ORCID Profile
0000-0003-3228-6501
Current Organisation
University of Nottingham
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Publisher: PeerJ
Date: 12-02-2018
DOI: 10.7717/PEERJ-CS.147
Abstract: This article describes the motivation, design, and progress of the Journal of Open Source Software (JOSS). JOSS is a free and open-access journal that publishes articles describing research software. It has the dual goals of improving the quality of the software submitted and providing a mechanism for research software developers to receive credit. While designed to work within the current merit system of science, JOSS addresses the dearth of rewards for key contributions to science made in the form of software. JOSS publishes articles that encapsulate scholarship contained in the software itself, and its rigorous peer review targets the software components: functionality, documentation, tests, continuous integration, and the license. A JOSS article contains an abstract describing the purpose and functionality of the software, references, and a link to the software archive. The article is the entry point of a JOSS submission, which encompasses the full set of software artifacts. Submission and review proceed in the open, on GitHub. Editors, reviewers, and authors work collaboratively and openly. Unlike other journals, JOSS does not reject articles requiring major revision while not yet accepted, articles remain visible and under review until the authors make adequate changes (or withdraw, if unable to meet requirements). Once an article is accepted, JOSS gives it a digital object identifier (DOI), deposits its metadata in Crossref, and the article can begin collecting citations on indexers like Google Scholar and other services. Authors retain copyright of their JOSS article, releasing it under a Creative Commons Attribution 4.0 International License. In its first year, starting in May 2016, JOSS published 111 articles, with more than 40 additional articles under review. JOSS is a sponsored project of the nonprofit organization NumFOCUS and is an affiliate of the Open Source Initiative (OSI).
Publisher: Center for Open Science
Date: 13-12-2021
Abstract: Associative learning is the process whereby humans and other animals learn the predictive relationship between cues in their environment. This process underlies simple forms of learning from rewards, such as classical and operant conditioning. In this chapter, we introduce the basics of associative learning and discuss the role that memory processes play in the establishment and maintenance of this learning. We then discuss the role that associative learning plays in human memory, including through paired associate learning, the enhancement of memory by reward, and the formation of episodic memories. Finally, we illustrate how the memory process influences choice in decision-making, where associative learning allows people to learn the values of different options. We conclude with some suggestions about how models of associative learning, memory, and choice can be integrated into a single theoretical framework.
Publisher: Springer Science and Business Media LLC
Date: 12-10-2022
DOI: 10.1038/S41597-022-01695-7
Abstract: We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets.
Publisher: Center for Open Science
Date: 19-12-2022
Abstract: Decision-making involves weighing up the outcome likelihood, potential rewards, and effort needed. Previous research has focused on the trade-offs between risk and reward or between effort and reward. Here we bridge this gap and examine how risk in effort levels influences choice. We focus on how two key properties of choice influence risk preferences for effort: changes in magnitude and probability. Two experiments assessed people’s risk attitudes for effort and an additional experiment provided a control condition using monetary gambles. All participants’ risk attitudes showed a strong interaction between changes in magnitude and probability, consistent with the classic fourfold pattern. Unlike with monetary outcomes, however, there was substantial heterogeneity in effort-based risk preferences: people who responded to effort as costly exhibited a “flipped” interaction pattern of risk preferences. The direction of the pattern depended on whether people treated effort as a loss of resources. Most, but not all, people treat effort as a loss and are more willing to take risks to avoid potentially high levels of effort.
Publisher: F1000 Research Ltd
Date: 20-07-2017
DOI: 10.12688/F1000RESEARCH.12037.1
Abstract: Peer review of research articles is a core part of our scholarly communication system. In spite of its importance, the status and purpose of peer review is often contested. What is its role in our modern digital research and communications infrastructure? Does it perform to the high standards with which it is generally regarded? Studies of peer review have shown that it is prone to bias and abuse in numerous dimensions, frequently unreliable, and can fail to detect even fraudulent research. With the advent of Web technologies, we are now witnessing a phase of innovation and experimentation in our approaches to peer review. These developments prompted us to examine emerging models of peer review from a range of disciplines and venues, and to ask how they might address some of the issues with our current systems of peer review. We examine the functionality of a range of social Web platforms, and compare these with the traits underlying a viable peer review system: quality control, quantified performance metrics as engagement incentives, and certification and reputation. Ideally, any new systems will demonstrate that they out-perform current models while avoiding as many of the biases of existing systems as possible. We conclude that there is considerable scope for new peer review initiatives to be developed, each with their own potential issues and advantages. We also propose a novel hybrid platform model that, at least partially, resolves many of the technical and social issues associated with peer review, and can potentially disrupt the entire scholarly communication system. Success for any such development relies on reaching a critical threshold of research community engagement with both the process and the platform, and therefore cannot be achieved without a significant change of incentives in research environments.
Publisher: American Psychological Association (APA)
Date: 12-2018
DOI: 10.1037/XGE0000414
Abstract: Extreme stimuli are often more salient in perception and memory than moderate stimuli. In risky choice, when people learn the odds and outcomes from experience, the extreme outcomes (best and worst) also stand out. This additional salience leads to more risk-seeking for relative gains than for relative losses-the opposite of what people do when queried in terms of explicit probabilities. Previous research has suggested that this pattern arises because the most extreme experienced outcomes are more prominent in memory. An important open question, however, is what makes these extreme outcomes more prominent? Here we assess whether extreme outcomes stand out because they fall at the edges of the experienced outcome distributions or because they are distinct from other outcomes. Across four experiments, proximity to the edge determined what was treated as extreme: Outcomes at or near the edge of the outcome distribution were both better remembered and more heavily weighted in choice. This prominence did not depend on two metrics of distinctiveness: lower frequency or distance from other outcomes. This finding adds to evidence from other domains that the values at the edges of a distribution have a special role. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
Publisher: Springer Science and Business Media LLC
Date: 31-10-2022
Publisher: Center for Open Science
Date: 18-09-2017
Abstract: In response to recommendations to redefine statistical significance to p ≤ .005, we propose that researchers should transparently report and justify all choices they make when designing a study, including the alpha level.
Publisher: Center for Open Science
Date: 13-12-2020
Abstract: When people make risky decisions based on past experience, they must rely on memory. The nature of the memory representations that support these decisions is not yet well understood. A key question concerns the extent to which people recall specific past episodes or whether they have learned a more abstract rule from their past experience. To address this question, we examined the precision of the memories used in risky decisions-from-experience. In three pre-registered experiments, we presented people with risky options, where the outcomes were drawn from continuous ranges (e.g., 100-190 or 500-590), and then assessed their memories for the outcomes experienced. In two preferential tasks, people were more risk seeking for high-value than low-value options, choosing as though they overweighted the outcomes from more extreme ranges. Moreover, in two preferential tasks and a parallel evaluation task, people were very poor at recalling the exact outcomes encountered, but rather confabulated outcomes that were consistent with the outcomes they had seen and were biased towards the more extreme ranges encountered. This common pattern suggests that the observed decision bias in the preferential task reflects a basic cognitive process to overweight extreme outcomes in memory. These results highlight the importance of the edges of the distribution in providing the encoding context for memory recall. They also suggest that episodic memory influences decision-making through gist memory and not through direct recall of specific instances.
Publisher: Center for Open Science
Date: 21-09-2017
Abstract: Both memory and choice are influenced by context: Memory is enhanced when encoding and retrieval contexts match, and choice is swayed by available options. Here, we assessed how context influences risky choice in an experience-based task. Within a single session, we created two separate contexts by presenting blocks of trials in distinct backgrounds. Risky choices were context-dependent given the same choice, people chose differently depending on other outcomes experienced in that context. Choices reflected an overweighting of the most extreme outcomes within each local context, rather than the global context of all outcomes. When tested in the non-trained context, people chose according to the context at encoding and not retrieval. In subsequent memory tests, people displayed biases specific to distinct contexts: extreme outcomes from each context were more accessible and judged as more frequent. These results pose a challenge for theories of choice that rely on retrieval as guiding choice.
Publisher: Center for Open Science
Date: 08-04-2022
Abstract: In recent years, the scientific community has called for improvements in the credibility, robustness, and reproducibility of research, characterized by increased interest and promotion of open and transparent research practices. While progress has been positive, there is a lack of consideration about how this approach can be embedded into undergraduate and postgraduate research training. Specifically, a critical overview of the literature which investigates how integrating open and reproducible science may influence student outcomes is needed. In this paper, we provide the first critical review of literature surrounding the integration of open and reproducible scholarship into teaching and learning and its associated outcomes in students. Our review highlighted how embedding open and reproducible scholarship appears to be associated with (1) students’ scientific literacies (i.e., students’ understanding of open research, consumption of science, and the development of transferable skills) (2) student engagement (i.e., motivation and engagement with learning, collaboration, and engagement in open research), and (3) students’ attitudes towards science (i.e., trust in science and confidence in research findings). However, our review also identified a need for more robust and rigorous methods within pedagogical research, including more interventional and experimental evaluations of teaching practice. We discuss implications for teaching and learning scholarship.
Publisher: Center for Open Science
Date: 18-08-2017
Abstract: Extreme stimuli are often more salient in perception and memory than moderate stimuli. In risky choice, when people learn the odds and outcomes from experience, the extreme outcomes (best and worst) also stand out. This additional salience leads to more risk-seeking for relative gains than for relative losses—the opposite of what people do when queried in terms of explicit probabilities. Previous research has suggested that this pattern arises because the most extreme experienced outcomes are more prominent in memory. An important open question, however, is what makes these extreme outcomes more prominent? Here we assess whether extreme outcomes stand out because they fall at the edges of the experienced outcome distributions or because they are distinct from other outcomes. Across four experiments, proximity to the edge determined what was treated as extreme: Outcomes at or near the edge of the outcome distribution were both better remembered and more heavily weighted in choice. This prominence did not depend on two metrics of distinctiveness: lower frequency or distance from other outcomes. This finding adds to evidence from other domains that the values at the edges of a distribution have a special role.
Publisher: Springer Science and Business Media LLC
Date: 26-02-2018
Publisher: Springer Science and Business Media LLC
Date: 17-11-2022
Publisher: Center for Open Science
Date: 21-06-2023
Abstract: Many real-world decisions involving rare events also involve extreme outcomes. Despite this confluence, decisions-from-experience research has focused on the impact of rare but non-extreme outcomes. In those situations, people typically choose as if they underestimate the probability of a rare outcome happening. Separately, people have been shown to overestimate the probability of an extreme outcome happening. Here, for the first time, we examine the confluence of these two competing biases in decisions from experience. In a between-subjects behavioural experiment, we examine people’s risk preferences for rare extreme outcomes and for rare non-extreme outcomes. When outcomes are both rare and extreme, people’s risk preferences shift away from traditional risk patterns for rare events: they underweight those extreme and rare events less. We simulate these results using a small-s le model of decision-making that accounts for the overweighting of extreme events. The additive effect of these decision biases on risk preferences reveals that to understand real-world risk for rare events we must also consider the extremity of the outcomes.
Publisher: Wiley
Date: 10-08-2023
DOI: 10.1002/ALZ.13412
Abstract: Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia. We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases. A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort. The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice. There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other in idual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias
Publisher: F1000 Research Ltd
Date: 29-11-2017
DOI: 10.12688/F1000RESEARCH.12037.3
Abstract: Peer review of research articles is a core part of our scholarly communication system. In spite of its importance, the status and purpose of peer review is often contested. What is its role in our modern digital research and communications infrastructure? Does it perform to the high standards with which it is generally regarded? Studies of peer review have shown that it is prone to bias and abuse in numerous dimensions, frequently unreliable, and can fail to detect even fraudulent research. With the advent of web technologies, we are now witnessing a phase of innovation and experimentation in our approaches to peer review. These developments prompted us to examine emerging models of peer review from a range of disciplines and venues, and to ask how they might address some of the issues with our current systems of peer review. We examine the functionality of a range of social Web platforms, and compare these with the traits underlying a viable peer review system: quality control, quantified performance metrics as engagement incentives, and certification and reputation. Ideally, any new systems will demonstrate that they out-perform and reduce the biases of existing models as much as possible. We conclude that there is considerable scope for new peer review initiatives to be developed, each with their own potential issues and advantages. We also propose a novel hybrid platform model that could, at least partially, resolve many of the socio-technical issues associated with peer review, and potentially disrupt the entire scholarly communication system. Success for any such development relies on reaching a critical threshold of research community engagement with both the process and the platform, and therefore cannot be achieved without a significant change of incentives in research environments.
Publisher: Springer Science and Business Media LLC
Date: 09-02-2023
Publisher: F1000 Research Ltd
Date: 11-2017
DOI: 10.12688/F1000RESEARCH.12037.2
Abstract: Peer review of research articles is a core part of our scholarly communication system. In spite of its importance, the status and purpose of peer review is often contested. What is its role in our modern digital research and communications infrastructure? Does it perform to the high standards with which it is generally regarded? Studies of peer review have shown that it is prone to bias and abuse in numerous dimensions, frequently unreliable, and can fail to detect even fraudulent research. With the advent of web technologies, we are now witnessing a phase of innovation and experimentation in our approaches to peer review. These developments prompted us to examine emerging models of peer review from a range of disciplines and venues, and to ask how they might address some of the issues with our current systems of peer review. We examine the functionality of a range of social Web platforms, and compare these with the traits underlying a viable peer review system: quality control, quantified performance metrics as engagement incentives, and certification and reputation. Ideally, any new systems will demonstrate that they out-perform and reduce the biases of existing models as much as possible. We conclude that there is considerable scope for new peer review initiatives to be developed, each with their own potential issues and advantages. We also propose a novel hybrid platform model that could, at least partially, resolve many of the socio-technical issues associated with peer review, and potentially disrupt the entire scholarly communication system. Success for any such development relies on reaching a critical threshold of research community engagement with both the process and the platform, and therefore cannot be achieved without a significant change of incentives in research environments.
Publisher: Center for Open Science
Date: 12-09-2022
Abstract: How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments testing accuracy of predictions of societal change in domains commonly studied in the social sciences: ideological preferences, political polarization, life satisfaction, sentiment on social media, and gender-career and racial bias. Following provision of historical trend data on the domain, social scientists submitted pre-registered monthly forecasts for a year (Tournament 1 N=86 teams/359 forecasts), with an opportunity to update forecasts based on new data six months later (Tournament 2 N=120 teams/546 forecasts). Benchmarking forecasting accuracy revealed that social scientists’ forecasts were on average no more accurate than simple statistical models (historical means, random walk, or linear regressions) or the aggregate forecasts of a s le from the general public (N=802). However, scientists were more accurate if they had scientific expertise in a prediction domain, were interdisciplinary, used simpler models, and based predictions on prior data.
Publisher: Center for Open Science
Date: 07-2021
Abstract: When deciding between different courses of action, both the potential outcomes and the costs of making a choice should be considered. To date, most studies of risk-sensitive choice have focused on the probability of different reward amounts. Here we studied choice between options that varied in the riskiness of the effort (number of responses) required. People made repeated choices between pairs of options that required them to click different numbers of sequentially presented response circles. Easy (low effort) options led to small numbers of response circles, whereas hard (high effort) options led to larger numbers of response circles. For both easy and hard options, safe options led to a fixed effort, whereas risky options led to variable effort that, with a 50/50 chance, required more or less effort. Participants who showed a preference for easier over harder options (63% in Experiment 1 and 93% in Experiment 2) were risk averse overall. Participants were more risk averse for decisions involving hard options than for decisions involving easy options. On subsequent memory tests, people most readily recalled the hardest outcome, and they overestimated its frequency of occurrence. Strikingly, memory for the effort associated with each risky option strongly correlated with risky choices for both easy-effort and hard-effort choices, suggesting that the memory may determine choices based on risky effort.
Publisher: SAGE Publications
Date: 28-04-2021
Abstract: Both memory and choice are influenced by context: Memory is enhanced when encoding and retrieval contexts match, and choice is swayed by available options. Here, we assessed how context influences risky choice in an experience-based task in two main experiments (119 and 98 participants retained, respectively) and two additional experiments reported in the Supplemental Material available online (152 and 106 participants retained, respectively). Within a single session, we created two separate contexts by presenting blocks of trials in distinct backgrounds. Risky choices were context dependent given the same choice, people chose differently depending on other outcomes experienced in that context. Choices reflected an overweighting of the most extreme outcomes within each local context rather than the global context of all outcomes. When tested in the nontrained context, people chose according to the context at encoding and not retrieval. In subsequent memory tests, people displayed biases specific to distinct contexts: Extreme outcomes from each context were more accessible and judged as more frequent. These results pose a challenge for theories of choice that rely on retrieval as guiding choice.
Publisher: American Psychological Association (APA)
Date: 12-2017
DOI: 10.1037/XLM0000416
Abstract: The authors investigated how humans use multiple landmarks to locate a goal. Participants searched for a hidden goal location along a line between 2 distinct landmarks on a computer screen. On baseline trials, the location of the landmarks and goal varied, but the distance between each of the landmarks and the goal was held constant, with 1 landmark always closer to the goal. In Experiment 1, some baseline trials provided both landmarks, and some provided only 1 landmark. On probe trials, both landmarks were shifted apart relative to the previously learned goal location. Participants searched between the locations specified by the 2 landmarks and their search locations were shifted more toward the nearer landmark, suggesting a weighted integration of the conflicting landmarks. Moreover, the observed variance in search responses when both cues were presented in their normal locations was reduced compared to the variance on tests with single landmarks. However, the variance reduction and the weightings of the landmarks did not always show Bayesian optimality. In Experiment 2, some participants were trained only with each of the single landmarks. On subsequent tests with the 2 cues in conflict, searching did not shift toward the nearer landmark and the variance of search responses of these single-cue trained participants was larger than their variance on single-landmark tests, and even larger than the variance predicted by using the 2 landmarks alternatively on different trials. Taken together, these results indicate that cue combination occurs only when the landmarks are presented together during the initial learning experience. (PsycINFO Database Record
Publisher: Springer Science and Business Media LLC
Date: 28-04-2023
Publisher: Linnaeus University
Date: 10-07-2023
Abstract: Most of the commonly used and endorsed guidelines for systematic review protocols and reporting standards have been developed for intervention research. These excellent guidelines have been adopted as the gold-standard for systematic reviews as an evidence synthesis method. In the current paper, we highlight some issues that may arise from adopting these guidelines beyond intervention designs, including in basic behavioural, cognitive, experimental, and exploratory research. We have adapted and built upon the existing guidelines to establish a complementary, comprehensive, and accessible tool for designing, conducting, and reporting Non-Intervention, Reproducible, and Open Systematic Reviews (NIRO-SR). NIRO-SR is a checklist composed of two parts that provide itemised guidance on the preparation of a systematic review protocol for pre-registration (Part A) and reporting the review (Part B) in a reproducible and transparent manner. This paper, the tool, and an open repository (osf.io/f3brw) provide a comprehensive resource for those who aim to conduct a high quality, reproducible, and transparent systematic review of non-intervention studies.
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Christopher Madan.