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1. Automating creativity assessment with SemDis: An open platform for computing semantic distance

   https://doi.org/10.3758/s13428-020-01453-w  

   Beaty, Roger E. ; Johnson, Dan R. ( April 2021 , Behavior Research Methods)

   null (Ed.)

   Abstract Creativity research requires assessing the quality of ideas and products. In practice, conducting creativity research often involves asking several human raters to judge participants’ responses to creativity tasks, such as judging the novelty of ideas from the alternate uses task (AUT). Although such subjective scoring methods have proved useful, they have two inherent limitations—labor cost (raters typically code thousands of responses) and subjectivity (raters vary on their perceptions and preferences)—raising classic psychometric threats to reliability and validity. We sought to address the limitations of subjective scoring by capitalizing on recent developments in automated scoring of verbal creativity via semantic distance, a computational method that uses natural language processing to quantify the semantic relatedness of texts. In five studies, we compare the top performing semantic models (e.g., GloVe, continuous bag of words) previously shown to have the highest correspondence to human relatedness judgements. We assessed these semantic models in relation to human creativity ratings from a canonical verbal creativity task (AUT; Studies 1–3) and novelty/creativity ratings from two word association tasks (Studies 4–5). We find that a latent semantic distance factor—comprised of the common variance from five semantic models—reliably and strongly predicts human creativity and novelty ratings across a range of creativity tasks. We also replicate an established experimental effect in the creativity literature (i.e., the serial order effect) and show that semantic distance correlates with other creativity measures, demonstrating convergent validity. We provide an open platform to efficiently compute semantic distance, including tutorials and documentation ( https://osf.io/gz4fc/ ). 

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2. Toytree: A minimalist tree visualization and manipulation library for Python

   https://doi.org/10.1111/2041-210X.13313  

   Eaton, Deren A. R. ; Matschiner, ed., Michael ( November 2019 , Methods in Ecology and Evolution)

   Toytreeis a lightweight Python library for programmatically visualizing and manipulating tree‐based data structures. It implements a minimalist design aesthetic and modern plotting architecture suited for interactive coding in IPython/Jupyter.

   Tree drawings are generated in HTML using thetoyplotlibrary backend, and display natively in Jupyter notebooks with interactivity features. Tree drawings can be combined with other plotting functions from thetoyplotlibrary (e.g. scatterplots, histograms) to create composite figures on a shared coordinate grid, and can be exported to additional formats including PNG, PDF and SVG.

   To parse and store tree data,toytreeuses a modified fork of theete3TreeNode object, which includes functions for manipulating, annotating and comparing trees.Toytreeintegrates these functions with a plotting layout to allow node values to be extracted from trees in the correct order to style nodes for plotting. In addition,toytreeprovides functions for parsing additional tree formats, generating random trees, inferring consensus trees and drawing grids or clouds from multiple trees to visualize discordance.

   The goal oftoytreeis to provide a simple Python equivalent to commonly used tree manipulation and plotting libraries in R, and in doing so, to promote further development of phylogenetic and other tree‐based methods in Python.Toytreeis released under the GPLv3 license. Source code is available on GitHub and documentation is available athttps://toytree.readthedocs.io.

    

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3. Towards Automated Ethogramming: Cognitively-Inspired Event Segmentation for Streaming Wildlife Video Monitoring

   https://doi.org/10.1007/s11263-023-01781-2  

   Mounir, Ramy ; Shahabaz, Ahmed ; Gula, Roman ; Theuerkauf, Jörn ; Sarkar, Sudeep ( April 2023 , International Journal of Computer Vision)

   Advances in visual perceptual tasks have been mainly driven by the amount, and types, of annotations of large-scale datasets. Researchers have focused on fully-supervised settings to train models using offline epoch-based schemes. Despite the evident advancements, limitations and cost of manually annotated datasets have hindered further development for event perceptual tasks, such as detection and localization of objects and events in videos. The problem is more apparent in zoological applications due to the scarcity of annotations and length of videos-most videos are at most ten minutes long. Inspired by cognitive theories, we present a self-supervised perceptual prediction framework to tackle the problem of temporal event segmentation by building a stable representation of event-related objects. The approach is simple but effective. We rely on LSTM predictions of high-level features computed by a standard deep learning backbone. For spatial segmentation, the stable representation of the object is used by an attention mechanism to filter the input features before the prediction step. The self-learned attention maps effectively localize the object as a side effect of perceptual prediction. We demonstrate our approach on long videos from continuous wildlife video monitoring, spanning multiple days at 25 FPS. We aim to facilitate automated ethogramming by detecting and localizing events without the need for labels. Our approach is trained in an online manner on streaming input and requires only a single pass through the video, with no separate training set. Given the lack of long and realistic (includes real-world challenges) datasets, we introduce a new wildlife video dataset–nest monitoring of the Kagu (a flightless bird from New Caledonia)–to benchmark our approach. Our dataset features a video from 10 days (over 23 million frames) of continuous monitoring of the Kagu in its natural habitat. We annotate every frame with bounding boxes and event labels. Additionally, each frame is annotated with time-of-day and illumination conditions. We will make the dataset, which is the first of its kind, and the code available to the research community. We find that the approach significantly outperforms other self-supervised, traditional (e.g., Optical Flow, Background Subtraction) and NN-based (e.g., PA-DPC, DINO, iBOT), baselines and performs on par with supervised boundary detection approaches (i.e., PC). At a recall rate of 80%, our best performing model detects one false positive activity every 50 min of training. On average, we at least double the performance of self-supervised approaches for spatial segmentation. Additionally, we show that our approach is robust to various environmental conditions (e.g., moving shadows). We also benchmark the framework on other datasets (i.e., Kinetics-GEBD, TAPOS) from different domains to demonstrate its generalizability. The data and code are available on our project page:https://aix.eng.usf.edu/research_automated_ethogramming.html

    

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4. Creative thinking and brain network development in schoolchildren

   https://doi.org/10.1111/desc.13389  

   Duval, Philippe Eon ; Fornari, Eleonora ; Décaillet, Marion ; Ledoux, Jean‐Baptiste ; Beaty, Roger E. ; Denervaud, Solange ( March 2023 , Developmental Science)

   Fostering creative minds has always been a premise to ensure adaptation to new challenges of human civilization. While some alternative educational settings (i.e., Montessori) were shown to nurture creative skills, it is unknown how they impact underlying brain mechanisms across the school years. This study assessed creative thinking and resting‐state functional connectivity via fMRI in 75 children (4–18 y.o.) enrolled either in Montessori or traditional schools. We found that pedagogy significantly influenced creative performance and underlying brain networks. Replicating past work, Montessori‐schooled children showed higher scores on creative thinking tests. Using static functional connectivity analysis, we found that Montessori‐schooled children showed decreased within‐network functional connectivity of the salience network. Moreover, using dynamic functional connectivity, we found that traditionally‐schooled children spent more time in a brain state characterized by high intra‐default mode network connectivity. These findings suggest that pedagogy may influence brain networks relevant to creative thinking—particularly the default and salience networks. Further research is needed, like a longitudinal study, to verify these results given the implications for educational practitioners. A video abstract of this article can be viewed athttps://www.youtube.com/watch?v=xWV_5o8wB5g.

   Most executive jobs are prospected to be obsolete within several decades, so creative skills are seen as essential for the near future.

   School experience has been shown to play a role in creativity development, however, the underlying brain mechanisms remained under‐investigated yet.

   Seventy‐five 4–18 years‐old children, from Montessori or traditional schools, performed a creativity task at the behavioral level, and a 6‐min resting‐state MR scan.

   We uniquely report preliminary evidence for the impact of pedagogy on functional brain networks.

    

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5. Anacapa Toolkit : An environmental DNA toolkit for processing multilocus metabarcode datasets

   https://doi.org/10.1111/2041-210X.13214  

   Curd, Emily E. ; Gold, Zack ; Kandlikar, Gaurav S. ; Gomer, Jesse ; Ogden, Max ; O'Connell, Taylor ; Pipes, Lenore ; Schweizer, Teia M. ; Rabichow, Laura ; Lin, Meixi ; et al ( June 2019 , Methods in Ecology and Evolution)

   Environmental DNA (eDNA) metabarcoding is a promising method to monitor species and community diversity that is rapid, affordable and non‐invasive. The longstanding needs of the eDNA community are modular informatics tools, comprehensive and customizable reference databases, flexibility across high‐throughput sequencing platforms, fast multilocus metabarcode processing and accurate taxonomic assignment. Improvements in bioinformatics tools make addressing each of these demands within a single toolkit a reality.

   The new modular metabarcode sequence toolkitAnacapa(https://github.com/limey-bean/Anacapa/) addresses the above needs, allowing users to build comprehensive reference databases and assign taxonomy to raw multilocus metabarcode sequence data. A novel aspect ofAnacapais its database building module, “Creating Reference libraries Using eXisting tools” (CRUX), which generates comprehensive reference databases for specific user‐defined metabarcoding loci. TheQuality Control and ASV Parsingmodule sorts and processes multiple metabarcoding loci and processes merged, unmerged and unpaired reads maximizing recovered diversity.DADA2then detects amplicon sequence variants (ASVs) and theAnacapa Classifiermodule aligns these ASVs toCRUX‐generated reference databases usingBowtie2. Lastly, taxonomy is assigned to ASVs with confidence scores using a Bayesian Lowest Common Ancestor (BLCA) method. TheAnacapa Toolkitalso includes anrpackage,ranacapa, for automated results exploration through standard biodiversity statistical analysis.

   Benchmarking tests verify that theAnacapa Toolkiteffectively and efficiently generates comprehensive reference databases that capture taxonomic diversity, and can assign taxonomy to both MiSeq and HiSeq‐length sequence data. We demonstrate the value of theAnacapa Toolkitin assigning taxonomy to seawater eDNA samples collected in southern California.

   TheAnacapa Toolkitimproves the functionality of eDNA and streamlines biodiversity assessment and management by generating metabarcode specific databases, processing multilocus data, retaining a larger proportion of sequencing reads and expanding non‐traditional eDNA targets. All the components of theAnacapa Toolkitare open and available in a virtual container to ease installation.

    

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