dataesr/scientific-paragraphs-categorization huggingface dataset:
The present study had several limitations. First, the questionnaires were completed online, limiting participants to those with Internet access. However, the online recruitment method allowed us to collect data from respondents all over France, despite the lockdown restrictions on travel. The size of the sample remains an issue, specifically regarding education level. Second, participants' follow-up could have been better, as there was considerable loss to follow-up. In the COVID-19 pandemic chaos, participants may have missed follow-up emails. Third, we could have explored affective dynamics using repeated measurements, with network analyses to identify virtuous profiles and positive functioning, as well as negative feedback loops between affect and regulation  (Pavani et al. , 2017). The cumulative effects of lockdowns on affect (sadness or boredom) and emotion regulation strategy use may well have damaged the psychological health of the French population.
Search strategy The following databases were searched for peer-reviewed empirical articles in June 2022: Web of Science, PsycINFO, and Pubmed. Our search strategy was based on the conjunction of two criteria: (1) the presence of "individual differences" in the title or in the abstract and (2) the presence of the terms "heuristics and biases" OR "cognitive bias" OR "cognitive biases" OR "behavioral biases" OR "rationality" OR "Decision-Making Competence" in the title or the abstract. All entries were imported in Zotero to remove duplicates, after which titles and abstracts were screened independently by two coders, according to predefined eligibility criteria. Noteworthy, this search strategy had two implications. First, we likely missed relevant papers as we did not enter every single HB as a keyword, thereby limiting the comprehensiveness of our inventory.
Results Figure  1  displays the PRISMA flowchart with full detail of this process. The complete search resulted in 1429 articles, leaving 1,091 articles once duplicates were removed. After title and abstract screening, 109 articles met the inclusion criteria and were eligible for full-text assessment. One study was subsequently excluded because the author published the same data in another article. A total of 108 studies met eligibility criteria and were included in the review.
Participants To qualify for the study, participants (n = 95) had to be aged over 18 and under 65 (Table  1 ), without specific or restrictive diets (e. g. , vegetarian, vegan, etc. ). To be able to participate in the study, participants had to have access to the Internet and a computer with an AZERTY keyboard. The participants were informed about the conditions of the study and were remunerated at the end with a 10-euro voucher. The sample size required to reach a power of 0. 9 with an alpha of 0. 05 was 54 participants; the power with 95 participants was 0. 993. The participants completed the task once, from home, using a link that was sent to them. The participants were recruited in the area of Dijon (France), from the Chemosens Platform's PanelSens database. This database complies with national data protection guidelines and has been examined by French National authorities (Commission Nationale Informatique et Libertés-CNIL-135n = 1.
Data Analysis A two-way permutational ANOVA was performed over a table containing attributes (good and bad), object categories (pulses and cereals), and reaction time (RT) in columns, with participants in lines. If significant differences were found, the ANOVA analyses were followed by a Tukey's HSD test for multiple comparisons between and within groups  [27, 28]. Descriptive data analysis was obtained by calculating the mean values for all the RTs obtained and the sum of the errors for participants. The frequency of errors made by a participant for each paired category (pulses + positive; pulses + negative; cereals + positive; cereals + negative) was calculated and then converted into a rank. The paired category for which a participant made more frequent errors obtained the highest rank.
Analyzing these generated images enables us to identify the distinguishing features preferred by each unit. Here, we started with 3 words of varying length (AIR, PAIN, and SQUARE) and found the unit with the highest activation in each layer. Next, for each unit, we generated its most preferred stimuli using the network distillation toolbox (see methods). In the early layers (V1 and V2), units preferred dark-oriented lines and curved shapes against a white background (Figure  5 ), presumably reflecting the background of the training word dataset. Consistent with earlier reports  (Zeiler & Fergus, 2013) , feature complexity and receptive field size increased in later layers of the network. Remarkably, from IT and output, the automated image optimization process recovered word fragments that partially matched the word identity originally used to select the units (Figure  5 ).
1% in MEG), and were faster and more accurate when the test stimulus was closer to the fovea (figure  S2 ). From whole-brain 7T fMRI data (n = 16; 1. 2 mm isotropic), using a combination of functional localizer and anatomical maps, we extracted four individual regions of interest (ROIs) along the ventral visual pathway: V1-V3, V4, LOC, and VWFA (see methods). In each region, using voxels as features, we fitted a linear classifier to decode the following factors: word position, letter identity, absolute letter position, and ordinal letter position. To avoid overfitting, we performed 3-fold crossvalidation (see methods; MEG data). Consistent with earlier findings, decoding accuracy for word position was highest in early visual areas, but progressively decreased along the ventral visual pathway, ultimately becoming statistically insignificant within VWFA (Figure  6 ).
Methods Model architecture and training: Among the many available convolutional neural networks that can predict neural responses along the ventral visual pathway, we chose CORnet-Z architecture for two reasons: 1) It has a modular structure that resembles the stages of processing in the visual cortex (V1, V2, V4, IT, avgpool IT or H, output), 2) It has fewer parameters, thus lowering the training time while achieving high levels of accuracy on synthetic word datasets  (Hannagan et al. , 2021). Similar to our previous work, we first trained this network on the ImageNet dataset (phase 1), which contains ~1. 3 million images across 1000 categories. This was considered an illiterate network, which encodes the visual properties of objects but not text. Next, we extended the number of output nodes to 2000 (1000 images + 1000 words), with full connectivity to H layer units, and retrained the entire network jointly on ImageNet and a synthetic word dataset (phase 2), which also contained 1.
3 million images of 1000 words. This was considered a literate network. We trained separate networks in 5 different languages: French, English, Chinese, Telugu, and Malayalam. To investigate the mechanisms of bilingualism  (Zhan et al. , 2023) , two additional networks were trained on bilingual stimuli: English + Chinese, and English + French. To estimate the variability across training sessions, each literate network was trained starting from the same five instances of illiterate networks. Thus, there were a total of 35 literate and 5 illiterate networks. Pytorch libraries were used to train these networks with stochastic gradient descent on a categorical cross-entropy loss. The learning rate (initial value = 0. 01) was scheduled to decrease linearly with a step size of 10 and a default gamma value of 0.
1. Phase 1 training lasted for 50 epochs and phase 2 training for another 30 epochs. The classification accuracy did not improve further with more epochs. Stimuli: To improve network performance, ImageNet images were transformed using standard operations such as "RandomResizedCrop" and "Normalize". The images were of dimension 224x224x3. To avoid cropping out some letters in the word dataset, the default scale parameter of RandomResizedCrop was changed such that 90% of the original image was retained. For fair comparisons, other operations such as flipping were not performed on the Imagenet dataset as the same operation would create mirror words in the Word dataset, which is not typical in reading. The English and French words included frequent words of length between 3-8 letters. The Chinese words were 1-2 characters long.
The Telugu and Malayalam words were 1-4 characters long, which approximates the physical length of the chosen English/French words. The synthetic dataset comprised 1300 stimuli per word for training and 50 stimuli per word for testing. These variants were created by varying position (-50 to +50 along the horizontal axis, and -30 to +30 along the vertical axis), size (30 to 70 pts), fonts, and case (for English and French). For each language, 5 different fonts were chosen: 2 for the train set, i. e. Arial and Times New Roman for English and French; FangSong and Yahei for Chinese; Nirmala and NotoSansTelugu for Telugu; and Arima and AnekMalayalam for Malayalam; and another 3 fonts for the test set, i. e. Comic Sans, Courier, and Calibri for English and French; Kaiti, simhei, and simsum for Chinese; NotoSerifTelugu, TenaliRamakrishna, and TiroTelugu for Telugu; and Nirmala, NotoSansMalayalam, and NotoSerifMalayalam for Malayalam.
The bigrams stimuli used in the dissimilarity analysis (Figure  1C -D) were taken from earlier studies  (Agrawal et al. , 2019 (Agrawal et al. , , 2020)). Identification of word selective units: Similar to fMRI localizer analysis, a unit was identified as word selective if its responses to words were greater than the responses to nonword categories: faces, houses, bodies, and tools by 3 standard deviations. The body and house images were taken from the ImageNet dataset. For tools, we used the "ALET" tools dataset, and Face images were taken from the "Caltech Faces 1999" dataset. We randomly chose 400-word stimuli and 100 images each from the other categories for identifying category-selective units. Dissimilarity measure: For each layer, we vectorized the activation values, and estimated the pairwise dissimilarity value using correlation metric i.
e. , d = 1-r. where r is the correlation coefficient between any two activation vectors. Letter selectivity: For each word-selective unit, its tuning profile across letters was estimated by identifying the letter that evoked maximum response at either of the 8 possible positions. First, we obtained 26x8 = 208 responses, followed by the max operation across positions, and were further sorted to identify the most and least preferred letter for a given unit. Activation maximization: We used the Lucent toolbox (https://github. com/greentfrapp/lucent) to generate images that maximally activate a given unit. Given the convolutional structure of the early stages of the network, all units within a given channel have the same features but at different spatial locations. Thus, we only estimated the preferred input for the unit whose receptive field was at the center of the image.
e. , after two event-related and one localizer run. The subjects were instructed to close their eyes and relax during the anatomical scan. Data preprocessing: The raw functional data underwent distortion correction using FSL topup (https://fsl. fmrib. ox. ac. uk/fsl/fslwiki/topup). This distortion-corrected data was further processed using the SPM12 toolbox (https://www. fil. ion. ucl. ac. uk/spm/software/spm12). The functional images were realigned, slice-time corrected, co-registered with anatomical images, segmented, and finally normalized to the MNI305 anatomical template. All SPM parameters were set to default and the voxel size after normalization was set to 1. 2x1. 2x1. 2 mm 3. Before normalization, the data were denoised using GLMdenoise  (Kay et al. , 2013) , which is known to improve signal-to-noise ratio.
Stimuli: The stimuli were identical to the fMRI study Task design: As in the fMRI, a series of letter string stimuli were presented to the participants, who had to respond with a button press if all the letters were identical. Each stimulus was shown for 200ms followed by a 300ms blank screen with a fixation cross. In a run, each stimulus was repeated 15 times, plus 60 target trials. The study comprised three such runs in total. Data acquisition: Participants performed the tasks while sitting inside an electromagnetically shielded room. The magnetic component of their brain activity was recorded with a 306-channel, whole-head MEG by Elekta Neuromag (Helsinki, Finland). 102 triplets, each comprising one magnetometer and two orthogonal planar gradiometers composed MEG helmet. The brain signals were acquired at a sampling rate of 1000 Hz with a hardware highpass filter at 0.
03 Hz. Eye movements and heartbeats were monitored with vertical and horizontal electrooculograms (EOGs) and electrocardiograms (ECGs). Head position inside the helmet was measured at the beginning of each run with an isotrack Polhemus Inc. system from the location of four coils placed over frontal and mastoidian skull areas. Data preprocessing: The MEG data underwent several preprocessing steps. Initially, the data was inspected to identify any sensors showing excessive noise or poor signal quality. This was performed using a custom script in which sensors displaying deviations from the median variance across time by more than 6 standard deviations were identified as deviant sensors and excluded from further analysis. To remove environmental noise and artifacts related to head movements, Maxwell filtering was applied using the MaxFilter tool in MNE-Python.
This step compensated for head position changes analyze the neural signals corresponding to these factors, a linear classifier was used. Specifically, logistic regression with a "liblinear" solver was performed after scaling the data using "robustscalar" from the sci-kit learn package in Python. To avoid overfitting, a 3-fold cross-validation was performed using the MNE function "cross_val_multiscore". For decoding the position information, the classifier was trained on the data from any two letter pairs, and tested on the third one. Similarly, for decoding letter identity, the classifier was trained on any two ordinal positions and tested on the remaining one. In fMRI, the voxels within each region were used as features, and in MEG, the channels were used as features with data averaged within 10ms time-bins.
Experiments should control the duration of the interval between memory encoding and bedtime/sleep onset, as well as the participants' activities during this interval. To minimize interference during this interval, participants should go to bed as soon as possible after memory encoding. Such designs are more feasible when participants sleep in the lab during the experiment. If, however, participants sleep at home after the learning session, then mobile actigraphy or, as lesscompelling substitutes, sleep diaries and post-experiment questionnaires, should be employed to assess the duration of this interval and the activities performed. This information then should be appropriately considered in data analysis.
Box 2 | Open science practices Other fields of cognitive neuroscience and psychology using techniques such as fMRI have engaged in open science initiatives by, for example, depositing raw data in open-access databases (e. g. , OpenfMRI 162 ), which have recently been extended to other neuroimaging and electrophysiological methods such as EEG (OpenNEURO 163 ). However, sleep and memory research lags in adopting open science practices  164  , despite positive examples emerging  [165] [166] [167]. Research transparency is further hindered by the lack of pre-registration of the studies on sleep and memory (but see  168  for an exception).
Data deposition Publicly available data enables re-analysis using new techniques. For example, For example, recent studies identified two REM microstate types (tonic vs. phasic), each with different characteristics  169, 170. Access to previous sleep EEG datasets would enable testing the role of REM microstructure in memory consolidation. Open databases facilitate re-analysis and meta-analyses, addressing non-significant results and spurious findings. To maximize the benefits of previous research in the scientific community, sleep researchers should embrace open science  171  and make data publicly available. Platforms like Open Science Framework 172 , OpenNeuro. org  163 , Scientific Data, or sleepdata. org offer repositories. Developing a specific open database for sleep research with EEG, polysomnographic and behavioral data would further benefit this field.
Pre-registration Another way of increasing transparency of research is to pre-register studies before data collection  173, 174. Pre-registration includes the specification of the research question, experimental design, participant population, sample size and planned analysis methods. Pre-registration is already the gold standard in many fields of research, including for clinical trials in medical research  175, 176. The neuroscience and psychology fields increasingly recognize the importance of pre-registration as well. Yet, this option has been largely neglected in sleep and memory research so far. Studies can be pre-registered in different ways. One option is pre-registration in independent online registries like the Open Science Framework or ClinicalTrials. gov. In these registries, researchers provide a detailed description of their planned study that can be accessed by other researchers as well as journal editors and reviewers to determine whether the pre-specified plan was followed adequately.
Another option is to write a registered report, which is a novel publication type offered by an increasing number of journals (e. g. , Plos Biology, eLife, eNeuro, Nature Human Behaviour, Cortex). A registered report usually undergoes two stages of peer review, first before data collection to determine the appropriateness of the research plan and methodology, and then after data collection covering the full research report including the results. If the first round of peer review is successful, the authors are typically offered 'in principle acceptance' by the journal, allowing the results to be published irrespective of the actual findings. Both procedures, pre-registration in online registries and registered reports, increase the quality of research by reducing inappropriate data analysis practices, including p-hacking, HARKing (hypothesizing after the results are known), and the application of unplanned statistical tests  [175] [176] [177].
Acknowledgements: This research was supported by the  ANR  grant awarded within the framework of the  Inserm CPJ  (to D. N. ); Hungary's  National Brain Research Program  (project  2017-1. 2. 1-NKP-2017-00002 ); the  Hungarian Scientific Research Fund  ( NKFIH-OTKA K 128016 , PI: D. N. ;  NKFIH-OTKA PD 124148 , PI: K. J. ;  NKFI FK 128100 , PI: P. S. );  Janos Bolyai Research Fellowship of the Hungarian Academy of Sciences  (to K. J. );  French National Agency for Research (ANR , grant  n°ANR-15-CE33-0003 , S. M. ). The authors thank  K. Schipper  for her help and comments on the manuscript.
Large sample sizes are particularly important in applied, observational contexts (e. g. , surveys, field settings), which typically involve more measurement error, and from which such data are more likely to be obtained. It is also important to use methodologies that can (1) identify patterns within extremely large datasets, (2) provide unbiased factor selection and prioritization, and (3) increase predictive accuracy by minimizing error variance. To address these questions, the current research involved almost 38,000 older adults randomly selected within 18 countries, an aggregate measure of subjective well-being, and 94 prospective SWB predictors. Factor analysis was conducted to create the aggregate SWB measure, and artificial-intelligence modeling (machine learning) was used to rank-order the top 50 potential predictors of SWB-from 15 categories-in terms of error reduction.
Because Wave 7 was a "special edition" (due to its emphasis on participants' childhoods), the data in the current study includes Waves 6 & 7 (2014 & 2016) to provide past and present information about participants. The data in this study were collected from 17 of the 26 European Union (EU) countries (Austria, Belgium, Croatia, Czech Republic, Denmark, Estonia, France, Germany, Greece, Italy, Luxembourg, Poland, Portugal, Slovenia, Spain, Sweden & Switzerland) and Israel, and includes a total of 37,991 participants. These participants-21,412 (56. 4%) of whom were female-ranged in ages from 50 to 102 years, with a mean age of 66. 1, a median age of 65, and a standard deviation of 9. 7 years. Subjective well-being. The SHARE data in the current research was developed, collected and analyzed by an interdisciplinary team of research scientists and clinicians.
These overall Euro-D scores were reverse-coded before being combined with the other two SWB components. It is increasingly clear to that positive and negative aspects of SWB do not simply operate as a single continuum  55  , indicating the importance of including negative SWB components, which is still relatively rare  7. Depression was included in this SWB measure because of it significant negative impact on SWB  56  , and because of the Euro-D's strong factor structure and internal reliability (Chronbachs > 0. 80)  57. A principal component factor analysis was conducted on the three SWB component scores using SPSS 28. 00. This unrotated analysis yielded the following factorial structures: Component 1 (eigenvalue) = 2. 03; Component 2 (eigenvalue) = 0. 57; and Component 3 (eigenvalue) = 0.
Not all of these categories were represented in all of these studies, nor did their individual predictors overlap completely with those in the current research. However, all of the previous predictors within the categories that correspond to those in the current study were significantly associated with SWB. This approach enables both direct comparisons with previous SHARE findings, while also providing results to address additional current and future SWB issues. Predictor variables. Given the exploratory nature of this study, all variables in the SHARE data that were relevant to the predictor categories were included in the initial set of predictors. In addition to standard category exemplars, this initial set of 94 variables (listed in Appendix A) also included less common predictors extracted from the SHARE database.
For example, beyond age and gender, demographic predictors included current participants' nationality and country of birth, while financial status included not only income, but also participants' ability to make ends meet. Given their conceptual overlap, many SWB predictors were relevant to more than one category. Thus, category designations were made in accordance with the primary themes of the research. For example, childhood loneliness was included in childhood experiences, periods of hunger were considered a marker of financial status, and current loneliness was categorized as social relationship factors. While most predictors were kept in their original form, some required transformations and/or aggregation by the SHARE research team. Specifically, due to the significant number of missing values for participant income, data imputation was conducted for income-related variables.