Reciprocal relations between cognitive neuroscience and formal cognitive models: opposites attract?

Reasoning Goals and Representational Decisions in Computational Cognitive Neuroscience: Lessons From the Drift Diffusion Model

Computational cognitive models are powerful tools for enhancing the quantitative and theoretical rigor of cognitive neuroscience. It is thus imperative that model users-researchers who develop models, use existing models, or integrate model-based findings into their own research-understand how these tools work and what factors need to be considered when engaging with them. To this end, we developed a philosophical toolkit that addresses core questions about computational cognitive models in the brain and behavioral sciences. Drawing on recent advances in the philosophy of modeling, we highlight the central role of model users' reasoning goals in the application and interpretation of formal models. We demonstrate the utility of this perspective by first offering a philosophical introduction to the highly popular drift diffusion model (DDM) and then providing a novel conceptual analysis of a long-standing debate about decision thresholds in the DDM. Contrary to most existing work, we suggest that the two model structures implicated in the debate offer complementary-rather than competing-explanations of speeded choice behavior. Further, we show how the type of explanation provided by each form of the model (parsimonious and normative) reflects the reasoning goals of the communities of users who developed them (cognitive psychometricians and theoretical decision scientists, respectively). We conclude our analysis by offering readers a principled heuristic for deciding which of the models to use, thus concretely demonstrating the conceptual and practical utility of philosophy for resolving meta-scientific challenges in the brain and behavioral sciences.

A multiscale distributed neural computing model database (NCMD) for neuromorphic architecture

Distributed neuromorphic architecture is a promising technique for on-chip processing of multiple tasks. Deploying the constructed model in a distributed neuromorphic system, however, remains time-consuming and challenging due to considerations such as network topology, connection rules, and compatibility with multiple programming languages. We proposed a multiscale distributed neural computing model database (NCMD), which is a framework designed for ARM-based multi-core hardware. Various neural computing components, including ion channels, synapses, and neurons, are encompassed in NCMD. We demonstrated how NCMD constructs and deploys multi-compartmental detailed neuron models as well as spiking neural networks (SNNs) in BrainS, a distributed multi-ARM neuromorphic system. We demonstrated that the electrodiffusive Pinsky-Rinzel (edPR) model developed by NCMD is well-suited for BrainS. All dynamic properties, such as changes in membrane potential and ion concentrations, can be easily explored. In addition, SNNs constructed by NCMD can achieve an accuracy of 86.67% on the test set of the Iris dataset. The proposed NCMD offers an innovative approach to applying BrainS in neuroscience, cognitive decision-making, and artificial intelligence research.

The effect of total sleep deprivation on working memory: evidence from diffusion model

STUDY OBJECTIVES

Working memory is crucial in human daily life and is vulnerable to sleep loss. The current study investigated the impact of sleep deprivation on working memory from the information processing perspective, to explore whether sleep deprivation affects the working memory via impairing information manipulation.

METHODS

Thirty-seven healthy adults attended two counterbalanced protocols: a normal sleep night and a total sleep deprivation (TSD). The N-back and the psychomotor vigilance task (PVT) assessed working memory and sustained attention. Response time distribution and drift-diffusion model analyses were applied to explore cognitive process alterations.

RESULTS

TSD increased the loading effect of accuracy, but not the loading effect of response time in the N-back task. TSD reduced the speed of information accumulation, increased the variability of the speed of accumulation, and elevated the decision threshold only in 1-back task. Moreover, the slow responses of PVT and N-back were severely impaired after TSD, mainly due to increased information accumulation variability.

CONCLUSIONS

The present study provides a new perspective to investigate behavioral performance by using response time distribution and drift-diffusion models, revealing that sleep deprivation affected multicognitive processes underlying working memory, especially information accumulation processes.

A spurious correlation between difference scores in evidence-accumulation model parameters

Evidence-accumulation models are a useful tool for investigating the cognitive processes that give rise to behavioural data patterns in reaction times (RTs) and error rates. In their simplest form, evidence-accumulation models include three parameters: The average rate of evidence accumulation over time (drift rate) and the amount of evidence that needs to be accumulated before a response becomes selected (boundary) both characterise the response-selection process; a third parameter summarises all processes before and after the response-selection process (non-decision time). Researchers often compute experimental effects as simple difference scores between two within-subject conditions and such difference scores can also be computed on model parameters. In the present paper, we report spurious correlations between such model parameter difference scores, both in empirical data and in computer simulations. The most pronounced spurious effect is a negative correlation between boundary difference and non-decision difference, which amounts to r = - .70 or larger. In the simulations, we only observed this spurious negative correlation when either (a) there was no true difference in model parameters between simulated experimental conditions, or (b) only drift rate was manipulated between simulated experimental conditions; when a true difference existed in boundary separation, non-decision time, or all three main parameters, the correlation disappeared. We suggest that care should be taken when using evidence-accumulation model difference scores for correlational approaches because the parameter difference scores can correlate in the absence of any true inter-individual differences at the population level.

Evidence accumulation modelling in the wild: understanding safety-critical decisions

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.

Social learning across adolescence: A Bayesian neurocognitive perspective

Adolescence is a period of social re-orientation in which we are generally more prone to peer influence and the updating of our beliefs based on social information, also called social learning, than in any other stage of our life. However, how do we know when to use social information and whose information to use and how does this ability develop across adolescence? Here, we review the social learning literature from a behavioral, neural and computational viewpoint, focusing on the development of brain systems related to executive functioning, value-based decision-making and social cognition. We put forward a Bayesian reinforcement learning framework that incorporates social learning about value associated with particular behavior and uncertainty in our environment and experiences. We discuss how this framework can inform us about developmental changes in social learning, including how the assessment of uncertainty and the ability to adaptively discriminate between information from different social sources change across adolescence. By combining reward-based decision-making in the domains of both informational and normative influence, this framework explains both negative and positive social peer influence in adolescence.

Relating a Spiking Neural Network Model and the Diffusion Model of Decision-Making

Many models of decision making assume accumulation of evidence to threshold as a core mechanism to predict response probabilities and response times. A spiking neural network model (Wang, 2002) instantiates these mechanisms at the level of biophysically-plausible pools of neurons with excitatory and inhibitory connections, and has numerous model parameters tuned by physiological measures. The diffusion model (Ratcliff, 1978) is a cognitive model that can be fitted to a range of behaviors and conditions. We investigated how parameters of the cognitive-level diffusion model relate to the parameters of a neural-level spiking model. In each simulated "experiment", we generated "data" from the spiking neural network by factorially combining a manipulation of choice difficulty (via the input to the spiking model) and a manipulation of one of the core parameters of the spiking model. We then fitted the diffusion model to these simulated data to observe how manipulation of each core spiking model parameter mapped on to fitted drift rate, response threshold, and non-decision time. Manipulations of parameters in the spiking model related to input sensitivity, threshold, and stimulus processing time mapped on to their conceptual analogues in the diffusion model, namely drift rate, threshold, and non-decision time. Manipulations of parameters in the spiking model with no direct analogue to the diffusion model, non-stimulus-specific background input, strength of recurrent excitation, and receptor conductances, mapped on to threshold in the diffusion model. We discuss implications of these results for interpretations of fits of the diffusion model to behavioral data.

The lexical categorization model: A computational model of left ventral occipito-temporal cortex activation in visual word recognition

To characterize the functional role of the left-ventral occipito-temporal cortex (lvOT) during reading in a quantitatively explicit and testable manner, we propose the lexical categorization model (LCM). The LCM assumes that lvOT optimizes linguistic processing by allowing fast meaning access when words are familiar and filtering out orthographic strings without meaning. The LCM successfully simulates benchmark results from functional brain imaging described in the literature. In a second evaluation, we empirically demonstrate that quantitative LCM simulations predict lvOT activation better than alternative models across three functional magnetic resonance imaging studies. We found that word-likeness, assumed as input into a lexical categorization process, is represented posteriorly to lvOT, whereas a dichotomous word/non-word output of the LCM could be localized to the downstream frontal brain regions. Finally, training the process of lexical categorization resulted in more efficient reading. In sum, we propose that word recognition in the ventral visual stream involves word-likeness extraction followed by lexical categorization before one can access word meaning.

Visual word recognition is a critical process for reading and relies on the human brain’s left ventral occipito-temporal (lvOT) regions. However, the lvOTs specific function in visual word recognition is not yet clear. We propose that these occipito-temporal brain systems are critical for lexical categorization, i.e., the process of determining whether an orthographic percept is a known word or not, so that further lexical and semantic processing can be restricted to those percepts that are part of our "mental lexicon". We demonstrate that a computational model implementing this process, the lexical categorization model, can explain seemingly contradictory benchmark results from the published literature. We further use functional magnetic resonance imaging to show that the lexical categorization model successfully predicts brain activation in the left ventral occipito-temporal cortex elicited during a word recognition task. It does so better than alternative models proposed so far. Finally, we provide causal evidence supporting this model by empirically demonstrating that training the process of lexical categorization improves reading performance.

Neurocomputational models of altruistic decision-making and social motives: Advances, pitfalls, and future directions

This article discusses insights from computational models and social neuroscience into motivations, precursors, and mechanisms of altruistic decision-making and other-regard. We introduce theoretical and methodological tools for researchers who wish to adopt a multilevel, computational approach to study behaviors that promote others' welfare. Using examples from recent studies, we outline multiple mental and neural processes relevant to altruism. To this end, we integrate evidence from neuroimaging, psychology, economics, and formalized mathematical models. We introduce basic mechanisms-pertinent to a broad range of value-based decisions-and social emotions and cognitions commonly recruited when our decisions involve other people. Regarding the latter, we discuss how decomposing distinct facets of social processes can advance altruistic models and the development of novel, targeted interventions. We propose that an accelerated synthesis of computational approaches and social neuroscience represents a critical step towards a more comprehensive understanding of altruistic decision-making. We discuss the utility of this approach to study lifespan differences in social preference in late adulthood, a crucial future direction in aging global populations. Finally, we review potential pitfalls and recommendations for researchers interested in applying a computational approach to their research. This article is categorized under: Economics > Interactive Decision-Making Psychology > Emotion and Motivation Neuroscience > Cognition Economics > Individual Decision-Making.

Cognitive Control of Working Memory: A Model-Based Approach

Working memory (WM)-based decision making depends on a number of cognitive control processes that control the flow of information into and out of WM and ensure that only relevant information is held active in WM's limited-capacity store. Although necessary for successful decision making, recent work has shown that these control processes impose performance costs on both the speed and accuracy of WM-based decisions. Using the reference-back task as a benchmark measure of WM control, we conducted evidence accumulation modeling to test several competing explanations for six benchmark empirical performance costs. Costs were driven by a combination of processes, running outside of the decision stage (longer non-decision time) and showing the inhibition of the prepotent response (lower drift rates) in trials requiring WM control. Individuals also set more cautious response thresholds when expecting to update WM with new information versus maintain existing information. We discuss the promise of this approach for understanding cognitive control in WM-based decision making.

Identifying the neural dynamics of category decisions with computational model-based functional magnetic resonance imaging

Successful categorization requires a careful coordination of attention, representation, and decision making. Comprehensive theories that span levels of analysis are key to understanding the computational and neural dynamics of categorization. Here, we build on recent work linking neural representations of category learning to computational models to investigate how category decision making is driven by neural signals across the brain. We uniquely combine functional magnetic resonance imaging with drift diffusion and exemplar-based categorization models to show that trial-by-trial fluctuations in neural activation from regions of occipital, cingulate, and lateral prefrontal cortices are linked to category decisions. Notably, only lateral prefrontal cortex activation was associated with exemplar-based model predictions of trial-by-trial category evidence. We propose that these brain regions underlie distinct functions that contribute to successful category learning.

Distractors Selectively Modulate Electrophysiological Markers of Perceptual Decisions

Current models of perceptual decision-making assume that choices are made after evidence in favor of an alternative accumulates to a given threshold. This process has recently been revealed in human EEG recordings, but an unresolved issue is how these neural mechanisms are modulated by competing, yet task-irrelevant, stimuli. In this study, we tested 20 healthy participants on a motion direction discrimination task. Participants monitored two patches of random dot motion simultaneously presented on either side of fixation for periodic changes in an upward or downward motion, which could occur equiprobably in either patch. On a random 50% of trials, these periods of coherent vertical motion were accompanied by simultaneous task-irrelevant, horizontal motion in the contralateral patch. Our data showed that these distractors selectively increased the amplitude of early target selection responses over scalp sites contralateral to the distractor stimulus, without impacting on responses ipsilateral to the distractor. Importantly, this modulation mediated a decrement in the subsequent buildup rate of a neural signature of evidence accumulation and accounted for a slowing of RTs. These data offer new insights into the functional interactions between target selection and evidence accumulation signals, and their susceptibility to task-irrelevant distractors. More broadly, these data neurally inform future models of perceptual decision-making by highlighting the influence of early processing of competing stimuli on the accumulation of perceptual evidence.

The role of oxytocin in delay of gratification and flexibility in non-social decision making

Oxytocin is well-known for its impact on social cognition. This specificity for the social domain, however, has been challenged by findings suggesting a domain-general allostatic function for oxytocin by promoting future-oriented and flexible behavior. In this pre-registered study, we tested the hypothesized domain-general function of oxytocin by assessing the impact of intranasal oxytocin (24 IU) on core aspects of human social (inequity aversion) and non-social decision making (delay of gratification and cognitive flexibility) in 49 healthy volunteers (within-subject design). In intertemporal choice, patience was higher under oxytocin than under placebo, although this difference was evident only when restricting the analysis to the first experimental session (between-group comparison) due to carry-over effects. Further, oxytocin increased cognitive flexibility in reversal learning as well as generosity under conditions of advantageous but not disadvantageous inequity. Our findings show that oxytocin affects both social and non-social decision making, supporting theoretical accounts of domain-general functions of oxytocin.

Group decisions based on confidence weighted majority voting

Background

It has repeatedly been reported that, when making decisions under uncertainty, groups outperform individuals. Real groups are often replaced by simulated groups: Instead of performing an actual group discussion, individual responses are aggregated by a numerical computation. While studies have typically used unweighted majority voting (MV) for this aggregation, the theoretically optimal method is confidence weighted majority voting (CWMV)—if independent and accurate confidence ratings from the individual group members are available. To determine which simulations (MV vs. CWMV) reflect real group processes better, we applied formal cognitive modeling and compared simulated group responses to real group responses.

Results

Simulated group decisions based on CWMV matched the accuracy of real group decisions, while simulated group decisions based on MV showed lower accuracy. CWMV predicted the confidence that groups put into their group decisions well. However, real groups treated individual votes to some extent more equally weighted than suggested by CWMV. Additionally, real groups tend to put lower confidence into their decisions compared to CWMV simulations.

Conclusion

Our results highlight the importance of taking individual confidences into account when simulating group decisions: We found that real groups can aggregate individual confidences in a way that matches statistical aggregations given by CWMV to some extent. This implies that research using simulated group decisions should use CWMV instead of MV as a benchmark to compare real groups to.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41235-021-00279-0.

Gaussian process linking functions for mind, brain, and behavior

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.

A distributed dynamic brain network mediates linguistic tone representation and categorization

Successful categorization requires listeners to represent the incoming sensory information, resolve the "blooming, buzzing confusion" inherent to noisy sensory signals, and leverage the accumulated evidence towards making a decision. Despite decades of intense debate, the neural systems underlying speech categorization remain unresolved. Here we assessed the neural representation and categorization of lexical tones by native Mandarin speakers (N = 31) across a range of acoustic and contextual variabilities (talkers, perceptual saliences, and stimulus-contexts) using functional magnetic imaging (fMRI) and an evidence accumulation model of decision-making. Univariate activation and multivariate pattern analyses reveal that the acoustic-variability-tolerant representations of tone category are observed within the middle portion of the left superior temporal gyrus (STG). Activation patterns in the frontal and parietal regions also contained category-relevant information that was differentially sensitive to various forms of variability. The robustness of neural representations of tone category in a distributed fronto-temporoparietal network is associated with trial-by-trial decision-making parameters. These findings support a hybrid model involving a representational core within the STG that operates dynamically within an extensive frontoparietal network to support the representation and categorization of linguistic pitch patterns.

A Bayesian brain model of adaptive behavior: an application to the Wisconsin Card Sorting Task

Adaptive behavior emerges through a dynamic interaction between cognitive agents and changing environmental demands. The investigation of information processing underlying adaptive behavior relies on controlled experimental settings in which individuals are asked to accomplish demanding tasks whereby a hidden regularity or an abstract rule has to be learned dynamically. Although performance in such tasks is considered as a proxy for measuring high-level cognitive processes, the standard approach consists in summarizing observed response patterns by simple heuristic scoring measures. With this work, we propose and validate a new computational Bayesian model accounting for individual performance in the Wisconsin Card Sorting Test (WCST), a renowned clinical tool to measure set-shifting and deficient inhibitory processes on the basis of environmental feedback. We formalize the interaction between the task's structure, the received feedback, and the agent's behavior by building a model of the information processing mechanisms used to infer the hidden rules of the task environment. Furthermore, we embed the new model within the mathematical framework of the Bayesian Brain Theory (BBT), according to which beliefs about hidden environmental states are dynamically updated following the logic of Bayesian inference. Our computational model maps distinct cognitive processes into separable, neurobiologically plausible, information-theoretic constructs underlying observed response patterns. We assess model identification and expressiveness in accounting for meaningful human performance through extensive simulation studies. We then validate the model on real behavioral data in order to highlight the utility of the proposed model in recovering cognitive dynamics at an individual level. We highlight the potentials of our model in decomposing adaptive behavior in the WCST into several information-theoretic metrics revealing the trial-by-trial unfolding of information processing by focusing on two exemplary individuals whose behavior is examined in depth. Finally, we focus on the theoretical implications of our computational model by discussing the mapping between BBT constructs and functional neuroanatomical correlates of task performance. We further discuss the empirical benefit of recovering the assumed dynamics of information processing for both clinical and research practices, such as neurological assessment and model-based neuroscience.

Neural Components of Reading Revealed by Distributed and Symbolic Computational Models

Determining how the cognitive components of reading - orthographic, phonological, and semantic representations - are instantiated in the brain has been a longstanding goal of psychology and human cognitive neuroscience. The two most prominent computational models of reading instantiate different cognitive processes, implying different neural processes. Artificial neural network (ANN) models of reading posit non-symbolic, distributed representations. The dual-route cascaded (DRC) model instead suggests two routes of processing, one representing symbolic rules of spelling-sound correspondence, the other representing orthographic and phonological lexicons. These models are not adjudicated by behavioral data and have never before been directly compared in terms of neural plausibility. We used representational similarity analysis to compare the predictions of these models to neural data from participants reading aloud. Both the ANN and DRC model representations corresponded with neural activity. However, ANN model representations correlated to more reading-relevant areas of cortex. When contributions from the DRC model were statistically controlled, partial correlations revealed that the ANN model accounted for significant variance in the neural data. The opposite analysis, examining the variance explained by the DRC model with contributions from the ANN model factored out, revealed no correspondence to neural activity. Our results suggest that ANNs trained using distributed representations provide a better correspondence between cognitive and neural coding. Additionally, this framework provides a principled approach for comparing computational models of cognitive function to gain insight into neural representations.

An ontology-based modelling system (OBMS) for representing behaviour change theories applied to 76 theories

Background: To efficiently search, compare, test and integrate behaviour change theories, they need to be specified in a way that is clear, consistent and computable. An ontology-based modelling system (OBMS) has previously been shown to be able to represent five commonly used theories in this way. We aimed to assess whether the OBMS could be applied more widely and to create a database of behaviour change theories, their constructs and propositions. Methods: We labelled the constructs within 71 theories and used the OBMS to represent the relationships between the constructs. Diagrams of each theory were sent to authors or experts for feedback and amendment. The 71 finalised diagrams plus the five previously generated diagrams were used to create a searchable database of 76 theories in the form of construct-relationship-construct triples. We conducted a set of illustrative analyses to characterise theories in the database. Results: All 71 theories could be satisfactorily represented using this system. In total, 35 (49%) were finalised with no or very minor amendment. The remaining 36 (51%) were finalised after changes to the constructs (seven theories), relationships between constructs (15 theories) or both (14 theories) following author/expert feedback. The mean number of constructs per theory was 20 (min. = 6, max. = 72), with the mean number of triples per theory 31 (min. = 7, max. = 89). Fourteen distinct relationship types were used, of which the most commonly used was 'influences', followed by 'part of'. Conclusions: The OBMS can represent a wide array of behavioural theories in a precise, computable format. This system should provide a basis for better integration and synthesis of theories than has hitherto been possible.

Centroparietal activity mirrors the decision variable when tracking biased and time-varying sensory evidence

Decision-making is a fundamental human activity requiring explanation at the neurocognitive level. Current theoretical frameworks assume that, during sensory-based decision-making, the stimulus is sampled sequentially. The resulting evidence is accumulated over time as a decision variable until a threshold is reached and a response is initiated. Several neural signals, including the centroparietal positivity (CPP) measured from the human electroencephalogram (EEG), appear to display the accumulation-to-bound profile associated with the decision variable. Here, we evaluate the putative computational role of the CPP as a model-derived accumulation-to-bound signal, focussing on point-by-point correspondence between model predictions and data in order to go beyond simple summary measures like average slope. In two experiments, we explored the CPP under two manipulations (namely non-stationary evidence and probabilistic decision biases) that complement one another by targeting the shape and amplitude of accumulation respectively. We fit sequential sampling models to the behavioural data, and used the resulting parameters to simulate the decision variable, before directly comparing the simulated profile to the CPP waveform. In both experiments, model predictions deviated from our naïve expectations, yet showed similarities with the neurodynamic data, illustrating the importance of a formal modelling approach. The CPP appears to arise from brain processes that implement a decision variable (as formalised in sequential-sampling models) and may therefore inform our understanding of decision-making at both the representational and implementational levels of analysis, but at this point it is uncertain whether a single model can explain how the CPP varies across different kinds of task manipulation.

A Joint Modelling Approach to Analyze Risky Decisions by Means of Diffusion Tensor Imaging and Behavioural Data

Understanding dependencies between brain functioning and cognition is a challenging task which might require more than applying standard statistical models to neural and behavioural measures to be accomplished. Recent developments in computational modelling have demonstrated the advantage to formally account for reciprocal relations between mathematical models of cognition and brain functional, or structural, characteristics to relate neural and cognitive parameters on a model-based perspective. This would allow to account for both neural and behavioural data simultaneously by providing a joint probabilistic model for the two sources of information. In the present work we proposed an architecture for jointly modelling the reciprocal relation between behavioural and neural information in the context of risky decision-making. More precisely, we offered a way to relate Diffusion Tensor Imaging data to cognitive parameters of a computational model accounting for behavioural outcomes in the popular Balloon Analogue Risk Task (BART). Results show that the proposed architecture has the potential to account for individual differences in task performances and brain structural features by letting individual-level parameters to be modelled by a joint distribution connecting both sources of information. Such a joint modelling framework can offer interesting insights in the development of computational models able to investigate correspondence between decision-making and brain structural connectivity.

Mutual benefits: Combining reinforcement learning with sequential sampling models

Reinforcement learning models of error-driven learning and sequential-sampling models of decision making have provided significant insight into the neural basis of a variety of cognitive processes. Until recently, model-based cognitive neuroscience research using both frameworks has evolved separately and independently. Recent efforts have illustrated the complementary nature of both modelling traditions and showed how they can be integrated into a unified theoretical framework, explaining trial-by-trial dependencies in choice behavior as well as response time distributions. Here, we review a theoretical background of integrating the two classes of models, and review recent empirical efforts towards this goal. We furthermore argue that the integration of both modelling traditions provides mutual benefits for both fields, and highlight promises of this approach for cognitive modelling and model-based cognitive neuroscience.

Investigations into ventral prefrontal cortex using mediation models

The ventral prefrontal cortex (vPFC) is a major focus of investigation in neuroscience, particularly in the studies of emotion and emotion-cognition integration. A crucial question concerning the regulatory function of vPFC is how it is recruited, especially how the function maps onto the structure and determines appropriate behavior. In social exclusion studies, mediation model analyses suggest that vPFC regulates distress by disrupting anterior cingulate cortex (ACC) activities, whereas I recently report (Hu, 2018; Neuropsychologia) that ventral medial prefrontal cortex appears to defend the organism from acute stress by activating ACC. In this review, I synthesize and highlight functional imaging research with mediation analysis that over the past decades has begun to offer new insights into the brain mechanisms underlying vPFC. Toward this end, the first section of the paper outlines a model of the processes and neural systems involved in the interaction of emotion and cognition. The second and third sections survey recent research on emotional regulation with negative and positive pathways, respectively, emanating from vPFC. The fourth section summarizes the current dynamic network findings. Functional mediation analysis helps to identify signals within vPFC and others that are common and/or specific to particular information processing. Finally, I provide a personal perspective of the adoption of mediation model analysis in the investigations into vPFC.

Model-based cognitive neuroscience: Multifield mechanistic integration in practice

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.

Advances in techniques for imposing reciprocity in brain-behavior relations

To better understand human behavior, the emerging field of model-based cognitive neuroscience seeks to anchor psychological theory to the biological substrate from which behavior originates: the brain. Despite complex dynamics, many researchers in this field have demonstrated that fluctuations in brain activity can be related to fluctuations in components of cognitive models, which instantiate psychological theories. In this review, we discuss a number of approaches for relating brain activity to cognitive models, and expand on a framework for imposing reciprocity in the inference of mental operations from the combination of brain and behavioral data.

Affect and Decision Making: Insights and Predictions from Computational Models

In recent years interest in integrating the affective and decision sciences has skyrocketed. Immense progress has been made, but the complexities of each field, which can multiply when combined, present a significant obstacle. A carefully defined framework for integration is needed. The shift towards computational modeling in decision science provides a powerful basis and a path forward, but one whose synergistic potential will only be fully realized by drawing on the theoretical richness of the affective sciences. Reviewing research using a popular computational model of choice (the drift diffusion model), we discuss how mapping concepts to parameters reduces conceptual ambiguity and reveals novel hypotheses.

Neurally constrained modeling of speed-accuracy tradeoff during visual search: gated accumulation of modulated evidence

Stochastic accumulator models account for response times and errors in perceptual decision making by assuming a noisy accumulation of perceptual evidence to a threshold. Previously, we explained saccade visual search decision making by macaque monkeys with a stochastic multiaccumulator model in which accumulation was driven by a gated feed-forward integration to threshold of spike trains from visually responsive neurons in frontal eye field that signal stimulus salience. This neurally constrained model quantitatively accounted for response times and errors in visual search for a target among varying numbers of distractors and replicated the dynamics of presaccadic movement neurons hypothesized to instantiate evidence accumulation. This modeling framework suggested strategic control over gate or over threshold as two potential mechanisms to accomplish speed-accuracy tradeoff (SAT). Here, we show that our gated accumulator model framework can account for visual search performance under SAT instructions observed in a milestone neurophysiological study of frontal eye field. This framework captured key elements of saccade search performance, through observed modulations of neural input, as well as flexible combinations of gate and threshold parameters necessary to explain differences in SAT strategy across monkeys. However, the trajectories of the model accumulators deviated from the dynamics of most presaccadic movement neurons. These findings demonstrate that traditional theoretical accounts of SAT are incomplete descriptions of the underlying neural adjustments that accomplish SAT, offer a novel mechanistic account of decision-making mechanisms during speed-accuracy tradeoff, and highlight questions regarding the identity of model and neural accumulators. NEW & NOTEWORTHY A gated accumulator model is used to elucidate neurocomputational mechanisms of speed-accuracy tradeoff. Whereas canonical stochastic accumulators adjust strategy only through variation of an accumulation threshold, we demonstrate that strategic adjustments are accomplished by flexible combinations of both modulation of the evidence representation and adaptation of accumulator gate and threshold. The results indicate how model-based cognitive neuroscience can translate between abstract cognitive models of performance and neural mechanisms of speed-accuracy tradeoff.

On the importance of avoiding shortcuts in applying cognitive models to hierarchical data

Psychological experiments often yield data that are hierarchically structured. A number of popular shortcut strategies in cognitive modeling do not properly accommodate this structure and can result in biased conclusions. To gauge the severity of these biases, we conducted a simulation study for a two-group experiment. We first considered a modeling strategy that ignores the hierarchical data structure. In line with theoretical results, our simulations showed that Bayesian and frequentist methods that rely on this strategy are biased towards the null hypothesis. Secondly, we considered a modeling strategy that takes a two-step approach by first obtaining participant-level estimates from a hierarchical cognitive model and subsequently using these estimates in a follow-up statistical test. Methods that rely on this strategy are biased towards the alternative hypothesis. Only hierarchical models of the multilevel data lead to correct conclusions. Our results are particularly relevant for the use of hierarchical Bayesian parameter estimates in cognitive modeling.

Leave-One-Trial-Out, LOTO, a general approach to link single-trial parameters of cognitive models to neural data

A key goal of model-based cognitive neuroscience is to estimate the trial-by-trial fluctuations of cognitive model parameters in order to link these fluctuations to brain signals. However, previously developed methods are limited by being difficult to implement, time-consuming, or model-specific. Here, we propose an easy, efficient and general approach to estimating trial-wise changes in parameters: Leave-One-Trial-Out (LOTO). The rationale behind LOTO is that the difference between parameter estimates for the complete dataset and for the dataset with one omitted trial reflects the parameter value in the omitted trial. We show that LOTO is superior to estimating parameter values from single trials and compare it to previously proposed approaches. Furthermore, the method makes it possible to distinguish true variability in a parameter from noise and from other sources of variability. In our view, the practicability and generality of LOTO will advance research on tracking fluctuations in latent cognitive variables and linking them to neural data.

A Varying Role for Abstraction in Models of Category Learning Constructed from Neural Representations in Early Visual Cortex

The human capacity for visual categorization is core to how we make sense of the visible world. Although a substantive body of research in cognitive neuroscience has localized this capacity to regions of human visual cortex, relatively few studies have investigated the role of abstraction in how representations for novel object categories are constructed from the neural representation of stimulus dimensions. Using human fMRI coupled with formal modeling of observer behavior, we assess a wide range of categorization models that vary in their level of abstraction from collections of subprototypes to representations of individual exemplars. The category learning tasks range from simple linear and unidimensional category rules to complex crisscross rules that require a nonlinear combination of multiple dimensions. We show that models based on neural responses in primary visual cortex favor a variable, but often limited, extent of abstraction in the construction of representations for novel categories, which differ in degree across tasks and individuals.

Explaining the neural activity distribution associated with discrete movement sequences: Evidence for parallel functional systems

To explore the effects of practice we scanned participants with fMRI while they were performing four-key unfamiliar and familiar sequences, and compared the associated activities relative to simple control sequences. On the basis of a recent cognitive model of sequential motor behavior (C-SMB), we propose that the observed neural activity would be associated with three functional networks that can operate in parallel and that allow (a) responding to stimuli in a reaction mode, (b) sequence execution using spatial sequence representations in a central-symbolic mode, and (c) sequence execution using motor chunk representations in a chunking mode. On the basis of this model and findings in the literature, we predicted which neural areas would be active during execution of the unfamiliar and familiar keying sequences. The observed neural activities were largely in line with our predictions, and allowed functions to be attributed to the active brain areas that fit the three above functional systems. The results corroborate C-SMB’s assumption that at advanced skill levels the systems executing motor chunks and translating key-specific stimuli are racing to trigger individual responses. They further support recent behavioral indications that spatial sequence representations continue to be used.

Neuroscience and Education: A Bridge Astray?

It is common to encounter the belief that neuroscience holds promise for advancing education practice—a belief that is predicated on the assumption that neuroscientific findings can be scaled up to inform our understanding of behavior in complex education settings. In this article, we argue that this belief is not just far-fetched but misdirected. Although we acknowledge the value of neuroscience for understanding brain mechanisms, we argue that it is largely unnecessary for the development of effective learning interventions. We demonstrate how neuroscience findings have failed to generalize to classroom contexts by highlighting the recent popularity and failed results from brain-training research. We end by providing two recommendations for how future researchers and policy makers should address neuroscience and its potential for education applications.

Low and variable correlation between reaction time costs and accuracy costs explained by accumulation models: Meta-analysis and simulations

The underpinning assumption of much research on cognitive individual differences (or group differences) is that task performance indexes cognitive ability in that domain. In many tasks performance is measured by differences (costs) between conditions, which are widely assumed to index a psychological process of interest rather than extraneous factors such as speed-accuracy trade-offs (e.g., Stroop, implicit association task, lexical decision, antisaccade, Simon, Navon, flanker, and task switching). Relatedly, reaction time (RT) costs or error costs are interpreted similarly and used interchangeably in the literature. All of this assumes a strong correlation between RT-costs and error-costs from the same psychological effect. We conducted a meta-analysis to test this, with 114 effects across a range of well-known tasks. Counterintuitively, we found a general pattern of weak, and often no, association between RT and error costs (mean r = .17, range -.45 to .78). This general problem is accounted for by the theoretical framework of evidence accumulation models, which capture individual differences in (at least) 2 distinct ways. Differences affecting accumulation rate produce positive correlation. But this is cancelled out if individuals also differ in response threshold, which produces negative correlations. In the models, subtractions between conditions do not isolate processing costs from caution. To demonstrate the explanatory power of synthesizing the traditional subtraction method within a broader decision model framework, we confirm 2 predictions with new data. Thus, using error costs or RT costs is more than a pragmatic choice; the decision carries theoretical consequence that can be understood through the accumulation model framework. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Cognitive computational neuroscience

To learn how cognition is implemented in the brain, we must build computational models that can perform cognitive tasks, and test such models with brain and behavioral experiments. Cognitive science has developed computational models that decompose cognition into functional components. Computational neuroscience has modeled how interacting neurons can implement elementary components of cognition. It is time to assemble the pieces of the puzzle of brain computation and to better integrate these separate disciplines. Modern technologies enable us to measure and manipulate brain activity in unprecedentedly rich ways in animals and humans. However, experiments will yield theoretical insight only when employed to test brain-computational models. Here we review recent work in the intersection of cognitive science, computational neuroscience and artificial intelligence. Computational models that mimic brain information processing during perceptual, cognitive and control tasks are beginning to be developed and tested with brain and behavioral data.

Approximating Bayesian Inference through Model Simulation

The ultimate test of the validity of a cognitive theory is its ability to predict patterns of empirical data. Cognitive models formalize this test by making specific processing assumptions that yield mathematical predictions, and the mathematics allow the models to be fitted to data. As the field of cognitive science has grown to address increasingly complex problems, so too has the complexity of models increased. Some models have become so complex that the mathematics detailing their predictions are intractable, meaning that the model can only be simulated. Recently, new Bayesian techniques have made it possible to fit these simulation-based models to data. These techniques have even allowed simulation-based models to transition into neuroscience, where tests of cognitive theories can be biologically substantiated.

Orthographic and Phonological Representations in the Fusiform Cortex

Mental and neural representations of words are at the core of understanding the cognitive and neural mechanisms of reading. Despite extensive studies, the nature of visual word representation remains highly controversial due to methodological limitations. In particular, it is unclear whether the fusiform cortex contains only abstract orthographic representation, or represents both lower and higher level orthography as well as phonology. Using representational similarity analysis, we integrated behavioral ratings, computational models of reading and visual object recognition, and neuroimaging data to examine the nature of visual word representations in the fusiform cortex. Our results provided clear evidence that the middle and anterior fusiform represented both phonological and orthographic information. Whereas lower level orthographic information was represented at every stage of the ventral visual stream, abstract orthographic information was increasingly represented along the posterior-to-anterior axis. Furthermore, the left and right hemispheres were tuned to high- and low-frequency orthographic information, respectively. These results help to resolve the long-standing debates regarding the role of the fusiform in reading, and have significant implications for the development of psychological, neural, and computational theories of reading.

Unpacking buyer-seller differences in valuation from experience: A cognitive modeling approach

People often indicate a higher price for an object when they own it (i.e., as sellers) than when they do not (i.e., as buyers)-a phenomenon known as the endowment effect. We develop a cognitive modeling approach to formalize, disentangle, and compare alternative psychological accounts (e.g., loss aversion, loss attention, strategic misrepresentation) of such buyer-seller differences in pricing decisions of monetary lotteries. To also be able to test possible buyer-seller differences in memory and learning, we study pricing decisions from experience, obtained with the sampling paradigm, where people learn about a lottery's payoff distribution from sequential sampling. We first formalize different accounts as models within three computational frameworks (reinforcement learning, instance-based learning theory, and cumulative prospect theory), and then fit the models to empirical selling and buying prices. In Study 1 (a reanalysis of published data with hypothetical decisions), models assuming buyer-seller differences in response bias (implementing a strategic-misrepresentation account) performed best; models assuming buyer-seller differences in choice sensitivity or memory (implementing a loss-attention account) generally fared worst. In a new experiment involving incentivized decisions (Study 2), models assuming buyer-seller differences in both outcome sensitivity (as proposed by a loss-aversion account) and response bias performed best. In both Study 1 and 2, the models implemented in cumulative prospect theory performed best. Model recovery studies validated our cognitive modeling approach, showing that the models can be distinguished rather well. In summary, our analysis supports a loss-aversion account of the endowment effect, but also reveals a substantial contribution of simple response bias.

Unifying Theories of Psychedelic Drug Effects

How do psychedelic drugs produce their characteristic range of acute effects in perception, emotion, cognition, and sense of self? How do these effects relate to the clinical efficacy of psychedelic-assisted therapies? Efforts to understand psychedelic phenomena date back more than a century in Western science. In this article I review theories of psychedelic drug effects and highlight key concepts which have endured over the last 125 years of psychedelic science. First, I describe the subjective phenomenology of acute psychedelic effects using the best available data. Next, I review late 19th-century and early 20th-century theories-model psychoses theory, filtration theory, and psychoanalytic theory-and highlight their shared features. I then briefly review recent findings on the neuropharmacology and neurophysiology of psychedelic drugs in humans. Finally, I describe recent theories of psychedelic drug effects which leverage 21st-century cognitive neuroscience frameworks-entropic brain theory, integrated information theory, and predictive processing-and point out key shared features that link back to earlier theories. I identify an abstract principle which cuts across many theories past and present: psychedelic drugs perturb universal brain processes that normally serve to constrain neural systems central to perception, emotion, cognition, and sense of self. I conclude that making an explicit effort to investigate the principles and mechanisms of psychedelic drug effects is a uniquely powerful way to iteratively develop and test unifying theories of brain function.