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1. Uncovering the computational mechanisms underlying many-alternative choice

   https://doi.org/10.7554/eLife.57012  

   Thomas, Armin W ; Molter, Felix ; Krajbich, Ian ( April 2021 , eLife)

   null (Ed.)

   In our everyday lives, we often have to choose between many different options. When deciding what to order off a menu, for example, or what type of soda to buy in the supermarket, we have a range of possibilities to consider. So how do we decide what to go for? Researchers believe we make such choices by assigning a subjective value to each of the available options. But we can do this in several different ways. We could look at every option in turn, and then choose the best one once we have considered them all. This is a so-called ‘rational’ decision-making approach. But we could also consider each of the options one at a time and stop as soon as we find one that is good enough. This strategy is known as ‘satisficing’. In both approaches, we use our eyes to gather information about the items available. Most scientists have assumed that merely looking at an item – such as a particular brand of soda – does not affect how we feel about that item. But studies in which animals or people choose between much smaller sets of objects – usually up to four – suggest otherwise. The results from these studies indicate that looking at an item makes that item more attractive to the observer, thereby increasing its subjective value. Thomas et al. now show that gaze also plays an active role in the decision-making process when people are spoilt for choice. Healthy volunteers looked at pictures of up to 36 snack foods on a screen and were asked to select the one they would most like to eat. The researchers then recorded the volunteers’ choices and response times, and used eye-tracking technology to follow the direction of their gaze. They then tested which of the various decision-making strategies could best account for all the behaviour. The results showed that the volunteers’ behaviour was best explained by computer models that assumed that looking at an item increases its subjective value. Moreover, the results confirmed that we do not examine all items and then choose the best one. But neither do we use a purely satisficing approach: the volunteers chose the last item they had looked at less than half the time. Instead, we make decisions by comparing individual items against one another, going back and forth between them. The longer we look at an item, the more attractive it becomes, and the more likely we are to choose it. 

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2. The Deep Learning Epilepsy Detection Challenge: Design, Implementation, and Test of a New Crowd-Sourced AI Challenge Ecosystem

   Kiral, Isabell ; Roy, Subhrajit ; Mummert, Todd ; Braz, Alan ; Tsay, Jason ; Tang, Jianbin ; Asif, Umar ; Schaffter, Thomas ; Mehmet, Eren ; Picone, Joseph ; et al ( April 2019 , Challenges in Machine Learning Competitions for All (CiML))

   null (Ed.)

   The DeepLearningEpilepsyDetectionChallenge: design, implementation, andtestofanewcrowd-sourced AIchallengeecosystem Isabell Kiral*, Subhrajit Roy*, Todd Mummert*, Alan Braz*, Jason Tsay, Jianbin Tang, Umar Asif, Thomas Schaffter, Eren Mehmet, The IBM Epilepsy Consortium◊ , Joseph Picone, Iyad Obeid, Bruno De Assis Marques, Stefan Maetschke, Rania Khalaf†, Michal Rosen-Zvi† , Gustavo Stolovitzky† , Mahtab Mirmomeni† , Stefan Harrer† * These authors contributed equally to this work † Corresponding authors: rkhalaf@us.ibm.com, rosen@il.ibm.com, gustavo@us.ibm.com, mahtabm@au1.ibm.com, sharrer@au.ibm.com ◊ Members of the IBM Epilepsy Consortium are listed in the Acknowledgements section J. Picone and I. Obeid are with Temple University, USA. T. Schaffter is with Sage Bionetworks, USA. E. Mehmet is with the University of Illinois at Urbana-Champaign, USA. All other authors are with IBM Research in USA, Israel and Australia. Introduction This decade has seen an ever-growing number of scientific fields benefitting from the advances in machine learning technology and tooling. More recently, this trend reached the medical domain, with applications reaching from cancer diagnosis [1] to the development of brain-machine-interfaces [2]. While Kaggle has pioneered the crowd-sourcing of machine learning challenges to incentivise data scientists from around the world to advance algorithm and model design, the increasing complexity of problem statements demands of participants to be expert data scientists, deeply knowledgeable in at least one other scientific domain, and competent software engineers with access to large compute resources. People who match this description are few and far between, unfortunately leading to a shrinking pool of possible participants and a loss of experts dedicating their time to solving important problems. Participation is even further restricted in the context of any challenge run on confidential use cases or with sensitive data. Recently, we designed and ran a deep learning challenge to crowd-source the development of an automated labelling system for brain recordings, aiming to advance epilepsy research. A focus of this challenge, run internally in IBM, was the development of a platform that lowers the barrier of entry and therefore mitigates the risk of excluding interested parties from participating. The challenge: enabling wide participation With the goal to run a challenge that mobilises the largest possible pool of participants from IBM (global), we designed a use case around previous work in epileptic seizure prediction [3]. In this “Deep Learning Epilepsy Detection Challenge”, participants were asked to develop an automatic labelling system to reduce the time a clinician would need to diagnose patients with epilepsy. Labelled training and blind validation data for the challenge were generously provided by Temple University Hospital (TUH) [4]. TUH also devised a novel scoring metric for the detection of seizures that was used as basis for algorithm evaluation [5]. In order to provide an experience with a low barrier of entry, we designed a generalisable challenge platform under the following principles: 1. No participant should need to have in-depth knowledge of the specific domain. (i.e. no participant should need to be a neuroscientist or epileptologist.) 2. No participant should need to be an expert data scientist. 3. No participant should need more than basic programming knowledge. (i.e. no participant should need to learn how to process fringe data formats and stream data efficiently.) 4. No participant should need to provide their own computing resources. In addition to the above, our platform should further • guide participants through the entire process from sign-up to model submission, • facilitate collaboration, and • provide instant feedback to the participants through data visualisation and intermediate online leaderboards. The platform The architecture of the platform that was designed and developed is shown in Figure 1. The entire system consists of a number of interacting components. (1) A web portal serves as the entry point to challenge participation, providing challenge information, such as timelines and challenge rules, and scientific background. The portal also facilitated the formation of teams and provided participants with an intermediate leaderboard of submitted results and a final leaderboard at the end of the challenge. (2) IBM Watson Studio [6] is the umbrella term for a number of services offered by IBM. Upon creation of a user account through the web portal, an IBM Watson Studio account was automatically created for each participant that allowed users access to IBM's Data Science Experience (DSX), the analytics engine Watson Machine Learning (WML), and IBM's Cloud Object Storage (COS) [7], all of which will be described in more detail in further sections. (3) The user interface and starter kit were hosted on IBM's Data Science Experience platform (DSX) and formed the main component for designing and testing models during the challenge. DSX allows for real-time collaboration on shared notebooks between team members. A starter kit in the form of a Python notebook, supporting the popular deep learning libraries TensorFLow [8] and PyTorch [9], was provided to all teams to guide them through the challenge process. Upon instantiation, the starter kit loaded necessary python libraries and custom functions for the invisible integration with COS and WML. In dedicated spots in the notebook, participants could write custom pre-processing code, machine learning models, and post-processing algorithms. The starter kit provided instant feedback about participants' custom routines through data visualisations. Using the notebook only, teams were able to run the code on WML, making use of a compute cluster of IBM's resources. The starter kit also enabled submission of the final code to a data storage to which only the challenge team had access. (4) Watson Machine Learning provided access to shared compute resources (GPUs). Code was bundled up automatically in the starter kit and deployed to and run on WML. WML in turn had access to shared storage from which it requested recorded data and to which it stored the participant's code and trained models. (5) IBM's Cloud Object Storage held the data for this challenge. Using the starter kit, participants could investigate their results as well as data samples in order to better design custom algorithms. (6) Utility Functions were loaded into the starter kit at instantiation. This set of functions included code to pre-process data into a more common format, to optimise streaming through the use of the NutsFlow and NutsML libraries [10], and to provide seamless access to the all IBM services used. Not captured in the diagram is the final code evaluation, which was conducted in an automated way as soon as code was submitted though the starter kit, minimising the burden on the challenge organising team. Figure 1: High-level architecture of the challenge platform Measuring success The competitive phase of the "Deep Learning Epilepsy Detection Challenge" ran for 6 months. Twenty-five teams, with a total number of 87 scientists and software engineers from 14 global locations participated. All participants made use of the starter kit we provided and ran algorithms on IBM's infrastructure WML. Seven teams persisted until the end of the challenge and submitted final solutions. The best performing solutions reached seizure detection performances which allow to reduce hundred-fold the time eliptologists need to annotate continuous EEG recordings. Thus, we expect the developed algorithms to aid in the diagnosis of epilepsy by significantly shortening manual labelling time. Detailed results are currently in preparation for publication. Equally important to solving the scientific challenge, however, was to understand whether we managed to encourage participation from non-expert data scientists. Figure 2: Primary occupation as reported by challenge participants Out of the 40 participants for whom we have occupational information, 23 reported Data Science or AI as their main job description, 11 reported being a Software Engineer, and 2 people had expertise in Neuroscience. Figure 2 shows that participants had a variety of specialisations, including some that are in no way related to data science, software engineering, or neuroscience. No participant had deep knowledge and experience in data science, software engineering and neuroscience. Conclusion Given the growing complexity of data science problems and increasing dataset sizes, in order to solve these problems, it is imperative to enable collaboration between people with differences in expertise with a focus on inclusiveness and having a low barrier of entry. We designed, implemented, and tested a challenge platform to address exactly this. Using our platform, we ran a deep-learning challenge for epileptic seizure detection. 87 IBM employees from several business units including but not limited to IBM Research with a variety of skills, including sales and design, participated in this highly technical challenge. 

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3. Benchmark Bird Surveys Help Quantify Counting Accuracy in a Citizen-Science Database

   https://doi.org/10.3389/fevo.2021.568278  

   Robinson, W. Douglas ; Hallman, Tyler A. ; Hutchinson, Rebecca A. ( February 2021 , Frontiers in ecology and evolution)

   null (Ed.)

   The growth of biodiversity data sets generated by citizen scientists continues to accelerate. The availability of such data has greatly expanded the scale of questions researchers can address. Yet, error, bias, and noise continue to be serious concerns for analysts, particularly when data being contributed to these giant online data sets are difficult to verify. Counts of birds contributed to eBird, the world’s largest biodiversity online database, present a potentially useful resource for tracking trends over time and space in species’ abundances. We quantified counting accuracy in a sample of 1,406 eBird checklists by comparing numbers contributed by birders (N = 246) who visited a popular birding location in Oregon, USA, with numbers generated by a professional ornithologist engaged in a long-term study creating benchmark (reference) measurements of daily bird counts. We focused on waterbirds, which are easily visible at this site. We evaluated potential predictors of count differences, including characteristics of contributed checklists, of each species, and of time of day and year. Count differences were biased toward undercounts, with more than 75% of counts being below the daily benchmark value. Median count discrepancies were −29.1% (range: 0 to −42.8%; N = 20 species). Model sets revealed an important influence of each species’ reference count, which varied seasonally as waterbird numbers fluctuated, and of percent of species known to be present each day that were included on each checklist. That is, checklists indicating a more thorough survey of the species richness at the site also had, on average, smaller count differences. However, even on checklists with the most thorough species lists, counts were biased low and exceptionally variable in their accuracy. To improve utility of such bird count data, we suggest three strategies to pursue in the future. (1) Assess additional options for analytically determining how to select checklists that include less biased count data, as well as exploring options for correcting bias during the analysis stage. (2) Add options for users to provide additional information that helps analysts choose checklists, such as an option for users to tag checklists where they focused on obtaining accurate counts. (3) Explore opportunities to effectively calibrate citizen-science bird count data by establishing a formalized network of marquis sites where dedicated observers regularly contribute carefully collected benchmark data. 

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4. A Text-Analytic Method for Identifying Text Recycling in STEM Research Reports

   Anson, Ian G ; Moskovitz, Cary ; Anson, Chris M ( November 2019 , The journal of writing analytics)

   Background: Text recycling (hereafter TR)—the reuse of one’s own textual materials from one document in a new document—is a common but hotly debated and unsettled practice in many academic disciplines, especially in the context of peer-reviewed journal articles. Although several analytic systems have been used to determine replication of text—for example, for purposes of identifying plagiarism—they do not offer an optimal way to compare documents to determine the nature and extent of TR in order to study and theorize this as a practice in different disciplines. In this article, we first describe TR as a common phenomenon in academic publishing, then explore the challenges associated with trying to study the nature and extent of TR within STEM disciplines. We then describe in detail the complex processes we used to create a system for identifying TR across large corpora of texts, and the sentence-level string-distance lexical methods used to refine and test the system (White & Joy, 2004). The purpose of creating such a system is to identify legitimate cases of TR across large corpora of academic texts in different fields of study, allowing meaningful cross-disciplinary comparisons in future analyses of published work. The findings from such investigations will extend and refine our understanding of discourse practices in academic and scientific settings. Literature Review: Text-analytic methods have been widely developed and implemented to identify reused textual materials for detecting plagiarism, and there is considerable literature on such methods. (Instead of taking up space detailing this literature, we point readers to several recent reviews: Gupta, 2016; Hiremath & Otari, 2014; and Meuschke & Gipp, 2013). Such methods include fingerprinting, term occurrence analysis, citation analysis (identifying similarity in references and citations), and stylometry (statistically comparing authors’ writing styles; see Meuschke & Gipp, 2013). Although TR occurs in a wide range of situations, recent debate has focused on recycling from one published research paper to another—particularly in STEM fields (see, for example, Andreescu, 2013; Bouville, 2008; Bretag & Mahmud, 2009; Roig, 2008; Scanlon, 2007). An important step in better understanding the practice is seeing how authors actually recycle material in their published work. Standard methods for detecting plagiarism are not directly suitable for this task, as the objective is not to determine the presence or absence of reuse itself, but to study the types and patterns of reuse, including materials that are syntactically but not substantively distinct—such as “patchwriting” (Howard, 1999). In the present account of our efforts to create a text-analytic system for determining TR, we take a conventional alphabetic approach to text, in part because we did not aim at this stage of our project to analyze non-discursive text such as images or other media. However, although the project adheres to conventional definitions of text, with a focus on lexical replication, we also subscribe to context-sensitive approaches to text production. The results of applying the system to large corpora of published texts can potentially reveal varieties in the practice of TR as a function of different discourse communities and disciplines. Writers’ decisions within what appear to be canonical genres are contingent, based on adherence to or deviation from existing rules and procedures if and when these actually exist. Our goal is to create a system for analyzing TR in groups of texts produced by the same authors in order to determine the nature and extent of TR, especially across disciplinary areas, without judgment of scholars’ use of the practice. 

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5. Correlates of economic decisions in the dorsal and subgenual anterior cingulate cortices

   https://doi.org/10.1111/ejn.13865  

   Azab, Habiba ; Hayden, Benjamin Y. ( February 2018 , European Journal of Neuroscience)

   The anterior cingulate cortex can be divided into distinct ventral (subgenual, sgACC) and dorsal (dACC), portions. The role ofdACCin value‐based decision‐making is hotly debated, while the role of sgACCis poorly understood. We recorded neuronal activity in both regions in rhesus macaques performing a token‐gambling task. We find that both encode many of the same variables; including integrated offered values of gambles, primary as well as secondary reward outcomes, number of current tokens and anticipated rewards. Both regions exhibit memory traces for offer values and putative value comparison signals. Both regions use a consistent scheme to encode the value of the attended option. This result suggests that neurones do not appear to be specialized for specific offers (that is, neurones use anattentionalas opposed tolabelled linecoding scheme). We also observed some differences between the two regions: (i) coding strengths indACCwere consistently greater than those in sgACC, (ii) neurones in sgACCresponded especially to losses and in anticipation of primary rewards, while those indACCshowed more balanced responding and (iii) responses to the first offer were slightly faster in sgACC. These results indicate that sgACCanddACChave some functional overlap in economic choice, and are consistent with the idea, inspired by neuroanatomy, which sgACCmay serve as input todACC.

    

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