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Rasanan AHH, Evans NJ, Fontanesi L, Manning C, Huang-Pollock C, Matzke D, Heathcote A, Rieskamp J, Speekenbrink M, Frank MJ, Palminteri S, Lucas CG, Busemeyer JR, Ratcliff R, Rad JA. Beyond discrete-choice options. Trends Cogn Sci 2024:S1364-6613(24)00175-X. [PMID: 39138030 DOI: 10.1016/j.tics.2024.07.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 07/12/2024] [Accepted: 07/14/2024] [Indexed: 08/15/2024]
Abstract
While decision theories have evolved over the past five decades, their focus has largely been on choices among a limited number of discrete options, even though many real-world situations have a continuous-option space. Recently, theories have attempted to address decisions with continuous-option spaces, and several computational models have been proposed within the sequential sampling framework to explain how we make a decision in continuous-option space. This article aims to review the main attempts to understand decisions on continuous-option spaces, give an overview of applications of these types of decisions, and present puzzles to be addressed by future developments.
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Affiliation(s)
| | - Nathan J Evans
- School of Psychology, The University of Queensland, St Lucia, QLD 4072, Australia; Department of Psychology, Ludwig Maximilian University of Munich, Munich, Germany
| | - Laura Fontanesi
- Department of Psychology, University of Basel, Missionsstrasse 62A, 4055, Basel, Switzerland
| | | | | | - Dora Matzke
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Andrew Heathcote
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands; School of Psychological Sciences, University of Newcastle, Newcastle, Australia
| | - Jörg Rieskamp
- Department of Psychology, University of Basel, Missionsstrasse 62A, 4055, Basel, Switzerland
| | | | - Michael J Frank
- Department of Cognitive, Linguistic, and Psychological Sciences and Carney Institute for Brain Science, Brown University, Providence, RI, USA
| | - Stefano Palminteri
- Laboratoire de Neurosciences Cognitives Computationnelles, Institut National de la Santé et Recherche Médicale, Paris, France; Département d'Etudes Cognitives, Ecole Normale Supérieure, PSL Research University, Paris, France
| | | | - Jerome R Busemeyer
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Roger Ratcliff
- The Ohio State University, 1835 Neil Avenue, Columbus, OH, 43210, USA
| | - Jamal Amani Rad
- Choice Modelling Centre and Institute for Transport Studies, University of Leeds, Leeds LS2 9JT, UK.
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2
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Wu J, Li J, Eickhoff SB, Scheinost D, Genon S. The challenges and prospects of brain-based prediction of behaviour. Nat Hum Behav 2023; 7:1255-1264. [PMID: 37524932 DOI: 10.1038/s41562-023-01670-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 06/27/2023] [Indexed: 08/02/2023]
Abstract
Relating individual brain patterns to behaviour is fundamental in system neuroscience. Recently, the predictive modelling approach has become increasingly popular, largely due to the recent availability of large open datasets and access to computational resources. This means that we can use machine learning models and interindividual differences at the brain level represented by neuroimaging features to predict interindividual differences in behavioural measures. By doing so, we could identify biomarkers and neural correlates in a data-driven fashion. Nevertheless, this budding field of neuroimaging-based predictive modelling is facing issues that may limit its potential applications. Here we review these existing challenges, as well as those that we anticipate as the field develops. We focus on the impacts of these challenges on brain-based predictions. We suggest potential solutions to address the resolvable challenges, while keeping in mind that some general and conceptual limitations may also underlie the predictive modelling approach.
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Affiliation(s)
- Jianxiao Wu
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany.
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany.
| | - Jingwei Li
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, USA
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA
- Department of Biomedical Engineering, Yale School of Engineering and Applied Sciences, New Haven, CT, USA
| | - Sarah Genon
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany.
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany.
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3
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Souto-Maior C, Serrano Negron YL, Harbison ST. Nonlinear expression patterns and multiple shifts in gene network interactions underlie robust phenotypic change in Drosophila melanogaster selected for night sleep duration. PLoS Comput Biol 2023; 19:e1011389. [PMID: 37561813 PMCID: PMC10443883 DOI: 10.1371/journal.pcbi.1011389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 08/22/2023] [Accepted: 07/25/2023] [Indexed: 08/12/2023] Open
Abstract
All but the simplest phenotypes are believed to result from interactions between two or more genes forming complex networks of gene regulation. Sleep is a complex trait known to depend on the system of feedback loops of the circadian clock, and on many other genes; however, the main components regulating the phenotype and how they interact remain an unsolved puzzle. Genomic and transcriptomic data may well provide part of the answer, but a full account requires a suitable quantitative framework. Here we conducted an artificial selection experiment for sleep duration with RNA-seq data acquired each generation. The phenotypic results are robust across replicates and previous experiments, and the transcription data provides a high-resolution, time-course data set for the evolution of sleep-related gene expression. In addition to a Hierarchical Generalized Linear Model analysis of differential expression that accounts for experimental replicates we develop a flexible Gaussian Process model that estimates interactions between genes. 145 gene pairs are found to have interactions that are different from controls. Our method appears to be not only more specific than standard correlation metrics but also more sensitive, finding correlations not significant by other methods. Statistical predictions were compared to experimental data from public databases on gene interactions. Mutations of candidate genes implicated by our results affected night sleep, and gene expression profiles largely met predicted gene-gene interactions.
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Affiliation(s)
- Caetano Souto-Maior
- Laboratory of Systems Genetics, Systems Biology Center, National Heart Lung and Blood Institute, Bethesda, Maryland, United States of America
| | - Yazmin L. Serrano Negron
- Laboratory of Systems Genetics, Systems Biology Center, National Heart Lung and Blood Institute, Bethesda, Maryland, United States of America
| | - Susan T. Harbison
- Laboratory of Systems Genetics, Systems Biology Center, National Heart Lung and Blood Institute, Bethesda, Maryland, United States of America
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4
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Talaei N, Ghaderi A. Integration of structural brain networks is related to openness to experience: A diffusion MRI study with CSD-based tractography. Front Neurosci 2022; 16:1040799. [PMID: 36570828 PMCID: PMC9775296 DOI: 10.3389/fnins.2022.1040799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 11/23/2022] [Indexed: 12/13/2022] Open
Abstract
Openness to experience is one of the big five traits of personality which recently has been the subject of several studies in neuroscience due to its importance in understanding various cognitive functions. However, the neural basis of openness to experience is still unclear. Previous studies have found largely heterogeneous results, suggesting that various brain regions may be involved in openness to experience. Here we suggested that performing structural connectome analysis may shed light on the neural underpinnings of openness to experience as it provides a more comprehensive look at the brain regions that are involved in this trait. Hence, we investigated the involvement of brain network structural features in openness to experience which has not yet been explored to date. The magnetic resonance imaging (MRI) data along with the openness to experience trait score from the self-reported NEO Five-Factor Inventory of 100 healthy subjects were evaluated from Human Connectome Project (HCP). CSD-based whole-brain probabilistic tractography was performed using diffusion-weighted images as well as segmented T1-weighted images to create an adjacency matrix for each subject. Using graph theoretical analysis, we computed global efficiency (GE) and clustering coefficient (CC) which are measures of two important aspects of network organization in the brain: functional integration and functional segregation respectively. Results revealed a significant negative correlation between GE and openness to experience which means that the higher capacity of the brain in combining information from different regions may be related to lower openness to experience.
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Affiliation(s)
- Nima Talaei
- Department of Psychology, Faculty of Literature and Human Sciences, Shahid Bahonar University, Kerman, Iran,*Correspondence: Nima Talaei,
| | - Amirhossein Ghaderi
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada,Department of Psychology, University of Calgary, Calgary, AB, Canada
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5
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Zhao C, Zhan L, Thompson PM, Huang H. Revealing Continuous Brain Dynamical Organization with Multimodal Graph Transformer. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2022; 13431:346-355. [PMID: 39051031 PMCID: PMC11266984 DOI: 10.1007/978-3-031-16431-6_33] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/27/2024]
Abstract
Brain large-scale dynamics is constrained by the heterogeneity of intrinsic anatomical substrate. Little is known how the spatio-temporal dynamics adapt for the heterogeneous structural connectivity (SC). Modern neuroimaging modalities make it possible to study the intrinsic brain activity at the scale of seconds to minutes. Diffusion magnetic resonance imaging (dMRI) and functional MRI reveals the large-scale SC across different brain regions. Electrophysiological methods (i.e. MEG/EEG) provide direct measures of neural activity and exhibits complex neurobiological temporal dynamics which could not be solved by fMRI. However, most of existing multimodal analytical methods collapse the brain measurements either in space or time domain and fail to capture the spatio-temporal circuit dynamics. In this paper, we propose a novel spatio-temporal graph Transformer model to integrate the structural and functional connectivity in both spatial and temporal domain. The proposed method learns the heterogeneous node and graph representation via contrastive learning and multi-head attention based graph Transformer using multimodal brain data (i.e. fMRI, MRI, MEG and behavior performance). The proposed contrastive graph Transformer representation model incorporates the heterogeneity map constrained by T1-to-T2-weighted (T1w/T2w) to improve the model fit to structure-function interactions. The experimental results with multimodal resting state brain measurements demonstrate the proposed method could highlight the local properties of large-scale brain spatio-temporal dynamics and capture the dependence strength between functional connectivity and behaviors. In summary, the proposed method enables the complex brain dynamics explanation for different modal variants.
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Affiliation(s)
- Chongyue Zhao
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Liang Zhan
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Paul M Thompson
- Imaging Genetics Center, University of Southern California, Los Angeles, CA, USA
| | - Heng Huang
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA
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Xie H, Beaty RE, Jahanikia S, Geniesse C, Sonalkar NS, Saggar M. Spontaneous and deliberate modes of creativity: Multitask eigen-connectivity analysis captures latent cognitive modes during creative thinking. Neuroimage 2021; 243:118531. [PMID: 34469816 DOI: 10.1016/j.neuroimage.2021.118531] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 08/24/2021] [Accepted: 08/28/2021] [Indexed: 11/30/2022] Open
Abstract
Despite substantial progress in the quest of demystifying the brain basis of creativity, several questions remain open. One such issue concerns the relationship between two latent cognitive modes during creative thinking, i.e., deliberate goal-directed cognition and spontaneous thought generation. Although an interplay between deliberate and spontaneous thinking is often implicated in the creativity literature (e.g., dual-process models), a bottom-up data-driven validation of the cognitive processes associated with creative thinking is still lacking. Here, we attempted to capture the latent modes of creative thinking by utilizing a data-driven approach on a novel continuous multitask paradigm (CMP) that widely sampled a hypothetical two-dimensional cognitive plane of deliberate and spontaneous thinking in a single fMRI session. The CMP consisted of eight task blocks ranging from undirected mind wandering to goal-directed working memory task, while also included two widely-used creativity tasks, i.e., alternate uses task (AUT) and remote association task (RAT). Using eigen-connectivity (EC) analysis on the multitask whole-brain functional connectivity (FC) patterns, we embedded the multitask FCs into a low-dimensional latent space. The first two latent components, as revealed by the EC analysis, broadly mapped onto the two cognitive modes of deliberate and spontaneous thinking, respectively. Further, in this low-dimensional space, both creativity tasks were located in the upper right corner of high deliberate and spontaneous thinking (creative cognitive space). Neuroanatomically, the creative cognitive space was represented by not only increased intra-network connectivity within executive control and default mode network, but also by higher coupling between the two canonical brain networks. Further, individual differences reflected in the low-dimensional connectivity embeddings were related to differences in deliberate and spontaneous thinking abilities. Altogether, using a continuous multitask paradigm and a data-driven approach, we provide initial empirical evidence for the contribution of both deliberate and spontaneous modes of cognition during creative thinking.
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Affiliation(s)
- Hua Xie
- Department of Psychiatry and Behavioral Sciences, Stanford University, USA
| | - Roger E Beaty
- Department of Psychology, Pennsylvania State University, USA
| | - Sahar Jahanikia
- Department of Psychiatry and Behavioral Sciences, Stanford University, USA
| | | | | | - Manish Saggar
- Department of Psychiatry and Behavioral Sciences, Stanford University, USA.
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Desender K, Ridderinkhof KR, Murphy PR. Understanding neural signals of post-decisional performance monitoring: An integrative review. eLife 2021; 10:e67556. [PMID: 34414883 PMCID: PMC8378845 DOI: 10.7554/elife.67556] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 08/08/2021] [Indexed: 12/22/2022] Open
Abstract
Performance monitoring is a key cognitive function, allowing to detect mistakes and adapt future behavior. Post-decisional neural signals have been identified that are sensitive to decision accuracy, decision confidence and subsequent adaptation. Here, we review recent work that supports an understanding of late error/confidence signals in terms of the computational process of post-decisional evidence accumulation. We argue that the error positivity, a positive-going centro-parietal potential measured through scalp electrophysiology, reflects the post-decisional evidence accumulation process itself, which follows a boundary crossing event corresponding to initial decision commitment. This proposal provides a powerful explanation for both the morphological characteristics of the signal and its relation to various expressions of performance monitoring. Moreover, it suggests that the error positivity -a signal with thus far unique properties in cognitive neuroscience - can be leveraged to furnish key new insights into the inputs to, adaptation, and consequences of the post-decisional accumulation process.
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Affiliation(s)
- Kobe Desender
- Brain and Cognition, KU LeuvenLeuvenBelgium
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-EppendorfHamburgGermany
| | - K Richard Ridderinkhof
- Department of Psychology, University of AmsterdamAmsterdamNetherlands
- Amsterdam center for Brain and Cognition (ABC), University of AmsterdamAmsterdamNetherlands
| | - Peter R Murphy
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-EppendorfHamburgGermany
- Trinity College Institute of Neuroscience, Trinity College DublinDublinIreland
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Niu H, Chen Y, West BJ. Why Do Big Data and Machine Learning Entail the Fractional Dynamics? ENTROPY (BASEL, SWITZERLAND) 2021; 23:297. [PMID: 33671047 PMCID: PMC7997214 DOI: 10.3390/e23030297] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 02/23/2021] [Accepted: 02/24/2021] [Indexed: 11/16/2022]
Abstract
Fractional-order calculus is about the differentiation and integration of non-integer orders. Fractional calculus (FC) is based on fractional-order thinking (FOT) and has been shown to help us to understand complex systems better, improve the processing of complex signals, enhance the control of complex systems, increase the performance of optimization, and even extend the enabling of the potential for creativity. In this article, the authors discuss the fractional dynamics, FOT and rich fractional stochastic models. First, the use of fractional dynamics in big data analytics for quantifying big data variability stemming from the generation of complex systems is justified. Second, we show why fractional dynamics is needed in machine learning and optimal randomness when asking: "is there a more optimal way to optimize?". Third, an optimal randomness case study for a stochastic configuration network (SCN) machine-learning method with heavy-tailed distributions is discussed. Finally, views on big data and (physics-informed) machine learning with fractional dynamics for future research are presented with concluding remarks.
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Affiliation(s)
- Haoyu Niu
- Electrical Engineering and Computer Science Department, University of California, Merced, CA 95340, USA;
| | - YangQuan Chen
- Mechanical Engineering Department, University of California, Merced, CA 95340, USA
| | - Bruce J. West
- Office of the Director, Army Research Office, Research Triangle Park, NC 27709, USA;
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Shiffrin RM, Bassett DS, Kriegeskorte N, Tenenbaum JB. The brain produces mind by modeling. Proc Natl Acad Sci U S A 2020; 117:29299-29301. [PMID: 33229525 PMCID: PMC7703556 DOI: 10.1073/pnas.1912340117] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/28/2023] Open
Affiliation(s)
- Richard M Shiffrin
- Psychological and Brain Sciences Department, Indiana University, Bloomington, IN 47405;
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104
| | | | - Joshua B Tenenbaum
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139-4307
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