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Model-Based Cognitive Neuroscience: A Conceptual Introduction

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Abstract

This tutorial chapter shows how the separate fields of mathematical psychology and cognitive neuroscience can interact to their mutual benefit. Historically, the field of mathematical psychology is mostly concerned with formal theories of behavior, whereas cognitive neuroscience is mostly concerned with empirical measurements of brain activity. Despite these superficial differences in method, the ultimate goal of both disciplines is the same: to understand the workings of human cognition. In recognition of this common purpose, mathematical psychologists have recently started to apply their models in cognitive neuroscience, and cognitive neuroscientists have borrowed and extended key ideas that originated from mathematical psychology. This chapter consists of three main sections: the first describes the field of mathematical psychology, the second describes the field of cognitive neuroscience, and the third describes their recent combination: model-based cognitive neuroscience.

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... DDMs capture simultaneously which choices are made and when they occur across the full distribution of response times (RTs). They provide a powerful computational account for studying brain-behavior mappings because they decompose choices into distinct latent components that together resemble the dynamic decision process [19][20][21]. Each component is represented by a quantifiable parameter with well-established psychological interpretation [19][20][21]. ...
... They provide a powerful computational account for studying brain-behavior mappings because they decompose choices into distinct latent components that together resemble the dynamic decision process [19][20][21]. Each component is represented by a quantifiable parameter with well-established psychological interpretation [19][20][21]. However, the DDM is just one instance of a broader class of sequential sample models (SSMs; [30]), each with its own assumptions about the underlying decision dynamics [19,[21][22][23]. ...
... Each component is represented by a quantifiable parameter with well-established psychological interpretation [19][20][21]. However, the DDM is just one instance of a broader class of sequential sample models (SSMs; [30]), each with its own assumptions about the underlying decision dynamics [19,[21][22][23]. In this study, we show how one can leverage and test neurobiologically derived hypotheses for these alternative models of evidence accumulation. ...
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The basal ganglia (BG) play a key role in decision-making, preventing impulsive actions in some contexts while facilitating fast adaptations in others. The specific contributions of different BG structures to this nuanced behavior remain unclear, particularly under varying situations of noisy and conflicting information that necessitate ongoing adjustments in the balance between speed and accuracy. Theoretical accounts suggest that dynamic regulation of the amount of evidence required to commit to a decision (a dynamic “decision boundary”) may be necessary to meet these competing demands. Through the application of novel computational modeling tools in tandem with direct neural recordings from human BG areas, we find that neural dynamics in the theta band manifest as variations in a collapsing decision boundary as a function of conflict and uncertainty. We collected intracranial recordings from patients diagnosed with either Parkinson’s disease (PD) (n = 14) or dystonia (n = 3) in the subthalamic nucleus (STN), globus pallidus internus (GPi), and globus pallidus externus (GPe) during their performance of a novel perceptual discrimination task in which we independently manipulated uncertainty and conflict. To formally characterize whether these task and neural components influenced decision dynamics, we leveraged modified diffusion decision models (DDMs). Behavioral choices and response time distributions were best characterized by a modified DDM in which the decision boundary collapsed over time, but where the onset and shape of this collapse varied with conflict. Moreover, theta dynamics in BG structures modulated the onset and shape of this collapse but differentially across task conditions. In STN, theta activity was related to a prolonged decision boundary (indexed by slower collapse and therefore more deliberate choices) during high conflict situations. Conversely, rapid declines in GPe theta during low conflict conditions were related to rapidly collapsing boundaries and expedited choices, with additional complementary decision bound adjustments during high uncertainty situations. Finally, GPi theta effects were uniform across conditions, with increases in theta associated with a prolongation of decision bound collapses. Together, these findings provide a nuanced understanding of how our brain thwarts impulsive actions while nonetheless enabling behavioral adaptation amidst noisy and conflicting information.
... Relatedly, the second critical issue in current methodological designs stems from optimisation of effects using inconsistent analyses. When data is analysed using any statistical technique, a model involving parameters and assumptions is applied to the data (Forstmann & Wagenmakers, 2015b). The way that data is analysed is meant to say something about the overall pattern observable in the data, such that reflects the psychological process being studied. ...
... Statistical analysis then becomes an additional and powerful tool that helps us to understand the process being studied. As a result of this, application of statistical modelling can also massively boost power to detect effects that are directly relevant to the research question (Forstmann & Wagenmakers, 2015b), however these parameters must first be identified, specified and rigorously tested (Heathcote, Brown, & Wagenmakers, 2015). ...
... For instance, choice reaction time has a strong modelling history with great improvements in the ability of this field to predict decision-making (Brown & Heathcote, 2008;Logan, Cowan, & Davis, 1984;Ratcliff & McKoon, 2008;Ratcliff & Smith, 2004). Advances in this field have led to successful predictive models that can be applied to other fields, such as neuroscience and schizophrenia (Culbreth, Westbrook, Daw, Botvinick, & Barch, 2016;Forstmann & Wagenmakers, 2015b;Heathcote, Suraev, et al., 2015;Smith & Ratcliff, 2015). However, perhaps the most relevant advance in this field has been acute, strategic predictions of responding across research groups, with rigorously and meaningfully defined boundaries for what constitutes effects. ...
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Fear conditioning and extinction is a construct integral to understanding trauma-, stress- and anxiety-related disorders. In the laboratory, associative learning paradigms that pair aversive with neutral stimuli are used as analogues to real-life fear learning. These studies use physiological indices, such as skin conductance, to sensitively measure rates and intensity of learning and extinction. In this review, we discuss some of the potential limitations in interpreting and analysing physiological data during the acquisition or extinction of conditioned fear. We argue that the utmost attention should be paid to the development of modelling approaches of physiological data in associative learning paradigms, by illustrating the lack of replicability and interpretability of results in current methods. We also show that statistical significance may be easily achieved in this paradigm without more stringent data and data analysis reporting requirements, leaving this particular field vulnerable to misleading conclusions. This review is written so that issues and potential solutions are accessible to researchers without mathematical training. We conclude the review with some suggestions that all laboratories should be able to implement, including visualising the full data set in publications and adopting modelling, or at least regression-based, approaches.
... They specify the algorithms through which inputs to the model (e.g., rewards following actions) are transformed into outputs (e.g., selection of actions). In this way, computational models offer a formal account of the parts of psychological processes and their functions, as well as a set of testable predictions (Farell & Lewandowsky, 2018;Forstmann & Wagenmakers, 2015;Smaldino, 2017). ...
... As a result of these benefits, computational models are becoming increasingly popular in fields outside of the strict scope of cognitive science. Formal models of cognitive processes are used to understand psychopathology (Grahek et al., 2019), emotion (Eldar et al., 2016), the relationship between emotion and cognition (Grahek et al., 2020), and the neural implementation of cognitive processes (Forstmann & Wagenmakers, 2015). ...
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Discussions about the replicability of psychological studies have primarily focused on improving research methods and practices, with less attention paid to the role of well-specified theories in facilitating the production of reliable empirical results. The field is currently in need of clearly articulated steps to theory specification and development, particularly regarding frameworks that may generalize across different fields of psychology. Here we focus on two approaches to theory specification and development that are typically associated with distinct research traditions: computational modeling and construct validation. We outline the points of convergence and divergence between them to illuminate the anatomy of a scientific theory in psychology—what a well-specified theory should contain and how it should be interrogated and revised through iterative theory-development processes. We propose how these two approaches can be used in complementary ways to increase the quality of explanations and the precision of predictions offered by psychological theories.
... A cognitive model of a task constructed in a cognitive architecture is runnable and produces a sequence of behaviors (Cox, Oates, & Perlis, 2011). Cognitive architectures have been used to create cognitive models of a variety of intelligent systems (Forstmann & Wagenmakers, 2015). A cognitive architecture is a generalpurpose control system inspired by scientific theories developed to explain cognition in animals and humans (Langley et al., 2009). ...
... According to a cognitive models are established to explore various areas of cognition; attention and multitasking, judgment and choice in decision making and skill building in dynamic conditions (Gonzalez, 2002). Cognitive architectures have been utilized to developed cognitive models of a range of intelligent systems (Forstmann & Wagenmakers, 2015). A cognitive model of a task built in a cognitive architecture is runnable and provide a string of conducts (Byrne, 2003). ...
Chapter
A cognitive model is a computational model of internal information processing mechanisms of the brain for the purposes of comprehension and prediction. CARINA metacognitive architecture runs cognitive models. However, CARINA does not currently have mechanisms to store and learn from cognitive models executed in the past. Semantic knowledge representation is a field of study which concentrates on using formal symbols to a collection of propositions, objects, object properties, and relations among objects. In CARINA Beliefs are a form of represent the semantic knowledge. The aim of this chapter is to formally describe a CARINA-based cognitive model through of denotational mathematics and to represent these models using a technique of semantic knowledge representation called beliefs. All the knowledge received by CARINA is stored in the semantic memory in the form of beliefs. Thus, a cognitive model represented through beliefs will be ready to be stored in semantic memory of the metacognitive architecture CARINA. Finally, an illustrative example is presented.
... Model-based cognitive neuroscience (MBCN) is a burgeoning intersection of cognitive neuroscience and cognitive psychology (especially in the form of mathematical psychology; Forstmann & Wagenmakers, 2015b;Forstmann, Wagenmakers, Eichele, Brown, & Serences, 2011;Gläscher & O'Doherty, 2010;Love, 2015Love, , 2016O'Doherty, Hampton, & Kim, 2007;Turner et al., 2017;Turner, Van Maanen, & Forstmann, 2015). There is now an edited book (Forstmann & Wagenmakers, 2015a) and special issue of the Journal of Mathematical Psychology ...
... 66-68). For Forstmann and Wagenmakers (2015b), the goals of MBCN are to show "how mathematical models can advance cognitive neuroscience, and how cognitive neuroscience can provide constraint for mathematical models" (p. 153). ...
Article
Autonomist accounts of cognitive science suggest that cognitive model building and theory construction (can or should) proceed independently of findings in neuroscience. Common functionalist justifications of autonomy rely on there being relatively few constraints between neural structure and cognitive function. In contrast, an integrative mechanistic perspective stresses the mutual constraining of structure and function. In this article, I show how Model-Based Cognitive Neuroscience (MBCN) epitomizes the integrative mechanistic perspective and concentrates the most revolutionary elements of the cognitive neuroscience revolution. I also show how the prominent subset account of functional realization supports the integrative mechanistic perspective I take on MBCN and use it to clarify the intralevel and interlevel components of integration.
... This accumulation process is represented by a decision variable, which on average moves towards the threshold that is supported by the sensory evidence. Once the accumulated evidence hits one of these thresholds, the agent makes the corresponding choice (Ratcliff and Rouder, 1998;Forstmann et al., 2015;Ratcliff, 1978;Shadlen and Newsome, 2001;Ratcliff and McKoon, 2008;Bogacz et al., 2010). ...
... Our results confirmed both of these predictions, both replicating our previous findings regarding the effect of pre-SMA inhibition on decision thresholds (Tosun et al., 2017) using more precise localization, and also providing a more complete empirical test of the cortico-striatal theory of SAT by testing for bi-directionality of this modulatory effect. Note that similar to the results of a number of previous studies (Erhan and Balcı, 2017;Forstmann and Wagenmakers, 2015;Georgiev et al., 2016;Tosun et al., 2017;Voss et al., 2004Voss et al., , 2013, these effects were observed in the absence of detectable effects at the level of behavioral outputs (i.e., accuracy or RT). We think that the mere possibility of having such dissociations demonstrates the value of computational approaches to decision-making particularly in elucidating its neural basis. ...
... Cai et al. note that the human brain can be considered as one of the most complex living structures of the known world [1]. Forstmann and Wagenmakers note the complexity of the brain because of its composition of billions of neurons, synapsis, blood vessels, glial cells, neural stem cells, and layered tissues [2]. For many decades, researchers have been attempting to model the human brain. ...
... This phase develops conversation diagram of each software agent. The MaSE conversation model defines the conversation of two software agents as a coordination protocol[1] [2]. In a conversational event, two agent classes participate, one is an initiator and the second is the responder. ...
Article
The human brain is one of the most complex living structures in the known Universe. It consists of billions of neurons and synapses. Due to its intrinsic complexity, it can be a formidable task to accurately depict brain's structure and functionality. In the past, numerous studies have been conducted on modeling brain disease, structure, and functionality. Some of these studies have employed Agent-based approaches including multiagent-based simulation models as well as brain complex networks. While these models have all been developed using agent-based computing, however, to our best knowledge, none of them have employed the use of Agent-Oriented Software Engineering (AOSE) methodologies in developing the brain or disease model. This is a problem because without due process, developed models can miss out on important requirements. AOSE has the unique capability of merging concepts from multiagent systems, agent-based modeling, artificial intelligence, besides concepts from distributed systems. AOSE involves the various tested software engineering principles in various phases of the model development ranging from analysis, design, implementation, and testing phases. In this paper, we employ the use of three different AOSE methodologies for modeling the Multiple Sclerosis brain disease namely GAIA, TROPOS, and MASE. After developing the models, we further employ the use of Exploratory Agent-based Modeling (EABM) to develop an actual model replicating previous results as a proof of concept. The key objective of this study is to demonstrate and explore the viability and effectiveness of AOSE methodologies in the development of complex brain structure and cognitive process models. Our key finding include demonstration that AOSE methodologies can be considerably helpful in modeling various living complex systems, in general, and the human brain, in particular.
... Thus, the choice of this model broadens the cognitive descriptors derived from the task performance, and makes it an interesting tool to search for novel markers that could ultimately help characterizing clinical populations. The model-based cognitive neuroscience approach in this analysis is intended to capture underlying cognitive processes and their associated brain activation patterns which may be overlooked in basic reaction times and accuracy analysis (Forstmann & Wagenmakers, 2015). ...
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Efficient learning of letters–speech sound associations results in the specialization of visual and audiovisual brain regions, which is crucial for the development of proficient reading skills. However, the brain dynamics underlying this learning process remain poorly understood, and the involvement of learning and performance monitoring networks remains underexplored. Here we applied two mutually dependent feedback learning tasks in which novel symbol–speech sound associations were learned by 39 healthy adults. We employed functional magnetic resonance (fMRI) along with a reinforcement learning drift diffusion model to characterize trial-by-trial learning in behavior and brain. The model-based analysis showed that posterior–occipital activations during stimulus processing were positively modulated by trial-wise learning, as indicated by the increase in association strength between audiovisual pairs. Prediction errors, describing the update mechanism to learn from feedback across trials, modulated activations in several mid-frontal, striatal, and cingulate regions. Both tasks yielded similar patterns of results, despite differences in their relative difficulty. This study elucidates the processes involved in audiovisual learning that contribute to rapid visual specialization within a single experimental session and delineates a set of coactivated regions involved in learning from feedback. Our paradigm provides a framework to advance our understanding of the neurobiology of learning and reading development.
... One key issue with this proposal is whether the notorious opacity of neural networks, including language models, should be seen as a fundamental impediment to their ability to generate scientific explanations. Explanatory models in cognitive science often take the form of mathematical or computational models that encode theoretical constructs and hypotheses about mechanisms (Forstmann and Wagenmakers, 2015). From this perspective, the lack of simplicity, transparency, and theoretical grounding of deep neural networks appears to undermine them as explanatory models. ...
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This chapter critically examines the potential contributions of modern language models to theoretical linguistics. Despite their focus on engineering goals, these models' ability to acquire sophisticated linguistic knowledge from mere exposure to data warrants a careful reassessment of their relevance to linguistic theory. I review a growing body of empirical evidence suggesting that language models can learn hierarchical syntactic structure and exhibit sensitivity to various linguistic phenomena, even when trained on developmentally plausible amounts of data. While the competence/performance distinction has been invoked to dismiss the relevance of such models to linguistic theory, I argue that this assessment may be premature. By carefully controlling learning conditions and making use of causal intervention methods, experiments with language models can potentially constrain hypotheses about language acquisition and competence. I conclude that closer collaboration between theoretical linguists and computational researchers could yield valuable insights, particularly in advancing debates about linguistic nativism.
... Kenny et al., 2020; see also Section 'Do my statistical models capture the multi-level structure of joint action data?') or conducting intra-brain analyses to ensure that neural responses of interest are present within each member of a group before examining inter-brain correspondence of those responses (e.g. A. Zamm et al., 2021; see Section 'What should I consider when initially inspecting the data?'). Finally, computational modeling can be a useful tool for examining connections across levels, including linking behavioral patterns to brain activity and mapping functional descriptions (e.g. at an algorithmic level) to explanatory mechanisms (at a physiological level) (Forstmann and Wagenmakers, 2015;Frank and Badre, 2015). More specifically, computational models require researchers to quantitatively describe reciprocal interactions between brain and behavior during joint action. ...
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Developments in cognitive neuroscience have led to the emergence of hyperscanning, the simultaneous measurement of brain activity from multiple people. Hyperscanning is useful for investigating social cognition, including joint action, because of its ability to capture neural processes that occur within and between people as they coordinate actions toward a shared goal. Here, we provide a practical guide for researchers considering using hyperscanning to study joint action and seeking to avoid frequently raised concerns from hyperscanning skeptics. We focus specifically on EEG hyperscanning, which is widely available and optimally suited for capturing fine-grained temporal dynamics of action coordination. Our guidelines cover questions that are likely to arise when planning a hyperscanning project, ranging from whether hyperscanning is appropriate for answering one’s research questions to considerations for study design, dependent variable selection, data analysis, and visualization. By following clear guidelines that facilitate careful consideration of the theoretical implications of research design choices and other methodological decisions, joint action researchers can mitigate interpretability issues and maximize the benefits of hyperscanning paradigms.
... However, there are numerous outstanding questions regarding the neural basis of several WM subprocesses. The following subsections take a closer look at behavioral and neural evidence for key WM subprocesses, through the lens of model-based cognitive neuroscience (Corrado & Doya, 2007;Forstmann & Wagenmakers, 2015;Friston, 2009;Love, 2016;Trutti et al., 2021;Turner et al., 2017aTurner et al., , 2019b. In doing so, we place several benchmark behavioral phenomena in the context of current cognitive and neurocomputational theory and draw attention to methodological challenges associated with linking brain and behavior, which may be resolved through the methods of model-based cognitive neuroscience. ...
Chapter
Working memory (WM) refers to a set of processes that makes task-relevant information accessible to higher-level cognitive processes including abstract reasoning, decision-making, learning, and reading comprehension. In this chapter, we introduce the concept of WM and outline key behavioral and neural evidence for a number of critical subprocesses that support WM and which have become recent targets of cognitive neuroscience. We discuss common approaches to linking brain and behavior in WM research seeking to identify the neural basis of WM subprocesses. We draw attention to limitations of common approaches and suggest that much progress could be made by applying several of the recent methodological advances in model-based cognitive neuroscience discussed throughout this book (see Chapters “An Introduction to EEG/MEG for Model-Based Cognitive Neuroscience”, “Ultra-High Field Magnetic Resonance Imaging for Model-Based Neuroscience”, “Advancements in Joint Modeling of Neural and Behavioral Data”, “Cognitive Models as a Tool to Link Decision Behavior With EEG Signals”, and “Linking Models with Brain Measures”). Overall, the purpose of this chapter is to give a broad overview of WM as seen through the lens of model-based cognitive neuroscience and to summarize our current state of knowledge of WM subprocesses and their neural basis. We hope to outline a path forward to a more complete neurocomputational understanding of WM.
... The choice of this model broadens the cognitive descriptors that can be derived, and makes it an interesting tool to search for novel markers that could ultimately help characterizing clinical populations. The model-based cognitive neuroscience approach in this analysis is intended to capture underlying cognitive processes and their associated brain activation patterns which may be overlooked in a basic reaction times and accuracy analysis 37 . ...
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Efficient learning of letters-speech sound associations leads to specialization of visual and audiovisual brain regions and is necessary to develop adequate reading skills. We still do not understand the brain dynamics of this learning process, and the involvement of learning and performance monitoring networks is still underexplored. Here we examined a feedback learning task with two mutually dependent parts in which novel symbol-speech sound associations were learned by 39 healthy adults. We used functional magnetic resonance (fMRI) and a reinforcement learning drift diffusion model that described learning across trials. The model-based analysis showed that posterior-occipital activations during stimulus processing were positively modulated by the trial-by-trial learning, described by the increase in association strength of each audiovisual pair. Prediction errors, describing the update mechanism to learn with feedback across trials, modulated activations in several mid-frontal, striatal and cingulate regions. The two task parts yielded a similar pattern of results although they varied in their relative difficulty. This study demonstrates which processes during audiovisual learning contribute to the rapid visual specialization within an experimental session and delineates a set of coactivated regions engaged in learning from feedback. Our paradigm provides a framework to advance our understanding of the neurobiology of learning and reading development.
... This advantage of self-information can be explained by improved attentional processing (Alexopoulos et al., 2012;Sui & Rotshtein, 2019), or improved visual awareness . One of the key benefits of integrating computational models of cognition with neural data is that it allows researchers to formulate explicit inferences about the specific task-related cognitive processes associated with EEG signals (Forstmann et al., 2016;Forstmann & Wagenmakers, 2015;Ratcliff et al., 2016). Of particular interest is a recent study by Sui et al. (2023) reporting EEG markers of the SPE in the matching task. ...
Article
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Self-prioritization is a very influential modulator of human information processing. Still, little is known about the time-frequency dynamics of the self-prioritization network. In this EEG study, we used the familiarity-confound free matching task to investigate the spectral dynamics of self-prioritization and their underlying cognitive functions in a drift-diffusion model. Participants (N = 40) repeatedly associated arbitrary geometric shapes with either "the self" or "a stranger." Behavioral results demonstrated prominent self-prioritization effects (SPEs) in reaction time and accuracy. Remarkably, EEG cluster analysis also revealed two significant SPEs, one in delta/theta power (2-7 Hz) and one in beta power (19-29 Hz). Drift-diffusion modeling indicated that beta activity was associated with evidence accumulation, whereas delta/theta activity was associated with response selection. The decreased beta suppression of the SPE might indicate more efficient sensorimotor processing of self-associated stimulus-response features, whereas the increased delta/theta SPE might refer to the facilitated retrieval of self-relevant features across a widely distributed associative self-network. These novel oscillatory biomarkers of self-prioritization indicate their function as an associative glue for the self-concept.
... In the cognitive (neuro)sciences, EAMs have been most widely applied to simple, highly controlled decision-making tasks (e.g., brightness discrimination, random dot motion, lexical decision, stop-signal, go/no-go tasks). The simplicity of highly controlled tasks enables precise, targeted measurement of cognitive processes and facilitates interpretation of neurophysiological measures (e.g., EEG, fMRI) [2,[10][11][12]. However, such tasks are seldom representative of the more complex and cognitively demanding decision-making contexts that humans face in the modern workplace [13,14]. ...
Preprint
Decision-making behavior is often understood using the framework of evidence accumulation models (EAMs). Nowadays, EAMs are applied to various domains of decision-making with the underlying assumption that the latent cognitive constructs proposed by EAMs are consistent across these domains. In this study we investigate both the extent to which the parameters of EAMs are related between four different decision-making domains and across different time points. To that end, we make use of the novel joint modelling approach, that explicitly includes relationships between parameters, such as covariances or underlying factors, in one combined joint model. Consequently, this joint model also accounts for measurement error and uncertainty within the estimation of these relations. We found that EAM parameters were consistent between time points on three of the four decision-making tasks. For our between-task analysis we constructed a joint model with a factor analysis on the parameters of the different tasks. Our two factor joint model indicated that information processing ability was related between the different decision-making domains. However, other cognitive constructs such as the degree of response caution and urgency were only comparable on some domains.
... In the cognitive (neuro)sciences, EAMs have been most widely applied to simple, highly controlled decision-making tasks (e.g., brightness discrimination, random dot motion, lexical decision, stop-signal, go/no-go tasks). The simplicity of highly controlled tasks enables precise, targeted measurement of cognitive processes and facilitates interpretation of neurophysiological measures (e.g., EEG, fMRI) [2,[10][11][12]. However, such tasks are seldom representative of the more complex and cognitively demanding decision-making contexts that humans face in the modern workplace [13,14]. ...
Article
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Evidence accumulation models (EAMs) are a class of computational cognitive model used to understand the latent cognitive processes that underlie human decisions and response times (RTs). They have seen widespread application in cognitive psychology and neuroscience. However, historically, the application of these models was limited to simple decision tasks. Recently, researchers have applied these models to gain insight into the cognitive processes that underlie observed behaviour in applied domains, such as air-traffic control (ATC), driving, forensic and medical image discrimination, and maritime surveillance. Here, we discuss how this modelling approach helps researchers understand how the cognitive system adapts to task demands and interventions, such as task automation. We also discuss future directions and argue for wider adoption of cognitive modelling in Human Factors research.
... We also identified what motor features (Fig. 2b) are linked to what parameters of decision making (i.e., bridging motor and decision parameters; . The drift rate (v) represents the rate at which information is accumulated and depends on the quality of information extracted from the stimulus (Forstmann & Wagenmakers, 2015;Ratcliff & McKoon, 2008). Boundary separation (a, b) -the distance between two optionsreflects the amount of information a participant needs to accumulate prior to making a response. ...
Article
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Mouse tracking, a new action-based measure of behavior, has advanced theories of decision making with the notion that cognitive and social decision making is fundamentally dynamic. Implicit in this theory is that people's decision strategies, such as discounting delayed rewards, are stable over task design and that mouse trajectory features correspond to specific segments of decision making. By applying the hierarchical drift diffusion model and the Bayesian delay discounting model, we tested these assumptions. Specifically, we investigated the extent to which the "mouse-tracking" design of decision-making tasks (delay discounting task, DDT and stop-signal task, SST) deviate from the standard "keypress" design of decision making tasks. We found remarkable agreement in delay discounting rates (intertemporal impatience) obtained in the keypress and mouse-tracking versions of DDT (ρ = 0.90) even though these tasks were given about 1 week apart. Rates of evidence accumulation converged well in the two versions (DDT, ρ = .86; SST, ρ = .55). Omission/commission error in SST showed high agreement (ρ = .42, ρ = .53). Mouse-motion features such as maximum velocity and AUC (area under the curve) correlated well with nondecision time (ρ = -.42) and boundary separation (ρ = .44)-the amount of information needed to accumulate prior to making a response. These results indicate that the response time (RT) and motion-based decision tasks converge well at a fundamental level, and that mouse-tracking features such as AUC and maximum velocity do indicate the degree of decision conflict and impulsivity.
... EEG or fMRI measurements could be correlated with the strength of each gradient in order to elucidate their neural underpinnings, or the model fit jointly to behavioural and neural data. (see Forstmann & Wagenmakers, 2015) 16 Financial decision making is a related area in which the GOAL architecture also has the potential to enhance our understanding. For example, Gathergood et al. (2019) describe several heuristics that consumers might use to determine how to allocate repayments when managing multiple credit card debts, all of which are all linked to the distance gradient. ...
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We present a unified model of the dynamics of goal-directed motivation and decision-making. The model-referred to as the GOAL architecture-provides a quantitative framework for integrating theories of goal pursuit and for relating their predictions to different types of data. The GOAL architecture proposes that motivation changes over time according to three gradients that capture the effects of the distance to the goal (i.e., the progress remaining), the time to the deadline, and the rate of progress required to achieve the goal. This enables the integration and comparison of six theoretical perspectives that make different predictions about how these dynamics unfold when pursuing approach and avoidance goals. Hierarchical Bayesian modeling was used to analyze data from three experiments which manipulate distance to goal, time to deadline, and goal type (approach vs. avoidance), and data from the naturalistic context of professional basketball. The results show that people rely on the distance and rate gradients, and to a lesser degree the time gradient, when making resource allocation decisions during goal pursuit, although the relative influence of the gradients depends on the goal type. We also demonstrate how the GOAL architecture can be used to answer questions about the influence of goal importance. Our findings suggest that goal pursuit unfolds in a complex manner that cannot be accounted for by any one previous theoretical perspective, but that is well-characterized by our unified framework. This research highlights the importance of theoretical integration for understanding motivation and decision-making during goal pursuit. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
... In this article, we extended an existing framework for connecting mind, brain, and behavior by specifying Gaussian process linking functions for the dynamics of both latent and manifest variables. This extension allows a few key advantages that will be important in the field of model-based cognitive neuroscience (4,7,8,44). First, our approach inherits all of the advantages that a hierarchical structure provides, such as statistical reciprocity through conditionally independent variables within a global model and flexibly accounting for missing observations (9,45,46). ...
Article
The link between mind, brain, and behavior has mystified philosophers and scientists for millennia. Recent progress has been made by forming statistical associations between manifest variables of the brain (e.g., electroencephalogram [EEG], functional MRI [fMRI]) and manifest variables of behavior (e.g., response times, accuracy) through hierarchical latent variable models. Within this framework, one can make inferences about the mind in a statistically principled way, such that complex patterns of brain–behavior associations drive the inference procedure. However, previous approaches were limited in the flexibility of the linking function, which has proved prohibitive for understanding the complex dynamics exhibited by the brain. In this article, we propose a data-driven, nonparametric approach that allows complex linking functions to emerge from fitting a hierarchical latent representation of the mind to multivariate, multimodal data. Furthermore, to enforce biological plausibility, we impose both spatial and temporal structure so that the types of realizable system dynamics are constrained. To illustrate the benefits of our approach, we investigate the model’s performance in a simulation study and apply it to experimental data. In the simulation study, we verify that the model can be accurately fitted to simulated data, and latent dynamics can be well recovered. In an experimental application, we simultaneously fit the model to fMRI and behavioral data from a continuous motion tracking task. We show that the model accurately recovers both neural and behavioral data and reveals interesting latent cognitive dynamics, the topology of which can be contrasted with several aspects of the experiment.
... Triangulating across areas of cognitive neuroscience provides strong leads on how regions involved in metamemory contribute to judgments, but strong tests of componentprocess accounts are still only beginning to emerge. One reason for the slow emergence of component process accounts in metamemorycompared to other areas of cognitive neuroscience (e.g., Forstmann & Wagenmakers, 2015) is that metamemory lacks well-developed algorithmic models of latent cue utilization and decision processes that can be leveraged to provide quantifiable predictions for how these processes are engaged (e.g., Love, 2020). Such algorithmic models tend to depend heavily on ground-truth estimates of which cues are available in the task environment, which is a challenge in metacognition where many cues are likely internally generated and thus less observable. ...
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Neurocognitive research on metamemory thus far has mostly focused on localizing brain regions that track metacognitive judgments and distinguishing metacognitive processing from primary cognition. With much known about the localization of metamemory in the brain, there is a growing opportunity to develop a more algorithmic characterization of the brain processes underlying metamemory. We briefly review some current neurocognitive metamemory research, including relevant brain regions and theories about their role in metamemory. We review some computational neuroimaging approaches and, as an illustrative example, describe their use in studies on the delayed-JOL (judgments of learning) effect. Finally, we discuss how researchers might apply computational approaches to several unresolved questions in the behavioral metamemory literature. Such research could provide a bridge between cognitive and neurocognitive research on metamemory and provide novel insights into the algorithms underlying metamemory judgments, thus informing theory and methodology in both areas.
... Moreover, comparisons can be made under changing conditions, i.e., during learning and development (Elman et al., 1996). As such, ANNs can serve as explanatory mechanisms in cognitive neuroscience and behavioral psychology, embracing recent model-based approaches (Forstmann and Wagenmakers, 2015). ...
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New developments in AI and neuroscience are revitalizing the quest to understanding natural intelligence, offering insight about how to equip machines with human-like capabilities. This paper reviews some of the computational principles relevant for understanding natural intelligence and, ultimately, achieving strong AI. After reviewing basic principles, a variety of computational modeling approaches is discussed. Subsequently, I concentrate on the use of artificial neural networks as a framework for modeling cognitive processes. This paper ends by outlining some of the challenges that remain to fulfill the promise of machines that show human-like intelligence.
... In this way, theoretically distinct aspects of the decision process can be separated statistically (Voss et al., 2004). DDMs have thus been proposed to provide deeper insights into the observed behaviour compared with merely using accuracies or RTs (Forstmann & Wagenmakers, 2015;Wagenmakers, 2009). ...
Article
Professional magicians regularly use pantomimed grasps (i.e., movements towards imagined objects) to deceive audiences. To do so, they learn to shape their hands similarly for real and pantomimed grasps. Here we tested whether this form of motor expertise provides them a significant benefit when processing pantomimed grasps. To this aim, in a one-interval discrimination design, we asked 17 professional magicians and 17 naïve controls to watch video clips of reach-to-grasp movements recorded from naïve participants and judge whether the observed movement was real or pantomimed. All video clips were edited to spatially occlude the grasped object (either present or imagined). Data were analysed within a drift diffusion model approach. Fitting different models showed that, whereas magicians and naïve performed similarly when observing real grasps, magicians had a specific advantage compared with naïve at discriminating pantomimed grasps. These findings suggest that motor expertise may be crucial for detecting relevant cues from hand movement during the discrimination of pantomimed grasps. Results are discussed in terms of motor recalibration.
... We also identified what motor features (Fig. 2b) are linked to what parameters of decision making (i.e., bridging motor and decision parameters; . The drift rate (v) represents the rate at which information is accumulated and depends on the quality of information extracted from the stimulus (Forstmann & Wagenmakers, 2015;Ratcliff & McKoon, 2008). Boundary separation (a, b) -the distance between two optionsreflects the amount of information a participant needs to accumulate prior to making a response. ...
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In decision-making tasks, participants are commonly instructed to respond by pressing a key. This design provides information about how fast and accurate an individual can respond but does not allow a researcher to directly study the process of response selection. Recently, mouse cursor tracking has been applied to offset this limitation. However, it is unclear whether RT/accuracy-based measures and mouse movement features (e.g. velocity) assess the same cognitive processes. To clarify the relationship between mouse movement features and cognitive processes, we developed mouse-tracking versions of the stop-signal and delay discounting tasks and investigated a) whether people respond similarly in the tasks with traditional design and tasks employing mouse cursor tracking; b) which features of the decision-making process mouse movement measures correspond with. Although participants responded similarly in tasks with traditional and mouse tracking design, only a few mouse movement features were related to the elements of the decision-making process.
... One way would be examine the correlation between these measurements of brain activity and the strength of each gradient in order to elucidate the nueral processes underlying each one. Another method would be to fit the model directly to the neural activity measures, so that the estimated gradients are informed by both the behavioral and neural data (see Forstmann & Wagenmakers, 2015) Capturing the Process ...
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We present a unified model of the spatial and temporal dynamics of motivation duringgoal pursuit. We use the model to integrate and compare six theoretical perspectivesthat make different predictions about how motivation changes as a personcomes closer to achieving a goal, as a deadline looms, and as a function ofwhether the goal is being approached or avoided. We fit the model to data fromthree experiments that examine how these factors combine to produce changes inmotivation over time. We show that motivation changes in a complex manner thatcannot be accounted for by any one previous theoretical perspective, but that iswell-characterized by our unified model. Our findings highlight the importance oftheoretical integration when attempting to understand the factors driving motivationand decision making in the context of goal pursuit.
... In this article, we extended an existing framework for connecting mind, brain and behavior by specifying Gaussian process linking functions for the dynamics of both latent and manifest variables. This extension allows a few key advantages that will be important in the field of model-based cognitive neuroscience [5,8,9,35]. First, our approach inherits all the advantages that a hierarchical structure provides, such as statistical reciprocity through conditionally independent variables within a global model, and flexibly accounting for missing observations [1,10,36]. ...
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The link between mind, brain, and behavior has mystified philosophers and scientists for millennia. Recent progress has been made by forming statistical associations between manifest variables of the brain (e.g., EEG, fMRI) and manifest variables of behavior (e.g., response times, accuracy) through hierarchical latent variable models (Turner, Forstmann, & Steyver, 2019). Within this framework, one can make inferences about the mind in a statistically principled way, such that complex patterns of brain-behavior associations drive the inference procedure. However, previous approaches were limited in the flexibility of the linking function, which has proven prohibitive for understanding the complex dynamics exhibited by the brain. In this article, we propose a data-driven, non-parametric approach that allows complex linking functions to emerge from fitting a hierarchical latent representation of the mind to multivariate, multimodal data. Furthermore, to enforce biological plausibility, we impose both spatial and temporal structure so that the types of realizable system dynamics are constrained. To illustrate the benefits of our approach, we investigate the model’s performance in a simulation study and apply it to experimental data. In the simulation study, we verify that the model can be accurately fit to simulated data, and latent dynamics can be well recovered. In an experimental application, we simultaneously fit the model to fMRI and behavioral data from a continuous motion tracking task. We show that the model accurately recovers both neural and behavioral data, and reveals interesting latent cognitive dynamics. Finally, we provide a test of the model’s generalizability by assessing its predictive accuracy in a cross-validation test.
... Drift-diffusion modeling (DDM) is a mathematical approach (Forstmann and Wagenmakers, 2015) that decomposes observational data into latent processes underlying decisionmaking. Constructs elucidated by DDM include caution, encoding, motor response duration, strength, and quality of evidence presented by the stimulus, and bias (i.e., implicit or explicit preference for one response over another) (White et al., 2011White and Poldrack, 2014). ...
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Hypertension accelerates brain aging, resulting in cognitive dysfunction with advancing age. Exercise is widely recommended for adults with hypertension to attenuate cognitive dysfunction. Whether acute exercise benefits cognitive function in this at-risk population is unknown. The purpose of this study was to compare the effects of acute aerobic exercise on cognitive function in 30 middle-aged hypertensive (HTN) and 30 age, sex, and body mass index (BMI)-matched non-HTN adults (56 ± 6 years, BMI 28.2 ± 2.9 kg/m2; 32 men). Subjects underwent cognitive testing pre/post 30-min cycling (≈55% peak oxygen consumption). Cognition was assessed using standard metrics of accuracy and reaction time (RT) across memory recognition, 2-back, and Flanker tasks. Behavioral data was further analyzed using drift-diffusion modeling to examine underlying components of decision-making (strength of evidence, caution, bias) and RT (non-decision time). Exercise elicited similar changes in cognitive function in both HTN and non-HTN groups (p > 0.05). Accuracy was unaltered for Flanker and 2-back tasks, while hits and false alarms increased for memory recognition post-exercise (p < 0.05). Modeling results indicated changes in memory hits/false alarms were due to significant changes in stimulus bias post-exercise. RT decreased for Flanker and memory recognition tasks and was driven by reductions in post-exercise non-decision time (p < 0.05). Our data indicate acute exercise resulted in similar, beneficial cognitive responses in both middle-age HTN and non-HTN adults, marked by unaltered task accuracy, and accelerated RT post-exercise. Additionally, drift-diffusion modeling revealed that beneficial acceleration of cognitive processing post-exercise (RT) is driven by changes in non-decision components (encoding/motor response) rather than the decision-making process itself.
... Drift-diffusion modeling (DDM) is a mathematical means [14] of decomposing observational data A C C E P T E D M A N U S C R I P T into underlying processes of decision-making. Rather than relying solely on accuracy and hit RT to describe behavior changes, DDM incorporates all available behavioral data (accuracy, correct/error RT, shape of RT distributions). ...
Article
High altitude sojourn is broadly associated with impaired cognitive function, although there are inconsistencies within the literature. Incorporation of mathematical modeling to gain insight into latent aspects of decision-making may strengthen the ability to characterize changes in cognitive function during high altitude sojourn. This study sought to examine the effects of high altitude on cognitive function and underlying constructs of decision-making during an 11-d incremental ascent to 5160 m in 18 healthy adults (26 ± 12 yrs). Participants underwent cognitive testing at 116 m, 3440 m, 4240 m, and 5160 m. Cognitive function was assessed using standard metrics of accuracy and reaction time (RT) during working memory (2-back) and attention (Flanker) tasks. Behavioral data were additionally analyzed using drift-diffusion modeling to interrogate latent neural (strength of evidence, non-decision time) and behavioral (caution, bias) processes of decision-making. Flanker accuracy was unaltered during incremental ascent to high altitude, while 2-back accuracy decreased at 5160 m (p < 0.01). RT was faster at 4240 m for the Flanker, and faster at all altitudes compared to 116 m for the 2-back (p < 0.01). Incremental ascent to high altitude elicited modest reductions in caution and non-decision time, increases in bias and strength of evidence for non-match items during the 2-back (0.04 ≥ p > 0.01). These data indicate that while RT may appear to improve during incremental ascent to high altitude, increases in speed may be driven by participants 1) accumulating less evidence before initiating a response (i.e., less cautious) and 2) preferentially attending to (more biased), and extracting more evidence from, frequent/easier stimuli, rather than improved processing per se. Taken together, changes in cognitive function during incremental ascent to high altitude may reflect subtle changes in neural and behavioral components of decision-making intended to reduce cognitive load and conserve brain resources under challenging environmental conditions.
... The former strength of these methods allowed for a compelling account of the cascade of top-down regulation in the network of regions involved in SAT. The latter allowed for analyses that assessed how parameters from a formal cognitive model relate to individual differences in connectivity, in line with recent calls for a "model-based" cognitive neuroscience (Forstmann & Wagenmakers, 2015). It also allowed heterogeneous relationships between ROIs (e.g., between the striatum and pre-SMA) to be characterized as such. ...
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Several plausible theories of the neural implementation of speed/accuracy trade‐off (SAT), the phenomenon in which individuals may alternately emphasize speed or accuracy during the performance of cognitive tasks, have been proposed, and multiple lines of evidence point to the involvement of the pre‐supplemental motor area (pre‐SMA). However, as the nature and directionality of the pre‐SMA's functional connections to other regions involved in cognitive control and task processing are not known, its precise role in the top‐down control of SAT remains unclear. Although recent advances in cross‐sectional path modeling provide a promising way of characterizing these connections, such models are limited by their tendency to produce multiple equivalent solutions. In a sample of healthy adults (N = 18), the current study uses the novel approach of Group Iterative Multiple Model Estimation for Multiple Solutions (GIMME‐MS) to assess directed functional connections between the pre‐SMA, other regions previously linked to control of SAT, and regions putatively involved in evidence accumulation for the decision task. Results reveal a primary role of the pre‐SMA for modulating activity in regions involved in the decision process but suggest that this region receives top‐down input from the DLPFC. Findings also demonstrate the utility of GIMME‐MS and solution‐reduction methods for obtaining valid directional inferences from connectivity path models.
... Cognitive models are developed to study different aspects of cognition; attention and multitasking, judgment and choice in decision-making and skill acquisition in dynamic situations [4]. Cognitive architectures have been used to create cognitive models of a variety of intelligent systems [5]. A cognitive model of a task constructed in a cognitive architecture is runnable and produces a sequence of behaviors [6]. ...
Conference Paper
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Cognitive modeling is a fundamental tool used to understand the processes that underlying behavior, and has become a standard technique in the cognitive sciences. The central goals of cognitive modeling are: to describe, to predict and to prescribe human behavior through computational models of cognitive processes commonly called cognitive models. Cognitive modeling depends on the use of cognitive architectures. A cognitive architecture is a general framework for specifying computational behavioral models of human cognitive performance. CARINA is a cognitive architecture for the development of cognitive agents in digital educational environments. This paper presents a formal representation of a cognitive model for cognitive architecture CARINA. Denotational mathematics was used to formally describe the specification of cognitive models in CARINA. As an example a cognitive model in the domain of cognitive arithmetic was implemented in CARINA.
... Cognitive models from mathematical psychology, which describe the computational and neural mechanisms that allow humans to complete cognitive tasks (Forstmann and Wagenmakers 2015), may be able to better index cognitive changes during abstinence and test mechanistic hypotheses about their causes for two reasons. First, such models provide frameworks where different mechanistic processes that are hypothesized to underlie withdrawal effects (e.g., reduced efficiency vs. mind wandering) can be distinguished, even if they have similar effects on behavioral summary statistics (e.g., RT variability). ...
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Rationale Cigarette smokers often experience cognitive decrements during abstinence from tobacco, and these decrements may have clinical relevance in the context of smoking cessation interventions. However, limitations of the behavioral summary statistics used to measure cognitive effects of abstinence, response times (RT) and accuracy rates, may restrict the field’s ability to identify robust abstinence effects on task performance and test mechanistic hypotheses about the etiology of these cognitive changes. Objectives The current study explored whether a measurement approach based on mathematical models of cognition, which make the cognitive mechanisms necessary to perform choice RT tasks explicit, would be able to address these limitations. Methods The linear ballistic accumulator model (LBA: Brown and Heathcote, Cogn Psychol 57(3):153-178, 2008) was fit to an existing data set from a study that evaluated the impact of overnight abstinence on flanker task performance. Results The model-based analysis provided evidence that smokers’ rates of mind wandering increased during abstinence, and was able to index this effect while controlling for participants’ strategy changes that were related to the specific experimental paradigm used. Conclusion Mind wandering is a putative explanation for cognitive withdrawal symptoms during smoking cessation and may be indexed using the LBA. More broadly, the use of formal model-based analyses in future research on this topic has the potential to allow for strong and specific tests of mechanistic explanations for these symptoms.
... Moreover, comparisons can be made under changing conditions, i.e., during learning and development (Elman et al., 1996). As such, ANNs can serve as explanatory mechanisms in cognitive neuroscience and behavioral psychology, embracing recent model-based approaches (Forstmann and Wagenmakers, 2015). ...
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New developments in AI and neuroscience are revitalizing the quest to understanding natural intelligence, offering insight about how to equip machines with human-like capabilities. This paper reviews some of the computational principles relevant for understanding natural intelligence and, ultimately, achieving strong AI. After reviewing basic principles, a variety of computational modeling approaches is discussed. Subsequently, I concentrate on the use of artificial neural networks as a framework for modeling cognitive processes. This paper ends by outlining some of the challenges that remain to fulfill the promise of machines that show human-like intelligence.
... Our results revealed that humans indeed exhibit a more cautious choice behavior by setting higher decision thresholds as a result of right pre-SMA inhibition compared with the inhibition of a control region, vertex. Importantly, this difference was present despite the null effects revealed when accuracy levels and RTs were compared in isolation or even in a unified fashion (i.e., IES, RCS, LISAS), reflecting the importance of modelbased approaches in cognitive neuroscience research (Ly et al., in press;Erhan & Balcı, 2017;Georgiev et al., 2016;Forstmann & Wagenmakers, 2015;Voss, Nagler, & Lerche, 2013;Voss, Rothermund, & Voss 2004). Along with the increase in the decision thresholds, we also found that the weight assigned to accuracy relative to reward rate (i.e., accuracy bias; Balcı et al., 2011;Bogacz et al., 2006;Maddox & Bohil, 1998) was significantly higher in the right pre-SMA as compared with the vertex inhibition condition. ...
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Decisions are made based on the integration of available evidence. The noise in evidence accumulation leads to a particular speed–accuracy tradeoff in decision-making, which can be modulated and optimized by adaptive decision threshold setting. Given the effect of pre-SMA activity on striatal excitability, we hypothesized that the inhibition of pre-SMA would lead to higher decision thresholds and an increased accuracy bias. We used offline continuous theta burst stimulation to assess the effect of transient inhibition of the right pre-SMA on the decision processes in a free-response two-alternative forced-choice task within the drift diffusion model framework. Participants became more cautious and set higher decision thresholds following right pre-SMA inhibition compared with inhibition of the control site (vertex). Increased decision thresholds were accompanied by an accuracy bias with no effects on post-error choice behavior. Participants also exhibited higher drift rates as a result of pre-SMA inhibition compared with the vertex inhibition. These results, in line with the striatal theory of speed–accuracy tradeoff, provide evidence for the functional role of pre-SMA activity in decision threshold modulation. Our results also suggest that pre-SMA might be a part of the brain network associated with the sensory evidence integration.
... In this way, theoretically distinct aspects of the cognitive decision process can be separated statistically. DDMs have thus been proposed to provide a highly detailed measure of participants' performance, deeper insights into the observed behavior and consequently, drive theoretical advances [19,20]. Indeed, a major advantage of the DDMs is the high degree of information utilization. ...
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Individuals show significant variations in performing a motor act. Previous studies in the action observation literature have largely ignored this ubiquitous, if often unwanted, characteristic of motor performance, assuming movement patterns to be highly similar across repetitions and individuals. In the present study, we examined the possibility that individual variations in motor style directly influence the ability to understand and predict others’ actions. To this end, we first recorded grasping movements performed with different intents and used a two-step cluster analysis to identify quantitatively ‘clusters’ of movements performed with similar movement styles (Experiment 1). Next, using videos of the same movements, we proceeded to examine the influence of these styles on the ability to judge intention from action observation (Experiments 2 and 3). We found that motor styles directly influenced observers’ ability to ‘read’ others’ intention, with some styles always being less ‘readable’ than others. These results provide experimental support for the significance of motor variability for action prediction, suggesting that the ability to predict what another person is likely to do next directly depends on her individual movement style.
... Encoding can be viewed as a model-based approach where internal latent states of computational models are fitted to neural and behavioral responses as subjects are engaged in that particular task (Anderson et al., 2008;Ashby & Waldschmidt, 2008;Forstmann & Wagenmakers, 2015). The advantages of this approach are that (i) a good match between internal states and observed regional brain responses can be taken as evidence for a neural correlate of that state and (ii) different computational models can be tested against each other by comparing their fit to observed data. ...
... Unfortunately, this can lead to mixed conclusions when slower RT indicates a processing deficit [30], while preservation of accuracy may suggest the contrary [9]. Drift-diffusion modeling (DDM) is a descriptive mathematical approach that decomposes observational data (hits, misses, RTs) into latent processes [32][33][34]. DDM utilizes all available behavioral data (accuracy, correct/error RTs, shape of correct/error RT distributions) to provide insight into whether changes in cognition are due to neurological (i.e. encoding, motor response) or behavioral (i.e. ...
Chapter
The Towers of Hanoi is a mathematical problem, which consists of three pegs, and a number “n” of disks of distinct sizes which can slide onto any peg. A cognitive model is a theoretical, empirical and computational representation of mental processes which belong to a cognitive function. A cognitive model generates a human-like performance for developing tasks, correcting errors, using strategies, and acquiring knowledge. A cognitive model constructed in a cognitive architecture is characterized to be runnable and producing specific behaviors. A cognitive architecture is a general-purpose control framework based on scientific theories to specify computational models of human cognitive performance. CARINA is a metacognitive architecture for artificial intelligent agents, obtained from the MISM Metacognitive Meta model. The objective of this paper is to present a cognitive model based on NGOMS-L to solve the algorithm of the Towers of Hanoi that can be runnable in the metacognitive architecture CARINA. The methodology used for the analysis of the cognitive task was: pre-processing stage, processing stage, classification of subjects, description of cognitive task in natural language and finally description of the cognitive task in NGOMS-L. The results obtained showed that of the four subjects originally selected, three of them were able to solve the problem and only one abandoned the problem. In the classification made, a successful and an unsuccessful subject was selected, to represent the cognitive task in natural language and finally express in the NGOMS-L notation the different Goals, Operators, Methods, and Selection Rules.
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Neuroscientific theories of attention-deficit/hyperactivity disorder (ADHD) alternately posit that cognitive aberrations in the disorder are due to acute attentional lapses, slowed neural processing, or reduced signal-to-noise ratios. However, they make similar predictions about behavioral summary statistics (response times [RTs] and accuracy), hindering the field’s ability to produce strong and specific tests of these theories. The current study uses the linear ballistic accumulator (LBA; Brown & Heathcote, 2008), a mathematical model of choice RT tasks, to distinguish between competing theory predictions. Children with ADHD (n = 80) and age-matched controls (n = 32) completed a numerosity discrimination paradigm at 2 levels of difficulty, and RT data were fit to the LBA model to test theoretical predictions. Individuals with ADHD displayed slowed processing of evidence for correct responses (signal) relative to their peers but comparable processing of evidence for error responses (noise) and between-trial variability in processing (performance lapses). The findings are inconsistent with accounts that posit an increased incidence of attentional lapses in the disorder and provide partial support for those that posit slowed neural processing and lower signal-to-noise ratios. Results also highlight the utility of well-developed cognitive models for distinguishing between the predictions of etiological theories of psychopathology.
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A computational architecture modeling the relation between perception and action is proposed. Basic brain processes representing synaptic plasticity are first abstracted through asynchronous communication protocols and implemented as virtual microcircuits. These are used in turn to build mesoscale circuits embodying parallel cognitive processes. Encoding these circuits into symbolic expressions gives finally rise to neuro-inspired programs that are compiled into pseudo-code to be interpreted by a virtual machine. Quantitative evaluation measures are given by the modification of synapse weights over time. This approach is illustrated by models of simple forms of behaviors exhibiting cognition up to the third level of animal awareness. As a potential benefit, symbolic models of emergent psychological mechanisms could lead to the discovery of the learning processes involved in the development of cognition. The executable specifications of an experimental platform allowing for the reproduction of simulated experiments are given in appendix.
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Several recent commentaries suggest that, for psychological science to move beyond “homuncular” explanations for cognitive control, it is critically important to examine the role of basic and computationally well-defined processes (e.g. cognitive processing speed). Correlational evidence has previously linked slow speed to working memory (WM) deficits in ADHD, but the directionality of this relationship has not been investigated experimentally and the mechanisms through which speed may influence WM are unclear. Herein, we demonstrate in school-aged children with and without ADHD, that manipulating speed (indexed with the diffusion model) within a WM paradigm reduces WM capacity due to an increase in cognitive load, in a manner that is consistent with predictions of the time-based resource-sharing model of WM. Results suggest slow speed is a plausible cause of WM deficits in ADHD, provide a mechanistic account of this relationship, and urge the exploration of non-executive neurocognitive processes in clinical research on etiology.
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Cognitive neuroscientists sometimes apply formal models to investigate how the brain implements cognitive processes. These models describe behavioral data in terms of underlying, latent, variables linked to hypothesized cognitive processes. A goal of model-based cognitive neuroscience is to link these variables to brain measurements, which can advance progress in both cognitive and neuroscientific research. However, the details and the philosophical approach for this linking problem can vary greatly. We propose a continuum of approaches which differ in the degree of tight, quantitative, and explicit hypothesizing. We describe this continuum using four points along it, which we dub ``qualitative structural'', ``qualitative predictive'', ``quantitative predictive'', and ``single model'' linking approaches. We further illustrate by providing examples from three research fields (decision making, reinforcement learning, and symbolic reasoning) for the different linking approaches.
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The authors propose and test an exemplar-based random walk model for predicting response times in tasks of speeded, multidimensional perceptual classification. The model combines elements of R. M. Nosofsky's (1986) generalized context model of categorization and G. D. Logan's (1988) instance-based model of automaticity. In the model, exemplars race among one another to be retrieved from memory, with rates determined by their similarity to test items. The retrieved exemplars provide incremental information that enters into a random walk process for making classification decisions. The model predicts correctly effects of within- and between-categories similarity, individual-object familiarity, and extended practice on classification response times. It also builds bridges between the domains of categorization and automaticity.
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In many response time tasks, people slow down after they make an error. This phenomenon of post-error slowing (PES) is thought to reflect an increase in response caution, that is, a heightening of response thresholds in order to increase the probability of a correct response at the expense of response speed. In many empirical studies, PES is quantified as the difference in response time (RT) between post-error trials and post-correct trials. Here we demonstrate that this standard measurement method is prone to contamination by global fluctuations in performance over the course of an experiment. Diffusion model simulations show how global fluctuations in performance can cause either spurious detection of PES or masking of PES. Both confounds are highly undesirable and can be eliminated by a simple solution: quantify PES as the difference in RT between post-error trials and the associated pre-error trials. Experimental data are used as an empirical illustration.
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The standard signal detection theory (SDT) approach to mm-alternative forced choice uses the proportion correct as the outcome variable and assumes that there is no response bias. The assumption of no bias is not made for theoretical reasons, but rather because it simplifies the model and estimation of its parameters. The SDT model for mmAFC with bias is presented, with the cases of two, three, and four alternatives considered in detail. Two approaches to fitting the model are noted: maximum likelihood estimation with Gaussian quadrature and Bayesian estimation with Markov chain Monte Carlo. Both approaches are examined in simulations. SAS and OpenBUGS programs to fit the models are provided, and an application to real-world data is presented.
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In this paper we develop a general quantum-like model of decision making. Here updating of probability is based on linear algebra, the von Neumann–Lüders projection postulate, Born’s rule, and the quantum representation of the state space of a composite system by the tensor product. This quantum-like model generalizes the classical Bayesian inference in a natural way. In our approach the latter appears as a special case corresponding to the absence of relative phases in the mental state. By taking into account a possibility of the existence of correlations which are encoded in relative phases we developed a more general scheme of decision making. We discuss natural situations inducing deviations from the classical Bayesian scheme in the process of decision making by cognitive systems: in situations that can be characterized as objective and subjective mental uncertainties. Further, we discuss the problem of base rate fallacy. In our formalism, these “irrational” (non-Bayesian) inferences are represented by quantum-like bias operations acting on the mental state.
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The prediction presented is based upon the empirically well sustained magnitude production representation that arose in both of Luce’s global psychophysical theories for subjective intensity of binary and unary continua coupled with Torgerson’s (1961) conjecture that respondents fail to distinguish subjective differences from subjective ratios. When applied to eqisections and fractionation the conjecture implies that the cognitive distortion function of the magnitude production representation is the identity function, which is firmly rejected by existing data.
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In this paper, we provide a tutorial exposition on the Bayesian approach in analyzing structural equation models (SEMs). SEMs, which can be regarded as regression models with observed and latent variables, have been widely applied to substantive research. However, the classical methods and most commercial software in this area are based on the covariance structure approach, which would encounter serious difficulties when dealing with complicated models and/or data structures. In contrast, the Bayesian approach has much more flexibility in handling complex situations. We give a brief introduction to SEMs and a detailed description of how to apply the Bayesian approach to this kind of model. Advantages of the Bayesian approach are discussed, and results obtained from a simulation study are provided for illustration. The intended audience is statisticians/methodologists who either know about SEMs or simple Bayesian statistics, and Ph.D. students in statistics, psychometrics, or mathematical psychology.
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The Ratcliff diffusion model for simple two-choice decisions (e.g., Ratcliff, 197863. Ratcliff , R. 1978 . A theory of memory retrieval . Psychological Review , 85 : 59 – 108 . [CrossRef], [Web of Science ®]View all references; Ratcliff & McKoon, 200868. Ratcliff , R. and McKoon , G. 2008 . The diffusion decision model: Theory and data for two-choice decision tasks . Neural Computation , 20 : 873 – 922 . [CrossRef], [PubMed], [Web of Science ®]View all references) has two outstanding advantages. First, the model generally provides an excellent fit to the observed data (i.e., response accuracy and the shape of RT distributions, both for correct and error responses). Second, the parameters of the model can be mapped on to latent psychological processes such as the speed of information accumulation, response caution, and a priori bias. In recent years, the advantages of the Ratcliff diffusion model have become increasingly clear. Current advances in methodology allow all researchers to fit the diffusion model to data easily. Recent applications to ageing, lexical decision, IQ, practice, the implicit association test, and the accessory stimulus effect serve to highlight the added value of a diffusion model perspective on simple decision making.
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The content of verbal theories is usually relatively easy to communicate. We often have only a vague idea about certain psychological phenomena. It is impossible to express such vague ideas in the language of mathematics. In particular, we usually need verbal theories when a new research field is emerging. At an early stage of such an enterprise, only a few results are available which are often not well understood. Only rough theoretical ideas are likely to advance such an initial stage. By contrast, successful mathematical models require a rather robust data basis. Finally, there is no doubt that great verbal theories exist not only in psychology but also in other fields of science. Despite these advantages of verbal theories, the author is strongly convinced that verbal theories are often insufficient to explain cognitive phenomena in a satisfactory way. In order to support this statement, it is useful to explain briefly the goal of cognitive psychology, before the merits of mathematical models can be outlined. Because this issue is so fundamental and thus not restricted to memory models, the author approaches this meta-theoretical issue from a rather general perspective without referring to specific memory models. The author also uses an example from his current research on temporal cognition to support and illustrate some general claims. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
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In contrast to the well-established roles of the striatum in movement generation and value-based decisions, its contributions to perceptual decisions lack direct experimental support. Here, we show that electrical microstimulation in the monkey caudate nucleus influences both choice and saccade response time on a visual motion discrimination task. Within a drift-diffusion framework, these effects consist of two components. The perceptual component biases choices toward ipsilateral targets, away from the neurons’ predominantly contralateral response fields. The choice bias is consistent with a nonzero starting value of the diffusion process, which increases and decreases decision times for contralateral and ipsilateral choices, respectively. The nonperceptual component decreases and increases nondecision times toward contralateral and ipsilateral targets, respectively, consistent with the caudate’s role in saccade generation. The results imply a causal role for the caudate in perceptual decisions used to select saccades that may be distinct from its role in executing those saccades. Video Abstract eyJraWQiOiI4ZjUxYWNhY2IzYjhiNjNlNzFlYmIzYWFmYTU5NmZmYyIsImFsZyI6IlJTMjU2In0.eyJzdWIiOiI0NzMyN2FiMGNjNzRhNmU3NzIwODUyNjEzODE2YmJjOSIsImtpZCI6IjhmNTFhY2FjYjNiOGI2M2U3MWViYjNhYWZhNTk2ZmZjIiwiZXhwIjoxNjA2MTI2NzQ4fQ.ZZieRLRP0RhG9Lfp4AI_2mzcNx_jGpezyCfP1FaFa4Xuh27DA9EXS1bFOZMNRVyS9W_4dnrbs3d-0-LqB8tM5WvuewUO2eK2ts06HtrJwBIUB2Dz2s7H3VBd2XCxBNfGm9JdWixcCBjclQsCceq3Gjc3903WbseQVS_aQ0IV-HBSsziOtf2mOJf4txcZ-lEC0LXwtu2qWE1sqMRYQmmCFaqSTrmu98kFzje9QVo53EyfNffo3dVTuajSp_avvMl2gMB-gk9lMITfcZY7GIXrSDnOfEzk2CNm0rBl3OyjieWLoOyAfWLYN3-cyTatawFXb2cYOhu5yvx8CStkKR_gFQ (mp4, (9.28 MB) Download video
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Experimental studies of decision-making have put a strong emphasis on choices between two alternatives. However, real-life decisions often involve multiple alternatives. This article provides an overview of theoretical frameworks that have been proposed to account for behavioral data from both economic and perceptual multialternative decision-making. We further review recent neurophysiological data collected in conjunction with decision-making behavior. These neural recordings provide constraints on putative models of the decision mechanism. For example, the time course of inhibition provides insight into how the competition between alternatives is mediated. Furthermore, whereas decision-related neural activity seems to reach a common threshold at the end of the decision period, the starting point tends to depend systematically on the number of alternatives. We discuss candidate mechanisms that could drive the reduction in firing rates on decisions among multiple alternatives.
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We systematically mistreat psychological phenomena, both logically and clinically. This article explores three contentions: that the dominant discourse in modern cognitive, affective, and clinical neuroscience assumes that we know how psychology/biology causation works when we do not; that there are serious intellectual, clinical, and policy costs to pretending we do know; and that crucial scientific and clinical progress will be stymied as long as we frame psychology, biology, and their relationship in currently dominant ways. The arguments are developed with emphasis on misguided attempts to localize psychological function via neuroimaging, misunderstandings about the role of genetics in psychopathology, and unfortunate constraints on health-care policy and clinical service delivery. A particular challenge, articulated but not resolved in this article, is determining what constitutes adequate explanation in the relationship between psychology and biology.
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Cognitive neuroscientists study how the brain implements particular cognitive processes such as perception, learning, and decision-making. Traditional approaches in which experiments are designed to target a specific cognitive process have been supplemented by two recent innovations. First, formal cognitive models can decompose observed behavioral data into multiple latent cognitive processes,