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Wang HE, Triebkorn P, Breyton M, Dollomaja B, Lemarechal JD, Petkoski S, Sorrentino P, Depannemaecker D, Hashemi M, Jirsa VK. Virtual brain twins: from basic neuroscience to clinical use. Natl Sci Rev 2024; 11:nwae079. [PMID: 38698901 PMCID: PMC11065363 DOI: 10.1093/nsr/nwae079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 02/05/2024] [Accepted: 02/20/2024] [Indexed: 05/05/2024] Open
Abstract
Virtual brain twins are personalized, generative and adaptive brain models based on data from an individual's brain for scientific and clinical use. After a description of the key elements of virtual brain twins, we present the standard model for personalized whole-brain network models. The personalization is accomplished using a subject's brain imaging data by three means: (1) assemble cortical and subcortical areas in the subject-specific brain space; (2) directly map connectivity into the brain models, which can be generalized to other parameters; and (3) estimate relevant parameters through model inversion, typically using probabilistic machine learning. We present the use of personalized whole-brain network models in healthy ageing and five clinical diseases: epilepsy, Alzheimer's disease, multiple sclerosis, Parkinson's disease and psychiatric disorders. Specifically, we introduce spatial masks for relevant parameters and demonstrate their use based on the physiological and pathophysiological hypotheses. Finally, we pinpoint the key challenges and future directions.
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Affiliation(s)
- Huifang E Wang
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Paul Triebkorn
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Martin Breyton
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
- Service de Pharmacologie Clinique et Pharmacosurveillance, AP–HM, Marseille, 13005, France
| | - Borana Dollomaja
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Jean-Didier Lemarechal
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Spase Petkoski
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Pierpaolo Sorrentino
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Damien Depannemaecker
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Meysam Hashemi
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Viktor K Jirsa
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
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McMaster KL, Kendeou P, Kim J, Butterfuss R. Efficacy of a Technology-Based Early Language Comprehension Intervention: A Randomized Control Trial. J Learn Disabil 2024; 57:139-152. [PMID: 37366054 DOI: 10.1177/00222194231182974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/28/2023]
Abstract
We examined the efficacy of a Technology-Based Early Language Comprehension Intervention (TeLCI) designed to teach inferencing in a non-reading context. A group of Grades 1 and 2 students from 2 elementary schools in the U.S. Midwest identified as at risk of comprehension difficulties were assigned randomly to a business-as-usual control group or to use TeLCI over an 8-week period. TeLCI comprised three learning modules per week that involved (a) learning new vocabulary, (b) watching fiction or nonfiction videos, and (c) answering inferential questions. Students also engaged in small-group read-alouds with their teachers once per week. Students who experienced TeLCI improved their inferencing and benefited from scaffolding and feedback provided during the intervention. Students' pre- to posttest inferencing gains were comparable with those of control students. Female students and those receiving special education services appeared less likely to benefit from TeLCI, whereas multilingual students were more likely to respond. Further work is needed to determine the optimal conditions under which TeLCI will benefit young children.
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Santangelo BE, Apgar M, Colorado ASB, Martin CG, Sterrett J, Wall E, Joachimiak MP, Hunter LE, Lozupone CA. Integrating biological knowledge for mechanistic inference in the host-associated microbiome. Front Microbiol 2024; 15:1351678. [PMID: 38638909 PMCID: PMC11024261 DOI: 10.3389/fmicb.2024.1351678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 02/26/2024] [Indexed: 04/20/2024] Open
Abstract
Advances in high-throughput technologies have enhanced our ability to describe microbial communities as they relate to human health and disease. Alongside the growth in sequencing data has come an influx of resources that synthesize knowledge surrounding microbial traits, functions, and metabolic potential with knowledge of how they may impact host pathways to influence disease phenotypes. These knowledge bases can enable the development of mechanistic explanations that may underlie correlations detected between microbial communities and disease. In this review, we survey existing resources and methodologies for the computational integration of broad classes of microbial and host knowledge. We evaluate these knowledge bases in their access methods, content, and source characteristics. We discuss challenges of the creation and utilization of knowledge bases including inconsistency of nomenclature assignment of taxa and metabolites across sources, whether the biological entities represented are rooted in ontologies or taxonomies, and how the structure and accessibility limit the diversity of applications and user types. We make this information available in a code and data repository at: https://github.com/lozuponelab/knowledge-source-mappings. Addressing these challenges will allow for the development of more effective tools for drawing from abundant knowledge to find new insights into microbial mechanisms in disease by fostering a systematic and unbiased exploration of existing information.
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Affiliation(s)
- Brook E. Santangelo
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, United States
| | - Madison Apgar
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, United States
| | | | - Casey G. Martin
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, United States
| | - John Sterrett
- Department of Integrative Physiology, University of Colorado, Boulder, CO, United States
| | - Elena Wall
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, United States
| | - Marcin P. Joachimiak
- Lawrence Berkeley National Laboratory, Environmental Genomics and Systems Biology Division, Biosystems Data Science Department, Berkeley, CA, United States
| | - Lawrence E. Hunter
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, United States
| | - Catherine A. Lozupone
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, United States
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Brückner DB, Broedersz CP. Learning dynamical models of single and collective cell migration: a review. Rep Prog Phys 2024; 87:056601. [PMID: 38518358 DOI: 10.1088/1361-6633/ad36d2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 03/22/2024] [Indexed: 03/24/2024]
Abstract
Single and collective cell migration are fundamental processes critical for physiological phenomena ranging from embryonic development and immune response to wound healing and cancer metastasis. To understand cell migration from a physical perspective, a broad variety of models for the underlying physical mechanisms that govern cell motility have been developed. A key challenge in the development of such models is how to connect them to experimental observations, which often exhibit complex stochastic behaviours. In this review, we discuss recent advances in data-driven theoretical approaches that directly connect with experimental data to infer dynamical models of stochastic cell migration. Leveraging advances in nanofabrication, image analysis, and tracking technology, experimental studies now provide unprecedented large datasets on cellular dynamics. In parallel, theoretical efforts have been directed towards integrating such datasets into physical models from the single cell to the tissue scale with the aim of conceptualising the emergent behaviour of cells. We first review how this inference problem has been addressed in both freely migrating and confined cells. Next, we discuss why these dynamics typically take the form of underdamped stochastic equations of motion, and how such equations can be inferred from data. We then review applications of data-driven inference and machine learning approaches to heterogeneity in cell behaviour, subcellular degrees of freedom, and to the collective dynamics of multicellular systems. Across these applications, we emphasise how data-driven methods can be integrated with physical active matter models of migrating cells, and help reveal how underlying molecular mechanisms control cell behaviour. Together, these data-driven approaches are a promising avenue for building physical models of cell migration directly from experimental data, and for providing conceptual links between different length-scales of description.
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Affiliation(s)
- David B Brückner
- Institute of Science and Technology Austria, Am Campus 1, 3400 Klosterneuburg, Austria
| | - Chase P Broedersz
- Department of Physics and Astronomy, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
- Arnold Sommerfeld Center for Theoretical Physics and Center for NanoScience, Department of Physics, Ludwig-Maximilian-University Munich, Theresienstr. 37, D-80333 Munich, Germany
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Valle D, Mintz J, Brack IV. Estimation and interpretation problems and solutions when using proportion covariates in linear regression models. Ecology 2024; 105:e4256. [PMID: 38361276 DOI: 10.1002/ecy.4256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 12/28/2023] [Accepted: 01/18/2024] [Indexed: 02/17/2024]
Abstract
Proportion variables, also known as compositional data, are very common in ecology. Unfortunately, few scientists are aware of how compositional data, when used as covariates, can adversely impact statistical analysis. We describe here how proportion covariates result in multicollinearity and parameter identifiability problems. Using simulated data on bird species richness as a function of land use, we show how these problems manifest when fitting a wide range of models in R, both in a frequentist and Bayesian framework. In particular, we show that similar models can often generate substantially different parameter estimates, leading to very different conclusions. Dropping a covariate or the intercept from the model can solve the multicollinearity and parameter identifiability problems. Unfortunately, these solutions do not fix the inherent challenges associated with interpreting parameter estimates. To this end, we propose focusing the interpretation on the difference of slope parameters to avoid the inherent unidentifiability of individual parameters. We also propose conditional plots with two x-axes and marginal plots as visualization techniques that can help users better interpret their modeling results. We illustrate these problems and proposed solutions using empirical data from the North American Breeding Bird Survey. The practical and straightforward approaches suggested in this article will help the fitting of linear models and interpretation of its results when some of the covariates are proportions.
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Affiliation(s)
- Denis Valle
- School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, Florida, USA
| | - Jeffrey Mintz
- School of Natural Resources and Environment, University of Florida, Gainesville, Florida, USA
| | - Ismael Verrastro Brack
- School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, Florida, USA
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Uchida T, Lair N, Ishiguro H, Dominey PF. Dissociable Neural Mechanisms for Human Inference Processing Predicted by Static and Contextual Language Models. Neurobiol Lang (Camb) 2024; 5:248-263. [PMID: 38645620 PMCID: PMC11025649 DOI: 10.1162/nol_a_00090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 11/07/2022] [Indexed: 04/23/2024]
Abstract
Language models (LMs) continue to reveal non-trivial relations to human language performance and the underlying neurophysiology. Recent research has characterized how word embeddings from an LM can be used to generate integrated discourse representations in order to perform inference on events. The current research investigates how such event knowledge may be coded in distinct manners in different classes of LMs and how this maps onto different forms of human inference processing. To do so, we investigate inference on events using two well-documented human experimental protocols from Metusalem et al. (2012) and McKoon and Ratcliff (1986), compared with two protocols for simpler semantic processing. Interestingly, this reveals a dissociation in the relation between local semantics versus event-inference depending on the LM. In a series of experiments, we observed that for the static LMs (word2vec/GloVe), there was a clear dissociation in the relation between semantics and inference for the two inference tasks. In contrast, for the contextual LMs (BERT/RoBERTa), we observed a correlation between semantic and inference processing for both inference tasks. The experimental results suggest that inference as measured by Metusalem and McKoon rely on dissociable processes. While the static models are able to perform Metusalem inference, only the contextual models succeed in McKoon inference. Interestingly, these dissociable processes may be linked to well-characterized automatic versus strategic inference processes in the psychological literature. This allows us to make predictions about dissociable neurophysiological markers that should be found during human inference processing with these tasks.
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Affiliation(s)
- Takahisa Uchida
- Ishiguro Lab, Graduate School of Engineering Science, Osaka University, Osaka, Japan
| | - Nicolas Lair
- INSERM UMR1093-CAPS, Université Bourgogne Franche-Comté, UFR des Sciences du Sport, Dijon, France
- Robot Cognition Laboratory, Marey Institute, Dijon, France
| | - Hiroshi Ishiguro
- Ishiguro Lab, Graduate School of Engineering Science, Osaka University, Osaka, Japan
| | - Peter Ford Dominey
- INSERM UMR1093-CAPS, Université Bourgogne Franche-Comté, UFR des Sciences du Sport, Dijon, France
- Robot Cognition Laboratory, Marey Institute, Dijon, France
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Wang Y, Yao T, Lin Y, Ge H, Huang B, Gao Y, Wu J. Association between gut microbiota and pan-dermatological diseases: a bidirectional Mendelian randomization research. Front Cell Infect Microbiol 2024; 14:1327083. [PMID: 38562964 PMCID: PMC10982508 DOI: 10.3389/fcimb.2024.1327083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 03/05/2024] [Indexed: 04/04/2024] Open
Abstract
Background Gut microbiota has been associated with dermatological problems in earlier observational studies. However, it is unclear whether gut microbiota has a causal function in dermatological diseases. Methods Thirteen dermatological diseases were the subject of bidirectional Mendelian randomization (MR) research aimed at identifying potential causal links between gut microbiota and these diseases. Summary statistics for the Genome-Wide Association Study (GWAS) of gut microbiota and dermatological diseases were obtained from public datasets. With the goal of evaluating the causal estimates, five acknowledged MR approaches were utilized along with multiple testing corrections, with inverse variance weighted (IVW) regression serving as the main methodology. Regarding the taxa that were causally linked with dermatological diseases in the forward MR analysis, reverse MR was performed. A series of sensitivity analyses were conducted to test the robustness of the causal estimates. Results The combined results of the five MR methods and sensitivity analysis showed 94 suggestive and five significant causal relationships. In particular, the genus Eubacterium_fissicatena_group increased the risk of developing psoriasis vulgaris (odds ratio [OR] = 1.32, pFDR = 4.36 × 10-3), family Bacteroidaceae (OR = 2.25, pFDR = 4.39 × 10-3), genus Allisonella (OR = 1.42, pFDR = 1.29 × 10-2), and genus Bacteroides (OR = 2.25, pFDR = 1.29 × 10-2) increased the risk of developing acne; and the genus Intestinibacter increased the risk of urticaria (OR = 1.30, pFDR = 9.13 × 10-3). A reverse MR study revealed insufficient evidence for a significant causal relationship. In addition, there was no discernible horizontal pleiotropy or heterogeneity. Conclusion This study provides novel insights into the causality of gut microbiota in dermatological diseases and therapeutic or preventive paradigms for cutaneous conditions.
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Affiliation(s)
- Yingwei Wang
- Department of Dermatology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, China
| | - Tao Yao
- Department of Cardiology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yunlu Lin
- Department of Cardiology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, China
| | - Hongping Ge
- Department of Dermatology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, China
| | - Bixin Huang
- Department of Dermatology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yu Gao
- Department of Dermatology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jianming Wu
- Department of Dermatology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, China
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Creswell R, Shepherd KM, Lambert B, Mirams GR, Lei CL, Tavener S, Robinson M, Gavaghan DJ. Understanding the impact of numerical solvers on inference for differential equation models. J R Soc Interface 2024; 21:20230369. [PMID: 38442857 PMCID: PMC10914510 DOI: 10.1098/rsif.2023.0369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 01/30/2024] [Indexed: 03/07/2024] Open
Abstract
Most ordinary differential equation (ODE) models used to describe biological or physical systems must be solved approximately using numerical methods. Perniciously, even those solvers that seem sufficiently accurate for the forward problem, i.e. for obtaining an accurate simulation, might not be sufficiently accurate for the inverse problem, i.e. for inferring the model parameters from data. We show that for both fixed step and adaptive step ODE solvers, solving the forward problem with insufficient accuracy can distort likelihood surfaces, which might become jagged, causing inference algorithms to get stuck in local 'phantom' optima. We demonstrate that biases in inference arising from numerical approximation of ODEs are potentially most severe in systems involving low noise and rapid nonlinear dynamics. We reanalyse an ODE change point model previously fit to the COVID-19 outbreak in Germany and show the effect of the step size on simulation and inference results. We then fit a more complicated rainfall run-off model to hydrological data and illustrate the importance of tuning solver tolerances to avoid distorted likelihood surfaces. Our results indicate that, when performing inference for ODE model parameters, adaptive step size solver tolerances must be set cautiously and likelihood surfaces should be inspected for characteristic signs of numerical issues.
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Affiliation(s)
- Richard Creswell
- Department of Computer Science, University of Oxford, Oxford, Oxfordshire, UK
| | | | - Ben Lambert
- Department of Statistics, University of Oxford, Oxford, Oxfordshire, UK
| | - Gary R. Mirams
- School of Mathematical Sciences, University of Nottingham, Nottingham, Nottinghamshire, UK
| | - Chon Lok Lei
- Institute of Translational Medicine and Department of Biomedical Sciences, Faculty of Health Sciences, University of Macau, Taipa, Macao
| | - Simon Tavener
- Department of Mathematics, Colorado State University, Fort Collins, CO, USA
| | - Martin Robinson
- Department of Computer Science, University of Oxford, Oxford, Oxfordshire, UK
| | - David J. Gavaghan
- Department of Computer Science, University of Oxford, Oxford, Oxfordshire, UK
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Seurat J, Gerbino KR, Meyer JR, Borin JM, Weitz JS. Design, optimization, and inference of multiphasic decay of infectious virus particles. bioRxiv 2024:2024.02.23.581735. [PMID: 38464262 PMCID: PMC10925204 DOI: 10.1101/2024.02.23.581735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
The loss of virus particles is typically considered to arise from a first-order kinetic process. Signals of deviations from this exponential decay are often de-prioritized. Here, we propose methods to evaluate if a design is adequate to evaluate evidence for multiphasic virus particle decay and to optimize the sampling times of decay experiments, accounting for uncertainties in viral kinetics. First, we evaluate 1500 synthetic scenarios of biphasic decays, with varying decay rates and initial proportions of subpopulations. Robust inference of multiphasic decay is more likely when the faster decaying subpopulation predominates insofar as early samples are taken to resolve the faster decay rate. Overall, we find that design optimization leads to a better precision of estimation while reducing the number of samples. It helps to estimate adequately the fastest decay in 54% of situations vs. 41% using a non-optimized design. We then apply these methods to infer multiple decay rates associated with the decay of ΦD9, an evolved isolate derived from phage Φ21. A pilot experiment confirmed that ΦD9 decay is multiphasic, but was unable to resolve the rate or proportion of the fast decay subpopulation(s). We then applied optimal design methods to propose new ΦD9 sampling times. Using this strategy, we were able to robustly estimate both decay rates and their respective subpopulations. Notably, we conclude that the vast majority (94%) of the population decays at a rate 16-fold higher than a slow decaying population. Altogether, these results provide methods to quantitatively estimate heterogeneity in viral decay.
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Affiliation(s)
- Jérémy Seurat
- Institut de Biologie, École Normale Supérieure, 75005 Paris, France
| | - Krista R. Gerbino
- Division of Biological Sciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Justin R. Meyer
- Division of Biological Sciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Joshua M. Borin
- Division of Biological Sciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Joshua S. Weitz
- Institut de Biologie, École Normale Supérieure, 75005 Paris, France
- Department of Biology, University of Maryland, College Park, MD 20742, USA
- Department of Physics, University of Maryland, College Park, MD 20742, USA
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Boldea O, Alipoor A, Pei S, Shaman J, Rozhnova G. Age-specific transmission dynamics of SARS-CoV-2 during the first 2 years of the pandemic. PNAS Nexus 2024; 3:pgae024. [PMID: 38312225 PMCID: PMC10837015 DOI: 10.1093/pnasnexus/pgae024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 01/09/2024] [Indexed: 02/06/2024]
Abstract
During its first 2 years, the SARS-CoV-2 pandemic manifested as multiple waves shaped by complex interactions between variants of concern, non-pharmaceutical interventions, and the immunological landscape of the population. Understanding how the age-specific epidemiology of SARS-CoV-2 has evolved throughout the pandemic is crucial for informing policy decisions. In this article, we aimed to develop an inference-based modeling approach to reconstruct the burden of true infections and hospital admissions in children, adolescents, and adults over the seven waves of four variants (wild-type, Alpha, Delta, and Omicron BA.1) during the first 2 years of the pandemic, using the Netherlands as the motivating example. We find that reported cases are a considerable underestimate and a generally poor predictor of true infection burden, especially because case reporting differs by age. The contribution of children and adolescents to total infection and hospitalization burden increased with successive variants and was largest during the Omicron BA.1 period. However, the ratio of hospitalizations to infections decreased with each subsequent variant in all age categories. Before the Delta period, almost all infections were primary infections occurring in naive individuals. During the Delta and Omicron BA.1 periods, primary infections were common in children but relatively rare in adults who experienced either reinfections or breakthrough infections. Our approach can be used to understand age-specific epidemiology through successive waves in other countries where random community surveys uncovering true SARS-CoV-2 dynamics are absent but basic surveillance and statistics data are available.
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Affiliation(s)
- Otilia Boldea
- Department of Econometrics and OR, Tilburg School of Economics and Management, Tilburg University, Tilburg 5037 AB, The Netherlands
| | - Amir Alipoor
- Department of Econometrics and OR, Tilburg School of Economics and Management, Tilburg University, Tilburg 5037 AB, The Netherlands
| | - Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
- Columbia Climate School, Columbia University, New York, NY 10025, USA
| | - Ganna Rozhnova
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht 3584 CX, The Netherlands
- Center for Complex Systems Studies (CCSS), Utrecht University, Utrecht 3584 CE, The Netherlands
- Faculdade de Ciências, Universidade de Lisboa, Lisbon PT1749-016, Portugal
- BioISI—Biosystems & Integrative Sciences Institute, Faculdade de Ciências, Universidade de Lisboa, Lisbon PT1749-016, Portugal
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Frascaroli J, Leder H, Brattico E, Van de Cruys S. Aesthetics and predictive processing: grounds and prospects of a fruitful encounter. Philos Trans R Soc Lond B Biol Sci 2024; 379:20220410. [PMID: 38104599 PMCID: PMC10725766 DOI: 10.1098/rstb.2022.0410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 12/01/2023] [Indexed: 12/19/2023] Open
Abstract
In the last few years, a remarkable convergence of interests and results has emerged between scholars interested in the arts and aesthetics from a variety of perspectives and cognitive scientists studying the mind and brain within the predictive processing (PP) framework. This convergence has so far proven fruitful for both sides: while PP is increasingly adopted as a framework for understanding aesthetic phenomena, the arts and aesthetics, examined under the lens of PP, are starting to be seen as important windows into our mental functioning. The result is a vast and fast-growing research programme that promises to deliver important insights into our aesthetic encounters as well as a wide range of psychological phenomena of general interest. Here, we present this developing research programme, describing its grounds and highlighting its prospects. We start by clarifying how the study of the arts and aesthetics encounters the PP picture of mental functioning (§1). We then go on to outline the prospects of this encounter for the fields involved: philosophy and history of art (§2), psychology of aesthetics and neuroaesthetics (§3) and psychology and neuroscience more generally (§4). The upshot is an ambitious but well-defined framework within which aesthetics and cognitive science can partner up to illuminate crucial aspects of the human mind. This article is part of the theme issue 'Art, aesthetics and predictive processing: theoretical and empirical perspectives'.
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Affiliation(s)
| | - Helmut Leder
- Faculty of Psychology and Cognitive Science Research Hub, University of Vienna, 1010 Vienna, Austria
| | - Elvira Brattico
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, and Royal Academy of Music Aarhus/Aalborg, 8000 Aarhus, Denmark
- Department of Education, Psychology, Communication, University of Bari Aldo Moro, 70121 Bari, Italy
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12
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Li S, Liu Y, Shen LC, Yan H, Song J, Yu DJ. GMFGRN: a matrix factorization and graph neural network approach for gene regulatory network inference. Brief Bioinform 2024; 25:bbad529. [PMID: 38261340 PMCID: PMC10805180 DOI: 10.1093/bib/bbad529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 12/08/2023] [Accepted: 12/19/2023] [Indexed: 01/24/2024] Open
Abstract
The recent advances of single-cell RNA sequencing (scRNA-seq) have enabled reliable profiling of gene expression at the single-cell level, providing opportunities for accurate inference of gene regulatory networks (GRNs) on scRNA-seq data. Most methods for inferring GRNs suffer from the inability to eliminate transitive interactions or necessitate expensive computational resources. To address these, we present a novel method, termed GMFGRN, for accurate graph neural network (GNN)-based GRN inference from scRNA-seq data. GMFGRN employs GNN for matrix factorization and learns representative embeddings for genes. For transcription factor-gene pairs, it utilizes the learned embeddings to determine whether they interact with each other. The extensive suite of benchmarking experiments encompassing eight static scRNA-seq datasets alongside several state-of-the-art methods demonstrated mean improvements of 1.9 and 2.5% over the runner-up in area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC). In addition, across four time-series datasets, maximum enhancements of 2.4 and 1.3% in AUROC and AUPRC were observed in comparison to the runner-up. Moreover, GMFGRN requires significantly less training time and memory consumption, with time and memory consumed <10% compared to the second-best method. These findings underscore the substantial potential of GMFGRN in the inference of GRNs. It is publicly available at https://github.com/Lishuoyy/GMFGRN.
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Affiliation(s)
- Shuo Li
- School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing, 210094, China
| | - Yan Liu
- School of information Engineering, Yangzhou University, 196 West Huayang, Yangzhou, 225000, China
| | - Long-Chen Shen
- School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing, 210094, China
| | - He Yan
- School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing, 210094, China
| | - Jiangning Song
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
- Monash Data Futures Institute, Monash University, Melbourne, Victoria 3800, Australia
| | - Dong-Jun Yu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing, 210094, China
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13
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Abstract
Social psychologists attempt to explain how we interact by appealing to basic principles of how we think. To make good on this ambition, they are increasingly relying on an interconnected set of formal tools that model inference, attribution, value-guided decision making, and multi-agent interactions. By reviewing progress in each of these areas and highlighting the connections between them, we can better appreciate the structure of social thought and behavior, while also coming to understand when, why, and how formal tools can be useful for social psychologists.
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Affiliation(s)
- Fiery Cushman
- Department of Psychology, Harvard University, Cambridge, Massachusetts, USA;
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14
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Prat-Carrabin A, Meyniel F, Azeredo da Silveira R. Resource-rational account of sequential effects in human prediction. eLife 2024; 13:e81256. [PMID: 38224341 PMCID: PMC10789490 DOI: 10.7554/elife.81256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 12/11/2023] [Indexed: 01/16/2024] Open
Abstract
An abundant literature reports on 'sequential effects' observed when humans make predictions on the basis of stochastic sequences of stimuli. Such sequential effects represent departures from an optimal, Bayesian process. A prominent explanation posits that humans are adapted to changing environments, and erroneously assume non-stationarity of the environment, even if the latter is static. As a result, their predictions fluctuate over time. We propose a different explanation in which sub-optimal and fluctuating predictions result from cognitive constraints (or costs), under which humans however behave rationally. We devise a framework of costly inference, in which we develop two classes of models that differ by the nature of the constraints at play: in one case the precision of beliefs comes at a cost, resulting in an exponential forgetting of past observations, while in the other beliefs with high predictive power are favored. To compare model predictions to human behavior, we carry out a prediction task that uses binary random stimuli, with probabilities ranging from 0.05 to 0.95. Although in this task the environment is static and the Bayesian belief converges, subjects' predictions fluctuate and are biased toward the recent stimulus history. Both classes of models capture this 'attractive effect', but they depart in their characterization of higher-order effects. Only the precision-cost model reproduces a 'repulsive effect', observed in the data, in which predictions are biased away from stimuli presented in more distant trials. Our experimental results reveal systematic modulations in sequential effects, which our theoretical approach accounts for in terms of rationality under cognitive constraints.
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Affiliation(s)
- Arthur Prat-Carrabin
- Department of Economics, Columbia UniversityNew YorkUnited States
- Laboratoire de Physique de l’École Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de ParisParisFrance
| | - Florent Meyniel
- Cognitive Neuroimaging Unit, Institut National de la Santé et de la Recherche Médicale, Commissariat à l’Energie Atomique et aux Energies Alternatives, Centre National de la Recherche Scientifique, Université Paris-Saclay, NeuroSpin centerGif-sur-YvetteFrance
- Institut de neuromodulation, GHU Paris, Psychiatrie et Neurosciences, Centre Hospitalier Sainte-Anne, Pôle Hospitalo-Universitaire 15, Université Paris CitéParisFrance
| | - Rava Azeredo da Silveira
- Laboratoire de Physique de l’École Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de ParisParisFrance
- Institute of Molecular and Clinical Ophthalmology BaselBaselSwitzerland
- Faculty of Science, University of BaselBaselSwitzerland
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15
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Chetty A, Blekhman R. Multi-omic approaches for host-microbiome data integration. Gut Microbes 2024; 16:2297860. [PMID: 38166610 PMCID: PMC10766395 DOI: 10.1080/19490976.2023.2297860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 12/18/2023] [Indexed: 01/05/2024] Open
Abstract
The gut microbiome interacts with the host through complex networks that affect physiology and health outcomes. It is becoming clear that these interactions can be measured across many different omics layers, including the genome, transcriptome, epigenome, metabolome, and proteome, among others. Multi-omic studies of the microbiome can provide insight into the mechanisms underlying host-microbe interactions. As more omics layers are considered, increasingly sophisticated statistical methods are required to integrate them. In this review, we provide an overview of approaches currently used to characterize multi-omic interactions between host and microbiome data. While a large number of studies have generated a deeper understanding of host-microbiome interactions, there is still a need for standardization across approaches. Furthermore, microbiome studies would also benefit from the collection and curation of large, publicly available multi-omics datasets.
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Affiliation(s)
- Ashwin Chetty
- Committee on Genetics, Genomics and Systems Biology, The University of Chicago, Chicago, IL, USA
| | - Ran Blekhman
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA
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16
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Croydon-Veleslavov IA, Stumpf MPH. Repeated Decision Stumping Distils Simple Rules from Single-Cell Data. J Comput Biol 2024; 31:21-40. [PMID: 38170180 DOI: 10.1089/cmb.2021.0613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2024] Open
Abstract
Single-cell data afford unprecedented insights into molecular processes. But the complexity and size of these data sets have proved challenging and given rise to a large armory of statistical and machine learning approaches. The majority of approaches focuses on either describing features of these data, or making predictions and classifying unlabeled samples. In this study, we introduce repeated decision stumping (ReDX) as a method to distill simple models from single-cell data. We develop decision trees of depth one-hence "stumps"-to identify in an inductive manner, gene products involved in driving cell fate transitions, and in applications to published data we are able to discover the key players involved in these processes in an unbiased manner without prior knowledge. Our algorithm is deliberately targeting the simplest possible candidate hypotheses that can be extracted from complex high-dimensional data. There are three reasons for this: (1) the predictions become straightforwardly testable hypotheses; (2) the identified candidates form the basis for further mechanistic model development, for example, for engineering and synthetic biology interventions; and (3) this approach complements existing descriptive modeling approaches and frameworks. The approach is computationally efficient, has remarkable predictive power, including in simulation studies where the ground truth is known, and yields robust and statistically stable predictors; the same set of candidates is generated by applying the algorithm to different subsamples of experimental data.
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Affiliation(s)
- Ivan A Croydon-Veleslavov
- Department of Life Sciences, Centre for Integrative Systems Biology and Bioinformatics, Imperial College London, London, United Kingdom
| | - Michael P H Stumpf
- Department of Life Sciences, Centre for Integrative Systems Biology and Bioinformatics, Imperial College London, London, United Kingdom
- School of BioSciences, University of Melbourne, Parkville, Australia
- School of Mathematics and Statistics, University of Melbourne, Parkville, Australia
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17
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Jarovi J, Pilkiw M, Takehara-Nishiuchi K. Prefrontal neuronal ensembles link prior knowledge with novel actions during flexible action selection. Cell Rep 2023; 42:113492. [PMID: 37999978 DOI: 10.1016/j.celrep.2023.113492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 10/23/2023] [Accepted: 11/10/2023] [Indexed: 11/26/2023] Open
Abstract
We make decisions based on currently perceivable information or an internal model of the environment. The medial prefrontal cortex (mPFC) and its interaction with the hippocampus have been implicated in the latter, model-based decision-making; however, the underlying computational properties remain incompletely understood. We have examined mPFC spiking and hippocampal oscillatory activity while rats flexibly select new actions using a known associative structure of environmental cues and outcomes. During action selection, the mPFC reinstates representations of the associative structure. These awake reactivation events are accompanied by synchronous firings among neurons coding the associative structure and those coding actions. Moreover, their functional coupling is strengthened upon the reactivation events leading to adaptive actions. In contrast, only cue-coding neurons improve functional coupling during hippocampal sharp wave ripples. Thus, the lack of direct experience disconnects the mPFC from the hippocampus to independently form self-organized neuronal ensemble dynamics linking prior knowledge with novel actions.
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Affiliation(s)
- Justin Jarovi
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON M5S 3G5, Canada
| | - Maryna Pilkiw
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON M5S 3G5, Canada
| | - Kaori Takehara-Nishiuchi
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON M5S 3G5, Canada; Department of Psychology, University of Toronto, Toronto, ON M5S 3G3, Canada; Collaborative Program in Neuroscience, University of Toronto, Toronto, ON M5S 1A8, Canada.
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18
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Privé F, Albiñana C, Arbel J, Pasaniuc B, Vilhjálmsson BJ. Inferring disease architecture and predictive ability with LDpred2-auto. Am J Hum Genet 2023; 110:2042-2055. [PMID: 37944514 PMCID: PMC10716363 DOI: 10.1016/j.ajhg.2023.10.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 10/15/2023] [Accepted: 10/17/2023] [Indexed: 11/12/2023] Open
Abstract
LDpred2 is a widely used Bayesian method for building polygenic scores (PGSs). LDpred2-auto can infer the two parameters from the LDpred model, the SNP heritability h2 and polygenicity p, so that it does not require an additional validation dataset to choose best-performing parameters. The main aim of this paper is to properly validate the use of LDpred2-auto for inferring multiple genetic parameters. Here, we present a new version of LDpred2-auto that adds an optional third parameter α to its model, for modeling negative selection. We then validate the inference of these three parameters (or two, when using the previous model). We also show that LDpred2-auto provides per-variant probabilities of being causal that are well calibrated and can therefore be used for fine-mapping purposes. We also introduce a formula to infer the out-of-sample predictive performance r2 of the resulting PGS directly from the Gibbs sampler of LDpred2-auto. Finally, we extend the set of HapMap3 variants recommended to use with LDpred2 with 37% more variants to improve the coverage of this set, and we show that this new set of variants captures 12% more heritability and provides 6% more predictive performance, on average, in UK Biobank analyses.
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Affiliation(s)
- Florian Privé
- National Centre for Register-based Research, Aarhus University, Aarhus, Denmark.
| | - Clara Albiñana
- National Centre for Register-based Research, Aarhus University, Aarhus, Denmark
| | - Julyan Arbel
- University Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, Grenoble, France
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Bjarni J Vilhjálmsson
- National Centre for Register-based Research, Aarhus University, Aarhus, Denmark; Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark; Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute, Cambridge, MA, USA
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19
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Cartmill EA. Overcoming bias in the comparison of human language and animal communication. Proc Natl Acad Sci U S A 2023; 120:e2218799120. [PMID: 37956297 PMCID: PMC10666095 DOI: 10.1073/pnas.2218799120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2023] Open
Abstract
Human language is a powerful communicative and cognitive tool. Scholars have long sought to characterize its uniqueness, but each time a property is proposed to set human language apart (e.g., reference, syntax), some (attenuated) version of that property is found in animals. Recently, the uniqueness argument has shifted from linguistic rules to cognitive capacities underlying them. Scholars argue that human language is unique because it relies on ostension and inference, while animal communication depends on simple associations and largely hardwired signals. Such characterizations are often borne out in published data, but these empirical findings are driven by radical differences in the ways animal and human communication are studied. The field of animal communication has been dramatically shaped by the "code model," which imagines communication as involving information packets that are encoded, transmitted, decoded, and interpreted. This framework standardized methods for studying meaning in animal signals, but it does not allow for the nuance, ambiguity, or contextual variation seen in humans. The code model is insidious. It is rarely referenced directly, but it significantly shapes how we study animals. To compare animal communication and human language, we must acknowledge biases resulting from the different theoretical models used. By incorporating new approaches that break away from searching for codes, we may find that animal communication and human language are characterized by differences of degree rather than kind.
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Affiliation(s)
- Erica A. Cartmill
- Department of Anthropology, University of California, Los Angeles, CA90095
- Department of Psychology, University of California, Los Angeles, CA90095
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20
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Bohus KA, Cesana-Arlotti N, Martín-Salguero A, Bonatti LL. The scope and role of deduction in infant cognition. Curr Biol 2023; 33:4014-4020.e5. [PMID: 37659416 DOI: 10.1016/j.cub.2023.08.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 06/19/2023] [Accepted: 08/09/2023] [Indexed: 09/04/2023]
Abstract
The origins of the human capacity for logically structured thought are still a mystery. Studies on young humans, which can be particularly informative, present conflicting results. Infants seem able to generate competing hypotheses1,2,3 and monitor the certainty or probability of one-shot outcomes,4,5,6,7,8 suggesting the existence of an articulated language of thought.9 However, sometimes toddlers10 and even children younger than 411,12,13,14 fail tasks seemingly requiring the same representational abilities. One fundamental test for the presence of logical abilities is the concept of disjunction as a way into the conception of alternative possibilities, and of disjunctive elimination as a way to prune them. Here, we document their widespread presence in 19-month-old infants. In a word-referent association task, both bilingual and monolingual infants display a pattern of oculomotor inspection previously found to be a hallmark of disjunctive reasoning in adults and children,15,16 showing that the onset of logical reasoning is not crucially dependent on language experience. The pattern appears when targets are novel, but also when both objects and words are known, though likely not yet sedimented into a mature lexicon. Disjunctive reasoning also surfaces in a non-linguistic location search task, not prompted by violated expectations, showing that infants reason by elimination spontaneously. Together, these results help answer long-standing empirical and philosophical puzzles about the role of logic in early knowledge development, suggesting that by increasing confidence in some options while eliminating alternatives, logic provides scaffolding for the organization of knowledge about the world, language, and language-world relations.
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Affiliation(s)
- Kinga Anna Bohus
- Center for Brain and Cognition, DTIC, Universitat Pompeu Fabra, Ramon Trias Fargas, 25-27, 08005 Barcelona, Spain.
| | | | - Ana Martín-Salguero
- Center for Brain and Cognition, DTIC, Universitat Pompeu Fabra, Ramon Trias Fargas, 25-27, 08005 Barcelona, Spain; Institut Jean Nicod, Département d'études cognitives, École Normale Supérieure, EHESS, CNRS, PSL University, 29 Rue d'Ulm, 75005 Paris, France
| | - Luca Lorenzo Bonatti
- Center for Brain and Cognition, DTIC, Universitat Pompeu Fabra, Ramon Trias Fargas, 25-27, 08005 Barcelona, Spain; ICREA, Pg. Lluís Companys 23, 08010 Barcelona, Spain.
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21
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Watson SI, Akinyemi JO, Hemming K. Permutation-based multiple testing corrections for P $$ P $$ -values and confidence intervals for cluster randomized trials. Stat Med 2023; 42:3786-3803. [PMID: 37340888 PMCID: PMC10962558 DOI: 10.1002/sim.9831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 05/17/2023] [Accepted: 05/29/2023] [Indexed: 06/22/2023]
Abstract
In this article, we derive and compare methods to derive P-values and sets of confidence intervals with strong control of the family-wise error rates and coverage for estimates of treatment effects in cluster randomized trials with multiple outcomes. There are few methods for P-value corrections and deriving confidence intervals, limiting their application in this setting. We discuss the methods of Bonferroni, Holm, and Romano and Wolf and adapt them to cluster randomized trial inference using permutation-based methods with different test statistics. We develop a novel search procedure for confidence set limits using permutation tests to produce a set of confidence intervals under each method of correction. We conduct a simulation-based study to compare family-wise error rates, coverage of confidence sets, and the efficiency of each procedure in comparison to no correction using both model-based standard errors and permutation tests. We show that the Romano-Wolf type procedure has nominal error rates and coverage under non-independent correlation structures and is more efficient than the other methods in a simulation-based study. We also compare results from the analysis of a real-world trial.
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Affiliation(s)
- Samuel I. Watson
- Institute of Applied Health ResearchUniversity of BirminghamBirminghamUK
| | | | - Karla Hemming
- Institute of Applied Health ResearchUniversity of BirminghamBirminghamUK
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22
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Vinci-Booher S, Schlichting ML, Preston AR, Pestilli F. Development of human hippocampal subfield microstructure and relation to associative inference. Cereb Cortex 2023; 33:10207-10220. [PMID: 37557916 PMCID: PMC10502573 DOI: 10.1093/cercor/bhad276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 06/30/2023] [Accepted: 07/01/2023] [Indexed: 08/11/2023] Open
Abstract
The hippocampus is a complex brain structure composed of subfields that each have distinct cellular organizations. While the volume of hippocampal subfields displays age-related changes that have been associated with inference and memory functions, the degree to which the cellular organization within each subfield is related to these functions throughout development is not well understood. We employed an explicit model testing approach to characterize the development of tissue microstructure and its relationship to performance on 2 inference tasks, one that required memory (memory-based inference) and one that required only perceptually available information (perception-based inference). We found that each subfield had a unique relationship with age in terms of its cellular organization. While the subiculum (SUB) displayed a linear relationship with age, the dentate gyrus (DG), cornu ammonis field 1 (CA1), and cornu ammonis subfields 2 and 3 (combined; CA2/3) displayed nonlinear trajectories that interacted with sex in CA2/3. We found that the DG was related to memory-based inference performance and that the SUB was related to perception-based inference; neither relationship interacted with age. Results are consistent with the idea that cellular organization within hippocampal subfields might undergo distinct developmental trajectories that support inference and memory performance throughout development.
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Affiliation(s)
- Sophia Vinci-Booher
- Indiana University, Psychological and Brain Sciences, 1101 E. 10th St., Bloomington, Indiana, 47405, United States
- Vanderbilt University, Psychology and Human Development, 230 Appleton Pl., Nashville, TN 37203, United States
| | - Margaret L Schlichting
- University of Toronto, Psychology, 100 St. George St., Toronto, ON M5S 3G3, Canada
- University of Texas at Austin, Psychology, 108 E. Dean Keeton Street, Austin, TX 78712, United States
| | - Alison R Preston
- University of Texas at Austin, Psychology, 108 E. Dean Keeton Street, Austin, TX 78712, United States
| | - Franco Pestilli
- University of Texas at Austin, Psychology, 108 E. Dean Keeton Street, Austin, TX 78712, United States
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23
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Douven I, Verheyen S, Elqayam S, Gärdenfors P, Osta-Vélez M. Similarity-based reasoning in conceptual spaces. Front Psychol 2023; 14:1234483. [PMID: 37731876 PMCID: PMC10508908 DOI: 10.3389/fpsyg.2023.1234483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Accepted: 08/14/2023] [Indexed: 09/22/2023] Open
Abstract
Whereas the validity of deductive inferences can be characterized in terms of their logical form, this is not true for all inferences that appear pre-theoretically valid. Nonetheless, philosophers have argued that at least some of those inferences-sometimes called "similarity-based inferences" -can be given a formal treatment with the help of similarity spaces, which are mathematical spaces purporting to represent human similarity judgments. In these inferences, we conclude that a given property pertains to a category of items on the grounds that the same property pertains to a similar category of items. We look at a specific proposal according to which the strength of such inferences is a function of the distance, as measured in the appropriate similarity space, between the category referenced in the premise and the category referenced in the conclusion. We report the outcomes of three studies that all support the said proposal.
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Affiliation(s)
- Igor Douven
- IHPST / CNRS / Panthéon–Sorbonne University, Paris, France
| | - Steven Verheyen
- Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam, Netherlands
| | - Shira Elqayam
- School of Applied Social Sciences, De Montfort University, Leicester, United Kingdom
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24
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Poissonnier LA, Hartmann Y, Czaczkes TJ. Ants combine object affordance with latent learning to make efficient foraging decisions. Proc Natl Acad Sci U S A 2023; 120:e2302654120. [PMID: 37603741 PMCID: PMC10468611 DOI: 10.1073/pnas.2302654120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 07/18/2023] [Indexed: 08/23/2023] Open
Abstract
The affordance of an object refers to its functional properties. For example, a bowl has the affordance of holding water, but a sieve does not. Here, we report that ants learn the affordance of a novel object without this attribute being rewarded, and use the memory of this affordance to avoid predicted, but never experienced, crowding. Ants were trained to feeders, which could support either only one ant or many. Two feeders were encountered, each of identical design but differently scented. After training, on the outward journey, half the ants encounter nestmates, which had fed on food matching one of the training feeders. Encountering returning nestmates reduced preference for the feeder matching the scent of the encountered nestmates, but only for ants trained on a limited-access feeder; ants trained on an unlimited feeder were unaffected. In other words, only if ants know that the food access is limited, and receive information that this feeder is heavily visited, do they reduce their preference for this feeder. To achieve this, the ants had to combine memories of the feeders' affordance with the presence of nestmates. Then they had to use semantic knowledge that restricted food access combined with nestmate presence predicts a likelihood of crowding, or a rule such as "if the food is restricted and there are nestmates on the path, go to another food source." Regardless of the mechanism, these results demonstrate that ants latently learn the affordance of their surroundings, an unexpected cognitive ability for an invertebrate.
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Affiliation(s)
- Laure-Anne Poissonnier
- Animal Comparative Economics Laboratory, Department of Zoology and Evolutionary Biology, University of Regensburg, RegensburgD-95053, Germany
| | - Yannick Hartmann
- Animal Comparative Economics Laboratory, Department of Zoology and Evolutionary Biology, University of Regensburg, RegensburgD-95053, Germany
| | - Tomer J. Czaczkes
- Animal Comparative Economics Laboratory, Department of Zoology and Evolutionary Biology, University of Regensburg, RegensburgD-95053, Germany
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25
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Dichio V, De Vico Fallani F. Statistical models of complex brain networks: a maximum entropy approach. Rep Prog Phys 2023; 86:102601. [PMID: 37437559 DOI: 10.1088/1361-6633/ace6bc] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Accepted: 07/12/2023] [Indexed: 07/14/2023]
Abstract
The brain is a highly complex system. Most of such complexity stems from the intermingled connections between its parts, which give rise to rich dynamics and to the emergence of high-level cognitive functions. Disentangling the underlying network structure is crucial to understand the brain functioning under both healthy and pathological conditions. Yet, analyzing brain networks is challenging, in part because their structure represents only one possible realization of a generative stochastic process which is in general unknown. Having a formal way to cope with such intrinsic variability is therefore central for the characterization of brain network properties. Addressing this issue entails the development of appropriate tools mostly adapted from network science and statistics. Here, we focus on a particular class of maximum entropy models for networks, i.e. exponential random graph models, as a parsimonious approach to identify the local connection mechanisms behind observed global network structure. Efforts are reviewed on the quest for basic organizational properties of human brain networks, as well as on the identification of predictive biomarkers of neurological diseases such as stroke. We conclude with a discussion on how emerging results and tools from statistical graph modeling, associated with forthcoming improvements in experimental data acquisition, could lead to a finer probabilistic description of complex systems in network neuroscience.
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Affiliation(s)
- Vito Dichio
- Sorbonne Universite, Paris Brain Institute-ICM, CNRS, Inria, Inserm, AP-HP, Hopital de la Pitie Salpêtriere, F-75013 Paris, France
| | - Fabrizio De Vico Fallani
- Sorbonne Universite, Paris Brain Institute-ICM, CNRS, Inria, Inserm, AP-HP, Hopital de la Pitie Salpêtriere, F-75013 Paris, France
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26
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Andrade J, Duggan J. Anchoring the mean generation time in the SEIR to mitigate biases in ℜ 0 estimates due to uncertainty in the distribution of the epidemiological delays. R Soc Open Sci 2023; 10:230515. [PMID: 37538746 PMCID: PMC10394422 DOI: 10.1098/rsos.230515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 07/13/2023] [Indexed: 08/05/2023]
Abstract
The basic reproduction number, ℜ 0 , is of paramount importance in the study of infectious disease dynamics. Primarily, ℜ 0 serves as an indicator of the transmission potential of an emerging infectious disease and the effort required to control the invading pathogen. However, its estimates from compartmental models are strongly conditioned by assumptions in the model structure, such as the distributions of the latent and infectious periods (epidemiological delays). To further complicate matters, models with dissimilar delay structures produce equivalent incidence dynamics. Following a simulation study, we reveal that the nature of such equivalency stems from a linear relationship between ℜ 0 and the mean generation time, along with adjustments to other parameters in the model. Leveraging this knowledge, we propose and successfully test an alternative parametrization of the SEIR model that produces accurate ℜ 0 estimates regardless of the distribution of the epidemiological delays, at the expense of biases in other quantities deemed of lesser importance. We further explore this approach's robustness by testing various transmissibility levels, generation times and data fidelity (overdispersion). Finally, we apply the proposed approach to data from the 1918 influenza pandemic. We anticipate that this work will mitigate biases in estimating ℜ 0 .
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Affiliation(s)
- Jair Andrade
- Data Science Institute and School of Computer Science, University of Galway, Galway, Republic of Ireland
| | - Jim Duggan
- Insight Centre for Data Analytics and School of Computer Science, University of Galway, Galway, Republic of Ireland
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27
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Nizharadze T, Becker NB, Höfer T. Quantitating CD8 + T cell memory development. Trends Immunol 2023; 44:519-529. [PMID: 37277233 DOI: 10.1016/j.it.2023.05.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 05/03/2023] [Accepted: 05/04/2023] [Indexed: 06/07/2023]
Abstract
In acute immune responses to infection, memory T cells develop that can spawn recall responses. This process has not been observable directly in vivo. Here we highlight the utility of mathematical inference to derive quantitatively testable models of mammalian CD8+ T cell memory development from complex experimental data. Previous inference studies suggested that precursors of memory T cells arise early during the immune response. Recent work has both validated a crucial prediction of this T cell diversification model and refined the model. While multiple developmental routes to distinct memory subsets might exist, a branch point occurs early in proliferating T cell blasts, from which separate differentiation pathways emerge for slowly dividing precursors of re-expandable memory cells and rapidly dividing effectors.
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Affiliation(s)
- Tamar Nizharadze
- Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Faculty of Biosciences, Heidelberg University, 69120 Heidelberg, Germany
| | - Nils B Becker
- Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Thomas Höfer
- Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Faculty of Biosciences, Heidelberg University, 69120 Heidelberg, Germany.
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28
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Akuno AO, Ramírez-Ramírez LL, Espinoza JF. Inference on a Multi-Patch Epidemic Model with Partial Mobility, Residency, and Demography: Case of the 2020 COVID-19 Outbreak in Hermosillo, Mexico. Entropy (Basel) 2023; 25:968. [PMID: 37509915 PMCID: PMC10378648 DOI: 10.3390/e25070968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 06/02/2023] [Accepted: 06/14/2023] [Indexed: 07/30/2023]
Abstract
Most studies modeling population mobility and the spread of infectious diseases, particularly those using meta-population multi-patch models, tend to focus on the theoretical properties and numerical simulation of such models. As such, there is relatively scant literature focused on numerical fit, inference, and uncertainty quantification of epidemic models with population mobility. In this research, we use three estimation techniques to solve an inverse problem and quantify its uncertainty for a human-mobility-based multi-patch epidemic model using mobile phone sensing data and confirmed COVID-19-positive cases in Hermosillo, Mexico. First, we utilize a Brownian bridge model using mobile phone GPS data to estimate the residence and mobility parameters of the epidemic model. In the second step, we estimate the optimal model epidemiological parameters by deterministically inverting the model using a Darwinian-inspired evolutionary algorithm (EA)-that is, a genetic algorithm (GA). The third part of the analysis involves performing inference and uncertainty quantification in the epidemic model using two Bayesian Monte Carlo sampling methods: t-walk and Hamiltonian Monte Carlo (HMC). The results demonstrate that the estimated model parameters and incidence adequately fit the observed daily COVID-19 incidence in Hermosillo. Moreover, the estimated parameters from the HMC method yield large credible intervals, improving their coverage for the observed and predicted daily incidences. Furthermore, we observe that the use of a multi-patch model with mobility yields improved predictions when compared to a single-patch model.
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Affiliation(s)
- Albert Orwa Akuno
- Departamento de Probabilidad y Estadística, Centro de Investigación en Matemáticas A.C., Jalisco s/n, Colonia Valenciana, Guanajuato C.P. 36023, Gto, Mexico
| | - L Leticia Ramírez-Ramírez
- Departamento de Probabilidad y Estadística, Centro de Investigación en Matemáticas A.C., Jalisco s/n, Colonia Valenciana, Guanajuato C.P. 36023, Gto, Mexico
| | - Jesús F Espinoza
- Departamento de Matemáticas, Universidad de Sonora, Rosales y Boulevard Luis Encinas, Hermosillo C.P. 83000, Sonora, Mexico
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29
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Miller AMP, Jacob AD, Ramsaran AI, De Snoo ML, Josselyn SA, Frankland PW. Emergence of a predictive model in the hippocampus. Neuron 2023; 111:1952-1965.e5. [PMID: 37015224 PMCID: PMC10293047 DOI: 10.1016/j.neuron.2023.03.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 01/23/2023] [Accepted: 03/08/2023] [Indexed: 04/05/2023]
Abstract
The brain organizes experiences into memories that guide future behavior. Hippocampal CA1 population activity is hypothesized to reflect predictive models that contain information about future events, but little is known about how they develop. We trained mice on a series of problems with or without a common statistical structure to observe how memories are formed and updated. Mice that learned structured problems integrated their experiences into a predictive model that contained the solutions to upcoming novel problems. Retrieving the model during learning improved discrimination accuracy and facilitated learning. Using calcium imaging to track CA1 activity during learning, we found that hippocampal ensemble activity became more stable as mice formed a predictive model. The hippocampal ensemble was reactivated during training and incorporated new activity patterns from each training problem. These results show how hippocampal activity supports building predictive models by organizing new information with respect to existing memories.
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Affiliation(s)
- Adam M P Miller
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada
| | - Alex D Jacob
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada; Department of Psychology, University of Toronto, Toronto, ON, Canada
| | - Adam I Ramsaran
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada; Department of Psychology, University of Toronto, Toronto, ON, Canada
| | - Mitchell L De Snoo
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada; Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
| | - Sheena A Josselyn
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada; Department of Psychology, University of Toronto, Toronto, ON, Canada; Department of Physiology, University of Toronto, Toronto, ON, Canada; Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada; Brain, Mind, & Consciousness Program, Canadian Institute for Advanced Research, Toronto, ON, Canada
| | - Paul W Frankland
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada; Department of Psychology, University of Toronto, Toronto, ON, Canada; Department of Physiology, University of Toronto, Toronto, ON, Canada; Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada; Child & Brain Development Program, Canadian Institute for Advanced Research, Toronto, ON, Canada.
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30
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Phipps N, Shang JJ, Teo TH, Wey IC. Pre-Computing Batch Normalisation Parameters for Edge Devices on a Binarized Neural Network. Sensors (Basel) 2023; 23:5556. [PMID: 37420727 DOI: 10.3390/s23125556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 05/11/2023] [Accepted: 06/12/2023] [Indexed: 07/09/2023]
Abstract
Binarized Neural Network (BNN) is a quantized Convolutional Neural Network (CNN), reducing the precision of network parameters for a much smaller model size. In BNNs, the Batch Normalisation (BN) layer is essential. When running BN on edge devices, floating point instructions take up a significant number of cycles to perform. This work leverages the fixed nature of a model during inference, to reduce the full-precision memory footprint by half. This was achieved by pre-computing the BN parameters prior to quantization. The proposed BNN was validated through modeling the network on the MNIST dataset. Compared to the traditional method of computation, the proposed BNN reduced the memory utilization by 63% at 860-bytes without any significant impact on accuracy. By pre-computing portions of the BN layer, the number of cycles required to compute is reduced to two cycles on an edge device.
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Affiliation(s)
- Nicholas Phipps
- Engineering Product Development, Singapore University of Technology and Design, Singapore 487372, Singapore
- Department of Electrical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
| | - Jin-Jia Shang
- Engineering Product Development, Singapore University of Technology and Design, Singapore 487372, Singapore
- Department of Electrical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
| | - Tee Hui Teo
- Engineering Product Development, Singapore University of Technology and Design, Singapore 487372, Singapore
| | - I-Chyn Wey
- Department of Electrical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
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31
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Achimova A, Scontras G, Eisemann E, Butz MV. Active Iterative Social Inference in Multi-Trial Signaling Games. Open Mind (Camb) 2023; 7:111-129. [PMID: 37416076 PMCID: PMC10320816 DOI: 10.1162/opmi_a_00074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 02/10/2023] [Indexed: 07/08/2023] Open
Abstract
Human behavioral choices can reveal intrinsic and extrinsic decision-influencing factors. We investigate the inference of choice priors in situations of referential ambiguity. In particular, we use the scenario of signaling games and investigate to which extent study participants profit from actively engaging in the task. Previous work has revealed that speakers are able to infer listeners' choice priors upon observing ambiguity resolution. However, it was also shown that only a small group of participants was able to strategically construct ambiguous situations to create learning opportunities. This paper sets to address how prior inference unfolds in more complex learning scenarios. In Experiment 1, we examine whether participants accumulate evidence about inferred choice priors across a series of four consecutive trials. Despite the intuitive simplicity of the task, information integration turns out to be only partially successful. Integration errors result from a variety of sources, including transitivity failure and recency bias. In Experiment 2, we investigate how the ability to actively construct learning scenarios affects the success of prior inference and whether the iterative settings improve the ability to choose utterances strategically. The results suggest that full task engagement and explicit access to the reasoning pipeline facilitates the invocation of optimal utterance choices as well as the accurate inference of listeners' choice priors.
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Affiliation(s)
- Asya Achimova
- Research Training Group 1808 “Ambiguity: Production and Perception”, University of Tübingen, Tübingen, Germany
- Department of General and Computational Linguistics, University of Tübingen, Tübingen, Germany
| | - Gregory Scontras
- Department of Language Science, University of California, Irvine, USA
| | - Ella Eisemann
- Institute of Vocational Education and Work Studies, Technische Universität Berlin, Berlin, Germany
| | - Martin V. Butz
- Research Training Group 1808 “Ambiguity: Production and Perception”, University of Tübingen, Tübingen, Germany
- Department of Computer Science and Department of Psychology, University of Tübingen, Tübingen, Germany
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32
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Dichio V, Zeng HL, Aurell E. Statistical genetics in and out of quasi-linkage equilibrium. Rep Prog Phys 2023; 86:052601. [PMID: 36944245 DOI: 10.1088/1361-6633/acc5fa] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 03/21/2023] [Indexed: 06/18/2023]
Abstract
This review is about statistical genetics, an interdisciplinary topic between statistical physics and population biology. The focus is on the phase ofquasi-linkage equilibrium(QLE). Our goals here are to clarify under which conditions the QLE phase can be expected to hold in population biology and how the stability of the QLE phase is lost. The QLE state, which has many similarities to a thermal equilibrium state in statistical mechanics, was discovered by M Kimura for a two-locus two-allele model, and was extended and generalized to the global genome scale byNeher&Shraiman (2011). What we will refer to as the Kimura-Neher-Shraiman theory describes a population evolving due to the mutations, recombination, natural selection and possibly genetic drift. A QLE phase exists at sufficiently high recombination rate (r) and/or mutation ratesµwith respect to selection strength. We show how in QLE it is possible to infer the epistatic parameters of the fitness function from the knowledge of the (dynamical) distribution of genotypes in a population. We further consider the breakdown of the QLE regime for high enough selection strength. We review recent results for the selection-mutation and selection-recombination dynamics. Finally, we identify and characterize a new phase which we call the non-random coexistence where variability persists in the population without either fixating or disappearing.
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Affiliation(s)
- Vito Dichio
- Sorbonne Université, Paris Brain Institute-ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, F-75013 Paris, France
| | - Hong-Li Zeng
- School of Science, Nanjing University of Posts and Telecommunications, New Energy Technology Engineering Laboratory of Jiangsu Province, Nanjing 210023, People's Republic of China
| | - Erik Aurell
- Department of Computational Science and Technology, KTH-Royal Institute of Technology, AlbaNova University Center, SE-106 91 Stockholm, Sweden
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33
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Amith MT, Cui L, Roberts K, Tao C. Application of an ontology for model cards to generate computable artifacts for linking machine learning information from biomedical research. Proc Int World Wide Web Conf 2023; 2023:820-825. [PMID: 38327770 PMCID: PMC10848146 DOI: 10.1145/3543873.3587601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Model card reports provide a transparent description of machine learning models which includes information about their evaluation, limitations, intended use, etc. Federal health agencies have expressed an interest in model cards report for research studies using machine-learning based AI. Previously, we have developed an ontology model for model card reports to structure and formalize these reports. In this paper, we demonstrate a Java-based library (OWL API, FaCT++) that leverages our ontology to publish computable model card reports. We discuss future directions and other use cases that highlight applicability and feasibility of ontology-driven systems to support FAIR challenges.
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Affiliation(s)
| | - Licong Cui
- The University of Texas Health Science Center at Houston, USA
| | - Kirk Roberts
- The University of Texas Health Science Center at Houston, USA
| | - Cui Tao
- The University of Texas Health Science Center at Houston, USA
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34
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Luo S, Zhang Z, Wang Z, Yang X, Chen X, Zhou T, Zhang J. Inferring transcriptional bursting kinetics from single-cell snapshot data using a generalized telegraph model. R Soc Open Sci 2023; 10:221057. [PMID: 37035293 PMCID: PMC10073913 DOI: 10.1098/rsos.221057] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 03/13/2023] [Indexed: 06/19/2023]
Abstract
Gene expression has inherent stochasticity resulting from transcription's burst manners. Single-cell snapshot data can be exploited to rigorously infer transcriptional burst kinetics, using mathematical models as blueprints. The classical telegraph model (CTM) has been widely used to explain transcriptional bursting with Markovian assumptions. However, growing evidence suggests that the gene-state dwell times are generally non-exponential, as gene-state switching is a multi-step process in organisms. Therefore, interpretable non-Markovian mathematical models and efficient statistical inference methods are urgently required in investigating transcriptional burst kinetics. We develop an interpretable and tractable model, the generalized telegraph model (GTM), to characterize transcriptional bursting that allows arbitrary dwell-time distributions, rather than exponential distributions, to be incorporated into the ON and OFF switching process. Based on the GTM, we propose an inference method for transcriptional bursting kinetics using an approximate Bayesian computation framework. This method demonstrates an efficient and scalable estimation of burst frequency and burst size on synthetic data. Further, the application of inference to genome-wide data from mouse embryonic fibroblasts reveals that GTM would estimate lower burst frequency and higher burst size than those estimated by CTM. In conclusion, the GTM and the corresponding inference method are effective tools to infer dynamic transcriptional bursting from static single-cell snapshot data.
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Affiliation(s)
- Songhao Luo
- Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou, Guangdong Province 510275, People's Republic of China
- School of Mathematics, Sun Yat-sen University, Guangzhou, Guangdong Province 510275, People's Republic of China
| | - Zhenquan Zhang
- Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou, Guangdong Province 510275, People's Republic of China
- School of Mathematics, Sun Yat-sen University, Guangzhou, Guangdong Province 510275, People's Republic of China
| | - Zihao Wang
- Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou, Guangdong Province 510275, People's Republic of China
- School of Mathematics, Sun Yat-sen University, Guangzhou, Guangdong Province 510275, People's Republic of China
| | - Xiyan Yang
- School of Financial Mathematics and Statistics, Guangdong University of Finance, Guangzhou 510521, People's Republic of China
| | - Xiaoxuan Chen
- Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou, Guangdong Province 510275, People's Republic of China
- School of Mathematics, Sun Yat-sen University, Guangzhou, Guangdong Province 510275, People's Republic of China
| | - Tianshou Zhou
- Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou, Guangdong Province 510275, People's Republic of China
- School of Mathematics, Sun Yat-sen University, Guangzhou, Guangdong Province 510275, People's Republic of China
| | - Jiajun Zhang
- Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou, Guangdong Province 510275, People's Republic of China
- School of Mathematics, Sun Yat-sen University, Guangzhou, Guangdong Province 510275, People's Republic of China
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35
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Wang Y, He S. Using Fano factors to determine certain types of gene autoregulation. ArXiv 2023:arXiv:2301.06692v2. [PMID: 36713249 PMCID: PMC9882590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
The expression of one gene might be regulated by its corresponding protein, which is called autoregulation. Although gene regulation is a central topic in biology, autoregulation is much less studied. In general, it is extremely difficult to determine the existence of autoregulation with direct biochemical approaches. Nevertheless, some papers have observed that certain types of autoregulations are linked to noise levels in gene expression. We generalize these results by two propositions on discrete-state continuous-time Markov chains. These two propositions form a simple but robust method to infer the existence of autoregulation in certain scenarios from gene expression data. This method only depends on the Fano factor, namely the ratio of variance and mean of the gene expression level. Compared to other methods for inferring autoregulation, our method only requires non-interventional one-time data, and does not need to estimate parameters. Besides, our method has few restrictions on the model. We apply this method to four groups of experimental data and find some genes that might have autoregulation. Some inferred autoregulations have been verified by experiments or other theoretical works.
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Affiliation(s)
- Yue Wang
- Department of Computational Medicine, University of California, Los Angeles, California, United States of America
- Institut des Hautes Études Scientifiques, Bures-sur-Yvette, Essonne, France
| | - Siqi He
- Simons Center for Geometry and Physics, Stony Brook University, Stony Brook, New York, United States of America
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36
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Baczkowski BM, Haaker J, Schwabe L. Inferring danger with minimal aversive experience. Trends Cogn Sci 2023; 27:456-467. [PMID: 36941184 DOI: 10.1016/j.tics.2023.02.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 01/11/2023] [Accepted: 02/23/2023] [Indexed: 03/22/2023]
Abstract
Learning about threats is crucial for survival and fundamentally rests upon Pavlovian conditioning. However, Pavlovian threat learning is largely limited to detecting known (or similar) threats and involves first-hand exposure to danger, which inevitably poses a risk of harm. We discuss how individuals leverage a rich repertoire of mnemonic processes that operate largely in safety and significantly expand our ability to recognize danger beyond Pavlovian threat associations. These processes result in complementary memories - acquired individually or through social interactions - that represent potential threats and the relational structure of our environment. The interplay between these memories allows danger to be inferred rather than directly learned, thereby flexibly protecting us from potential harm in novel situations despite minimal prior aversive experience.
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Affiliation(s)
- Blazej M Baczkowski
- Department of Cognitive Psychology, Universität Hamburg, Von-Melle-Park 5, 20146 Hamburg, Germany
| | - Jan Haaker
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Lars Schwabe
- Department of Cognitive Psychology, Universität Hamburg, Von-Melle-Park 5, 20146 Hamburg, Germany.
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37
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Trott S, Reed S, Kaliblotzky D, Ferreira V, Bergen B. The Role of Prosody in Disambiguating English Indirect Requests. Lang Speech 2023; 66:118-142. [PMID: 35422153 DOI: 10.1177/00238309221087715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Ambiguity pervades language. The sentence "My office is really hot" could be interpreted as a complaint about the temperature or as an indirect request to turn on the air conditioning. How do comprehenders determine a speaker's intended interpretation? One possibility is that speakers and comprehenders exploit prosody to overcome the pragmatic ambiguity inherent in indirect requests. In a pre-registered behavioral experiment, we find that human listeners can successfully determine whether a given utterance was intended as a request at a rate above chance (55%), above and beyond the prior probability of a given sentence being interpreted as a request. Moreover, we find that a classifier equipped with seven acoustic features can detect the original intent of an utterance with 65% accuracy. Finally, consistent with past work, the duration, pitch, and pitch slope of an utterance emerge both as significant correlates of a speaker's original intent and as predictors of comprehenders' pragmatic interpretation. These results suggest that human and machine comprehenders alike can use prosody to enrich the meaning of ambiguous utterances, such as indirect requests.
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Affiliation(s)
- Sean Trott
- Department of Cognitive Science, UC San Diego, USA
| | - Stefanie Reed
- Department of Linguistics, City University of New York Graduate Center, USA
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38
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Barnett HY, Villar SS, Geys H, Jaki T. A novel statistical test for treatment differences in clinical trials using a response-adaptive forward-looking Gittins Index Rule. Biometrics 2023; 79:86-97. [PMID: 34669968 PMCID: PMC7614356 DOI: 10.1111/biom.13581] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Accepted: 09/30/2021] [Indexed: 11/28/2022]
Abstract
The most common objective for response-adaptive clinical trials is to seek to ensure that patients within a trial have a high chance of receiving the best treatment available by altering the chance of allocation on the basis of accumulating data. Approaches that yield good patient benefit properties suffer from low power from a frequentist perspective when testing for a treatment difference at the end of the study due to the high imbalance in treatment allocations. In this work we develop an alternative pairwise test for treatment difference on the basis of allocation probabilities of the covariate-adjusted response-adaptive randomization with forward-looking Gittins Index (CARA-FLGI) Rule for binary responses. The performance of the novel test is evaluated in simulations for two-armed studies and then its applications to multiarmed studies are illustrated. The proposed test has markedly improved power over the traditional Fisher exact test when this class of nonmyopic response adaptation is used. We also find that the test's power is close to the power of a Fisher exact test under equal randomization.
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Affiliation(s)
| | - Sofía S Villar
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | | | - Thomas Jaki
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Medical and Pharmaceutical Statistics Research Unit, Lancaster University, Lancaster, UK
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van den Brink RL, Hagena K, Wilming N, Murphy PR, Büchel C, Donner TH. Flexible sensory-motor mapping rules manifest in correlated variability of stimulus and action codes across the brain. Neuron 2023; 111:571-584.e9. [PMID: 36476977 DOI: 10.1016/j.neuron.2022.11.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 10/27/2022] [Accepted: 11/11/2022] [Indexed: 12/12/2022]
Abstract
Humans and non-human primates can flexibly switch between different arbitrary mappings from sensation to action to solve a cognitive task. It has remained unknown how the brain implements such flexible sensory-motor mapping rules. Here, we uncovered a dynamic reconfiguration of task-specific correlated variability between sensory and motor brain regions. Human participants switched between two rules for reporting visual orientation judgments during fMRI recordings. Rule switches were either signaled explicitly or inferred by the participants from ambiguous cues. We used behavioral modeling to reconstruct the time course of their belief about the active rule. In both contexts, the patterns of correlations between ongoing fluctuations in stimulus- and action-selective activity across visual- and action-related brain regions tracked participants' belief about the active rule. The rule-specific correlation patterns broke down around the time of behavioral errors. We conclude that internal beliefs about task state are instantiated in brain-wide, selective patterns of correlated variability.
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Affiliation(s)
- Ruud L van den Brink
- Computational Cognitive Neuroscience Section, Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany.
| | - Keno Hagena
- Computational Cognitive Neuroscience Section, Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Niklas Wilming
- Computational Cognitive Neuroscience Section, Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Peter R Murphy
- Computational Cognitive Neuroscience Section, Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany; Trinity College Institute of Neuroscience and School of Psychology, Trinity College Dublin, D02 PN40 Dublin, Ireland; Department of Psychology, Maynooth University, Maynooth, Co. Kildare, Ireland
| | - Christian Büchel
- Institute for Systems Neuroscience, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Tobias H Donner
- Computational Cognitive Neuroscience Section, Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany.
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40
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Rosenman ETR, Friedberg R, Baiocchi M. Robust Designs for Prospective Randomized Trials Surveying Sensitive Topics. Am J Epidemiol 2023; 192:812-820. [PMID: 36749012 DOI: 10.1093/aje/kwad027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Revised: 11/09/2022] [Accepted: 02/01/2023] [Indexed: 02/08/2023] Open
Abstract
We consider the problem of designing a prospective randomized trial in which the outcome data will be self-reported, and will involve sensitive topics. Our interest is in how a researcher can adequately power her study when some respondents misreport the binary outcome of interest. To correct the power calculations, we first obtain expressions for the bias and variance induced by misreporting. We model the problem by assuming each individual in our study is a member of one "reporting class": aTrue-reporter, False-reporter, Never-reporter, or Always-reporter. We show that the joint distribution of reporting classes and "response classes" (characterizing individuals' response to the treatment) will exactly define the error terms for our causalestimate. We propose a novel procedure for determining adequate sample sizes under the worst-case power corresponding to a given level of misreporting. Our problem is motivated by prior experience implementing a randomized controlled trial of a sexual violence prevention program among adolescent girls in Kenya.
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Affiliation(s)
- Evan T R Rosenman
- Harvard Data Science Initiative, Harvard University, Cambridge, Massachusetts United States
| | - Rina Friedberg
- LinkedIn Data Science and Applied Research, Sunnyvale, California, United States
| | - Mike Baiocchi
- Stanford University School of Medicine, Stanford, California, United States
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41
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Dietler N, Lupo U, Bitbol AF. Impact of phylogeny on structural contact inference from protein sequence data. J R Soc Interface 2023; 20:20220707. [PMID: 36751926 PMCID: PMC9905998 DOI: 10.1098/rsif.2022.0707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2023] Open
Abstract
Local and global inference methods have been developed to infer structural contacts from multiple sequence alignments of homologous proteins. They rely on correlations in amino acid usage at contacting sites. Because homologous proteins share a common ancestry, their sequences also feature phylogenetic correlations, which can impair contact inference. We investigate this effect by generating controlled synthetic data from a minimal model where the importance of contacts and of phylogeny can be tuned. We demonstrate that global inference methods, specifically Potts models, are more resilient to phylogenetic correlations than local methods, based on covariance or mutual information. This holds whether or not phylogenetic corrections are used, and may explain the success of global methods. We analyse the roles of selection strength and of phylogenetic relatedness. We show that sites that mutate early in the phylogeny yield false positive contacts. We consider natural data and realistic synthetic data, and our findings generalize to these cases. Our results highlight the impact of phylogeny on contact prediction from protein sequences and illustrate the interplay between the rich structure of biological data and inference.
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Affiliation(s)
- Nicola Dietler
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Umberto Lupo
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Anne-Florence Bitbol
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
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42
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Lambert B, Lei CL, Robinson M, Clerx M, Creswell R, Ghosh S, Tavener S, Gavaghan DJ. Autocorrelated measurement processes and inference for ordinary differential equation models of biological systems. J R Soc Interface 2023. [PMCID: PMC9943878 DOI: 10.1098/rsif.2022.0725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023] Open
Abstract
Ordinary differential equation models are used to describe dynamic processes across biology. To perform likelihood-based parameter inference on these models, it is necessary to specify a statistical process representing the contribution of factors not explicitly included in the mathematical model. For this, independent Gaussian noise is commonly chosen, with its use so widespread that researchers typically provide no explicit justification for this choice. This noise model assumes ‘random’ latent factors affect the system in the ephemeral fashion resulting in unsystematic deviation of observables from their modelled counterparts. However, like the deterministically modelled parts of a system, these latent factors can have persistent effects on observables. Here, we use experimental data from dynamical systems drawn from cardiac physiology and electrochemistry to demonstrate that highly persistent differences between observations and modelled quantities can occur. Considering the case when persistent noise arises owing only to measurement imperfections, we use the Fisher information matrix to quantify how uncertainty in parameter estimates is artificially reduced when erroneously assuming independent noise. We present a workflow to diagnose persistent noise from model fits and describe how to remodel accounting for correlated errors.
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Affiliation(s)
- Ben Lambert
- Department of Mathematics, University of Exeter, Exeter EX4 4PY, UK
| | - Chon Lok Lei
- Faculty of Health Sciences, Institute of Translational Medicine, University of Macau, Macau, People’s Republic of China
| | - Martin Robinson
- Department of Computer Science, University of Oxford, Oxford OX1 3QG, UK
| | - Michael Clerx
- School of Mathematical Sciences, University of Nottingham, Nottingham NG7 2RD, UK
| | - Richard Creswell
- Department of Computer Science, University of Oxford, Oxford OX1 3QG, UK
| | - Sanmitra Ghosh
- MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK
| | - Simon Tavener
- Department of Mathematics, Colorado State University, Fort Collins, CO 80523, UK
| | - David J. Gavaghan
- Department of Computer Science, University of Oxford, Oxford OX1 3QG, UK
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43
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Narang S, Kaduk M, Chernyshev M, Karlsson Hedestam GB, Corcoran MM. Adaptive immune receptor genotyping using the corecount program. Front Immunol 2023; 14:1125884. [PMID: 37114042 PMCID: PMC10126697 DOI: 10.3389/fimmu.2023.1125884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 02/27/2023] [Indexed: 04/29/2023] Open
Abstract
We present a new Rep-Seq analysis tool called corecount, for analyzing genotypic variation in immunoglobulin (IG) and T cell receptor (TCR) genes. corecount is highly efficient at identifying V alleles, including those that are infrequently used in expressed repertoires and those that contain 3' end variation that are otherwise refractory to reliable identification during germline inference from expressed libraries. Furthermore, corecount facilitates accurate D and J gene genotyping. The output is highly reproducible and facilitates the comparison of genotypes from multiple individuals, such as those from clinical cohorts. Here, we applied corecount to the genotypic analysis of IgM libraries from 16 individuals. To demonstrate the accuracy of corecount, we Sanger sequenced all the heavy chain IG alleles (65 IGHV, 27 IGHD and 7 IGHJ) from one individual from whom we also produced two independent IgM Rep-seq datasets. Genomic analysis revealed that 5 known IGHV and 2 IGHJ sequences are truncated in current reference databases. This dataset of genomically validated alleles and IgM libraries from the same individual provides a useful resource for benchmarking other bioinformatic programs that involve V, D and J assignments and germline inference, and may facilitate the development of AIRR-Seq analysis tools that can take benefit from the availability of more comprehensive reference databases.
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44
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Mikulasch FA, Rudelt L, Wibral M, Priesemann V. Where is the error? Hierarchical predictive coding through dendritic error computation. Trends Neurosci 2023; 46:45-59. [PMID: 36577388 DOI: 10.1016/j.tins.2022.09.007] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 09/28/2022] [Accepted: 09/28/2022] [Indexed: 11/19/2022]
Abstract
Top-down feedback in cortex is critical for guiding sensory processing, which has prominently been formalized in the theory of hierarchical predictive coding (hPC). However, experimental evidence for error units, which are central to the theory, is inconclusive and it remains unclear how hPC can be implemented with spiking neurons. To address this, we connect hPC to existing work on efficient coding in balanced networks with lateral inhibition and predictive computation at apical dendrites. Together, this work points to an efficient implementation of hPC with spiking neurons, where prediction errors are computed not in separate units, but locally in dendritic compartments. We then discuss the correspondence of this model to experimentally observed connectivity patterns, plasticity, and dynamics in cortex.
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Affiliation(s)
- Fabian A Mikulasch
- Max-Planck-Institute for Dynamics and Self-Organization, Göttingen, Germany.
| | - Lucas Rudelt
- Max-Planck-Institute for Dynamics and Self-Organization, Göttingen, Germany
| | - Michael Wibral
- Göttingen Campus Institute for Dynamics of Biological Networks, Georg-August University, Göttingen, Germany
| | - Viola Priesemann
- Max-Planck-Institute for Dynamics and Self-Organization, Göttingen, Germany; Bernstein Center for Computational Neuroscience (BCCN), Göttingen, Germany; Department of Physics, Georg-August University, Göttingen, Germany
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45
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Yang Z, Zhao Y, Yu J, Mao X, Xu H, Huang L. An Intelligent Tongue Diagnosis System via Deep Learning on the Android Platform. Diagnostics (Basel) 2022; 12:2451. [PMID: 36292140 DOI: 10.3390/diagnostics12102451] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Revised: 09/22/2022] [Accepted: 09/29/2022] [Indexed: 11/17/2022] Open
Abstract
To quickly and accurately identify the pathological features of the tongue, we developed an intelligent tongue diagnosis system that uses deep learning on a mobile terminal. We also propose an efficient and accurate tongue image processing algorithm framework to infer the category of the tongue. First, a software system integrating registration, login, account management, tongue image recognition, and doctor-patient dialogue was developed based on the Android platform. Then, the deep learning models, based on the official benchmark models, were trained by using the tongue image datasets. The tongue diagnosis algorithm framework includes the YOLOv5s6, U-Net, and MobileNetV3 networks, which are employed for tongue recognition, tongue region segmentation, and tongue feature classification (tooth marks, spots, and fissures), respectively. The experimental results demonstrate that the performance of the tongue diagnosis model was satisfying, and the accuracy of the final classification of tooth marks, spots, and fissures was 93.33%, 89.60%, and 97.67%, respectively. The construction of this system has a certain reference value for the objectification and intelligence of tongue diagnosis.
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46
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Gower G, Ragsdale AP, Bisschop G, Gutenkunst RN, Hartfield M, Noskova E, Schiffels S, Struck TJ, Kelleher J, Thornton KR. Demes: a standard format for demographic models. Genetics 2022; 222:6730747. [PMID: 36173327 PMCID: PMC9630982 DOI: 10.1093/genetics/iyac131] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 08/23/2022] [Indexed: 11/12/2022] Open
Abstract
Understanding the demographic history of populations is a key goal in population genetics, and with improving methods and data, ever more complex models are being proposed and tested. Demographic models of current interest typically consist of a set of discrete populations, their sizes and growth rates, and continuous and pulse migrations between those populations over a number of epochs, which can require dozens of parameters to fully describe. There is currently no standard format to define such models, significantly hampering progress in the field. In particular, the important task of translating the model descriptions in published work into input suitable for population genetic simulators is labor intensive and error prone. We propose the Demes data model and file format, built on widely used technologies, to alleviate these issues. Demes provides a well-defined and unambiguous model of populations and their properties that is straightforward to implement in software, and a text file format that is designed for simplicity and clarity. We provide thoroughly tested implementations of Demes parsers in multiple languages including Python and C, and showcase initial support in several simulators and inference methods. An introduction to the file format and a detailed specification are available at https://popsim-consortium.github.io/demes-spec-docs/.
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Affiliation(s)
- Graham Gower
- Section for Molecular Ecology and Evolution, Globe Institute, University of Copenhagen, 1350 Copenhagen K, Denmark
| | - Aaron P Ragsdale
- Department of Integrative Biology, University of Wisconsin-Madison, WI 53706, USA
| | - Gertjan Bisschop
- Institute of Ecology and Evolution, The University of Edinburgh, EH9 3FL, UK
| | - Ryan N Gutenkunst
- Department of Molecular and Cellular Biology, University of Arizona, Tucson, AZ 85721, USA
| | - Matthew Hartfield
- Institute of Ecology and Evolution, The University of Edinburgh, EH9 3FL, UK
| | - Ekaterina Noskova
- Computer Technologies Laboratory, ITMO University, 197101, Saint-Petersburg, Russia
| | - Stephan Schiffels
- Max Planck Institute for Evolutionary Anthropology, 04103 Leipzig, Germany
| | - Travis J Struck
- Department of Molecular and Cellular Biology, University of Arizona, Tucson, AZ 85721, USA
| | - Jerome Kelleher
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, OX3 7LF, UK
| | - Kevin R Thornton
- Ecology and Evolutionary Biology, University of California, Irvine, CA 92697, USA
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47
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Rouault M, Weiss A, Lee JK, Drugowitsch J, Chambon V, Wyart V. Controllability boosts neural and cognitive signatures of changes-of-mind in uncertain environments. eLife 2022; 11:75038. [PMID: 36097814 PMCID: PMC9470160 DOI: 10.7554/elife.75038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 07/19/2022] [Indexed: 11/13/2022] Open
Abstract
In uncertain environments, seeking information about alternative choice options is essential for adaptive learning and decision-making. However, information seeking is usually confounded with changes-of-mind about the reliability of the preferred option. Here, we exploited the fact that information seeking requires control over which option to sample to isolate its behavioral and neurophysiological signatures. We found that changes-of-mind occurring with control require more evidence against the current option, are associated with reduced confidence, but are nevertheless more likely to be confirmed on the next decision. Multimodal neurophysiological recordings showed that these changes-of-mind are preceded by stronger activation of the dorsal attention network in magnetoencephalography, and followed by increased pupil-linked arousal during the presentation of decision outcomes. Together, these findings indicate that information seeking increases the saliency of evidence perceived as the direct consequence of one's own actions.
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Affiliation(s)
- Marion Rouault
- Laboratoire de Neurosciences Cognitives et Computationnelles, Institut National de la Santé et de la Recherche Médicale (Inserm), Paris, France.,Institut Jean Nicod, Centre National de la Recherche Scientifique (CNRS), Paris, France.,Département d'Études Cognitives, École Normale Supérieure, Université Paris Sciences et Lettres (PSL University), Paris, France
| | - Aurélien Weiss
- Laboratoire de Neurosciences Cognitives et Computationnelles, Institut National de la Santé et de la Recherche Médicale (Inserm), Paris, France.,Département d'Études Cognitives, École Normale Supérieure, Université Paris Sciences et Lettres (PSL University), Paris, France.,Université de Paris, Paris, France
| | - Junseok K Lee
- Laboratoire de Neurosciences Cognitives et Computationnelles, Institut National de la Santé et de la Recherche Médicale (Inserm), Paris, France.,Département d'Études Cognitives, École Normale Supérieure, Université Paris Sciences et Lettres (PSL University), Paris, France
| | - Jan Drugowitsch
- Department of Neurobiology, Harvard Medical School, Boston, United States
| | - Valerian Chambon
- Institut Jean Nicod, Centre National de la Recherche Scientifique (CNRS), Paris, France.,Département d'Études Cognitives, École Normale Supérieure, Université Paris Sciences et Lettres (PSL University), Paris, France
| | - Valentin Wyart
- Laboratoire de Neurosciences Cognitives et Computationnelles, Institut National de la Santé et de la Recherche Médicale (Inserm), Paris, France.,Département d'Études Cognitives, École Normale Supérieure, Université Paris Sciences et Lettres (PSL University), Paris, France
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48
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Abstract
statistics for genome-wide association studies (GWAS) are increasingly available for downstream analyses. Meanwhile, the popularity of causal inference methods has grown as we look to gather robust evidence for novel medical and public health interventions. This has led to the development of methods that use GWAS summary statistics for causal inference. Here, we describe these methods in order of their escalating complexity, from genetic associations to extensions of Mendelian randomization that consider thousands of phenotypes simultaneously. We also cover the assumptions and limitations of these approaches before considering the challenges faced by researchers performing causal inference using GWAS data. GWAS summary statistics constitute an important data source for causal inference research that offers a counterpoint to nongenetic methods when triangulating evidence. Continued efforts to address the challenges in using GWAS data for causal inference will allow the full impact of these approaches to be realized.
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Affiliation(s)
- Venexia M. Walker
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Jie Zheng
- MRC (Medical Research Council) Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom;
| | - Tom R. Gaunt
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - George Davey Smith
- MRC (Medical Research Council) Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom;
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49
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Noble S, Mejia AF, Zalesky A, Scheinost D. Improving power in functional magnetic resonance imaging by moving beyond cluster-level inference. Proc Natl Acad Sci U S A 2022; 119:e2203020119. [PMID: 35925887 DOI: 10.1073/pnas.2203020119] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Inference in neuroimaging typically occurs at the level of focal brain areas or circuits. Yet, increasingly, well-powered studies paint a much richer picture of broad-scale effects distributed throughout the brain, suggesting that many focal reports may only reflect the tip of the iceberg of underlying effects. How focal versus broad-scale perspectives influence the inferences we make has not yet been comprehensively evaluated using real data. Here, we compare sensitivity and specificity across procedures representing multiple levels of inference using an empirical benchmarking procedure that resamples task-based connectomes from the Human Connectome Project dataset (∼1,000 subjects, 7 tasks, 3 resampling group sizes, 7 inferential procedures). Only broad-scale (network and whole brain) procedures obtained the traditional 80% statistical power level to detect an average effect, reflecting >20% more statistical power than focal (edge and cluster) procedures. Power also increased substantially for false discovery rate- compared with familywise error rate-controlling procedures. The downsides are fairly limited; the loss in specificity for broad-scale and FDR procedures was relatively modest compared to the gains in power. Furthermore, the broad-scale methods we introduce are simple, fast, and easy to use, providing a straightforward starting point for researchers. This also points to the promise of more sophisticated broad-scale methods for not only functional connectivity but also related fields, including task-based activation. Altogether, this work demonstrates that shifting the scale of inference and choosing FDR control are both immediately attainable and can help remedy the issues with statistical power plaguing typical studies in the field.
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50
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Wang Y, Luan S, Gigerenzer G. Modeling fast-and-frugal heuristics. Psych J 2022; 11:600-611. [PMID: 35778774 DOI: 10.1002/pchj.576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 05/11/2022] [Indexed: 11/08/2022]
Abstract
Heuristics are simple rules that experts and laypeople rely on to make decisions under uncertainty as opposed to situations with calculable risk. The research program on fast-and-frugal heuristics studies formal models of heuristics and is motivated by Herbert Simon's seminal work on bounded rationality and satisficing. In this article, we first introduce the major theoretical principles (e.g., ecological rationality) and research approaches (e.g., competitive testing) that have been adopted in this research program, and then illustrate these principles and approaches with two heuristics: take-the-best and fast-and-frugal trees. We describe conditions under which simple heuristics predict as accurately as or better than more complex models, despite requiring less effort. We close by pointing out several issues that need to be further studied and better understood in the research on fast-and-frugal heuristics.
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Affiliation(s)
- Yuhui Wang
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Shenghua Luan
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
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