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König SD, Safo S, Miller K, Herman AB, Darrow DP. Flexible multi-step hypothesis testing of human ECoG data using cluster-based permutation tests with GLMEs. Neuroimage 2024; 290:120557. [PMID: 38423264 PMCID: PMC11268380 DOI: 10.1016/j.neuroimage.2024.120557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 02/22/2024] [Accepted: 02/26/2024] [Indexed: 03/02/2024] Open
Abstract
BACKGROUND Time series analysis is critical for understanding brain signals and their relationship to behavior and cognition. Cluster-based permutation tests (CBPT) are commonly used to analyze a variety of electrophysiological signals including EEG, MEG, ECoG, and sEEG data without a priori assumptions about specific temporal effects. However, two major limitations of CBPT include the inability to directly analyze experiments with multiple fixed effects and the inability to account for random effects (e.g. variability across subjects). Here, we propose a flexible multi-step hypothesis testing strategy using CBPT with Linear Mixed Effects Models (LMEs) and Generalized Linear Mixed Effects Models (GLMEs) that can be applied to a wide range of experimental designs and data types. METHODS We first evaluate the statistical robustness of LMEs and GLMEs using simulated data distributions. Second, we apply a multi-step hypothesis testing strategy to analyze ERPs and broadband power signals extracted from human ECoG recordings collected during a simple image viewing experiment with image category and novelty as fixed effects. Third, we assess the statistical power differences between analyzing signals with CBPT using LMEs compared to CBPT using separate t-tests run on each fixed effect through simulations that emulate broadband power signals. Finally, we apply CBPT using GLMEs to high-gamma burst data to demonstrate the extension of the proposed method to the analysis of nonlinear data. RESULTS First, we found that LMEs and GLMEs are robust statistical models. In simple simulations LMEs produced highly congruent results with other appropriately applied linear statistical models, but LMEs outperformed many linear statistical models in the analysis of "suboptimal" data and maintained power better than analyzing individual fixed effects with separate t-tests. GLMEs also performed similarly to other nonlinear statistical models. Second, in real world human ECoG data, LMEs performed at least as well as separate t-tests when applied to predefined time windows or when used in conjunction with CBPT. Additionally, fixed effects time courses extracted with CBPT using LMEs from group-level models of pseudo-populations replicated latency effects found in individual category-selective channels. Third, analysis of simulated broadband power signals demonstrated that CBPT using LMEs was superior to CBPT using separate t-tests in identifying time windows with significant fixed effects especially for small effect sizes. Lastly, the analysis of high-gamma burst data using CBPT with GLMEs produced results consistent with CBPT using LMEs applied to broadband power data. CONCLUSIONS We propose a general approach for statistical analysis of electrophysiological data using CBPT in conjunction with LMEs and GLMEs. We demonstrate that this method is robust for experiments with multiple fixed effects and applicable to the analysis of linear and nonlinear data. Our methodology maximizes the statistical power available in a dataset across multiple experimental variables while accounting for hierarchical random effects and controlling FWER across fixed effects. This approach substantially improves power leading to better reproducibility. Additionally, CBPT using LMEs and GLMEs can be used to analyze individual channels or pseudo-population data for the comparison of functional or anatomical groups of data.
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Affiliation(s)
- Seth D König
- Department of Psychiatry, University of Minnesota, USA; Department of Neurosurgery, University of Minnesota, USA
| | - Sandra Safo
- Department of Neurosurgery, Mayo Clinic, USA
| | - Kai Miller
- Department of Biostatistics, University of Minnesota, USA
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König SD, Safo S, Miller K, Herman AB, Darrow DP. Flexible Multi-Step Hypothesis Testing of Human ECoG Data using Cluster-based Permutation Tests with GLMEs. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.31.535153. [PMID: 37034791 PMCID: PMC10081325 DOI: 10.1101/2023.03.31.535153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Background Time series analysis is critical for understanding brain signals and their relationship to behavior and cognition. Cluster-based permutation tests (CBPT) are commonly used to analyze a variety of electrophysiological signals including EEG, MEG, ECoG, and sEEG data without a priori assumptions about specific temporal effects. However, two major limitations of CBPT include the inability to directly analyze experiments with multiple fixed effects and the inability to account for random effects (e.g. variability across subjects). Here, we propose a flexible multi-step hypothesis testing strategy using CBPT with Linear Mixed Effects Models (LMEs) and Generalized Linear Mixed Effects Models (GLMEs) that can be applied to a wide range of experimental designs and data types. Methods We first evaluate the statistical robustness of LMEs and GLMEs using simulated data distributions. Second, we apply a multi-step hypothesis testing strategy to analyze ERPs and broadband power signals extracted from human ECoG recordings collected during a simple image viewing experiment with image category and novelty as fixed effects. Third, we assess the statistical power differences between analyzing signals with CBPT using LMEs compared to CBPT using separate t-tests run on each fixed effect through simulations that emulate broadband power signals. Finally, we apply CBPT using GLMEs to high-gamma burst data to demonstrate the extension of the proposed method to the analysis of nonlinear data. Results First, we found that LMEs and GLMEs are robust statistical models. In simple simulations LMEs produced highly congruent results with other appropriately applied linear statistical models, but LMEs outperformed many linear statistical models in the analysis of "suboptimal" data and maintained power better than analyzing individual fixed effects with separate t-tests. GLMEs also performed similarly to other nonlinear statistical models. Second, in real world human ECoG data, LMEs performed at least as well as separate t-tests when applied to predefined time windows or when used in conjunction with CBPT. Additionally, fixed effects time courses extracted with CBPT using LMEs from group-level models of pseudo-populations replicated latency effects found in individual category-selective channels. Third, analysis of simulated broadband power signals demonstrated that CBPT using LMEs was superior to CBPT using separate t-tests in identifying time windows with significant fixed effects especially for small effect sizes. Lastly, the analysis of high-gamma burst data using CBPT with GLMEs produced results consistent with CBPT using LMEs applied to broadband power data. Conclusions We propose a general approach for statistical analysis of electrophysiological data using CBPT in conjunction with LMEs and GLMEs. We demonstrate that this method is robust for experiments with multiple fixed effects and applicable to the analysis of linear and nonlinear data. Our methodology maximizes the statistical power available in a dataset across multiple experimental variables while accounting for hierarchical random effects and controlling FWER across fixed effects. This approach substantially improves power and accuracy leading to better reproducibility. Additionally, CBPT using LMEs and GLMEs can be used to analyze individual channels or pseudo-population data for the comparison of functional or anatomical groups of data.
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Affiliation(s)
- Seth D König
- Department of Psychiatry, University of Minnesota
- Department of Neurosurgery, University of Minnesota
| | | | - Kai Miller
- Department of Biostatistics, University of Minnesota
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Fast deliberation is related to unconditional behaviour in iterated Prisoners' Dilemma experiments. Sci Rep 2022; 12:20287. [PMID: 36434077 PMCID: PMC9700794 DOI: 10.1038/s41598-022-24849-4] [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: 09/19/2022] [Accepted: 11/21/2022] [Indexed: 11/26/2022] Open
Abstract
People have different preferences for what they allocate for themselves and what they allocate to others in social dilemmas. These differences result from contextual reasons, intrinsic values, and social expectations. What is still an area of debate is whether these differences can be estimated from differences in each individual's deliberation process. In this work, we analyse the participants' reaction times in three different experiments of the Iterated Prisoner's Dilemma with the Drift Diffusion Model, which links response times to the perceived difficulty of the decision task, the rate of accumulation of information (deliberation), and the intuitive attitudes towards the choices. The correlation between these results and the attitude of the participants towards the allocation of resources is then determined. We observe that individuals who allocated resources equally are correlated with more deliberation than highly cooperative or highly defective participants, who accumulate evidence more quickly to reach a decision. Also, the evidence collection is faster in fixed neighbour settings than in shuffled ones. Consequently, fast decisions do not distinguish cooperators from defectors in these experiments, but appear to separate those that are more reactive to the behaviour of others from those that act categorically.
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Cognitive and affective processes of prosociality. Curr Opin Psychol 2021; 44:309-314. [PMID: 34864587 DOI: 10.1016/j.copsyc.2021.10.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 10/18/2021] [Accepted: 10/20/2021] [Indexed: 11/22/2022]
Abstract
One piece of the puzzle to prosocial behavior is understanding its underlying cognitive and affective processes. We discuss how modeling behavior in social dilemmas can be expanded by integrating cognitive theories and attention-based models of decision processes, and models of affective influences on prosocial decision-making. We review theories speaking to the interconnections of cognition and affect, identifying the need for further theory development regarding modeling moment-by-moment decision-making processes. We discuss how these theoretical perspectives are mirrored in empirical evidence, drawn from classical outcome-oriented as well as contemporary process-tracing research. Finally, we develop perspectives for future research trajectories aiming to further elucidate the processes by which prosocial decisions are formed, by linking process measures to usually unobservable cognitive and affective reactions.
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Bengart P, Gruendler T, Vogt B. Acute tryptophan depletion in healthy subjects increases preferences for negative reciprocity. PLoS One 2021; 16:e0249339. [PMID: 33784350 PMCID: PMC8009398 DOI: 10.1371/journal.pone.0249339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Accepted: 03/16/2021] [Indexed: 11/18/2022] Open
Abstract
Reciprocity motivates to reward those who are kind (= positive reciprocity) and to punish those who are unkind (= negative reciprocity). The neurotransmitter serotonin (5-HT) modulates human behavior in numerous social situations, such as retaliation in response to perceived unfairness. In a placebo-controlled study, we used acute tryptophan depletion (ATD) to investigate the influence of available serotonin on choice behavior and reciprocity in the Hawk-Dove game. This game illustrates a conflict situation and incorporates two potential strategies: the cooperative Dove strategy and the uncooperative, more aggressive Hawk strategy. After strategic choices, we elicited the subjects' expectations (= beliefs) regarding the opponent's choices and controlled for risk preferences and current mood. We defined strategy choices as negative reciprocity when the participants opted for Hawk in response to an expected Hawk. We hypothesized that the ATD-induced reduction of 5-HT availability would increase participants' preferences for negative reciprocity. Generalized estimating equations reveal no significant main effect of ATD on assessed belief, mood, or risk attitude. But assessment of ATD's marginal effects over beliefs suggests that ATD significantly increases the tendency for negative reciprocity, whereas positive reciprocity (Dove in response to an expected Dove) is unaffected. We could therefore demonstrate that 5-HT availability mediates (negative) reciprocal behavior in social decision-making.
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Affiliation(s)
- Paul Bengart
- Institute of Social Medicine and Health Systems Research, Otto von Guericke University Magdeburg, Magdeburg, Germany
| | - Theo Gruendler
- Department of Empirical Economics, Otto von Guericke University Magdeburg, Magdeburg, Germany
- Center for Military Mental Health, Military Hospital Berlin, Berlin, Germany
| | - Bodo Vogt
- Institute of Social Medicine and Health Systems Research, Otto von Guericke University Magdeburg, Magdeburg, Germany
- Department of Empirical Economics, Otto von Guericke University Magdeburg, Magdeburg, Germany
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Prosocial Preferences Condition Decision Effort and Ingroup Biased Generosity in Intergroup Decision-Making. Sci Rep 2020; 10:10132. [PMID: 32576839 PMCID: PMC7311554 DOI: 10.1038/s41598-020-64592-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Accepted: 01/31/2020] [Indexed: 11/25/2022] Open
Abstract
Ingroup favoritism and discrimination against outgroups are pervasive in social interactions. To uncover the cognitive processes underlying generosity towards in- and outgroup members, we employ eye-tracking in two pre registered studies. We replicate the well-established ingroup favoritism effect and uncover that ingroup compared to outgroup decision settings are characterized by systematic differences in information search effort (i.e., increased response times and number of fixations, more inspected information) and attention distribution. Surprisingly, these results showed a stronger dependency on the in- vs. out-group setting for more individualistic compared to prosocial participants: Whereas individualistic decision makers invested relatively less effort into information search when decisions involved out-group members, prosocial decision makers’ effort differed less between in- and outgroup decisions. Therein, choice and processing findings showed differences, indicating that inferences about the decision process from choices alone can be misleading. Implications for intergroup research and the regulation of intergroup conflict are discussed.
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Maier SU, Raja Beharelle A, Polanía R, Ruff CC, Hare TA. Dissociable mechanisms govern when and how strongly reward attributes affect decisions. Nat Hum Behav 2020; 4:949-963. [PMID: 32483344 DOI: 10.1038/s41562-020-0893-y] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2018] [Accepted: 05/04/2020] [Indexed: 11/09/2022]
Abstract
Theories and computational models of decision-making usually focus on how strongly different attributes are weighted in choice, for example, as a function of their importance or salience to the decision-maker. However, when different attributes affect the decision process is a question that has received far less attention. Here, we investigated whether the timing of attribute consideration has a unique influence on decision-making by using a time-varying drift diffusion model and data from four separate experiments. Experimental manipulations of attention and neural activity demonstrated that we can dissociate the processes that determine the relative weighting strength and timing of attribute consideration. Thus, the processes determining either the weighting strengths or the timing of attributes in decision-making can independently adapt to changes in the environment or goals. Quantifying these separate influences of timing and weighting on choice improves our understanding and predictions of individual differences in decision behaviour.
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Affiliation(s)
- Silvia U Maier
- Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, Zurich, Switzerland. .,Neuroscience Center Zurich, University of Zurich, Swiss Federal Institute of Technology Zurich, Zurich, Switzerland. .,Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland.
| | - Anjali Raja Beharelle
- Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, Zurich, Switzerland. .,Neuroscience Center Zurich, University of Zurich, Swiss Federal Institute of Technology Zurich, Zurich, Switzerland.
| | - Rafael Polanía
- Neuroscience Center Zurich, University of Zurich, Swiss Federal Institute of Technology Zurich, Zurich, Switzerland.,Decision Neuroscience Lab, Department of Health Sciences and Technology, ETH, Swiss Federal Institute of Technology, Zurich, Switzerland
| | - Christian C Ruff
- Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, Zurich, Switzerland.,Neuroscience Center Zurich, University of Zurich, Swiss Federal Institute of Technology Zurich, Zurich, Switzerland
| | - Todd A Hare
- Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, Zurich, Switzerland. .,Neuroscience Center Zurich, University of Zurich, Swiss Federal Institute of Technology Zurich, Zurich, Switzerland.
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Keung W, Hagen TA, Wilson RC. A divisive model of evidence accumulation explains uneven weighting of evidence over time. Nat Commun 2020; 11:2160. [PMID: 32358501 PMCID: PMC7195479 DOI: 10.1038/s41467-020-15630-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Accepted: 03/12/2020] [Indexed: 12/21/2022] Open
Abstract
Divisive normalization has long been used to account for computations in various neural processes and behaviours. The model proposes that inputs into a neural system are divisively normalized by the system’s total activity. More recently, dynamical versions of divisive normalization have been shown to account for how neural activity evolves over time in value-based decision making. Despite its ubiquity, divisive normalization has not been studied in decisions that require evidence to be integrated over time. Such decisions are important when the information is not all available at once. A key feature of such decisions is how evidence is weighted over time, known as the integration kernel. Here, we provide a formal expression for the integration kernel in divisive normalization, and show that divisive normalization quantitatively accounts for 133 human participants’ perceptual decision making behaviour, performing as well as the state-of-the-art Drift Diffusion Model, the predominant model for perceptual evidence accumulation. Divisive normalization is thought to be a ubiquitous computation in the brain, but has not been studied in decisions that require integrating evidence over time. Here, the authors show in humans that dynamic divisive normalization accounts for the uneven weighting of perceptual evidence over time.
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Affiliation(s)
- Waitsang Keung
- Department of Psychology, University of Arizona, Tucson, AZ, 85719, USA.
| | - Todd A Hagen
- Department of Psychology, University of Arizona, Tucson, AZ, 85719, USA
| | - Robert C Wilson
- Department of Psychology, University of Arizona, Tucson, AZ, 85719, USA.,Cognitive Science Program, University of Arizona, Tucson, AZ, 85719, USA
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Rahal RM, Fiedler S. Understanding cognitive and affective mechanisms in social psychology through eye-tracking. JOURNAL OF EXPERIMENTAL SOCIAL PSYCHOLOGY 2019. [DOI: 10.1016/j.jesp.2019.103842] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Abstract
AbstractDespite their popularity, dual process accounts of human reasoning and decision-making have come under intense scrutiny in recent years. Cognitive scientists and philosophers alike have come to question the theoretical foundations of the ‘standard view’ of dual process theory and have challenged the validity and relevance of evidence in support of it. Moreover, attempts to modify and refine dual process theory in light of these challenges have generated additional concerns about its applicability and refutability as a scientific theory. With these concerns in mind, this paper provides a critical review of dual process theory in economics, focusing on its role as a psychological framework for decision modeling in behavioral economics and neuroeconomics. I argue that the influx of criticisms against dual process theory challenge the descriptive accuracy of dualistic decision models in economics. In fact, the case can be made that the popularity of dual process theory in economics has less to do with the empirical success of dualistic decision models, and more to do with the convenience that the dual process narrative provides economists looking to explain-away decision anomalies. This leaves behavioral economists and neuroeconomists with something of a dilemma: either they stick to their purported ambitions to give a realistic description of human decision-making and give up the narrative, or they revise and restate their scientific ambitions.
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Abstract
Models of reinforcement learning (RL) are prevalent in the decision-making literature, but not all behavior seems to conform to the gradual convergence that is a central feature of RL. In some cases learning seems to happen all at once. Limited prior research on these "epiphanies" has shown evidence of sudden changes in behavior, but it remains unclear how such epiphanies occur. We propose a sequential-sampling model of epiphany learning (EL) and test it using an eye-tracking experiment. In the experiment, subjects repeatedly play a strategic game that has an optimal strategy. Subjects can learn over time from feedback but are also allowed to commit to a strategy at any time, eliminating all other options and opportunities to learn. We find that the EL model is consistent with the choices, eye movements, and pupillary responses of subjects who commit to the optimal strategy (correct epiphany) but not always of those who commit to a suboptimal strategy or who do not commit at all. Our findings suggest that EL is driven by a latent evidence accumulation process that can be revealed with eye-tracking data.
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Konovalov A, Krajbich I. Over a Decade of Neuroeconomics: What Have We Learned? ORGANIZATIONAL RESEARCH METHODS 2016. [DOI: 10.1177/1094428116644502] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
At its inception, neuroeconomics promised to revolutionize economics. That promise has not yet been realized, and neuroeconomics has seen limited penetration into mainstream economics. Nevertheless, it would be a mistake to declare that neuroeconomics has failed. Quite to the contrary, the yearly rate of neuroeconomics papers has roughly doubled since 2005. While the number of direct applications to economics remains limited, due to the infancy of the field, we have learned an amazing amount about how the brain makes decisions. In this article, we review some of the major topics that have emerged in neuroeconomics and highlight findings that we believe will form the basis for future applications to economics. When possible, we focus on existing applications to economics and future directions for that research.
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Affiliation(s)
- Arkady Konovalov
- Department of Economics, The Ohio State University, Columbus, OH, USA
| | - Ian Krajbich
- Department of Economics, The Ohio State University, Columbus, OH, USA
- Department of Psychology, The Ohio State University, Columbus, OH, USA
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