dataesr/scientific-paragraphs-categorization huggingface dataset:
We observe that these values are close to the (hidden) ground truth value (h = 1/ 40 = 0. 025) used to generate the sequence. In addition, the best-fit hazard rate value is higher for anticipatory pursuit compared to the true value and the rating scale measures. As an interim summary, this analysis reveals that relaxing the free parameter of the BBCP model improves the match of the model to the behavioral data, and that individual best-fitting hazard-rates are variable, especially for the Bet task. Future work might provide important insight about the analysis of these inter-individual differences in terms of each participant's preference for exploration versus exploitation across different cognitive tasks. The distribution of best-fitted values for each individual participant seemed to qualitatively cluster, but the dataset is still insufficiently large to support the significance of such observation at a quantitative level.
Eye movements experiment Eye movements were recorded continuously with an eye tracking system (Eyelink 1000, SR Research Ltd. , sampled at 1000 Hz), using the Python module Pylink 0. 1. 0 provided by Psy-choPy. Horizontal and vertical eye position data were transferred, stored, and analyzed offline using programs written using Jupyter notebooks. The data analyses were implemented using the Python libraries numpy, pandas and pylab. All the scripts for data analysis, as well as for stimulus presentation, data collection, and preparation of figures are available at https:// github. com/chloepasturel/AnticipatorySPEM. To minimize measurement errors, the participant's head movements were restrained using a chin and forehead rest, so that the eyes in primary gaze position were directed towards the center of the screen.
In order to enforce accuracy in gaze position and tracking, we implemented an automatic procedure of fixation control. If the distance between the gaze position and the central fixation point during the fixation epoch exceeded 2˚of visual angles, the fixation point started flickering and the counter for the fixation duration was reset to 0. The recorded horizontal and vertical raw gaze position data were numerically differentiated to obtain velocity measures. We adopted an automatic conjoint acceleration and velocity threshold method (the default saccade detection implemented by SR Research) to detect ocular saccades. Saccades and eye-blinks were excluded from eye velocity traces (and replaced by Not-a-Number values in the numerical arrays) before trial averaging and data fitting for the extraction of the oculomotor parameters of interest.
Note that this method for estimating anticipatory velocity led to qualitatively identical results to the estimation of the mean eye velocity within an arbitrary temporal window of anticipation, a more classical method that we implemented for instance in a previous study  [11]. Some trials were excluded from the analysis as the proportion of missing data-points, due to eye blinks or saccades was considered too large, namely when the missing data exceeded 45 ms during the gap or one third of the total target motion epoch (4. 36% of all trials). In addition, trials were also excluded when the eyemovement fitting procedure did not converge, after visual inspection, to a satisfactory match with the data (3. 25% of all trials). The python scripts used to analyze eye movements are available at https://github.
The Bet experiment The aim of the Bet experiment was to collect data related to the individual explicit estimates of the probability for the next outcome of a target motion direction. At the beginning of each trial, before the presentation of the moving target, participants had to answer to the question "How sure are you that the target will go left or right". This was performed by adjusting a cursor on the screen using the mouse (see Fig  1C ). The cursor could be placed at any point along a horizontal segment representing a linear rating scale with three ticks labeled as "Left", "Right" (at the extreme left and right end of the segment respectively), and "Unsure" in the middle. Participants had to validate their choice by clicking on the mouse left-button and the actual target motion was shown thereafter.
values can serve as a regressor for behavioral data to test whether human observers follow a similar strategy. In particular, the free parameter of the model (h), may be fitted to the behavioral dataset. Testing different hypothesis for the value of h thus allows to infer the agents' most likely belief in the (fixed) weight decay. Now, since we have defined a first generative model and the corresponding ideal observer (the forgetful agent), we next define a more complex model, in order to overcome some of the limits of the leaky integrator. Indeed, a first criticism could be that this model is too rigid and does not sufficiently account for the dynamics of contextual changes [60]  as the weight decay corresponds to assuming a priori a constant precision in the data sequence, contrary to more elaborate Bayesian models [61].
Main (1000-1500; currently 1691): Molecular networking (MN)  1  within the GNPS web platform (http://gnps. ucsd. edu)  2  has been used for the analysis of non-targeted mass spectrometry data in various fields  3, 4. MN relies on the principle that similar structures tend to form similar patterns in tandem mass spectra (MS 2 ). By computing pairwise spectral comparisons in a dataset, we create an MS 2 spectral network. This network is enriched by annotating the MS 2 spectra against MS 2 spectral libraries or compound databases (Figure  1 ); further, annotations can be propagated through the network  5. MN can be used to map the chemical space of complex samples to facilitate the discovery of new molecules, especially analogs of known compounds  2. For the data analysis of liquid chromatography-mass spectrometry (LC-MS 2 ) data, feature-based molecular networking (FBMN) combines MN with chromatographic feature finding tools  6.
Subsequently, ion species can be identified based on known mass differences  7  , resulting in MS  1  -based ion identity networks (IIN). We fully integrated IIN into MS  2  -based molecular networks in the GNPS environment. After feature grouping and identification of ion species, extracted data are uploaded to GNPS to run IIMN on the webserver. Resulting ion identity molecular networks contain two layers of feature (node) connectivity, linking ion identities of the same compound by MS  1  characteristics and structurally similar compounds by MS 2 spectral similarity (Figure  1 ). The IIMN modules in MZmine (Supplementary Figure  2 ) include new feature grouping and ion identity networking algorithms, as well as modules to visualize and analyze networking results. To validate the identification of ion species with IIMN, we created an LC-MS 2 benchmark dataset of a natural product mixture containing 300 compounds, in which we promoted adduct formation by post-column infusion of ammonium acetate or sodium acetate at different concentrations (Figure  2a-e ).
The IIMN networks can be depicted in alternative layouts that illustrate complementary results within the same dataset. It is also possible to collapse ion identity networks to reduce the redundancy of different ion species by merging them into a single "neutral molecule" (M) node (Figure  2c ). In this dataset, IIN successfully connects ion identities and reduces the size of a complex network by 56% to four major compounds. The increased connectivity facilitates the propagation of structure annotations to neighboring in-source fragments and an unannotated compound. Finally, the abundance change of identified adducts ([M+H] + , [M+NH4] + , [M+Na] + ) in our benchmark dataset is in agreement with the different postcolumn salt infusion conditions (H2O, NaAcetate or NH4Acetate, Figure  2f ) which validates ion species identification on a dataset level.
To test the workflow with data generated from various sample types and on different experimental platforms, 24 public datasets were processed by different authors using the MZmine workflow (Figure  2g , Supplementary Table  1 ). Here, the application of IIMN to identify post-column induced ion species can be particularly useful for the screening of biologicallyrelevant metal-binding compounds. In a native ESI-based metabolomics study, IIMN specifically revealed that the known siderophore yersiniabactin also acts as a zincophore (Supplementary Figure  3 )  12. In a dataset with 88 animal bile acid extracts, multiple smaller networks and unconnected nodes were combined to a large network of free bile acids and those conjugated to amino acids or sulfate, resulting in higher connectivity in the network (Supplementary Figure  4 ).
IIMN also yielded additional structural information in the case of mold samples from Stachybotrys chartarum (Supplementary Figure  5 ). The increasing number of aliphatic hydroxyl groups in phenylspirodrimane derivatives was reflected by the maximum number of in-source water losses, whereas acetylation of hydroxy groups reduced this number. During the creation of IIMN networks, further layers of additional feature connections can be supplied. One example is a relationship between ion identity networks based on neutral mass differences that annotate putative structure modifications between compounds (Supplementary Figure  6 ). From a global view on all 24 datasets, IIMN successfully reduced the number of unconnected LC-MS 2 features and increased the connections to annotated compound structures (Supplementary Figure  7 , Supplementary Table  2 ).
In positive ion mode, most mass spectrometrists routinely consider H and Na adducts, but rarely NH4, Ca, and K adducts and in-source fragments that were commonly observed in the 24 datasets. Inspecting the relative distribution of ion identities within all datasets, marine samples, for instance, showed a higher percentage of NH4 adducts (24±5%) when compared to all other datasets (10±8%). Sodium adducts that were expected to be elevated in marine samples (due to anticipated higher salt contents in the original sample), in contrast, are evenly distributed between all datasets with an average of 26±6% (Figure  2g ). On average, protonated species contribute to 23±6% of the overall ion identities, indicating spectral bias in public MS 2 libraries such as MassBank of North America (66% [M+H] + ) and GNPS (65% [M+H] + ) (Supplementary Figure  8 ), and suggests that the community should provide MS 2 spectra for other ion species of the same molecules to reference libraries.
Here, IIMN can be used to expand the spectral libraries with additional adducts and in-source fragments in LC-MS experiments, which can significantly increase spectral library coverage and thus MS 2 annotation rates. By propagating high confident spectral matches (cosine > 0. 9 or authentic standards) to connected ion identities from the 24 public datasets and two datasets of natural products from the NIH 'ACONN' collection, we created spectral libraries with a total of 2,659 unique entries with a broader and more representative ion species coverage (e. g. , 24% [M+H]+, 22% multimeric species, 17% [M+Na] + , 15% in-source fragments, and 13% [M+NH4] + ). Such spectral libraries better represent ion species observed in typical metabolomics experiments (Supplementary Table  3  and Supplementary Figure  8 ).
In conclusion, by establishing relationships between different ion species originating from the same compound, IIMN facilitates molecular network interpretation and compound annotation. An exciting application of IIMN is the expansion of spectral libraries by (re)processing public datasets and propagating spectral library annotations to create library entries of connected ion identities. The identification of ion adducts can reveal novel ionophores, some of which will be biologically relevant and are still underappreciated in the function of small molecules  12, 13. The integration into FBMN and the GNPS environment provided a platform to utilize IIMN in other related bioinformatics tools, e. g. , SIRIUS  14  and CANOPUS  15. We anticipate that the new option to add orthogonal relationships between features to IIMN will stimulate the integration and development of additional tools for spectral alignment and measures of featurefeature relationships  16.
To reach a broad user base, we interfaced the IIMN workflow with three widely used open source MS processing tools (MZmine 17 , MS-DIAL  18  , and XCMS  7, 19  ). Detailed documentation and training videos are available online (https://ccms-ucsd. github. io/GNPSDocumentation/fbmniin/). Especially the option to directly submit IIMN analysis from MZmine to GNPS provides a simple entry point for new users. d) lists the structure of all GNPS library matches and e) propagated structures for D (based on A and C) and the in-source fragments A' to D'. This subset of structurally related compounds gives a first statistical proof for high correct annotation rates during IIN in MZmine as adduct formation responds to the corresponding salt infusion, e. g. , higher [M+Na] + abundances in the sodium acetate buffer infusion.
MZmine is the first tool to provide a direct submission to GNPS to start analysis jobs, consequently streamlining the workflow and lowering the entrancing energy needed to apply IIMN within GNPS. In detail, the metaCorrelate feature grouping algorithm searches for features with similar average retention times, chromatographic intensity profiles (feature shapes) with a minimum percentage of intra-sample correlation and overlap, and minimum feature intensity correlations across all samples (Supplementary Figure  2 ). The feature shape correlation is a vital filter to reduce false grouping significantly and can apply either a minimum Pearson correlation (favored) or cosine similarity. A requirement is at least five data points, two on each side of the peak apex. If a low MS 1 scan rate leads to chromatographic peaks with less than five data points, it is advisable to either redesign the acquisition method or to turn off the feature shape correlation.
Refer to the documentation and video tutorials on how to apply IIMN within MZmine and GNPS. The youtube playlist "MZmine: Ion Identity Molecular Networking" contains instructions on data processing for IIMN and FBMN, a minimalistic and full IIMN workflow within MZmine, and theoretical background to feature shape correlation and ion identity molecular networking. https://ccms-ucsd. github. io/GNPSDocumentation/fbmn-iin-mzmine/ https://www. youtube. com/playlist?list=PL4L2Xw5k8ITyxSyBdrcv70LDKsP8QNuyN.
IIMN with XCMS (CAMERA) The XCMS  19  Bioconductor package  21  is the most widely used software for processing untargeted LC-MS based metabolomics data. Its results can be further processed with the CAMERA  7  package to determine which of the extracted m/z-rt features might be adducts  7  or isotopes  22  of the same original compound. For the integration of XCMS and CAMERA into the IIMN workflow, novel utility functions were created (`getFeatureAnnotations` and `getEdgelist`) to extract and export MS 1 based feature and edge annotations (i. e. grouping of features to adduct/isotope groups of the same compound). In addition, the utility function `formatSpectraForGNPS` is used to export MS 2 spectra. These functions are available in the GitHub repository https://github. com/jorainer/xcms-gnps-tools.
R-markdown documents and python scripts with example analyses and descriptions are available in the documentation. (https://ccms-ucsd. github. io/GNPSDocumentation/fbmn-iin-xcms/) The files exported by these utility functions can be directly used for IIMN analysis on GNPS. Note that theoretically, it is possible to use RAMClust 8 , CliqueMS  23  , or other packages available for XCMS that perform ion annotation. The results of these packages need to be reformatted to the introduced generic supplementary edges format. The CAMERA integration might serve as a reference and starting point.
IIMN with MS-DIAL MS-DIAL  24  is a polyvalent mass spectrometry data processing software capable of processing various non-targeted LC-MS metabolomics experiments, including ion mobility mass spectrometry (http://prime. psc. riken. jp/compms/msdial/main. html). MS-DIAL supports IIMN since version 4. 1. After a standard data processing workflow with MS-DIAL, the "Alignment results" can be exported for IIMN analysis using the option "GNPS export". Detailed documentation and representative tutorials are available in the GNPS documentations (https://ccms-ucsd. github. io/GNPSDocumentation/fbmn-iin-msdial).
Dataset processing All 24 datasets (Supplementary Table  1 ) were processed with the MZmine workflow. As each dataset originates from a different study and was acquired with different LC-MS methods, variable feature detection and alignment parameters were applied, which are summarized in Supplementary Table  5. For all datasets, the same parameters were used for the feature grouping module (metaCorrelate) and the ion identity networking modules, with the only exception that the feature height correlation filter was turned off to group features for the post-column salt infusion experiments. As described previously, this filter should only be applied if the ionization conditions and detection sensitivity are kept constant over all samples. The post-column infusion of different salt solutions for this study promotes the formation of specific ion species in the ionization source.
1. A pair of features were grouped with a retention time tolerance of 0. 1 min, with a minimum overlapping intensity percentage of 50% in at least 2 samples in the whole dataset (gap-filled features excluded), a feature shape Pearson correlation greater equals 0. 85 with at least 5 data points and 2 data points on each edge, and a feature height Pearson correlation greater equals 0. 6 with at least 3 data points. 3. Two iterations were applied to add more ion identities to the resulting networks of step 2 with an unchanged m/z tolerance. a. To add a higher variety of adducts, a maximum multimers number of 2, a list of adducts ([M+H] + , [M+Na] + , [M+K] + , [M+NH4] + , [M-H+2Na] + , [M-H+Ca] + , [M- H+Fe] + , [M+2H] 2+ , [M+H+Na] 2+ , [M+H+NH4] 2+ , [M+Ca] 2+ ,.
Dataset statistics Ion identity molecular networking statistics on all datasets were extracted with a new MZmine module and exported to a comma-separated file (csv) for evaluation in Microsoft Excel. The module is included in the special IIMN build of MZmine. All available statistics were based on the spectral input file (mgf) and the resulting network file (graphml), which was downloaded from the dataset's corresponding GNPS results page. The graphml file contains all ion identity molecular networking results, namely, the nodes representing individual features and the edges between nodes. The mgf spectral summary file contains the corresponding MS 2 spectrum for each feature node. While classical MN and FBMN depend on MS 2 data for each node, IIN creates new MS  1  -based edges that might include nodes without an MS 2 spectrum in the resulting network.
For a comparison between FBMN and IIMN, only nodes present within both networks (with an MS 2 spectrum) are considered. A statistical summary and in-depth statistics on each dataset are provided in a supplementary Microsoft Excel workbook (Supplementary File. CC-BY-NC-ND 4. 0 International license made available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprint this version posted May 13, 2020. ; https://doi. org/10. 1101/2020. 05. 11. 088948 doi: bioRxiv preprint SI_IIMN_dataset_statistics. xlsx). Excerpts are summarized in Supplementary Table  2 , and the different statistical measures and metadata items are described in Supplementary Table  4. One important measure is the identification density, i.
e. , all identified nodes and nodes with a maximum distance of n edges to at least one identified compound. Supplementary Figure  7  highlights how the additional edges of ion identity networking increase the identification density in the datasets, measured over a maximum distance of 1 to 5 edges. The increased density over one edge reflects the new links between unidentified to an identified node by IIN edge. The identification density is increased for 21 datasets, two datasets with poor identification rates exhibit no change, and one dataset lacks identifications. The maximum identification density increases over one edge of +8% results in a total of 42% of the nodes being either identified or directly linked to an identified compound. The network of the corresponding dataset, i. e. , the post-column salt infusion study, contains a total of 22% identified nodes and 25% nodes with ion identity and MS 2 spectrum in 134 ion identity networks.
MZmine IIMN workflow for spectral library extraction To extract spectral library entries from any IIMN results, download the networking results as a graphml file from a GNPS job page and use the mgf file of that analysis. The special MZmine IIMN build offers the "GNPS results analysis" module in the tab "Tools" to create library entries based on these two files and provided metadata. The minimum GNPS library match score sets a threshold for the extraction of library entries. Furthermore, library matches can be filtered to also match the ion identity to the adduct of the library match. A simple comparison between the different reporting formats for adducts was implemented. It removes all spaces, square brackets, and plus symbols (e. g. , harmonizing M+H and [M+H] + ). Filters are available for new library entries with a minimum number of signals above a relative intensity threshold.
Use case -Compound structure information The ion identity molecular networking results for the Stachybotrys chartarum dataset (MSV000084134) prove that the ion identity annotations can yield structure relevant information. Putative molecular formula modifications (+O and +H2O) between chemical compounds can be verified by the maximum number of water losses that were annotated by IIN. The difference in the number of oxygens in the molecular formulas of phenylspirodrimane derivatives is reflected in additional losses of H2O within the corresponding IINs. The results are depicted in Supplementary Figure  5. The IIMN job can be accessed on GNPS (rerun of the original job after additional spectral library entries were added to the GNPS spectral libraries: https://gnps. ucsd. edu/ProteoSAFe/status.
Use case -Implementation of orthogonal supplementary edges Ion identity molecular networking was the initial driver to implement the option of supplementary edges into the FBMN workflow on GNPS. However, based on the generic format, any tool can create and export new relationships between features to link the corresponding nodes in feature-based molecular networks. As an example, we have implemented a new MZmine module to annotate neutral mass differences between ion identity networks as putative chemical modifications, in the format of supplementary edges. These edges connect two IIN if the neutral mass difference matches a user-defined modifications list. The IIMN MZmine workflow was applied to a dataset of 88 bile acid extracts from feces and gall bladder of various animals (MSV000084170).
IIN modification edges were based on the mass differences of +methyl (Me, CH2), +O, and +H2O. To exemplify the results, Supplementary Figure  6  shows a network cluster of glycocholic acid analogs. Library matching annotated most of the ion identity networks as glycine conjugated bile acids; Two IINs as glycocholic acid (+isomers) and two IINs as glycodeoxycholic acid (+isomers) with a mass accuracy of <2 ppm. The additional modification edges connect these structurally related compounds and increase the network density. Moreover, they help to infer putative molecular formulas and modified structures from an FBMN. In a second analysis of the same dataset, IINs were connected based on mass differences of the modification by taurine, glycine, and alanine conjugation. This resulted, in additional links between conjugated and free bile acid forms of cholic acid and deoxycholic acid.