dataesr/scientific-paragraphs-categorization huggingface dataset:
Visually presented math task from the York dataset In this task, participants performed addition with smaller or larger numbers, giving rise to easy and hard conditions. Participants saw an arithmetic expression on the screen for 1. 45 s and were then given two numbers as potential answers, for 1. 45 s. Each trial ended with a blank screen lasting for 0. 1 s. Each run consisted of 12 experimental blocks (with four trials per block) and four fixation blocks, resulting in a total time of 316 s. This task included two runs containing the two conditions, presented in a standard block design. Condition order was counterbalanced across runs, and run order was counterbalanced across participants for each task. The fixation blocks were used as the implicit baseline for the univariate analysis.
Spatial working memory task from the York dataset Participants had to keep track of four or eight sequentially presented locations in a 3 × 4 grid, giving rise to easy and hard spatial working memory conditions  (Fedorenko et al. 2011). Stimuli were presented at the center of the screen across four steps. Each of these steps lasted for 1 s and highlighted one location on the grid in the easy condition and two locations in the hard condition. This was followed by a decision phase, which showed two grids side by side. One grid contained the locations shown on the previous four steps, while the other contained one or two locations in the wrong place. Participants indicated their recognition of these locations in a two-alternative, forced-choice paradigm via a button press, and feedback was immediately provided.
Then we resampled the individual surface from fsaverage space into fs_LR ∼ 32 k space. After that, we extracted all the vertices belong to calcarine sulcus, central sulcus and transverse sulcus, respectively, for each individual. The use of individual-specific landmarks improved the precision of our study. Geodesic distance along the "midthickness" of the cortical surface (halfway between the pial and white matter) was calculated using the Connectome Workbench software with an algorithm that measures the shortest path between two vertices on a triangular surface mesh  (Mitchell et al. 1987; O'Rourke 1999). This method returns distance values that are independent of mesh density. Geodesic distance was extracted from surface geometry (GIFTI) files, following surface-based registration  (Robinson et al.
Control analysis of parcel size Given that parcel size (i. e. surface area) was correlated with the distance between parcels (see Results), we conducted control analyses examining the relationship between parcel surface area for each individual and task activation. The surface area of each parcel was calculated using surface geometry (GIFTI) files, using the Connectome Workbench software. Each vertex was assigned one third of the triangular area it defined. Surface area values were averaged across vertices that shared the same areal label in a given participant to get the parcel-based surface area.
3A ; r = 0. 131, P < 0. 001; r = 0. 109, P = 0. 005, FWE corrected): when this region was more distant from central sulcus, participants tended to show stronger auditory-verbal activation (Fig. 3E  and F ). Supplementary analyses confirmed that these positive and negative correlations with between activation and physical distance were also significant for each task separately (except for right 55b in the math task) (section 3. 1 in Supplementary Materials). To verify the functional specialization of these parcels, we used a second dataset that examined activation for visual semantic, math and spatial working memory tasks. We expect regions showing a negative correlation between sensory-motor distance and functional activation in the HCP data to activate specifically to auditory inputs, since shorter cortical distances should correspond to greater tethering to sensory inputs.
Since bilateral 55b in the language network showed activation in both the story and math tasks and no difference between these conditions in the HCP dataset, this region might be involved in lexicalphonological as opposed to conceptual aspects of language processing. To test this hypothesis, we examined whether this region responded to visually-presented words and non-words in the York dataset. Bilateral 55b showed activation over rest for visually presented words (left 55b: t = 12. 296, P = 2. 51e-12; right 55b: t = 2. 649, P = 0. 026) and no activation for the spatial working memory task (P > 0. 05). Left 55b but not right 55b showed activation over rest for visually presented nonwords (t = 4. 646, P = 0. 0002) and math problems (t = 4. 656, P = 0. 001). These findings are consistent with a role in heteromodal aspects of language processing.
To examine the extent to which these three parcels showing greater activation for the story task with greater distance from sensory-motor landmarks supported semantic processing across modalities, we assessed their responses to visually presented words and nonwords, math problems and spatial working memory using the York dataset. Left posterior inferior frontal sulcus in the language network responded to visual words and nonwords, with a stronger response for meaningful words (t = 4. 08, P < 0. 002). This parcel therefore showed a heteromodal semantic response across both auditorily and visually mediated language tasks. The parcel also showed a stronger response to more demanding math (t = 7. 009, P < 0. 0001) and spatial working memory (t = 3. 909, P < 0. 001) tasks, suggesting its contributions to cognition are not exclusively semantic.
e. compensatory increases in the BOLD response ref lecting neurocognitive weakness) because participants with better performance were presented with more difficult materials in the scanner for the HCP tasks we analyzed. The left inferior frontal sulcus also showed stronger activation in response to visual words than pronounceable nonwords, suggesting a multimodal semantic response for this site. This is consistent with previous findings that this region showed stronger activation for words than nonwords no matter whether the stimuli were visually or auditorily presented, even when these modalities were compared using the same participants and same scanning protocols  (Fedokenro et al. 2010). Furthermore, the language parcels defined in the HCP dataset  (Barch et al. 2013 ) greatly overlapped with the language parcels defined in another independent dataset  (Lipkin et al.
2022). Bilateral 47 m did not show stronger activation for written words versus nonwords, perhaps because the semantic demands in the visually-presented word task were much lower than for story comprehension. In addition, although the HCP and York datasets were scanned using different protocols, we used cutting-edge methods to improve cortical area localization and alignment across participants and datasets. We performed all the analysis on the surface to improve the alignment of areas across participants, since volume-based smoothing and registration substantially degrades localization compared with surfacebased approaches  (Coalson et al. 2018). The comparison between these datasets is therefore informative about the heteromodal versus unimodal tuning of each region, even though an ideal design would use the same scanning protocols and same materials in the same participants to establish how each parcel responds across modalities.
The quality of computer programs was measured using a qualitative rubric, and programs were also assessed using the Dr Scratch auto-grading platform. The Test of Nonverbal Intelligence (3rd edition, TONI-3) was used to test for confounding effects. While significant correlations between both measures of computational thinking and program quality were detected, regression analysis revealed that only applied computational thinking processes significantly predicted program quality (general computational thinking capability and non-verbal intelligence were not significant predictors). The results highlight the importance of students developing applied computational thinking procedural capabilities more than generalized computational thinking capabilities in order to improve the quality of their computer programs.
Brennan and Resnick (2012)  examined whether aspects of computational thinking were present in students' block-based Scratch programs. Grover et al. have manually evaluated computational thinking evident in students' Scratch programs, though without providing detail of the process and rubrics  (Grover, 2017; Grover, Pea, & Cooper, 2015). An increasingly renown innovation, Dr Scratch, combines automated assessment, examination of the inner workings of programs, and analysis of computational thinking to provide a measure of program quality for Scratch programs  (Moreno-León & Robles, 2015). One study has established a strong correlation (r = 0. 682) between the Dr Scratch automated assessment of computational thinking evident within students' Scratch programs and manual evaluation of computational thinking within Scratch programs by human experts  (Moreno-León et al.
Participants The sample for this study was drawn from 74 pre-service teachers completing a digital creativity and learning course at an Australian university. Among them 68% were female, 30% male and 2% preferred not to say. On average, participants were 23. 9 years old (SD = 5. 2). In terms of language proficiency, 97% indicated that they spoke English fluently or were native speakers. In terms of prior knowledge, 97% had no or only little prior programming experience and none of the participants were familiar with the Scratch programming environment that was used for the study.
Observing computational thinking processes To enable participants to demonstrate how much time they spent on computational thinking-relevant processes while programming, participants were set a Scratch programming task. Scratch itself was developed in 2003 at MIT Media Laboratory and publicly launched in 2007  (Resnick et al. , 2009). It is one the first and one of the 1 3 most popular open-source visual programming environments. In visual programming environments, users connects code blocks with each other instead of actual writing a code as common in real programming languages. To prepare students for the task, they were given 45 min to review the Scratch tutorials available from within the Scratch platform. They were also allowed to access these tutorials during the programming task. The task itself was defined as follows: "Program a story or a game where a hero has to overcome a challenge in order to defeat the villain(s).
Measuring program quality To measure participants' program quality, two measures were used. For one, a rubric scheme loosely based on "Clean code" of  Martin (2009)  was developed specially for this study. The program quality criteria were based on five categories: richness of project, variety of code usage, organization and tidiness, functionality of code and coding efficiency. Richness of project described how much was happening in the Scratch project. Lower scores were given when only one element was programmed to perform only one behaviour, while Scratch projects consisting of several programmed elements that were related to each other received higher scores. The variety of code usage depended on the kinds of code blocks were used. Scratch projects were rated lower when they mainly consisted of simple code chunks such as motion or looks and high when more advanced chunks like control or sensing were used.
The category organization and tidiness took into account the extent to which the control section in Scratch was organized, with more organized Scratch projects receiving higher scores. Functionality was assessed based on whether the intention of the Scratch project was clear and whether it worked as intended. Projects received higher scores when they ran smoothly and the intention was easy to understand. The category efficiency described the usage of code controlling the flow of execution, and the number unnecessary duplications. Lower scores were given to projects having many such duplicates, while more generalized and more abstract code scripts received higher scores. An example of a program with unnecessary duplication is shown in Fig. 1  (left), compared to a more efficiently represented code block in Fig.
1  (right). The five code quality categories were all rated on a scale including 0 (not evident), 1 (poor), 2 (satisfactory), 3 (good), up to 4 (excellent). A weighted mean over all categories was calculated to provide a general assessment. The weight for each category was based on their importance for program quality, resulting in extent and richness, variety, and functionality being weighted 20% each, efficiency 30%, and organization and tidiness 10% to the weighted mean. Quality criteria and the (weighted) scoring system of the scheme were discussed with two computer science education professionals to uphold the content validity of the measure. In addition, one of the CS education professionals rated the Scratch projects to obtain reliability assessment. Inter-rater reliability was high with ICC(3,1), 95% CI [0.
87, 0. 96]. The second measure for program quality was based on Dr Scratch (Moreno-León & Robles, 2015). Dr Scratch provides a measure of program quality based on seven dimensions relevant to CS: abstraction and problem decomposition, parallelism, logical thinking, synchronization, algorithmic notions of flow control, user interactivity and data representation. Dimensions are judged as 0 (not evident), 1 (Basic), 2 (Developing), and 3 (Proficient). Scores are aggregated over all dimensions resulting in a total evaluation score (mastery score) from 0 to 21. Mastery scores between 8 and 14 are regarded as general developing; lower than 8 is regarded as generally basic, and more than 14 as general proficient. High correlations between Dr Scratch mastery scores and experts judgments of program quality can be used as an indicator of satisfactory criterion validity (Moreno-León, Román-González, Harteveld, & Robles, 2017).
Analysis All statistical analysis was conducted using the R statistics programming environment. In order to acquire a sense of the data, basic descriptive statistics including means and standard deviations were calculated for all five measures (Bebras scores, time spent on different computational thinking processes, program quality rubric score, Dr Scratch score, TONI-3 non-verbal intelligence score). Because participants worked on the programming task in pairs, all programming assessments based on the rubric scheme, the additional Scratch evaluation assessment based on Dr Scratch, and the assessment of how much time participants spent on computational thinking behavior based on CTBS, were paired values. Scores on the Bebras tasks and TONI-3 test were averaged for each pair. Of the 37 pairs of participants who agreed to complete the Bebras test and have their final programs used in the study, 27 agreed to be video recorded for the purposes of the CTBS analysis, and 32 pairs agreed to complete the TONI-3 test.
Spearman's ρ were computed between all five measures using all available data, to determine whether the underlying variables were directly correlated. Finally, in order to account for the possibility of moderating variables, two regression models were estimated with the two program quality measures as outcomes (program quality rubric score and Dr Scratch score). These two regression models used the Bebras task scores, the CTBS, and the TONI-3 IQ scores as predictors, so that it was possible to detect if any of these were moderating variables.
Correlations between variables As a first step towards analyzing which of the two computational thinking measures (general computational thinking knowledge as measured by Bebras versus computational thinking processes as observed in practice) have a greater relationship to program quality, Spearman's ρ were computed (see Table  4 ). Correlation between the two measures of program quality (weighted means based on the developed rubric scheme and Dr Scratch mastery scores) revealed a significant relationship, ρ = 0. 61, p <. 001. Based on common interpretation of effect sizes  (Cohen, 1988) , this correlation can be interpreted as large. The large correlation between the two measures of program quality reveals a degree of consistency in their assessment of student programs. Significant positive correlations were found between general computational thinking knowledge (Bebras scores) and both measures of program quality, with a borderline small to medium effect sizes (see Table  5 ).
Discussion The general computational thinking knowledge scores (Bebras) and the computational thinking procedural performance (as indicated by the CTBS), were both positively correlated with both program quality measures (the rubric scheme and Dr Scratch mastery score). Therefore, a general interpretation could be that the higher the level of both general computational thinking knowledge and applied computational thinking in practice, the better the program quality. However, this interpretation would be premature because regression analyses revealed that only one -the applied computational thinking in practice -was a significant predictor of program quality when controlling for other variables such as the level of nonverbal intelligence and general computational thinking knowledge. The reason why the two different computational thinking measures predict programming differently might lie in different perspectives underlying the two different measures of computational thinking, and how these might mediate the relationship with program quality.
Correlations indicated that the more participants spent on applied computational thinking processes, the better the programming quality of their Scratch project. It must be pointed that this was mostly due to algorithmic design, with algorithmic design being the most frequently applied computational thinking activity measured. As stated before, participants were working on their code from the start of the session and so there is a logical interpretation that the longer and the more participants spent on algorithmic design, the better the quality of their programs. Even after controlling for other measures, this relationship was still significant and persisted in both regression models with the programming quality rubric and Dr Scratch project evaluation as outcome, respectively. What is even more remarkable is that computational thinking processes were significantly correlated with program quality even though the correctness of the computational thinking processes was not assessed in this study.
Some indicators of the computational thinking behavior are dependent on the environment used. For instance, the computational thinking component algorithmic design category of the scheme and encompasses all utterance and actions with the purpose of designing an algorithmic solution to a problem. The programming task in this study was designed in Scratch, for which the only way to create algorithmic solutions was to drag and drop code chunks together. If another programming environment were used, or indeed different programming problems, or even other cohorts of participants, other indicators may be identified. This potentially limits the generalization of the results of the study.
Data collection and outcomes Data were collected in a 15-min, self-administered questionnaire on a dedicated online platform (LimeSurvey. org). They were grouped into three different categories as follows: (i) sociodemographics: age, gender, education level (< secondary school diploma vs. ≥ secondary school diploma), living with a partner during the lockdown ('yes'/'no'), living with children during the lockdown ('yes'/'no'), number of people living in the house (including the participant) before and during the lockdown, having an external space in one's housing (e. g. , large terrace, garden) ('yes'/'no') before and during lockdown, town/city of residence before and during lockdown; (ii) cannabis use (before and during the lockdown): median number of daily intakes, form used (dried flowers vs.
Finally, the 'increased' category comprised participants who reported more daily intakes during the lockdown than before it. It is important to point out that we measured the number of daily intakes not the quantity of cannabis used daily. Changes in the use of psychoactive substances other than cannabis: For each substance, we combined the number of intakes per day and the number of days per month and compared the monthly frequency (i. e. , number of daily intakes by number of days) before and during lockdown ('increase' vs. 'decrease or unchanged'). Type of area of residence Using the French National Institute of Statistics and Economic Studies (INSEE) notion of the 'urban unit'  [28] , the type of town/city of residence was categorized into an urban (urban unit > 200 k inhabitants), semi-urban (urban unit between 10 and 200 k inhabitants) or rural (urban unit < 10 k inhabitants or rural town) area, according to INSEE's data for 2020.
Acknowledgements We thank all the study participants and community associations involved in recruiting them. We also thank the members of the scientific committee ( Laurent Appel ,  Martin Bastien ,  Laélia Briand Madrid ,  Cécile Donadille ,  Claire Duport ,  Muriel Grégoire ,  Maëla Le Brun Gadelius ,  Victor Martin ,  Salim Mezaache ,  Perrine Roux ,  Bruno Spire ). Finally, our thanks to  Jude Sweeney  ( Milan, Italy ) for the English revision and copyediting of our manuscript. Public involvement in research: This study was grounded in community-based participatory research. People from the cannabis-using community were involved in all steps of the study (enrolment, conception of the research question, interpretation of the results).
One aspect of natural language comprehension is understanding how many of what or whom a speaker is referring to. While previous work has documented the neural correlates of general number comprehension and quantity comparison, we investigate semantic number from a cross-linguistic perspective with the goal of identifying cortical regions involved in distinguishing plural from singular nouns. We use three fMRI datasets in which Chinese, French, and English native speakers listen to an audiobook of a children's story in their native language. We select these three languages because they differ in their number semantics. While Chinese lacks nominal pluralization, French and English nouns are overtly marked for number. We find a number of known semantic processing regions in common, including dorsomedial prefrontal cortex and the pars orbitalis, in which cortical activation is greater for plural than singular nouns and posit a cross-linguistic role for number in semantic comprehension.
Participants The English dataset includes 51 healthy, right-handed, young adults (32 female, mean age = 21. 3, range = 18-37). They self-identified as native English The English, French, and Chinese audiobooks are 94, 98, and 99 minutes in length, respectively. The presentations were divided into nine sections, each 8 lasting around ten minutes. Participants listened passively to the nine sections and completed four quiz questions after each section (36 questions in total). These questions were used to confirm participant comprehension of the story.