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61 results for “Judgment”
Moral judgments of intentional and accidental moral violations across Harm and Purity domains
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Disentangling the origins of confidence in speeded perceptual judgments through multimodal imaging
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Data to "Humans Can Visually Judge Grasp Quality and Refine Their Judgments Through Visual and Haptic Feedback"
<p>This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p>Maiello, G.^, Schepko, M.^, Klein, L. K., Paulun, V. C., and Fleming, R. W. (2021) Humans Can Visually Judge Grasp Quality and Refine Their Judgments Through Visual and Haptic Feedback. Front. Neurosci. 14:591898.<br> doi: 10.3389/fnins.2020.591898</p> <p>A preprint version of the manuscript is available at: https://doi.org/10.1101/2020.08.11.246173</p>
Improving causality perception judgments in schizophrenia spectrum disorder via transcranial direct current stimulation - Dataset
<p>Raw data related to the publication:</p> <p>Schülke, R., Schmitter, C. V., & Straube, B. (2023). Improving causality perception judgments in schizophrenia spectrum disorder via transcranial direct current stimulation. <em>Journal of Psychiatry and Neuroscience</em>, <em>48</em>(4), E245–E254. <a href="https://doi.org/10.1503/jpn.220184">https://doi.org/10.1503/jpn.220184</a></p> <p>Variables:</p> <ul> <li>Subject</li> <li>Condition – Stimulation condition; parietal (left parietal cathodal, right parietal anodal [LPC-RPA]), frontoparietal (left frontal cathodal, right parietal anodal [LFC-­RPA]), frontal (left frontal cathodal, right frontal anodal [LFC­-RFA])</li> <li>Timepoint – Before/After (stimulation)</li> <li>Angle – in degrees</li> <li>Angle_scaled – mean-centered and scaled Angle</li> <li>Delay_ms – in milliseconds</li> <li>Delay_ms_scaled – mean-centered and scaled Delayed_ms</li> <li>Causality – causal/non-causal (judgment)</li> <li>RT – reaction time in milliseconds</li> </ul> <p>In the original version of the data, the data had been incorrectly labelled: The data actually corresponding to the LFC-RPA condition had been incorrectly labelled as LPC-RPA, and the data actually corresponding to the LPC-RPA condition had been incorrectly labelled as LFC-RPA. This has been corrected with the 04/2024 version of the dataset.</p>
Rhyme judgment
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Forecasting Adversarial Actions Using Judgment Decomposition-Recomposition
<p>This repository contains the data and source code used in the research paper, "Forecasting Adversarial Actions Using Judgment Decomposition-Recomposition".</p>
M1-data-format-only compositions and two-rater judgments of composition validity
<p>The set of graph compositions produced by the implementation of M1 data-format-matching algorithm and the validity judgements of two of the authors MMW and WRH.</p>
Custom language model checkpoints used in "Testing the limits of natural language models for predicting human language judgments"
<p>Checkpoint files for an RNN, LSTM, BILSTM and n-gram models used the paper "Testing the limits of natural language models for predicting human language judgments"</p>
Once an optimist, always an optimist? Studying cognitive judgment bias in mice
<p>This repository contains raw data and analysis code for the manuscript entitled "Once an Optimist, Always an Optimist? Studying Cognitive Judgment Bias in Mice" from Marko Bračić, Lena Bohn, Viktoria Siewert, Vanessa von Kortzfleisch, Holger Schielzeth, Sylvia Kaiser, Norbert Sachser, S. Helene Richter, accepted for publication in the journal Behavioral Ecology.</p> <p>The aim of the study was to investigate the causes and stability of cognitive judgment bias (aka "optimism").</p> <p>Individuals differ in the way they judge ambiguous information: some individuals interpret ambiguous information in a more optimistic, and others in a more pessimistic way. Over the past two decades, such "optimistic" and "pessimistic" cognitive judgement biases (CJBs) have been utilized in animal welfare science as indicators of animals' emotional states. However, empirical studies on their ecological and evolutionary relevance are still lacking.</p> <p>We, therefore, aimed at transferring the concept of "optimism" and "pessimism" to behavioral ecology and investigated the role of genetic and environmental factors in modulating CJB in mice, using an automated, touchscreen-based active choice paradigm. In addition, we assessed the temporal stability of individual differences in CJB.</p> <p><span></span></p> <p>We show that the chosen genotypes (C57BL/6J and B6D2F1N) and environments ("scarce" and "complex") did not have a statistically significant influence on the responses in the CJB test. By contrast, they influenced anxiety-like behavior (assessed in the elevated plus maze (EPM), an open field test (OFT), and a free exploration test (FET)) with C57BL/6J mice and mice from the "complex" environment displaying less anxiety-like behavior than B6D2F1N mice and mice from the "scarce" environment. As the selected genotypes and environments did not explain the existing differences in CJB, future studies might investigate the impact of other genotypes and environmental conditions on CJB, and additionally, elucidate the role of other potential causes like endocrine profiles and epigenetic modifications. Furthermore, we show that individual differences in CJB were repeatable over a period of seven weeks, suggesting that CJB represents a temporally stable trait in laboratory mice. Therefore, we encourage the further study of CJB within an animal personality framework.</p>
Advice Taking under Uncertainty: The Impact of Genuine Advice versus Arbitrary Anchors on Judgment
<p>A major module of rational advice taking consists in the metacognitive ability to distinguish between credible advice and arbitrary anchors. Accordingly, we investigated the extent to which framing the very same information as either advice or anchor exerts a differential influence on quantitative judgments. Four experiments showed that although arbitrary anchors were given lower weight than advice, they nevertheless exerted a systematic impact on final judgments. Degree of integration was related to subjective confidence only in the advice condition, but not in the anchoring condition, suggesting that arbitrary anchors were not considered informative. Framing the source of advice as a human being versus as a computer did not affect our results. Only the aboutness of advice, that is, whether it targeted the focal judgment item, determined its influence on final judgments and on confidence. On the one hand, these findings speak to the (partial) sensitivity of human judges to the source and validity of advice under uncertainty. On the other hand, the persevering effect of arbitrary anchors demonstrates the dependence of judgments on unauthorized influences. Both findings together highlight the need to study advice taking from a metacognitive perspective.</p>
CLIP Features and Selected Relevance Judgments Subset for TRECVID Ad-hoc Search (2019-2023)
<div> <div> <div> <div> <div> </div> </div> </div> </div> </div> <div> <div> <div> <div> <div> <div> <p>This repository contains CLIP features and annotations for a subset of V3C images, based on their relevance to selected queries from the TREC Video Retrieval Evaluation (TRECVID) Ad-hoc Video Search (AVS) task. The data includes annotations for AVS queries and judgments conducted in TRECVID from 2019 to 2023 [1], using the V3C1 and V3C2 collections [2]. Specifically, the TRECVID-AVS collection covers 89 queries, with video shots manually labeled as relevant (1), non-relevant (0), or not annotated (-1).</p> <p>We used approximately 2.6 million keyframes extracted from these video shots, mapping the annotations to the corresponding keyframes (note that there may not be a one-to-one correspondence between TRECVID shotID since multiple frames might be extracted from a single shot). Image representations are based on CLIP ViT-H/14 - LAION-2B features [3]. The timestamps of the keyframes and their CLIP features are sourced from the VISIONE repository [4].</p> <p>Given the incomplete nature of the TRECVID ground truth (where only a subset of video segments were judged per query), we focused on queries with at least 200 positive and 1400 negative annotations. This resulted in 80 datasets, each containing 1500 images—10% labeled as relevant and 90% as non-relevant.</p> <h3>Contents of the Repository:</h3> <ol> <li> <p><strong>Query-specific CSV Files:</strong> For each of the 80 selected AVS query (e.g., <code>1591</code>), the corresponding CSV file (e.g., <code>1591.csv</code>) contains a column for each image, where:</p> <ul> <li><strong>VISIONE image ID</strong> is in the first row.</li> <li><strong>CLIP features</strong> are in the subsequent rows.</li> <li><strong>Relevance annotations</strong> are in the last row: <code>1</code> for relevant, <code>0</code> for non-relevant.</li> </ul> </li> <li> <p><strong>Post-processed Datasets:</strong></p> <ul> <li><code>dataset_normalized.zip</code>: L2-normalized CLIP features.</li> <li><code>dataset_softmax.zip</code>: CLIP features converted into probabilities using a softmax function.</li> <li><code>dataset_logistic.zip</code>: CLIP features converted into probabilities using a logistic function followed by L1 normalization.</li> </ul> </li> <li> <p><strong>Text Feature Data:</strong> <code>clip_laion_text_features.csv</code> contains additional details for each query, including the query ID, query text, and L2 normalized CLIP features extracted from the query text.</p> </li> </ol> <h3>Citation and Usage:</h3> <p>This data was used in the experiments described in:</p> <p>Lucia Vadicamo, Francesca Scotti, Alan Dearle, Richard Connor, <em>Comparative Analysis of Relevance Feedback Techniques for Image Retrieval</em>, in Proceedings of the 31st International Conference on Multimedia Modeling (MMM 2025).</p> <p>The data is released under a Creative Commons Attribution license. If you use it in your research, please cite the above work. </p> <h3>References:</h3> <p>[1]TRECVID Data: <a href="https://www-nlpir.nist.gov/projects/trecvid/trecvid.data.html">https://www-nlpir.nist.gov/projects/trecvid/trecvid.data.html</a><br>[2] Rossetto, L., Schuldt, H., Awad, G., Butt, A.A.: V3C - A research video collection. <em>In: International Conference on Multimedia Modeling</em>, pp. 349–360. Springer (2019).<br>[3] https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K<br>[4] VISIONE Repository: <a href="https://zenodo.org/records/8188570">https://zenodo.org/records/8188570</a></p> </div> </div> </div> </div> </div> </div> <p> </p> <p> </p> <p> </p>
NNS-500 Acceptability judgment task dataset based on the sentences written by non-native English speakers
<p><strong>Acceptability judgment task (AJT):</strong> AJT is a common method in empirical linguistics to gather information about the internal grammar of speakers of a language, which is considered a promising area to evaluate neural language models' linguistic knowledge. There is a Corpus of Linguistic Acceptability (CoLA) whose creators think Boolean judgments sufficient; similarly, some non-English resources cast acceptability as a binary classification task.</p> <p><strong>Dataset:</strong> NNS-500 dataset based on the sentences written by non-native speakers (which is important from the point of view of the source of unacceptable sentences) and labelled by a university English teacher is intended for testing the pre-trained neural networks. It has 350 acceptable and 150 unacceptable sentences, which is 70% of acceptability (this compares to 69.2% in the CoLA out-of-domain set). The dataset markup includes standard data: id number, sentence, indication of acceptability – 1, indication of unacceptability – 0, type of error (morphology, syntax, semantics), and detailed information about the source (group number with the year of admission to the university, number according to list of the group members, and gender of the student). For the use of the assessment of EFL learners' linguistic competence, the first 100 sentences of the dataset (id 1‒100) include the ones written by the study group with a high level of academic performance (Group A) and another 100 sentences of the dataset (id 101‒200) are taken from the writing assignments of students with a poor academic performance (Group B). From each group of students, 5 people were selected (a total 10 participants); 20 sentences were randomly selected from each student's written work (14 ‒ unacceptable, 6 ‒ acceptable); there are more sentences with errors, since they are very important for the error analysis. The rest of the dataset consists of 290 acceptable and 10 unacceptable sentences (id 201‒500) taken from the works of students of different study groups of an intermediate level.</p>
Moral Judgments in Narratives on Reddit: Investigating Moral Sparks via Social Commonsense and Linguistic Signals
<ol> <li>The file 'post_instances.jsonl' contains instances extracted from specific posts. In this file, instances that contain moral sparks are labeled as '1,' while others are labeled differently or as '0.'</li> <li>Each instance is scraped by using PushShift API by searching for an unique id. And each instance contains its comment ids that use ">" to quote excerpts in a post. We removed author names and make it left with ids and contexts. The "label" field is computed by using regular expressions to match predefined r/AmItheAsshole verdict codes.</li> <li>The sup_documents.pdf includes full lists of c-event clusters and parameters of linguistic features used in our paper.</li> <li>The regular expressions used to extract the verdicts are as follows:AUTHOR = (0, 'YTA', [<br> r'\m(?i:YWBTA?)\M',<br> r'\m(?i:YTAH?)\M',<br> r"(?e)(?i:"<br> r"you(?:'re| r| are| were| would be| will be) "<br> r"(?:(?:kind|sort) of |really |indeed |just |definitely |exactly |absolutely |certainly |obviously )?"<br> r"(?:an? |the )?"<br> r"(?:huge |big |giant )?"<br> r"(?:asshole|a-?hole)"<br> r"){e<=1}",<br> r"(?e)(?i:"<br> r"you "<br> r"(?:(?:kind|sort) of |really |indeed |just |definitely |exactly |absolutely |certainly |obviously )?"<br> r"(?: r| are| were)? (?:an? |the )?"<br> r"(?:huge |big |giant )?"<br> r"(?:asshole|a-?hole)"<br> r"){e<=1}"<br> ])<br> OTHER = (1, 'NTA', [<br> r'\m(?i:YWNBTA?)\M',<br> r'\m(?i:Y?NTAH?)\M',<br> r'(?e)(?i:'<br> r"you(?:'re| r| are| were| would| will) "<br> r"(?!both)"<br> r'(?:not| not be) '<br> r"(?:(?:kind|sort) of |really |indeed |just |definitely |exactly |absolutely |certainly |obviously )?"<br> r'(?:an? |the )?'<br> r"(?:asshole|a-?hole)"<br> r'){e<=1}',<br> r'(?e)(?i:'<br> r"(he|she)(?:'s|s| s| is| was)"<br> r"(?:(?:kind|sort) of |really |indeed |just |definitely |exactly |absolutely |certainly |obviously )?"<br> r'(?:an? |the )?'<br> r"(?:asshole|a-?hole)"<br> r'){e<=1}',<br> r'(?e)(?i:'<br> r"they(?:'re|r| r| are| were)"<br> r"(?:(?:kind|sort) of |really |indeed |just |definitely |exactly |absolutely |certainly |obviously )?"<br> r'(?:the )?'<br> r"(?:asshole|a-?hole)"<br> r'){e<=1}'<br> ])<br> EVERYBODY = (2, 'ESH', [<br> r'\m(?i:ESH)\M',<br> r'(?e)(?i:every(?:one|body) sucks here){e<=1}',<br> r'(?e)(?i:you both suck){e<=1}',<br> r'(?e)(?i:'<br> r"you(?:'re| r| are| were) "<br> r"(?:(?:kind|sort) of |really |indeed |just |definitely |exactly |absolutely |certainly |obviously )?"<br> r'both (?:the )? (?:assholes?|a-?holes?)){e<=1}',<br> r'(?e)(?i:'<br> r"you both"<br> r"(?:'re| r| are| were)? "<br> r"(?:(?:kind|sort) of |really |indeed |just |definitely |exactly |absolutely |certainly |obviously )?"<br> r'(?:the )?'<br> r"(?:assholes?|a-?holes?)"<br> r'){e<=1}',<br> r'(?e)(?i:'<br> r"there(?: r| are| were)(?: any| all) "<br> r"(?:(?:kind|sort) of |really |indeed |just |definitely |exactly |absolutely |certainly |obviously )?"<br> r'(?:assholes?|a-?holes?)){e<=1}'<br> <br> ])<br> NOBODY = (3, 'NAH', [<br> r'\m(?i:NAH?H)\M',<br> r'(?e)(?i:no (?:assholes|a-?holes|asshole) here)',<br> r'(?e)(?i:no one is the (?:asshole|a-?hole)){e<=1}',<br> r'(?e)(?i:'<br> r"you both"<br> r"(?:'re| r| are| were)? "<br> r"(?:(?:kind|sort) of |really |indeed |just |definitely |exactly |absolutely |certainly |obviously )?"<br> r'not (?:an? |the )?'<br> r"(?:assholes?|a-?holes?)"<br> r'){e<=1}',<br> r'(?e)(?i:'<br> r"you(?:'re| r| are| were) "<br> r"(?:(?:kind|sort) of |really |indeed |just |definitely |exactly |absolutely |certainly |obviously )?"<br> r'both not (?:an? |the )? (?:assholes?|a-?holes?)){e<=1}',<br> r'(?e)(?i:'<br> r"you "<br> r"both(?: weren't| aren't)? (?:an? |the )? (?:assholes?|a-?holes?)){e<=1}",<br> r'(?e)(?i:'<br> r"there(?: r| are| were)"<br> r' no '<br> r"(?:(?:kind|sort) of |really |indeed |just |definitely |exactly |absolutely |certainly |obviously )?"<br> r'(?:assholes?|a-?holes?)){e<=1}'<br> ])<br> INFO = (4, 'INFO', [<br> r'\m(?i:INFO)\M',<br> r'(?e)(?i:not enough info){e<=1}',<br> r'(?e)(?i:needs? more info){e<=1}',<br> r"(?e)(?i:more info(?:'s| is)? required){e<=1}"<br> ])</li> </ol> <p> </p>
Once an optimist, always an optimist? Studying cognitive judgment bias in mice
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Robustness of large language models in moral judgments
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Data for Improving the Quality of Quality Judgment by Luan, Tan, and Schooler
<p>The four .mat files are the ecological data we collected for the diamond and car tasks. There are three variables in each data file. The first is always the criterion variable (i.e., price for Diamond and fuel efficiency for Car) and the next two are the cue variables. </p> <p>The one Excel file contains all experimental data. Each sheet is labeled to identify the particular experiment in which the data come from. </p> <p> </p>
Improving judgment quality_LuanTan&Schooler_Data
<p>There are two Excel files. One file, "All Experiment Data", contains all experimental data. Each sheet is labeled to identify the particular experiment in which the data come from. </p> <p>The other file, "All Ecological Data", contains all ecological data we collected to analyze the task ecologies. Each sheet is labeled to identify a particular data set. </p> <p> </p>
Low-Amplitude Textures Explored with the Bare Finger: Roughness Judgments Follow an Inverted U-Shaped Function of Texture Period Modified by Texture Type
<p>Roughness is probably the most salient dimension pertaining to the perception of textures by touch and has been widely investigated. There is a controversy on how roughness relates to the texture’s spatial period and which factors influence this relation. Here, roughness during bare finger exploration of coarse textures is studied for different types of textures with elements of low height (0.3 mm). Participants were presented with square-wave gratings that were defined along one dimension and sine-wave gratings that were defined along one or two dimensions. Textures of each type varied in their spatial half period between 0.25 and 5.17 mm. Participants explored the textures by a lateral movement or a stationary finger contact. In all conditions judged roughness increased with spatial period up to a peak roughness and then decreased again. The exact function depended on the texture type, but hardly on exploration mode. We conclude that roughness is an inverted U-shaped function of texture period, if the textures are of low amplitude. The effects are explained by the interplay of two components contributing to the spatial code to roughness: variability in skin deformation due to the finger’s intrusion into the texture, which increases with the textures’ period up to a maximum (when the skin contacts the texture’s ground), and variability associated with the spatial frequency of the deformation, which decreases with spatial period.</p> <p><strong>Drewing</strong>, K. (2016). Low-Amplitude Textures Explored with the Bare Finger: Roughness Judgments Follow an Inverted U-Shaped Function of Texture Period Modified by Texture Type. <em>Haptics: Perception, Devices, Control, and Applications </em>(pp. 206-217). Springer: Heidelberg.</p> <p> </p> <p>The file DataPerTrialAndVp_Zenodo.txt contains all data relative to the publication.</p> <p>A description of the variables is contained in the file VARIABLE_CODES.txt</p>
Effect of the red uniform on the judgment of position or movement used in Wushu Routine, evaluated by practitioners of the modality
<p>Experiment 1 design was adopted a 2 (Color: red, blue) x 2 (Gender: male, female) ANOVA. 144 Participants were randomly assigned to four groups based on uniform color (red vs. blue) and practitioners' gender (male vs. female) in a 2 x 2 between-subject design according to the method of random number table. There were 36 male athletes and 36 female athletes in the red uniform group, and 36 male athletes and 36 female athletes in the blue uniform group. Experiment 2 used within-subjects design to conducted experiment separately for the blue and red groups. The female practitioners were randomly assigned to two groups according to the method of random number table and made ratings of the athletes once during the ovulation phase and once during the non-ovulation phase. The order in which the tasks were completed was balanced between subjects: Half of the females in the blue group were tested first during ovulation and then during non-ovulation; the other half were tested first during non-ovulation and then during ovulation. The red group did the same arrangement. They were instructed to look at each photograph for 5s and then rate the quality of the each athlete's movements. After scoring was completed, they were told to proceed to the questionnaire, which was designed to probe for awareness of the manipulation. Finally, look at the differences in the ratings of Wushu Routine athletes wearing different colored uniform by these different groups of participants, and whether the subjects in Experiment 1 would have been aware of color effect.</p><p>The results of Experiment 1 showed that both male and female athletes wearing red uniform (compared to blue uniform) received higher ratings, and the red effect was especially strong when male practitioners rated female athletes. The results of Experiment 2, in an all-female sample, showed that in most cases there was no difference in ratings made by women in the ovulation and non-ovulation phases of their menstrual cycle, with the exception of their ratings of male athletes wearing red; in this condition, women gave higher ratings when they were in the ovulation phase of their cycle.</p>
Corrective saccades influence velocity judgments and interception
<p>Here you can find the data for the paper "Corrective saccades influence velocity judgments and interception" by</p> <p>Alexander Goettker, Eli Brenner, Karl Gegenfurtner & Cristina de la Malla.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.