
115. 1. Pea RD
     2. Kurland DM

     (1984) On the cognitive effects of learning computer programming

     New Ideas in Psychology 2:137–168.

     https://doi.org/10.1016/0732-118X(84)90018-7

     • Google Scholar
116. Book

     1. Pennington N
     2. Grabowski B

     (1990)

     The Tasks of Programming

     Psychology of Programming.

     • Google Scholar
117. 1. Pereira F
     2. Lou B
     3. Pritchett B
     4. Ritter S
     5. Gershman SJ
     6. Kanwisher N
     7. Botvinick M
     8. Fedorenko E

     (2018) Toward a universal decoder of linguistic meaning from brain activation

     Nature Communications 9:1–13.

     https://doi.org/10.1038/s41467-018-03068-4

     • Google Scholar
118. 1. Perkins DN
     2. Simmons R

     (1988) Patterns of misunderstanding: an integrative model for science, math, and programming

     Review of Educational Research 58:303–326.

     https://doi.org/10.3102/00346543058003303

     • Google Scholar
119. 1. Pinel P
     2. Piazza M
     3. Le Bihan D
     4. Dehaene S

     (2004) Distributed and overlapping cerebral representations of number, size, and luminance during comparative judgments

     Neuron 41:983–993.

     https://doi.org/10.1016/S0896-6273(04)00107-2

     • PubMed
     • Google Scholar
120. 1. Pinel P
     2. Dehaene S

     (2010) Beyond hemispheric dominance: brain regions underlying the joint lateralization of language and arithmetic to the left hemisphere

     Journal of Cognitive Neuroscience 22:48–66.

     https://doi.org/10.1162/jocn.2009.21184

     • PubMed
     • Google Scholar
121. 1. Poldrack RA

     (2006) Can cognitive processes be inferred from neuroimaging data?

     Trends in Cognitive Sciences 10:59–63.

     https://doi.org/10.1016/j.tics.2005.12.004

     • PubMed
     • Google Scholar
122. 1. Poldrack RA

     (2011) Inferring mental states from neuroimaging data: from reverse inference to large-scale decoding

     Neuron 72:692–697.

     https://doi.org/10.1016/j.neuron.2011.11.001

     • PubMed
     • Google Scholar
123. 1. Prabhakaran V
     2. Smith JA
     3. Desmond JE
     4. Glover GH
     5. Gabrieli JD

     (1997) Neural substrates of fluid reasoning: an fMRI study of neocortical activation during performance of the raven's Progressive Matrices Test

     Cognitive Psychology 33:43–63.

     https://doi.org/10.1006/cogp.1997.0659

     • PubMed
     • Google Scholar
124. 1. Prat CS
     2. Madhyastha TM
     3. Mottarella MJ
     4. Kuo CH

     (2020) Relating natural language aptitude to individual differences in learning programming languages

     Scientific Reports 10:1–10.

     https://doi.org/10.1038/s41598-020-60661-8

     • PubMed
     • Google Scholar
125. 1. Pritchett BL
     2. Hoeflin C
     3. Koldewyn K
     4. Dechter E
     5. Fedorenko E

     (2018) High-level language processing regions are not engaged in action observation or imitation

     Journal of Neurophysiology 120:2555–2570.

     https://doi.org/10.1152/jn.00222.2018

     • PubMed
     • Google Scholar
126. 1. Regev M
     2. Honey CJ
     3. Simony E
     4. Hasson U

     (2013) Selective and invariant neural responses to spoken and written narratives

     Journal of Neuroscience 33:15978–15988.

     https://doi.org/10.1523/JNEUROSCI.1580-13.2013

     • PubMed
     • Google Scholar
127. 1. Reverberi C
     2. Shallice T
     3. D'Agostini S
     4. Skrap M
     5. Bonatti LL

     (2009) Cortical bases of elementary deductive reasoning: inference, memory, and metadeduction

     Neuropsychologia 47:1107–1116.

     https://doi.org/10.1016/j.neuropsychologia.2009.01.004

     • PubMed
     • Google Scholar
128. 1. Rogalsky C
     2. Rong F
     3. Saberi K
     4. Hickok G

     (2011) Functional anatomy of language and music perception: temporal and structural factors investigated using functional magnetic resonance imaging

     Journal of Neuroscience 31:3843–3852.

     https://doi.org/10.1523/JNEUROSCI.4515-10.2011

     • PubMed
     • Google Scholar
129. 1. Rosselli M
     2. Ardila A

     (1989) Calculation deficits in patients with right and left hemisphere damage

     Neuropsychologia 27:607–617.

     https://doi.org/10.1016/0028-3932(89)90107-3

     • PubMed
     • Google Scholar
130. 1. Sanner MF

     (1999)

     Python: a programming language for software integration and development

     Journal of Molecular Graphics & Modelling 17:57–61.

     • PubMed
     • Google Scholar
131. 1. Saxe R
     2. Brett M
     3. Kanwisher N

     (2006) Divide and conquer: a defense of functional localizers

     NeuroImage 30:1088–1096.

     https://doi.org/10.1016/j.neuroimage.2005.12.062

     • PubMed
     • Google Scholar
132. 1. Scott TL
     2. Gallée J
     3. Fedorenko E

     (2017) A new fun and robust version of an fMRI localizer for the frontotemporal language system

     Cognitive Neuroscience 8:167–176.

     https://doi.org/10.1080/17588928.2016.1201466

     • PubMed
     • Google Scholar
133. 1. Shain C
     2. Blank IA
     3. van Schijndel M
     4. Schuler W
     5. Fedorenko E

     (2020) fMRI reveals language-specific predictive coding during naturalistic sentence comprehension

     Neuropsychologia 138:107307.

     https://doi.org/10.1016/j.neuropsychologia.2019.107307

     • PubMed
     • Google Scholar
134. 1. Shashidhara S
     2. Mitchell DJ
     3. Erez Y
     4. Duncan J

     (2019a) Progressive recruitment of the frontoparietal Multiple-demand system with increased task complexity, time pressure, and reward

     Journal of Cognitive Neuroscience 31:1617–1630.

     https://doi.org/10.1162/jocn_a_01440

     • PubMed
     • Google Scholar
135. Preprint

     1. Shashidhara S
     2. Spronkers FS
     3. Erez Y

     (2019b) Individual-subject functional localization increases univariate activation but not multivariate pattern discriminability in the ‘multiple-demand’ frontoparietal network

     bioRxiv.

     https://doi.org/10.1101/661934

     • Google Scholar
136. 1. Sheremata SL
     2. Bettencourt KC
     3. Somers DC

     (2010) Hemispheric asymmetry in visuotopic posterior parietal cortex emerges with visual short-term memory load

     Journal of Neuroscience 30:12581–12588.

     https://doi.org/10.1523/JNEUROSCI.2689-10.2010

     • PubMed
     • Google Scholar
137. 1. Shuman M
     2. Kanwisher N

     (2004) Numerical magnitude in the human parietal lobe; tests of representational generality and domain specificity

     Neuron 44:557–569.

     https://doi.org/10.1016/j.neuron.2004.10.008

     • PubMed
     • Google Scholar
138. Conference

     1. Siegmund J
     2. Kästner C
     3. Apel S
     4. Parnin C
     5. Bethmann A
     6. Leich T
     7. Saake G
     8. Brechmann A

     (2014) Understanding understanding source code with functional magnetic resonance imaging

     Proceedings of the 36th International Conference on Software Engineering. pp. 378–389.

     https://doi.org/10.1145/2568225.2568252

     • Google Scholar
139. Conference

     1. Siegmund J
     2. Peitek N
     3. Parnin C
     4. Apel S
     5. Hofmeister J
     6. Kästner C
     7. Begel A
     8. Bethmann A
     9. Brechmann A

     (2017) Measuring neural efficiency of program comprehension

     Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering. pp. 140–150.

     https://doi.org/10.1145/3106237.3106268

     • Google Scholar
140. 1. Simony E
     2. Honey CJ
     3. Chen J
     4. Lositsky O
     5. Yeshurun Y
     6. Wiesel A
     7. Hasson U

     (2016) Dynamic reconfiguration of the default mode network during narrative comprehension

     Nature Communications 7:1–13.

     https://doi.org/10.1038/ncomms12141

     • Google Scholar
141. 1. Sullivan A
     2. Bers MU

     (2019) Computer science education in early childhood: the case of scratch jr

     Journal of Information Technology Education 18:113–138.

     https://doi.org/10.28945/4437

     • Google Scholar
142. 1. Tahmasebi AM
     2. Davis MH
     3. Wild CJ
     4. Rodd JM
     5. Hakyemez H
     6. Abolmaesumi P
     7. Johnsrude IS

     (2012) Is the link between anatomical structure and function equally strong at all cognitive levels of processing?

     Cerebral Cortex 22:1593–1603.

     https://doi.org/10.1093/cercor/bhr205

     • PubMed
     • Google Scholar
143. 1. Takayama Y
     2. Sugishita M
     3. Akiguchi I
     4. Kimura J

     (1994) Isolated acalculia due to left parietal lesion

     Archives of Neurology 51:286–291.

     https://doi.org/10.1001/archneur.1994.00540150084021

     • PubMed
     • Google Scholar
144. 1. Varley RA
     2. Klessinger NJ
     3. Romanowski CA
     4. Siegal M

     (2005) Agrammatic but numerate

     PNAS 102:3519–3524.

     https://doi.org/10.1073/pnas.0407470102

     • PubMed
     • Google Scholar
145. 1. Varley R
     2. Siegal M

     (2000) Evidence for cognition without grammar from causal reasoning and 'theory of mind' in an agrammatic aphasic patient

     Current Biology 10:723–726.

     https://doi.org/10.1016/S0960-9822(00)00538-8

     • PubMed
     • Google Scholar
146. 1. Vázquez-Rodríguez B
     2. Suárez LE
     3. Markello RD
     4. Shafiei G
     5. Paquola C
     6. Hagmann P
     7. van den Heuvel MP
     8. Bernhardt BC
     9. Spreng RN
     10. Misic B

     (2019) Gradients of structure-function tethering across neocortex

     PNAS 116:21219–21227.

     https://doi.org/10.1073/pnas.1903403116

     • PubMed
     • Google Scholar
147. Preprint

     1. Wen T
     2. Duncan J
     3. Mitchell DJ

     (2019) Representation of task episodes in human cortical networks

     bioRxiv.

     https://doi.org/10.1101/582858

     • Google Scholar
148. 1. Wing JM

     (2006) Computational thinking

     Communications of the ACM 49:33–35.

     https://doi.org/10.1145/1118178.1118215

     • Google Scholar
149. Book

     1. Wing J

     (2011)

     Research notebook: Computational thinking—What and why

     The Link Magazine.

     • Google Scholar
150. 1. Woolgar A
     2. Thompson R
     3. Bor D
     4. Duncan J

     (2011) Multi-voxel coding of stimuli, rules, and responses in human frontoparietal cortex

     NeuroImage 56:744–752.

     https://doi.org/10.1016/j.neuroimage.2010.04.035

     • PubMed
     • Google Scholar
151. 1. Woolgar A
     2. Duncan J
     3. Manes F
     4. Fedorenko E

     (2018) Fluid intelligence is supported by the multiple-demand system not the language system

     Nature Human Behaviour 2:200–204.

     https://doi.org/10.1038/s41562-017-0282-3

     • Google Scholar

Decision letter after peer review:

Thank you for submitting your article "Comprehension of computer code relies primarily on domain-general executive resources" for consideration by eLife. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Timothy Behrens as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: William Matchin (Reviewer #1); Ina Bornkessel-Schlesewsky (Reviewer #2).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, when editors judge that a submitted work as a whole belongs in eLife but that some conclusions require a modest amount of additional new data, as they do with your paper, we are asking that the manuscript be revised to either limit claims to those supported by data in hand, or to explicitly state that the relevant conclusions require additional supporting data.

Our expectation is that the authors will eventually carry out the additional experiments and report on how they affect the relevant conclusions either in a preprint on bioRxiv or medRxiv, or if appropriate, as a Research Advance in eLife, either of which would be linked to the original paper.

First, thank you for taking part in the review process.

As you know, eLife is invested in changing scientific publishing and experimenting to embody that change, even if that involves a degree of risk in order to find workable changes. In this spirit, the remit of the co-submission format is to ask if the scientific community is enriched by the data presented in the co-submitted manuscripts together more so than it would be by the papers apart, or if only one paper was presented to the community. In other words, are the conclusions that can be made are stronger or clearer when the manuscripts are considered together rather than separately? We felt that despite significant concerns with each paper individually, especially regarding the theoretical structures in which the experimental results could be interpreted, that this was the case.

We want to be very clear that in a non-co-submission case we would have substantial and serious concerns about the interpretability and robustness of the Liu et al. submission given its small sample size. Furthermore, the reviewers' concerns about the suitability of the control task differed substantially between the manuscripts. We share these concerns. However, despite these differences in control task and sample size, the Liu et al. and Ivanova et al. submissions nonetheless replicated each other – the language network was not implicated in processing programming code. The replication substantially mitigates the concerns shared by us and the reviewers about sample size and control tasks. The fact that different control tasks and sample sizes did not change the overall pattern of results, in our view, is affirmation of the robustness of the findings, and the value that both submissions presented together can offer the literature.

In sum, there were concerns that both submissions were exploratory in nature, lacking a strong theoretical focus, and relied on functional localizers on novel tasks. However, these concerns were mitigated by the following strengths. Both tasks ask a clear and interesting question. The results replicate each other despite task differences. In this way, the two papers strengthen each other. Specifically, the major concerns for each paper individually are ameliorated when considering them as a whole.

In your revisions, please address the concerns of the reviewers, including, specifically, the limits of interpretation of your results with regard to control task choice, the discussion of relevant literature mentioned by the reviewers, and most crucially, please contextualize your results with regard to the other submission's results.

Reviewer #1:

The manuscript is well-written and the methods are clear and rigorous, representing a clear advance on previous research comparing computer code programming to language. The conclusions with respect to which brain networks computer programming activates are compelling and well conveyed. This paper is useful to the extent that the conclusions are focused on the empirical findings: whether or not code activates language-related brain regions (answer: no). However, the authors appear to be also testing whether or not any of the mechanisms involved in language are recruited for computer programming. The problem with this goal is that the authors do not present or review a theory of the representations and mechanisms involved in computer programming, as has been developed for language (e.g. Adger, 2013; Bresnan, 2001; Chomsky, 1965, 1981, 1995; Goldberg, 1995; Hornstein, 2009; Jackendoff, 2002; Levelt, 1989; Lewis and Vasishth, 2005; Vosse and Kempen, 2000).

1) "The fact that coding can be learned in adulthood suggests that it may rely on existing cognitive systems." "Finally, code comprehension may rely on the system that supports comprehension of natural languages: to successfully process both natural and computer languages, we need to access stored meanings of words/tokens and combine them using hierarchical syntactic rules (Fedorenko et al., 2019; Murnane, 1993; Papert, 1993) – a similarity that, in theory, should make the language circuits well-suited for processing computer code." If we understand stored elements and computational structure in the broadest way possible without breaking this down more, many domains of cognition would be shared in this way. The authors should illustrate in more detail how the psychological structure of computer programming parallels language. Is there an example of hierarchical structure in computer code? What is the meaning of a variable/function in code, and how does this compare to meaning in language?

2) "Our findings, along with prior findings from math and logic (Amalric and Dehaene, 2019; Monti et al., 2009, 2012), argue against this possibility: the language system does not respond to meaningful structured input that is non-linguistic." This is an overly simple characterization of the word "meaningful". The meaning of math and logic are not the same as in language. Both mathematics and computer programming have logical structure to them, but the nature of this structure and the elements that are combined in language are different. Linguistic computations take as input complex atoms of computation that have phonological and conceptual properties. These atoms are commonly used to refer to entities "in the world" with complex semantic properties and often have rich associated imagery. Linguistic computations output complex, monotonically enhanced forms. So cute + dogs = cute dogs, chased + cute dogs = chased cute dogs, etc. This is very much unlike mathematics and computer programming, where we typically do not make reference to the "real world" using these expressions to interlocuters, and outputs of an expression are not monotonic, structure-preserving combinations of the input elements, and there is no semantic enhancement that occurs through increased computation. This bears much more discussion in the paper, if the authors intend to make claims regarding shared/distinct computations between computer programming and language.

3) More importantly, even if there were shared mechanisms between computer code programming and language, I'm not sure we can use reverse inference to strongly test this hypothesis. As Poldrack, 2006, pointed out, reverse inference is sharply limited by the extent to which we know how cognition maps onto the brain. This is a similar point to Poeppel and Embick, 2005, who pointed out that different mechanisms of language could be implemented in the brain in a large variety of ways, only one of which is big pieces of cortical tissue. In this sense, there could in fact be shared mechanisms between language and code (e.g. oscillatory dynamics, connectivity patterns, subcortical structures), but these mechanisms might not be aligned with the cortical territory associated with language-related brain regions. The authors should spend much additional time discussing these alternative possibilities.

Reviewer #2:

This carefully designed fMRI study examines an interesting question, namely how computer code – as a "cognitive/cultural invention" – is processed by the human brain. The study has a number of strengths, including: use of two very different programming languages (Python and Scratch Jr.) in two experiments; direct comparison between code problems and "content-matched sentence problems" to disentangle code comprehension from problem content; control for the impact of lexical information in code passages by replacing variable names with Japanese translations; and consideration of inter-individual differences in programming proficiency. I do, however, have some questions regarding the interpretation of the results in mechanistic terms, as detailed below.

1) Code comprehension versus underlying problem content

I am generally somewhat sceptical in regard to the use of functional localisers in view of the assumptions that necessarily enter into the definition of a localiser task. In addition, an overlap between the networks supporting two different tasks doesn't imply comparable neural processing mechanisms. With the present study, however, I was impressed by the authors' overall methodological approach. In particular, I found the supplementation of the localiser-based approach with the comparison between code problems and analogous sentence problems rather convincing.

However, while I agree that computational thinking does not require coding / code comprehension, it is less clear to me what code comprehension involves when it is stripped of the computational thinking aspect. Knowing how to approach a problem algorithmically strikes me as a central aspect of coding. What, then, is being measured by the code problem versus sentence problem comparison? Knowledge of how to implement a certain computational solution within a particular programming language? The authors touch upon this briefly in the Discussion section of the paper, but I am not fully convinced by their arguments. Specifically, they state:

"The process of code comprehension includes retrieving code-related knowledge from memory and applying it to the problems at hand. This application of task-relevant knowledge plausibly requires attention, working memory, inhibitory control, planning, and general flexible reasoning-cognitive processes long linked to the MD system […].”

Shouldn't all of this also apply (or even apply more strongly) to processing of the underlying problem content rather than to code comprehension per se?

According to the authors, the extent to which code-comprehension-related activity reflects problem content varies between different systems. They conclude that "MD responses to code […] do not exclusively reflect responses to problem content", while they argue on the basis of their voxel-wise correlation analysis that "the language system's response to code is largely (although not completely) driven by problem content. However, unless I have missed something, the latter analysis was only undertaken for the language system but not for the other systems under examination. Was there a particular reason for this? Also, what are the implications of observing problem content-driven responses within the language system for the authors' conclusion that this system is "functionally conservative"?

Overall, the paper would be strengthened by more clarity in regard to these issues – and specifically a more detailed discussion of what code comprehension may amount to in mechanistic terms when it is stripped of computational thinking.

2) Implications of using reading for the language localiser task

Given that reading is also a cultural invention, is it really fair to say that coding is being compared to the "language system" here rather than to the "reading system" (in view of the visual presentation of the language task)? The possible implications of this for the interpretation of the data should be considered.

3) Possible effects of verbalisation?

It appears possible that participants may have internally verbalised code problems – at least to a certain extent (and likely with a considerable degree of inter-individual variability). How might this have affected the results of the present study? Could verbalisation be related to the highly correlated response between code problems and language problems within the language system?

[…] In your revisions, please address the concerns of the reviewers, including, specifically, the limits of interpretation of your results with regard to control task choice, the discussion of relevant literature mentioned by the reviewers, and most crucially, please contextualize your results with regard to the other submission's results.

We thank the editor and the reviewers for their thoughtful evaluation. We have now addressed the reviewers’ comments (see below) and added a paragraph comparing our results with those of Liu et al.:

“The results of our work align well with the results of another recent study on program comprehension (Liu et al., 2020). […] This is precisely what Liu et al. find. Further, similar to us, Liu et al. conclude that it is the MD regions, not the language regions, that are primarily involved in program comprehension.”

We also added a reference to Liu et al.’s work in the Introduction:

“However, none of these prior studies sought to explicitly distinguish code comprehension from other programming-related processes, and none of them provide quantitative evaluations of putative shared responses to code and other tasks, such as working memory, math, or language (cf. Liu et al., 2020; see Discussion).”

Additionally, we have adjusted the statistical results in order to fix two minor bugs in our code (accidentally excluding results from one participant and using dummy coding instead of sum coding for Hemisphere when analyzing MD system activity). The resulting changes were numerically small and did not affect any of our conclusions. We also formatted supplementary figure/file references in accordance with the journal’s requirements.

Reviewer #1:

The manuscript is well-written and the methods are clear and rigorous, representing a clear advance on previous research comparing computer code programming to language. The conclusions with respect to which brain networks computer programming activates are compelling and well conveyed. This paper is useful to the extent that the conclusions are focused on the empirical findings: whether or not code activates language-related brain regions (answer: no). However, the authors appear to be also testing whether or not any of the mechanisms involved in language are recruited for computer programming. The problem with this goal is that the authors do not present or review a theory of the representations and mechanisms involved in computer programming, as has been developed for language (e.g. Adger, 2013; Bresnan, 2001; Chomsky, 1965, 1981, 1995; Goldberg, 1995; Hornstein, 2009; Jackendoff, 2002; Levelt, 1989; Lewis and Vasishth, 2005; Vosse and Kempen, 2000).

Thank you for the positive evaluation! We agree that the main value of our paper is examining the contributions of the two brain networks to computer programming. Unfortunately, the theories of representations/computations involved in computer code comprehension are quite underdeveloped and have very little experimental support. Thus, the main mechanistic distinction we investigate in this work is a high-level distinction between code comprehension and the processing of problem content. We have adjusted the framing and the title of our manuscript to tone down and/or clarify our theoretical stance (see responses to specific points below).

1) "The fact that coding can be learned in adulthood suggests that it may rely on existing cognitive systems." "Finally, code comprehension may rely on the system that supports comprehension of natural languages: to successfully process both natural and computer languages, we need to access stored meanings of words/tokens and combine them using hierarchical syntactic rules (Fedorenko et al., 2019; Murnane, 1993; Papert, 1993) – a similarity that, in theory, should make the language circuits well-suited for processing computer code." If we understand stored elements and computational structure in the broadest way possible without breaking this down more, many domains of cognition would be shared in this way. The authors should illustrate in more detail how the psychological structure of computer programming parallels language. Is there an example of hierarchical structure in computer code? What is the meaning of a variable/function in code, and how does this compare to meaning in language?

We have modified the sections to further illustrate potential structural similarities between language and code:

“Finally, code comprehension may rely on the system that supports comprehension of natural languages (Fedorenko et al., 2019; Murnane, 1993; Papert, 1993). Like language, computer code makes heavy use of hierarchical structures (e.g., loops, conditionals, and recursive statements), and, like language, it can convey an unlimited amount of meaningful information (e.g., describing objects or action sequences). These similarities could, in principle, make the language circuits well-suited for processing computer code.”

“Such accounts predict that the mechanisms supporting structure processing in language should also get engaged when we process structure in other domains, including computer code. […] Our finding builds upon these results to show that compositional input (here, variables and keywords combining into statements) and hierarchical structure (here, conditional statements and loops) do not necessarily engage language-specific regions.”

We have also added more details about meaning in language vs. code (see point 2).

2) "Our findings, along with prior findings from math and logic (Amalric and Dehaene, 2019; Monti et al., 2009, 2012), argue against this possibility: the language system does not respond to meaningful structured input that is non-linguistic." This is an overly simple characterization of the word "meaningful". The meaning of math and logic are not the same as in language. Both mathematics and computer programming have logical structure to them, but the nature of this structure and the elements that are combined in language are different. Linguistic computations take as input complex atoms of computation that have phonological and conceptual properties. These atoms are commonly used to refer to entities "in the world" with complex semantic properties and often have rich associated imagery. Linguistic computations output complex, monotonically enhanced forms. So cute + dogs = cute dogs, chased + cute dogs = chased cute dogs, etc. This is very much unlike mathematics and computer programming, where we typically do not make reference to the "real world" using these expressions to interlocuters, and outputs of an expression are not monotonic, structure-preserving combinations of the input elements, and there is no semantic enhancement that occurs through increased computation. This bears much more discussion in the paper, if the authors intend to make claims regarding shared/distinct computations between computer programming and language.

We thank the reviewer for raising this interesting and important point. We agree that it is important to clarify what we mean by “meaning”. We therefore significantly expanded the section in question to clarify our statement:

“Another similarity shared by computer programming and natural language is the use of symbols – units referring to concepts “out in the world”. […] And yet the communicative nature of this activity is not sufficient to recruit the language system, consistent with previous reports showing a neural dissociation between language and gesture processing (Jouravlev et al., 2019), intentional actions (Pritchett et al., 2018) or theory of mind tasks (Apperly et al., 2006; Dronkers et al., 1998; Jacoby et al., 2016; Paunov et al., 2019; Varley and Siegal, 2000).”

Note that this section addresses the comments about reference to external entities and the communicative intent (which, as we state above, we think are partially shared between language and code). The paragraph preceding these (see point 1) also addresses the point about compositionality, at least in its most basic definition. We agree that language has both semantic and compositional properties that make it “special” (rich associations, imagery, semantic enhancement), all of which might account for the high functional selectivity of the language regions. However, we think that the discussion of such possibly unique properties of language is outside the scope of this paper and therefore limit ourselves to listing the putative shared properties of language and code.

3) More importantly, even if there were shared mechanisms between computer code programming and language, I'm not sure we can use reverse inference to strongly test this hypothesis. As Poldrack, 2006, pointed out, reverse inference is sharply limited by the extent to which we know how cognition maps onto the brain. This is a similar point to Poeppel and Embick, 2005, who pointed out that different mechanisms of language could be implemented in the brain in a large variety of ways, only one of which is big pieces of cortical tissue. In this sense, there could in fact be shared mechanisms between language and code (e.g. oscillatory dynamics, connectivity patterns, subcortical structures), but these mechanisms might not be aligned with the cortical territory associated with language-related brain regions. The authors should spend much additional time discussing these alternative possibilities.

The reviewer is right in that we here focus on one way in which two cognitive functions might share resources (through cortical overlap; though note that our whole-brain analyses rule out overlap at the subcortical level, too). It is absolutely true that functionally distinct cortical areas could still (a) implement similar computations, and/or (b) interact, e.g., via oscillatory dynamics. We have addressed this point in a separate paragraph.

“Of course, the lack of consistent language system engagement in code comprehension does not mean that the mechanisms underlying language and code processing are completely different. […] However, the fact that we observed code-evoked activity primarily in the MD regions indicates that code comprehension does not load on the same neural circuits as language and needs to use domain-general MD circuits instead.”

Reviewer #2:

[…] I do, however, have some questions regarding the interpretation of the results in mechanistic terms, as detailed below.

1) Code comprehension versus underlying problem content

I am generally somewhat sceptical in regard to the use of functional localisers in view of the assumptions that necessarily enter into the definition of a localiser task. In addition, an overlap between the networks supporting two different tasks doesn't imply comparable neural processing mechanisms. With the present study, however, I was impressed by the authors' overall methodological approach. In particular, I found the supplementation of the localiser-based approach with the comparison between code problems and analogous sentence problems rather convincing.

However, while I agree that computational thinking does not require coding / code comprehension, it is less clear to me what code comprehension involves when it is stripped of the computational thinking aspect. Knowing how to approach a problem algorithmically strikes me as a central aspect of coding. What, then, is being measured by the code problem versus sentence problem comparison? Knowledge of how to implement a certain computational solution within a particular programming language?

The reviewer is right – it will be helpful to provide further clarifications of what it means to process problem content vs. process code in the Discussion section (in addition to the definition we provide in the Introduction). We address the specific points below. Additionally, prompted by the comments from reviewer 1, we added some more information about the putative cognitive processes underlying code comprehension and their possible parallels with language processing (see reviewer 1, points 1 and 2).

The authors touch upon this briefly in the Discussion section of the paper, but I am not fully convinced by their arguments. Specifically, they state:

"The process of code comprehension includes retrieving code-related knowledge from memory and applying it to the problems at hand. This application of task-relevant knowledge plausibly requires attention, working memory, inhibitory control, planning, and general flexible reasoning-cognitive processes long linked to the MD system […].

Shouldn't all of this also apply (or even apply more strongly) to processing of the underlying problem content rather than to code comprehension per se?

This paragraph indeed applies to both processes. We have adjusted it below, including examples of processing code vs. problem content:

“We found that responses in the MD system were driven both by the processing of problem content (e.g., summing the contents of an array) and by code comprehension (e.g., identifying variables referring to an array and its elements, interpreting a for-loop, realizing that the output of the program is the variable being updated inside the for-loop). […] The overlap was observed within brain regions whose topography grossly resembles that of the MD system.”

We also added a sentence about processes specific to code comprehension to the next paragraph:

“Furthermore, given that no regions outside of the MD system showed code-specific responses, it must be the case that code-specific knowledge representations are also stored within this system (see Hasson et al., 2015, for a general discussion of the lack of distinction between storage and computing resources in the brain). Such code-specific representations would likely include both knowledge specific to a programming language (e.g. the syntax marking an array in Java vs. Python) and knowledge of programming concepts that are shared across languages (e.g. for loops).”

According to the authors, the extent to which code-comprehension-related activity reflects problem content varies between different systems. They conclude that "MD responses to code […] do not exclusively reflect responses to problem content", while they argue on the basis of their voxel-wise correlation analysis that "the language system's response to code is largely (although not completely) driven by problem content. However, unless I have missed something, the latter analysis was only undertaken for the language system but not for the other systems under examination. Was there a particular reason for this?

The reason is that, for the MD system, we can make this inference based on the univariate analyses alone, but for the language system we cannot. To clarify this point, we have added an explanation to the section motivating the spatial correlation analysis and clarified our conclusion to highlight that both MD and language systems are sensitive to problem content. Finally, for completeness, we have also added a supplementary figure showing the spatial correlation plot for the MD system.

“Finally, we investigated whether the responses to Python code problems within the language system were driven by code comprehension specifically or rather by the underlying problem content. […] Thus, in both MD and language systems, response to Python code is driven both by problem content and by code-specific responses.

Overall, we found that the language system responded to code problems written in Python but not in ScratchJr. Furthermore, Python responses were driven not only by code comprehension, but also by the processing of problem content. We conclude that successful comprehension of computer code can proceed without engaging the language network.”

Also, what are the implications of observing problem content-driven responses within the language system for the authors' conclusion that this system is "functionally conservative"?

We thank the reviewer for drawing our attention to the fact that we do not explicitly discuss the possible explanations of the language system’s responses to Python code. We have added a paragraph to the Discussion section to fill this gap. It mentions both the content-driven responses and possible verbalization confounds (addressing point 3):

“More work is required to determine why the language system showed some activity in response to Python code. […] Further investigations of the role of the language system in computational thinking have the potential to shed light on the exact computations supported by these regions.”

Overall, the paper would be strengthened by more clarity in regard to these issues – and specifically a more detailed discussion of what code comprehension may amount to in mechanistic terms when it is stripped of computational thinking.

2) Implications of using reading for the language localiser task

Given that reading is also a cultural invention, is it really fair to say that coding is being compared to the "language system" here rather than to the "reading system" (in view of the visual presentation of the language task)? The possible implications of this for the interpretation of the data should be considered.

We believe that we are examining the language system rather than the reading system because (a) we are restricting our analysis to left hemisphere fronto-temporal parcels that encompass language-related activity (cf. the ventral visual areas that specifically support reading; Baker et al., 2007), (b) we are defining the fROIs based on the sentences vs. pronounceable nonwords contrast (both of these conditions involve reading, but only sentences have linguistic structure + meaning). Moreover, these regions have been previously shown to respond to both spoken and written language (Deniz et al., 2019; Fedorenko et al., 2010; Nakai et al., 2020; Regev et al., 2013; Scott et al., 2017), and damage to these regions leads to linguistic difficulties in both reading and listening (for comprehension), and writing and speaking (for production).

To clarify, we have added more information about the language system in the main text:

“These regions respond robustly to linguistic input, both visual and auditory (Deniz et al., 2019; Fedorenko et al., 2010; Nakai et al., 2020; Regev et al., 2013; Scott et al., 2017). However, they show little or no response to tasks in non-linguistic domains”

“The sentences > nonword-lists contrast isolates processes related to language comprehension (responses evoked by, e.g., visual perception and reading are subtracted out) and has been previously shown to reliably activate left-lateralized fronto-temporal language processing regions, be robust to changes in task and materials, and activate the same regions regardless of whether the materials were presented visually or auditorily (Fedorenko et al., 2010; Mahowald and Fedorenko, 2016; Scott et al., 2017).”

3) Possible effects of verbalisation?

It appears possible that participants may have internally verbalised code problems – at least to a certain extent (and likely with a considerable degree of inter-individual variability). How might this have affected the results of the present study? Could verbalisation be related to the highly correlated response between code problems and language problems within the language system?

Internal verbalization is indeed an important confound to keep in mind when trying to dissociate the neural correlates of language and other cognitive functions. It is not a major threat to claims that the language system is not engaged in code comprehension, so our main conclusion is not affected by this potential confound. However, the reviewer is right in pointing out that verbalization may underlie the language system’s responses to Python code. While this is possible, we think such an explanation is unlikely, since ScratchJr problems would have been even easier to verbalize, and yet they do not evoke language responses. We have included a section addressing verbalization in the Discussion; this text is also included in the response to point (1).

“More work is required to determine why the language system showed some activity in response to Python code. […] It is also inconsistent with observations that even behaviors that ostensibly require subvocal rehearsal (e.g., mathematical operations) do not engage the language system (see e.g., Amalric and Dehaene, 2019; Fedorenko et al., 2011).”

Author details

1. Anna A Ivanova

   1. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States
   2. McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, United States

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration

   For correspondence

   annaiv@mit.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1184-8299

2. Shashank Srikant

   Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, United States

   Contribution

   Conceptualization, Software, Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

3. Yotaro Sueoka

   1. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States
   2. McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, United States

   Contribution

   Data curation, Software, Investigation, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6417-9052

4. Hope H Kean

   1. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States
   2. McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, United States

   Contribution

   Data curation, Software, Formal analysis, Investigation, Methodology

   Competing interests

   No competing interests declared

5. Riva Dhamala

   Eliot-Pearson Department of Child Study and Human Development, Tufts University, Medford, United States

   Contribution

   Software, Investigation, Methodology

   Competing interests

   No competing interests declared

6. Una-May O'Reilly

   Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, United States

   Contribution

   Conceptualization, Supervision, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

7. Marina U Bers

   Eliot-Pearson Department of Child Study and Human Development, Tufts University, Medford, United States

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Methodology, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0206-1846

8. Evelina Fedorenko

   1. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States
   2. McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, United States

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Methodology, Project administration, Writing - review and editing

   For correspondence

   evelina9@mit.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3823-514X

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