Control adjustment costs limit goal flexibility: Empirical evidence and a computational account

A cornerstone of human intelligence is the ability to flexibly adjust our cognition and behavior as our goals change. For instance, achieving some goals requires efficiency, while others require caution. Adapting to these changing goals require corresponding adjustments in cognitive control (e.g., levels of attention, response thresholds). However, adjusting our control to meet new goals comes at a cost: we are better at achieving a goal in isolation than when transitioning between goals. The source of these control adjustment costs remains poorly understood, and the bulk of our understanding of such costs comes from settings in which participants transition between discrete task sets, rather than performance goals. Across four experiments, we show that adjustments in continuous control states incur a performance cost, and that a dynamical systems model can explain the source of these costs. Participants performed a single cognitively demanding task under varying performance goals (e.g., to be fast or to be accurate). We modeled control allocation to include a dynamic process of adjusting from one's current control state to a target state for a given performance goal. By incorporating inertia into this adjustment process, our model accounts for our empirical findings that people under-shoot their target control state more (i.e., exhibit larger adjustment costs) when (a) goals switch rather than remain fixed over a block (Study 1); (b) target control states are more distant from one another (Study 2); (c) less time is given to adjust to the new goal (Study 3); and (d) when anticipating having to switch goals more frequently (Study 4). Our findings characterize the costs of adjusting control to meet changing goals, and show that these costs can emerge directly from cognitive control dynamics. In so doing, they shed new light on the sources of and constraints on flexibility in human goal-directed behavior.

Decomposition of Reinforcement Learning Deficits in Disordered Gambling via Drift Diffusion Modeling and Functional Magnetic Resonance Imaging

Gambling disorder is associated with deficits in reward-based learning, but the underlying computational mechanisms are still poorly understood. Here, we examined this issue using a stationary reinforcement learning task in combination with computational modeling and functional resonance imaging (fMRI) in individuals that regular participate in gambling (n = 23, seven fulfilled one to three DSM 5 criteria for gambling disorder, sixteen fulfilled four or more) and matched controls (n = 23). As predicted, the gambling group exhibited substantially reduced accuracy, whereas overall response times (RTs) were not reliably different between groups. We then used comprehensive modeling using reinforcement learning drift diffusion models (RLDDMs) in combination with hierarchical Bayesian parameter estimation to shed light on the computational underpinnings of this performance deficit. In both groups, an RLDDM in which both non-decision time and decision threshold (boundary separation) changed over the course of the experiment accounted for the data best. The model showed good parameter and model recovery, and posterior predictive checks revealed that, in both groups, the model accurately reproduced the evolution of accuracies and RTs over time. Modeling revealed that, compared to controls, the learning impairment in the gambling group was linked to a more rapid reduction in decision thresholds over time, and a reduced impact of value-differences on the drift rate. The gambling group also showed shorter non-decision times. FMRI analyses replicated effects of prediction error coding in the ventral striatum and value coding in the ventro-medial prefrontal cortex, but there was no credible evidence for group differences in these effects. Taken together, our findings show that reinforcement learning impairments in disordered gambling are linked to both maladaptive decision threshold adjustments and a reduced consideration of option values in the choice process.

Adaptive decision-making depends on pupil-linked arousal in rats performing tactile discrimination tasks

Perceptual decision-making is a dynamic cognitive process and is shaped by many factors, including behavioral state, reward contingency, and sensory environment. To understand the extent to which adaptive behavior in decision-making is dependent on pupil-linked arousal, we trained head-fixed rats to perform perceptual decision-making tasks and systematically manipulated the probability of Go and No-go stimuli while simultaneously measuring their pupil size in the tasks. Our data demonstrated that the animals adaptively modified their behavior in response to the changes in the sensory environment. The response probability to both Go and No-go stimuli decreased as the probability of the Go stimulus being presented decreased. Analyses within the signal detection theory framework showed that while the animals' perceptual sensitivity was invariant, their decision criterion increased as the probability of the Go stimulus decreased. Simulation results indicated that the adaptive increase in the decision criterion will increase possible water rewards during the task. Moreover, the adaptive decision-making is dependent on pupil-linked arousal as the increase in the decision criterion was the largest during low pupil-linked arousal periods. Taken together, our results demonstrated that the rats were able to adjust their decision-making to maximize rewards in the tasks, and that adaptive behavior in perceptual decision-making is dependent on pupil-linked arousal.NEW & NOTEWORTHY Perceptual decision-making is a dynamic cognitive process and is shaped by many factors. However, the extent to which changes in sensory environment result in adaptive decision-making remains poorly understood. Our data provided new experimental evidence demonstrating that the rats were able to adaptively modify their decision criterion to maximize water reward in response to changes in the statistics of the sensory environment. Furthermore, the adaptive decision-making is dependent on pupil-linked arousal.

Muscular reflex gains reflect changes of mind in reaching

Decisions for action are accompanied by a continual processing of sensory information, sometimes resulting in a revision of the initial choice, called a change of mind (CoM). Although the motor system is tuned during the formation of a reach decision, it is unclear whether its preparatory state differs between CoM and non-CoM decisions. To test this, participants (n = 14) viewed a random-dot motion (RDM) stimulus of various coherence levels for a random viewing duration. At the onset of a mechanical perturbation that rapidly stretched the pectoralis muscle, they indicated the perceived motion direction by making a reaching movement to one of two targets. Using electromyography (EMG), we quantified the reflex gains of the pectoralis and posterior deltoid muscles. Results show that reflex gains scaled with both the coherence level and the viewing duration of the stimulus. We fit a drift diffusion model (DDM) to the behavioral choices. The decision variable (DV), derived from the DDM, correlated well with the measured reflex gain at the single-trial level. However, when matched on DV magnitude, reflex gains were significantly lower in CoM than non-CoM trials. We conclude that the internal state of the motor system, as measured by the spinal reflexes, reflects the continual deliberation on sensory evidence for action selection, including the postdecisional evidence that can lead to a change of mind.NEW & NOTEWORTHY Using behavioral findings, EMG, and computational modeling, we show that not only the perceptual decision outcome but also the accumulating evidence for that outcome is continuously sent to the relevant muscles. Moreover, we show that an upcoming change of mind can be detected in the motor periphery, suggesting that a correlate of the internal decision making process is being sent along.

How the time-of-day affects the EEG signatures of vigilance fluctuation

Previous research suggested the homeostatic effect on the top-down control system as a major factor for daytime vigilance decrement, yet how it alters the cognitive processes of vigilance remains unclear. Using EEG, the current study measured the vigilance of 28 participants under three states: the morning, the midafternoon after napping and no-nap. The drift-diffusion model was applied to decompose vigilant reaction time into decision and non-decision components. From morning to midafternoon, vigilance declined during sustained wakefulness, but remained stable after midday napping. Increased sleep pressure negatively affected decision time and drift rate, but did not significantly alter the non-decision process. Frontocentral N2 amplitude decreased from morning to no-nap afternoon, associated with slowing decision time. In contrast, parietal P3 had no diurnal alterations during sustained wakefulness, but enhanced after napping. Pre-stimulus parietooccipital alpha power enhanced under high sleep pressure relative to low, accompanied by more lapses in no-nap vs. post-napping conditions. The homeostasis effect is a major contributor to daily vigilance fluctuation, specifically targeting top-down control processes during the pre-stimulus and decision-making stages. Under the influence of sleep homeostasis, the speed of decision-making declines with degradation in target monitoring from morning to afternoon, leading to post-noon vigilance decrement.

Midfrontal theta phase underlies evidence accumulation and response thresholding in cognitive control

Cognitive control involves evidence accumulation and response thresholding, but the neural underpinnings of these 2 processes are poorly understood. Based on recent findings that midfrontal theta phase coordinates the correlation between theta power and reaction time during cognitive control, this study investigated whether and how theta phase would modulate the relationships between theta power and evidence accumulation and response thresholding in human participants when they performed a flanker task. Our results confirmed the modulation of theta phase on the correlations between ongoing midfrontal theta power and reaction time under both conditions. Using hierarchical drift-diffusion regression modeling, we found that in both conditions, theta power was positively associated with boundary separation in phase bins with optimal power-reaction time correlations, whereas the power-boundary correlation decreased to nonsignificance in phase bins with reduced power-reaction time correlations. In contrast, the power-drift rate correlation was not modulated by theta phase, but by cognitive conflict. Drift rate was positively correlated with theta power for the bottom-up processing in the non-conflict condition, whereas it was negatively correlated with theta power for the top-down control to address conflict. These findings suggest that evidence accumulation is likely to be a phase-coordinated continuous process, whereas thresholding may be a phase-specific transient process.

A process model account of the role of dopamine in intertemporal choice

Theoretical accounts disagree on the role of dopamine in intertemporal choice and assume that dopamine either promotes delay of gratification by increasing the preference for larger rewards or that dopamine reduces patience by enhancing the sensitivity to waiting costs. Here, we reconcile these conflicting accounts by providing empirical support for a novel process model according to which dopamine contributes to two dissociable components of the decision process, evidence accumulation and starting bias. We re-analyzed a previously published data set where intertemporal decisions were made either under the D2 antagonist amisulpride or under placebo by fitting a hierarchical drift diffusion model that distinguishes between dopaminergic effects on the speed of evidence accumulation and the starting point of the accumulation process. Blocking dopaminergic neurotransmission not only strengthened the sensitivity to whether a reward is perceived as worth the delay costs during evidence accumulation (drift rate) but also attenuated the impact of waiting costs on the starting point of the evidence accumulation process (bias). In contrast, re-analyzing data from a D1 agonist study provided no evidence for a causal involvement of D1R activation in intertemporal choices. Taken together, our findings support a novel, process-based account of the role of dopamine for cost-benefit decision making, highlight the potential benefits of process-informed analyses, and advance our understanding of dopaminergic contributions to decision making.

Pallidal neuromodulation of the explore/exploit trade-off in decision-making

Every decision that we make involves a conflict between exploiting our current knowledge of an action's value or exploring alternative courses of action that might lead to a better, or worse outcome. The sub-cortical nuclei that make up the basal ganglia have been proposed as a neural circuit that may contribute to resolving this explore-exploit 'dilemma'. To test this hypothesis, we examined the effects of neuromodulating the basal ganglia's output nucleus, the globus pallidus interna, in patients who had undergone deep brain stimulation (DBS) for isolated dystonia. Neuromodulation enhanced the number of exploratory choices to the lower value option in a two-armed bandit probabilistic reversal-learning task. Enhanced exploration was explained by a reduction in the rate of evidence accumulation (drift rate) in a reinforcement learning drift diffusion model. We estimated the functional connectivity profile between the stimulating DBS electrode and the rest of the brain using a normative functional connectome derived from heathy controls. Variation in the extent of neuromodulation induced exploration between patients was associated with functional connectivity from the stimulation electrode site to a distributed brain functional network. We conclude that the basal ganglia's output nucleus, the globus pallidus interna, can adaptively modify decision choice when faced with the dilemma to explore or exploit.

Space- and feature-based attention operate both independently and interactively within latent components of perceptual decision making

Top-down visual attention filters undesired stimuli while selected information is afforded the lion's share of limited cognitive resources. Multiple selection mechanisms can be deployed simultaneously, but how unique influences of each combine to facilitate behavior remains unclear. Previously, we failed to observe an additive perceptual benefit when both space-based attention (SBA) and feature-based attention (FBA) were cued in a sparse display (Liang & Scolari, 2020): FBA was restricted to higher order decision-making processes when combined with a valid spatial cue, whereas SBA additionally facilitated target enhancement. Here, we introduced a series of design modifications across three experiments to elicit both attention mechanisms within signal enhancement while also investigating the impacts on decision making. First, we found that when highly reliable spatial and feature cues made unique contributions to search (experiment 1), or when each cue component was moderately reliable (experiments 2a and 2b), both mechanisms were deployed independently to resolve the target. However, the same manipulations produced interactive attention effects within other latent decision-making components that depended on the probability of the integrated cueing object. Time spent before evidence accumulation was reduced and responses were more conservative for the most likely pre-cue combination-even when it included an invalid component. These data indicate that selection mechanisms operate on sensory signals invariably in an independent manner, whereas a higher-order dependency occurs outside of signal enhancement.

A practical introduction to using the drift diffusion model of decision-making in cognitive psychology, neuroscience, and health sciences

Recent years have seen a rapid increase in the number of studies using evidence-accumulation models (such as the drift diffusion model, DDM) in the fields of psychology and neuroscience. These models go beyond observed behavior to extract descriptions of latent cognitive processes that have been linked to different brain substrates. Accordingly, it is important for psychology and neuroscience researchers to be able to understand published findings based on these models. However, many articles using (and explaining) these models assume that the reader already has a fairly deep understanding of (and interest in) the computational and mathematical underpinnings, which may limit many readers' ability to understand the results and appreciate the implications. The goal of this article is therefore to provide a practical introduction to the DDM and its application to behavioral data - without requiring a deep background in mathematics or computational modeling. The article discusses the basic ideas underpinning the DDM, and explains the way that DDM results are normally presented and evaluated. It also provides a step-by-step example of how the DDM is implemented and used on an example dataset, and discusses methods for model validation and for presenting (and evaluating) model results. Supplementary material provides R code for all examples, along with the sample dataset described in the text, to allow interested readers to replicate the examples themselves. The article is primarily targeted at psychologists, neuroscientists, and health professionals with a background in experimental cognitive psychology and/or cognitive neuroscience, who are interested in understanding how DDMs are used in the literature, as well as some who may to go on to apply these approaches in their own work.

Evidence accumulation, not 'self-control', explains dorsolateral prefrontal activation during normative choice

What role do regions like the dorsolateral prefrontal cortex (dlPFC) play in normative behavior (e.g., generosity, healthy eating)? Some models suggest that dlPFC activation during normative choice reflects controlled inhibition or modulation of default hedonistic preferences. Here, we develop an alternative account, showing that evidence accumulation models predict trial-by-trial variation in dlPFC response across three fMRI paradigms and two self-control contexts (altruistic sacrifice and healthy eating). Using these models to simulate a variety of self-control dilemmas generated a novel prediction: although dlPFC activity might typically increase for norm-consistent choices, deliberate self-regulation focused on normative goals should decrease or even reverse this pattern (i.e., greater dlPFC response for hedonistic, self-interested choices). We confirmed these predictions in both altruistic and dietary choice contexts. Our results suggest that dlPFC response during normative choice may depend more on value-based evidence accumulation than inhibition of our baser instincts.

Temporal dynamics of decision making: A synthesis of computational and neurophysiological approaches

As interest in the temporal dynamics of decision-making has grown, researchers have increasingly turned to computational approaches such as the drift diffusion model (DDM) to identify how cognitive processes unfold during choice. At the same time, technological advances in noninvasive neurophysiological methods such as electroencephalography and magnetoencephalography now allow researchers to map the neural time course of decision making with millisecond precision. Combining these approaches can potentially yield important new insights into how choices emerge over time. Here we review recent research on the computational and neurophysiological correlates of perceptual and value-based decision making, from DDM parameters to scalp potentials and oscillatory neural activity. Starting with motor response preparation, the most well-understood aspect of the decision process, we discuss evidence that urgency signals and shifts in baseline activation, rather than shifts in the physiological value of the choice-triggering response threshold, are responsible for adjusting response times under speeded choice scenarios. Research on the neural correlates of starting point bias suggests that prestimulus activity can predict biases in motor choice behavior. Finally, studies examining the time dynamics of evidence construction and evidence accumulation have identified signals at frontocentral and centroparietal electrodes associated respectively with these processes, emerging 300-500 ms after stimulus onset. These findings can inform psychological theories of decision-making, providing empirical support for attribute weighting in value-based choice while suggesting theoretical alternatives to dual-process accounts. Further research combining computational and neurophysiological approaches holds promise for providing greater insight into the moment-by-moment evolution of the decision process. This article is categorized under: Psychology > Reasoning and Decision Making Neuroscience > Cognition Economics > Individual Decision-Making.

Neural Encoding of Active Multi-Sensing Enhances Perceptual Decision-Making via a Synergistic Cross-Modal Interaction

Most perceptual decisions rely on the active acquisition of evidence from the environment involving stimulation from multiple senses. However, our understanding of the neural mechanisms underlying this process is limited. Crucially, it remains elusive how different sensory representations interact in the formation of perceptual decisions. To answer these questions, we used an active sensing paradigm coupled with neuroimaging, multivariate analysis, and computational modeling to probe how the human brain processes multisensory information to make perceptual judgments. Participants of both sexes actively sensed to discriminate two texture stimuli using visual (V) or haptic (H) information or the two sensory cues together (VH). Crucially, information acquisition was under the participants' control, who could choose where to sample information from and for how long on each trial. To understand the neural underpinnings of this process, we first characterized where and when active sensory experience (movement patterns) is encoded in human brain activity (EEG) in the three sensory conditions. Then, to offer a neurocomputational account of active multisensory decision formation, we used these neural representations of active sensing to inform a drift diffusion model of decision-making behavior. This revealed a multisensory enhancement of the neural representation of active sensing, which led to faster and more accurate multisensory decisions. We then dissected the interactions between the V, H, and VH representations using a novel information-theoretic methodology. Ultimately, we identified a synergistic neural interaction between the two unisensory (V, H) representations over contralateral somatosensory and motor locations that predicted multisensory (VH) decision-making performance.SIGNIFICANCE STATEMENT In real-world settings, perceptual decisions are made during active behaviors, such as crossing the road on a rainy night, and include information from different senses (e.g., car lights, slippery ground). Critically, it remains largely unknown how sensory evidence is combined and translated into perceptual decisions in such active scenarios. Here we address this knowledge gap. First, we show that the simultaneous exploration of information across senses (multi-sensing) enhances the neural encoding of active sensing movements. Second, the neural representation of active sensing modulates the evidence available for decision; and importantly, multi-sensing yields faster evidence accumulation. Finally, we identify a cross-modal interaction in the human brain that correlates with multisensory performance, constituting a putative neural mechanism for forging active multisensory perception.

High-value decisions are fast and accurate, inconsistent with diminishing value sensitivity

What information about economic value is incorporated into decision-makers’ choices? Across the decision sciences, several prominent models ignore average value, assuming that only value differences are incorporated into the decision-making process, while others assume diminishing sensitivity to value, suggesting that it should be more difficult to choose between high-value options. Other models suggest that high-value decisions should, if anything, be treated as less important (holding value difference constant). Across three experiments with very different types of choices (food, art, and learned stimuli), we find violations of these predictions. Contrary to expectations, the presence of high-value options makes decisions easier while also inducing more effort to get them right.

It is a widely held belief that people’s choices are less sensitive to changes in value as value increases. For example, the subjective difference between $11 and $12 is believed to be smaller than between $1 and $2. This idea is consistent with applications of the Weber-Fechner Law and divisive normalization to value-based choice and with psychological interpretations of diminishing marginal utility. According to random utility theory in economics, smaller subjective differences predict less accurate choices. Meanwhile, in the context of sequential sampling models in psychology, smaller subjective differences also predict longer response times. Based on these models, we would predict decisions between high-value options to be slower and less accurate. In contrast, some have argued on normative grounds that choices between high-value options should be made with less caution, leading to faster and less accurate choices. Here, we model the dynamics of the choice process across three different choice domains, accounting for both discriminability and response caution. Contrary to predictions, we mostly observe faster and more accurate decisions (i.e., higher drift rates) between high-value options. We also observe that when participants are alerted about incoming high-value decisions, they exert more caution and not less. We rule out several explanations for these results, using tasks with both subjective and objective values. These results cast doubt on the notion that increasing value reduces discriminability.

Fast evidence accumulation in social anxiety disorder enhances decision making in a probabilistic reward task

Choices and response times in two-alternative decision-making tasks can be modeled by assuming that individuals steadily accrue evidence in favor of each alternative until a response boundary for one of them is crossed, at which point that alternative is chosen. Prior studies have reported that evidence accumulation during decision-making tasks takes longer in adults with psychopathology than in healthy controls, indicating that slow evidence accumulation may be transdiagnostic. However, few studies have examined perceptual decision making in anxiety disorders, where hypervigilance might enhance performance. Therefore, this study used the Hierarchical Drift Diffusion model to investigate evidence accumulation in adults with social anxiety disorder (SAD) and healthy controls as they performed a probabilistic reward task (PRT), in which social rewards were delivered for correct perceptual judgments. Adults with SAD completed the PRT before and after gaze-contingent music reward therapy (GCMRT), which trains attention allocation and has shown efficacy for SAD. Healthy controls also completed the PRT twice. Results revealed excellent performance in adults with SAD, especially after GCMRT: relative to controls, they showed faster evidence accumulation, better discriminability, and earned more rewards. These data highlight a positive effect of attention training on performance in anxious adults and show how a behavioral trait that is typically problematic-hypervigilance in SAD-can nevertheless confer advantages in certain contexts. The data also indicate that, in contrast to other forms of psychopathology, SAD is not characterized by slow evidence accumulation, at least in the context of the social PRT. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Accumulation of evidence during decision making in OCD patients

Decision-making often entails the accumulation of evidence. Previous studies suggested that people with obsessive-compulsive disorder (OCD) process decision-making differently from healthy controls. Both their compulsive behavior and obsessive thoughts may influence the evidence accumulation process, yet the previous studies disagreed on the reason. To address this question, we employed a probabilistic reasoning task in which subjects made two alternative forced choices by viewing a series of visual stimuli. These stimuli carried probabilistic information toward the choices. While the OCD patients achieved similar accuracy to the control, they took longer time and accumulated more evidence, especially in difficult trials in which the evidence strength was low. We further modeled the subjects' decision making as a leaky drifting diffusion process toward two collapsing bounds. The control group showed a higher drifting rate than the OCD group, indicating that the OCD group was less sensitive to evidence. Together, these results demonstrated that the OCD patients were less efficient than the control at transforming sensory information into evidence. However, their evidence accumulation was comparable to the healthy control, and they compensated for their decision-making accuracy with longer reaction times.

A causal role for the right frontal eye fields in value comparison

Recent studies have suggested close functional links between overt visual attention and decision making. This suggests that the corresponding mechanisms may interface in brain regions known to be crucial for guiding visual attention - such as the frontal eye field (FEF). Here, we combined brain stimulation, eye tracking, and computational approaches to explore this possibility. We show that inhibitory transcranial magnetic stimulation (TMS) over the right FEF has a causal impact on decision making, reducing the effect of gaze dwell time on choice while also increasing reaction times. We computationally characterize this putative mechanism by using the attentional drift diffusion model (aDDM), which reveals that FEF inhibition reduces the relative discounting of the non-fixated option in the comparison process. Our findings establish an important causal role of the right FEF in choice, elucidate the underlying mechanism, and provide support for one of the key causal hypotheses associated with the aDDM.

Disentangling cognitive processes in externalizing psychopathology using drift diffusion modeling: Antagonism, but not disinhibition, is associated with poor cognitive control

Although externalizing psychopathology has been linked to deficits in cognitive control, the cognitive processes underlying this association are unclear. Here, we provide a theoretical account of how research on cognitive processes can help to integrate and distinguish personality and psychopathology. We then apply this account to connect the two major subcomponents of externalizing, Antagonism and Disinhibition, with specific control processes using a battery of inhibitory control tasks and corresponding computational modeling. Participants (final N = 104) completed the flanker, go/no-go, and recent probes tasks, as well as normal and maladaptive personality inventories and measures of psychological distress. We fit participants' task behavior using a hierarchical drift diffusion model (DDM) to decompose their responses into specific cognitive processes. Using multilevel structural equation models, we found that Antagonism was associated with faster RTs on the flanker task and lower accuracy on flanker and go/no-go tasks. These results were complemented by DDM parameter associations: Antagonism was linked to decreased threshold and drift rate parameter estimates in the flanker task and a decreased drift rate on no-go trials. Altogether, our findings indicate that Antagonism is associated with specific impairments in fast (sub-second) inhibitory control processes involved in withholding prepared/prepotent responses and filtering distracting information. Disinhibition and momentary distress, however, were not associated with task performance.

Understanding neural signals of post-decisional performance monitoring: An integrative review

Performance monitoring is a key cognitive function, allowing to detect mistakes and adapt future behavior. Post-decisional neural signals have been identified that are sensitive to decision accuracy, decision confidence and subsequent adaptation. Here, we review recent work that supports an understanding of late error/confidence signals in terms of the computational process of post-decisional evidence accumulation. We argue that the error positivity, a positive-going centro-parietal potential measured through scalp electrophysiology, reflects the post-decisional evidence accumulation process itself, which follows a boundary crossing event corresponding to initial decision commitment. This proposal provides a powerful explanation for both the morphological characteristics of the signal and its relation to various expressions of performance monitoring. Moreover, it suggests that the error positivity -a signal with thus far unique properties in cognitive neuroscience - can be leveraged to furnish key new insights into the inputs to, adaptation, and consequences of the post-decisional accumulation process.

Evidence and Urgency Related EEG Signals during Dynamic Decision-Making in Humans

A successful class of models link decision-making to brain signals by assuming that evidence accumulates to a decision threshold. These evidence accumulation models have identified neuronal activity that appears to reflect sensory evidence and decision variables that drive behavior. More recently, an additional evidence-independent and time-variant signal, called urgency, has been hypothesized to accelerate decisions in the face of insufficient evidence. However, most decision-making paradigms tested with fMRI or EEG in humans have not been designed to disentangle evidence accumulation from urgency. Here we use a face-morphing decision-making task in combination with EEG and a hierarchical Bayesian model to identify neural signals related to sensory and decision variables, and to test the urgency-gating model. Forty females and 34 males took part (mean age, 23.4 years). We find that an evoked potential time locked to the decision, the centroparietal positivity, reflects the decision variable from the computational model. We further show that the unfolding of this signal throughout the decision process best reflects the product of sensory evidence and an evidence-independent urgency signal. Urgency varied across subjects, suggesting that it may represent an individual trait. Our results show that it is possible to use EEG to distinguish neural signals related to sensory evidence accumulation, decision variables, and urgency. These mechanisms expose principles of cognitive function in general and may have applications to the study of pathologic decision-making such as in impulse control and addictive disorders.SIGNIFICANCE STATEMENT Perceptual decisions are often described by a class of models that assumes that sensory evidence accumulates gradually over time until a decision threshold is reached. In the present study, we demonstrate that an additional urgency signal impacts how decisions are formed. This endogenous signal encourages one to respond as time elapses. We found that neural decision signals measured by EEG reflect the product of sensory evidence and an evidence-independent urgency signal. A nuanced understanding of human decisions, and the neural mechanisms that support it, can improve decision-making in many situations and potentially ameliorate dysfunction when it has gone awry.

Apathy in small vessel cerebrovascular disease is associated with deficits in effort-based decision making

Patients with small vessel cerebrovascular disease frequently suffer from apathy, a debilitating neuropsychiatric syndrome, the underlying mechanisms of which remain to be established. Here we investigated the hypothesis that apathy is associated with disrupted decision making in effort-based decision making, and that these alterations are associated with abnormalities in the white matter network connecting brain regions that underpin such decisions. Eighty-two patients with MRI evidence of small vessel disease were assessed using a behavioural paradigm as well as diffusion weighted MRI. The decision-making task involved accepting or rejecting monetary rewards in return for performing different levels of physical effort (hand grip force). Choice data and reaction times were integrated into a drift diffusion model that framed decisions to accept or reject offers as stochastic processes approaching a decision boundary with a particular drift rate. Tract-based spatial statistics were used to assess the relationship between white matter tract integrity and apathy, while accounting for depression. Overall, patients with apathy accepted significantly fewer offers on this decision-making task. Notably, while apathetic patients were less responsive to low rewards, they were also significantly averse to investing in high effort. Significant reductions in white matter integrity were observed to be specifically related to apathy, but not to depression. These included pathways connecting brain regions previously implicated in effort-based decision making in healthy people. The drift rate to decision parameter was significantly associated with both apathy and altered white matter tracts, suggesting that both brain and behavioural changes in apathy are associated with this single parameter. On the other hand, depression was associated with an increase in the decision boundary, consistent with an increase in the amount of evidence required prior to making a decision. These findings demonstrate altered effort-based decision making for reward in apathy, and also highlight dissociable mechanisms underlying apathy and depression in small vessel disease. They provide clear potential brain and behavioural targets for future therapeutic interventions, as well as modelling parameters that can be used to measure the effects of treatment at the behavioural level.

Does Attention Increase the Value of Choice Alternatives?

A growing recognition of the role of attention in decision-making has been driven by both the technology of eye tracking and the development of models that explicitly incorporate attention. One result of this convergence is the arresting claim that attention, by itself, can increase the perceived value of a decision alternative. In this review, we cover the origins of that claim, its empirical foundation, and the reasoning that supports it. The conclusion is that, to date, there is not sufficient evidence to support the claim. Alternative explanations for the extant evidentiary base are discussed, as is the balance between the bottom-up influence of empirical evidence and the top-down commitment to a conceptual framework.

Dual-Task Interference in a Simulated Driving Environment: Serial or Parallel Processing?

When humans are required to perform two or more tasks concurrently, their performance declines as the tasks get closer together in time. Here, we investigated the mechanisms of this cognitive performance decline using a dual-task paradigm in a simulated driving environment, and using drift-diffusion modeling, examined if the two tasks are processed in a serial or a parallel manner. Participants performed a lane change task, along with an image discrimination task. We systematically varied the time difference between the onset of the two tasks (Stimulus Onset Asynchrony, SOA) and measured its effect on the amount of dual-task interference. Results showed that the reaction times (RTs) of the two tasks in the dual-task condition were higher than those in the single-task condition. SOA influenced the RTs of both tasks when they were presented second and the RTs of the image discrimination task when it was presented first. Results of drift-diffusion modeling indicated that dual-task performance affects both the rate of evidence accumulation and the delays outside the evidence accumulation period. These results suggest that a hybrid model containing features of both parallel and serial processing best accounts for the results. Next, manipulating the predictability of the order of the two tasks, we showed that in unpredictable conditions, the order of the response to the two tasks changes, causing attenuation in the effect of SOA. Together, our findings suggest higher-level executive functions are involved in managing the resources and controlling the processing of the tasks during dual-task performance in naturalistic settings.

Transfer of information across repeated decisions in general and in obsessive-compulsive disorder

Real-life decisions are often repeated. Whether considering taking a job in a new city, or doing something mundane like checking if the stove is off, decisions are frequently revisited even if no new information is available. This mode of behavior takes a particularly pathological form in obsessive-compulsive disorder (OCD), which is marked by individuals' redeliberating previously resolved decisions. Surprisingly, little is known about how information is transferred across decision episodes in such circumstances, and whether and how such transfer varies in OCD. In two experiments, data from a repeated decision-making task and computational modeling revealed that both implicit and explicit memories of previous decisions affected subsequent decisions by biasing the rate of evidence integration. Further, we replicated previous work demonstrating impairments in baseline decision-making as a function of self-reported OCD symptoms, and found that information transfer effects specifically due to implicit memory were reduced, offering computational insight into checking behavior.

Effortful control is associated with executive attention: A computational study

INTRODUCTION

Effortful control (EC) is the self-regulatory aspect of temperament that is thought to reflect the efficiency of executive attention (EA). Findings on relationship between EC and performance on EA tasks among adults are still contradictory. This study used a computational approach to clarify whether greater self-reported EC reflects better EA.

METHODS

Four hundred twenty-seven healthy subjects completed the Adult Temperament Questionnaires and the Attention Network Task-revised, a conflict resolution task that gauges EA as the flanker effect (FE), that is, the difference in performances between incongruent and congruent trials. Here we also employed a drift-diffusion model in which parameters reflecting the actual decisional process (drift rate) and the extra-decisional time are extracted for congruent and incongruent trials.

RESULTS

EC was not correlated with the FE computed with the classic approach, but correlated positively with drift rate for the incongruent trials, even when controlling for the drift rate in the congruent condition and the extra-decisional time in the incongruent condition.

CONCLUSION

This study demonstrates an association between self-reported EC and EA among adults. Specifically, EC is not associated with overall response facilitation but specifically with a greater ability to make goal-oriented decisions when facing conflicting information.

Identifying Feigned Cognitive Impairment: Investigating the Utility of Diffusion Model Analyses

Forced-choice performance validity tests are routinely used for the detection of feigned cognitive impairment. The drift diffusion model deconstructs performance into distinct cognitive processes using accuracy and response time measures. It thereby offers a unique approach for gaining insight into examinees' speed-accuracy trade-offs and the cognitive processes that underlie their performance. The current study is the first to perform such analyses using a well-established forced-choice performance validity test. To achieve this aim, archival data of healthy participants, either simulating cognitive impairment in the Word Memory Test or performing it to the best of their ability, were analyzed using the EZ-diffusion model (N = 198). The groups differed in the three model parameters, with drift rate emerging as the best predictor of group membership. These findings provide initial evidence for the usefulness of the drift diffusion model in clarifying the cognitive processes underlying feigned cognitive impairment and encourage further research.

Associative and Identity Words Promote the Speed of Visual Categorization: A Hierarchical Drift Diffusion Account

Words can either boost or hinder the processing of visual information, which can lead to facilitation or interference of the behavioral response. We investigated the stage (response execution or target processing) of verbal interference/facilitation in the response priming paradigm with a gender categorization task. Participants in our study were asked to judge whether the presented stimulus was a female or male face that was briefly preceded by a gender word either congruent (prime: "man," target: "man"), incongruent (prime: "woman," target: "man") or neutral (prime: "day," target: "man") with respect to the face stimulus. We investigated whether related word-picture pairs resulted in faster reaction times in comparison to the neutral word-picture pairs (facilitation) and whether unrelated word-picture pairs resulted in slower reaction times in comparison to neutral word-picture pairs (interference). We further examined whether these effects (if any) map onto response conflict or aspects of target processing. In addition, identity ("man," "woman") and associative ("tie," "dress") primes were introduced to investigate the cognitive mechanisms of semantic and Stroop-like effects in response priming (introduced respectively by associations and identity words). We analyzed responses and reaction times using the drift diffusion model to examine the effect of facilitation and/or interference as a function of the prime type. We found that regardless of prime type words introduce a facilitatory effect, which maps to the processes of visual attention and response execution.

Dissecting the impact of depression on decision-making

BACKGROUND

Cognitive deficits in depressed adults may reflect impaired decision-making. To investigate this possibility, we analyzed data from unmedicated adults with Major Depressive Disorder (MDD) and healthy controls as they performed a probabilistic reward task. The Hierarchical Drift Diffusion Model (HDDM) was used to quantify decision-making mechanisms recruited by the task, to determine if any such mechanism was disrupted by depression.

METHODS

Data came from two samples (Study 1: 258 MDD, 36 controls; Study 2: 23 MDD, 25 controls). On each trial, participants indicated which of two similar stimuli was presented; correct identifications were rewarded. Quantile-probability plots and the HDDM quantified the impact of MDD on response times (RT), speed of evidence accumulation (drift rate), and the width of decision thresholds, among other parameters.

RESULTS

RTs were more positively skewed in depressed v. healthy adults, and the HDDM revealed that drift rates were reduced-and decision thresholds were wider-in the MDD groups. This pattern suggests that depressed adults accumulated the evidence needed to make decisions more slowly than controls did.

CONCLUSIONS

Depressed adults responded slower than controls in both studies, and poorer performance led the MDD group to receive fewer rewards than controls in Study 1. These results did not reflect a sensorimotor deficit but were instead due to sluggish evidence accumulation. Thus, slowed decision-making-not slowed perception or response execution-caused the performance deficit in MDD. If these results generalize to other tasks, they may help explain the broad cognitive deficits seen in depression.

Dopamine and Risky Decision-Making in Gambling Disorder

Gambling disorder is a behavioral addiction associated with impairments in value-based decision-making and cognitive control. These functions are thought to be regulated by dopamine within fronto-striatal circuits, but the role of altered dopamine neurotransmission in the etiology of gambling disorder remains controversial. Preliminary evidence suggests that increasing frontal dopamine tone might improve cognitive functioning in gambling disorder. We therefore examined whether increasing frontal dopamine tone via a single dose of the catechol-O-methyltransferase (COMT) inhibitor tolcapone would reduce risky choice in human gamblers (n = 14) in a randomized double-blind placebo-controlled crossover study. Data were analyzed using hierarchical Bayesian parameter estimation and a combined risky choice drift diffusion model (DDM). Model comparison revealed a nonlinear mapping from value differences to trial-wise drift rates, confirming recent findings. An increase in risk-taking under tolcapone versus placebo was about five times more likely, given the data, than a decrease [Bayes factor (BF) = 0.2]. Examination of drug effects on diffusion model parameters revealed that an increase in the value dependency of the drift rate under tolcapone was about thirteen times more likely than a decrease (BF = 0.073). In contrast, a reduction in the maximum drift rate under tolcapone was about seven times more likely than an increase (BF = 7.51). Results add to previous work on COMT inhibitors in behavioral addictions and to mounting evidence for the applicability of diffusion models in value-based decision-making. Future work should focus on individual genetic, clinical and cognitive factors that might account for heterogeneity in the effects of COMT inhibition.

Neural Correlates of Evidence and Urgency During Human Perceptual Decision-Making in Dynamically Changing Conditions

Current models of decision-making assume that the brain gradually accumulates evidence and drifts toward a threshold that, once crossed, results in a choice selection. These models have been especially successful in primate research; however, transposing them to human fMRI paradigms has proved it to be challenging. Here, we exploit the face-selective visual system and test whether decoded emotional facial features from multivariate fMRI signals during a dynamic perceptual decision-making task are related to the parameters of computational models of decision-making. We show that trial-by-trial variations in the pattern of neural activity in the fusiform gyrus reflect facial emotional information and modulate drift rates during deliberation. We also observed an inverse-urgency signal based in the caudate nucleus that was independent of sensory information but appeared to slow decisions, particularly when information in the task was ambiguous. Taken together, our results characterize how decision parameters from a computational model (i.e., drift rate and urgency signal) are involved in perceptual decision-making and reflected in the activity of the human brain.

The counterintuitive impact of responses and response times on parameter estimates in the drift diffusion model

Given a drift diffusion model with unknown drift and boundary parameters, we analyse the behaviour of maximum likelihood estimates with respect to changes of responses and response times. It is shown analytically that a single fast response time can dominate the estimation in that no matter how many correct answers a test taker provides, the estimate of the drift (ability) parameter decreases to zero. In addition, it is shown that although higher drift rates imply shorter response times, the reverse implication does not hold for the estimates: shorter response times can decrease the drift rate estimate. In the light of these analytical results, we illustrate the actual impact of the findings in a small simulation for a mental rotation test. The method of analysis outlined is applicable to a broader range of models, and we emphasize the need to further check currently used reaction time models within this framework.

A kind of magic: Enhanced detection of pantomimed grasps in professional magicians

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.

Translating a rodent measure of negative bias into humans: the impact of induced anxiety and unmedicated mood and anxiety disorders

BACKGROUND

Mood and anxiety disorders are ubiquitous but current treatment options are ineffective for many sufferers. Moreover, a number of promising pre-clinical interventions have failed to translate into clinical efficacy in humans. Improved treatments are unlikely without better animal-human translational pipelines. Here, we translate a rodent measure of negative affective bias into humans, exploring its relationship with (1) pathological mood and anxiety symptoms and (2) transient induced anxiety.

METHODS

Adult participants (age = 29 ± 11) who met criteria for mood or anxiety disorder symptomatology according to a face-to-face neuropsychiatric interview were included in the symptomatic group. Study 1 included N = 77 (47 = asymptomatic [female = 21]; 30 = symptomatic [female = 25]), study 2 included N = 47 asymptomatic participants (25 = female). Outcome measures were choice ratios, reaction times and parameters recovered from a computational model of reaction time - the drift diffusion model (DDM) - from a two-alternative-forced-choice task in which ambiguous and unambiguous auditory stimuli were paired with high and low rewards.

RESULTS

Both groups showed over 93% accuracy on unambiguous tones indicating intact discrimination, but symptomatic individuals demonstrated increased negative affective bias on ambiguous tones [proportion high reward = 0.42 (s.d. = 0.14)] relative to asymptomatic individuals [0.53 (s.d. = 0.17)] as well as a significantly reduced DDM drift rate. No significant effects were observed for the within-subjects anxiety-induction.

CONCLUSIONS

Humans with pathological anxiety symptoms directly mimic rodents undergoing anxiogenic manipulation. The lack of sensitivity to transient anxiety suggests the paradigm might be more sensitive to clinically relevant symptoms. Our results establish a direct translational pipeline (and candidate therapeutics screen) from negative affective bias in rodents to pathological mood and anxiety symptoms in humans.

Semantic Incongruency Interferes With Endogenous Attention in Cross-Modal Integration of Semantically Congruent Objects

Efficient multisensory integration is often influenced by other cognitive processes including, but not limited to, semantic congruency and focused endogenous attention. Semantic congruency can re-allocate processing resources to the location of a congruent stimulus, while attention can prioritize the integration of multi-sensory stimuli under focus. Here, we explore the robustness of this phenomenon in the context of three stimuli, two of which are in the focus of endogenous attention. Participants completed an endogenous attention task with a stimulus compound consisting of 3 different objects: (1) a visual object (V) in the foreground, (2) an auditory object (A), and (3) a visual background scene object (B). Three groups of participants focused their attention on either the visual object and auditory sound (Group VA, n = 30), the visual object and the background (VB, n = 27), or the auditory sound and the background (AB, n = 30), and judged the semantic congruency of the objects under focus. Congruency varied systematically across all 3 stimuli: All stimuli could be semantically incongruent (e.g., V, ambulance; A, church bell; and B, swimming-pool) or all could be congruent (e.g., V, lion; A, roar; and B, savannah), or two objects could be congruent with the remaining one incongruent to the other two (e.g., V, duck; A, quack; and B, phone booth). Participants exhibited a distinct pattern of errors: when participants attended two congruent objects (e.g., group VA: V, lion; A, roar), in the presence of an unattended, incongruent third object (e.g., B, bath room) they tended to make more errors than in any other stimulus combination. Drift diffusion modeling of the behavioral data revealed a significantly smaller drift rate in two-congruent-attended condition, indicating slower evidence accumulation, which was likely due to interference from the unattended, incongruent object. Interference with evidence accumulation occurred independently of which pair of objects was in the focus of attention, which suggests that the vulnerability of congruency judgments to incongruent unattended distractors is not affected by sensory modalities. A control analysis ruled out the simple explanation of a negative response bias. These findings implicate that our perceptual system is highly sensitive to semantic incongruencies even when they are not endogenously attended.

Characterizing the Manifest Probability Distributions of Three Latent Trait Models for Accuracy and Response Time

In this paper we study the statistical relations between three latent trait models for accuracies and response times: the hierarchical model (HM) of van der Linden (Psychometrika 72(3):287-308, 2007), the signed residual time model (SM) proposed by Maris and van der Maas (Psychometrika 77(4):615-633, 2012), and the drift diffusion model (DM) as proposed by Tuerlinckx and De Boeck (Psychometrika 70(4):629-650, 2005). One important distinction between these models is that the HM and the DM either assume or imply that accuracies and response times are independent given the latent trait variables, while the SM does not. In this paper we investigate the impact of this conditional independence property-or a lack thereof-on the manifest probability distribution for accuracies and response times. We will find that the manifest distributions of the latent trait models share several important features, such as the dependency between accuracy and response time, but we also find important differences, such as in what function of response time is being modeled. Our method for characterizing the manifest probability distributions is related to the Dutch identity (Holland in Psychometrika 55(6):5-18, 1990).

Predictably confirmatory: The influence of stereotypes during decisional processing

Stereotypes facilitate the processing of expectancy-consistent (vs expectancy-inconsistent) information, yet the underlying origin of this congruency effect remains unknown. As such, here we sought to identify the cognitive operations through which stereotypes influence decisional processing. In six experiments, participants responded to stimuli that were consistent or inconsistent with respect to prevailing gender stereotypes. To identify the processes underpinning task performance, responses were submitted to a hierarchical drift diffusion model (HDDM) analysis. A consistent pattern of results emerged. Whether manipulated at the level of occupational (Expts. 1, 3, and 5) or trait-based (Expts. 2, 4, and 6) expectancies, stereotypes facilitated task performance and influenced decisional processing via a combination of response and stimulus biases. Specifically, (1) stereotype-consistent stimuli were classified more rapidly than stereotype-inconsistent stimuli; (2) stereotypic responses were favoured over counter-stereotypic responses (i.e., starting-point shift towards stereotypic responses); (3) less evidence was required when responding to stereotypic than counter-stereotypic stimuli (i.e., narrower threshold separation for stereotypic stimuli); and (4) decisional evidence was accumulated more efficiently for stereotype-inconsistent than stereotype-consistent stimuli and when targets had a typical than atypical facial appearance. Collectively, these findings elucidate how stereotypes influence person construal.

Deep Brain Stimulation of the Subthalamic Nucleus Induces Impulsive Responses to Bursts of Sensory Evidence

Decisions are made through the integration of external and internal inputs until a threshold is reached, triggering a response. The subthalamic nucleus (STN) has been implicated in adjusting the decision bound to prevent impulsivity during difficult decisions. We combine model-based and model-free approaches to test the theory that the STN raises the decision bound, a process impaired by deep brain stimulation (DBS). Eight male and female human subjects receiving treatment for Parkinson's disease with bilateral DBS of the STN performed an auditory two-alternative forced choice task. By ending trials unpredictably, we collected reaction time (RT) trials in which subjects reached their decision bound and non-RT trials in which subjects were forced to make a decision with less evidence. A decreased decision bound would cause worse performance on RT trials, and we found this to be the case on left-sided RT trials. Drift diffusion modeling showed a negative drift rate. This implies that in the absence of new evidence, the amount of evidence accumulated tends to drift toward zero. If evidence is accumulated at a constant rate this results in the evidence accumulated reaching an asymptote, the distance of which from the bound was decreased by DBS (p = 0.0079, random shuffle test), preventing subjects from controlling impulsivity. Subjects were more impulsive to bursts of stimuli associated with conflict (p < 0.001, cluster mass test). In addition, DBS lowered the decision bound specifically after error trials, decreasing the probability of switching to a non-RT trial after an error compared to correct response (28% vs. 38%, p = 0.005, Fisher exact test). The STN appears to function in decision-making by modulating the decision bound and drift rate to allow the suppression of impulsive responses.

Paradoxical Decision-Making: A Framework for Understanding Cognition in Parkinson's Disease

People with Parkinson's disease (PD) show impaired decision-making when sensory and memory information must be combined. This recently identified impairment results from an inability to accumulate the proper amount of information needed to make a decision and appears to be independent of dopamine tone and reinforcement learning mechanisms. Although considerable work focuses on PD and decisions involving risk and reward, in this Opinion article we propose that the emerging findings in perceptual decision-making highlight the multisystem nature of PD, and that unraveling the neuronal circuits underlying perceptual decision-making impairment may help in understanding other cognitive impairments in people with PD. We also discuss how a decision-making framework may be extended to gain insights into mechanisms of motor impairments in PD.

Differential Bilateral Primary Motor Cortex tDCS Fails to Modulate Choice Bias and Readiness in Perceptual Decision Making

One of the critical factors that guide choice behavior is the prior bias of the decision-maker with respect to different options, namely, the relative readiness by which the decision-maker opts for a specific choice. Although previous neuroimaging work has shown decision bias related activity in the orbitofrontal cortex, intraparietal sulcus (IPS) and dorsolateral prefrontal cortex, in a recent work by Javadi et al. (2015), primary motor cortex was also implicated. By applying transcranial direct current stimulation (tDCS), they have revealed a causal role of the primary motor cortex excitability in the induction of response time (RT) differences and decision bias in the form of choice probability. The current study aimed to replicate these recent findings with an experimental design that contained a sham group to increase experimental control and an additional testing phase to investigate the possible after-effects of tDCS. The conventional decision outputs such as choice proportion and RT were analyzed along with the theory-driven estimates of choice bias and non-decision related components of RTs (e.g., motor implementation speed of choices made). None of the statistical comparisons favored the alternative hypotheses over the null hypotheses. Consequently, previous findings regarding the effect of primary motor cortex excitability on choice bias and response times could not be replicated with a more controlled experimental design that is recommended for tDCS studies (Horvath et al., 2015). This empirical discrepancy between the two studies adds to the evidence demonstrating inconsistent effects of tDCS in establishing causal relationships between cortical excitability and motor behavior.

Cognitive regulation alters social and dietary choice by changing attribute representations in domain-general and domain-specific brain circuits

Are some people generally more successful using cognitive regulation or does it depend on the choice domain? Why? We combined behavioral computational modeling and multivariate decoding of fMRI responses to identify neural loci of regulation-related shifts in value representations across goals and domains (dietary or altruistic choice). Surprisingly, regulatory goals did not alter integrative value representations in the ventromedial prefrontal cortex, which represented all choice-relevant attributes across goals and domains. Instead, the dorsolateral prefrontal cortex (DLPFC) flexibly encoded goal-consistent values and predicted regulatory success for the majority of choice-relevant attributes, using attribute-specific neural codes. We also identified domain-specific exceptions: goal-dependent encoding of prosocial attributes localized to precuneus and temporo-parietal junction (not DLPFC). Our results suggest that cognitive regulation operated by changing specific attribute representations (not integrated values). Evidence of domain-general and domain-specific neural loci reveals important divisions of labor, explaining when and why regulatory success generalizes (or doesn’t) across contexts and domains.

Dopamine Modulates the Efficiency of Sensory Evidence Accumulation During Perceptual Decision Making

BACKGROUND

Perceptual decision making is the process through which available sensory information is gathered and processed to guide our choices. However, the neuropsychopharmacological basis of this important cognitive function is largely elusive. Yet, theoretical considerations suggest that the dopaminergic system may play an important role.

METHODS

In a double-blind, randomized, placebo-controlled study design, we examined the effect of methylphenidate in 2 dosages (0.25 mg/kg and 0.5 mg/kg body weight) in separate groups of healthy young adults. We used a moving dots task in which the coherency of the direction of moving dots stimuli was manipulated in 3 levels (5%, 15%, and 35%). Drift diffusion modelling was applied to behavioral data to capture subprocesses of perceptual decision making.

RESULTS

The findings show that only the drift rate (v), reflecting the efficiency of sensory evidence accumulation, but not the decision criterion threshold (a) or the duration of nondecisional processes (Ter), is affected by methylphenidate vs placebo administration. Compared with placebo, administering 0.25 mg/kg methylphenidate increased v, but only in the 35% coherence condition. Administering 0.5 mg/kg methylphenidate did not induce modulations.

CONCLUSIONS

The data suggest that dopamine selectively modulates the efficacy of evidence accumulation during perceptual decision making. This modulation depends on 2 factors: (1) the degree to which the dopaminergic system is modulated using methylphenidate (i.e., methylphenidate dosage) and (2) the signal-to-noise ratio of the visual information. Dopamine affects sensory evidence accumulation only when dopamine concentration is not shifted beyond an optimal level and the incoming information is less noisy.

Mechanisms Underlying Decision-Making as Revealed by Deep-Brain Stimulation in Patients with Parkinson's Disease

To optimally balance opposing demands of speed and accuracy during decision-making, we must flexibly adapt how much evidence we require before making a choice. Such adjustments in decision thresholds have been linked to the subthalamic nucleus (STN), and therapeutic STN deep-brain stimulation (DBS) has been shown to interfere with this function. Here, we performed continuous as well as closed-loop DBS of the STN while Parkinson’s disease patients performed a perceptual decision-making task. Closed-loop STN DBS allowed temporally patterned STN stimulation and simultaneous recordings of STN activity. This revealed that DBS only affected patients’ ability to adjust decision thresholds if applied in a specific temporally confined time window during deliberation. Only stimulation in that window diminished the normal slowing of response times that occurred on difficult trials when DBS was turned off. Furthermore, DBS eliminated a relative, time-specific increase in STN beta oscillations and compromised its functional relationship with trial-by-trial adjustments in decision thresholds. Together, these results provide causal evidence that the STN is involved in adjusting decision thresholds in distinct, time-limited processing windows during deliberation.

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We performed temporally patterned stimulation of the subthalamic nucleus in humans

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During stimulation, Parkinson’s patients performed a perceptual decision-making task

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Stimulation effects on behavior were confined to a short window during deliberation

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Here, stimulation affected changes in decision thresholds during difficult decisions

The Influence of Feedback on Task-Switching Performance: A Drift Diffusion Modeling Account

Task-switching is an important cognitive skill that facilitates our ability to choose appropriate behavior in a varied and changing environment. Task-switching training studies have sought to improve this ability by practicing switching between multiple tasks. However, an efficacious training paradigm has been difficult to develop in part due to findings that small differences in task parameters influence switching behavior in a non-trivial manner. Here, for the first time we employ the Drift Diffusion Model (DDM) to understand the influence of feedback on task-switching and investigate how drift diffusion parameters change over the course of task switch training. We trained 316 participants on a simple task where they alternated sorting stimuli by color or by shape. Feedback differed in six different ways between subjects groups, ranging from No Feedback (NFB) to a variety of manipulations addressing trial-wise vs. Block Feedback (BFB), rewards vs. punishments, payment bonuses and different payouts depending upon the trial type (switch/non-switch). While overall performance was found to be affected by feedback, no effect of feedback was found on task-switching learning. Drift Diffusion Modeling revealed that the reductions in reaction time (RT) switch cost over the course of training were driven by a continually decreasing decision boundary. Furthermore, feedback effects on RT switch cost were also driven by differences in decision boundary, but not in drift rate. These results reveal that participants systematically modified their task-switching performance without yielding an overall gain in performance.

Electrophysiological correlates of the drift diffusion model in visual word recognition

This study was designed to explore the electrophysiological correlates of the diffusion models drift rate parameter in cognitive decision making. Eighty-two participants completed a lexical decision task while their stimulus-dependent event-related potentials (ERP) and theta frequency band power were measured. A mass univariate approach was applied to examine the timeline at which correlations were evident. Individual differences in drift rate parameter and condition-wise within-subject differences in drift rates for word emotionality and item repetition were found to be related to amplitude differences in the late positive complex (LPC). No relations to theta frequency band power changes were obtained. The drift rate parameter captures information accumulation of noisy evidence, while LPC amplitudes are discussed to reflect the strength of a memory trace. While these results point to a common underlying cognitive mechanism to explain drift rates and LPC modulation, they also provide a new angle on the timeline of visual word processing following word identification. Further confirmations of the results are needed to approve the LPC as neurophysiological marker of information accumulation. Hum Brain Mapp 38:5616-5627, 2017. © 2017 Wiley Periodicals, Inc.

Fronto-parietal Cortical Circuits Encode Accumulated Evidence with a Diversity of Timescales

Decision-making in dynamic environments often involves accumulation of evidence, in which new information is used to update beliefs and select future actions. Using in vivo cellular resolution imaging in voluntarily head-restrained rats, we examined the responses of neurons in frontal and parietal cortices during a pulse-based accumulation of evidence task. Neurons exhibited activity that predicted the animal's upcoming choice, previous choice, and graded responses that reflected the strength of the accumulated evidence. The pulsatile nature of the stimuli enabled characterization of the responses of neurons to a single quantum (pulse) of evidence. Across the population, individual neurons displayed extensive heterogeneity in the dynamics of responses to pulses. The diversity of responses was sufficiently rich to form a temporal basis for accumulated evidence estimated from a latent variable model. These results suggest that heterogeneous, often transient sensory responses distributed across the fronto-parietal cortex may support working memory on behavioral timescales. VIDEO ABSTRACT.

The Sustained Influence of an Error on Future Decision-Making

Post-error slowing (PES) is consistently observed in decision-making tasks after negative feedback. Yet, findings are inconclusive as to whether PES supports performance accuracy. We addressed the role of PES by employing drift diffusion modeling which enabled us to investigate latent processes of reaction times and accuracy on a large-scale dataset (>5,800 participants) of a visual search experiment with emotional face stimuli. In our experiment, post-error trials were characterized by both adaptive and non-adaptive decision processes. An adaptive increase in participants' response threshold was sustained over several trials post-error. Contrarily, an initial decrease in evidence accumulation rate, followed by an increase on the subsequent trials, indicates a momentary distraction of task-relevant attention and resulted in an initial accuracy drop. Higher values of decision threshold and evidence accumulation on the post-error trial were associated with higher accuracy on subsequent trials which further gives credence to these parameters' role in post-error adaptation. Finally, the evidence accumulation rate post-error decreased when the error trial presented angry faces, a finding suggesting that the post-error decision can be influenced by the error context. In conclusion, we demonstrate that error-related response adaptations are multi-component processes that change dynamically over several trials post-error.

Mind the gap: temporal discrimination and dystonia

BACKGROUND AND PURPOSE

One of the most widely studied perceptual measures of sensory dysfunction in dystonia is the temporal discrimination threshold (TDT) (the shortest interval at which subjects can perceive that there are two stimuli rather than one). However the elevated thresholds described may be due to a number of potential mechanisms as current paradigms test not only temporal discrimination but also extraneous sensory and decision-making parameters. In this study two paradigms designed to better quantify temporal processing are presented and a decision-making model is used to assess the influence of decision strategy.

METHODS

22 patients with cervical dystonia and 22 age-matched controls completed two tasks (i) temporal resolution (a randomized, automated version of existing TDT paradigms) and (ii) interval discrimination (rating the length of two consecutive intervals).

RESULTS

In the temporal resolution task patients had delayed (P = 0.021) and more variable (P = 0.013) response times but equivalent discrimination thresholds. Modelling these effects suggested this was due to an increased perceptual decision boundary in dystonia with patients requiring greater evidence before committing to decisions (P = 0.020). Patient performance on the interval discrimination task was normal.

CONCLUSIONS

Our work suggests that previously observed abnormalities in TDT may not be due to a selective sensory deficit of temporal processing as decision-making itself is abnormal in cervical dystonia.

The role of speed in ADHD-related working memory deficits: A time-based resource-sharing and diffusion model account

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.

Sources of noise during accumulation of evidence in unrestrained and voluntarily head-restrained rats

Decision-making behavior is often characterized by substantial variability, but its source remains unclear. We developed a visual accumulation of evidence task designed to quantify sources of noise and to be performed during voluntary head restraint, enabling cellular resolution imaging in future studies. Rats accumulated discrete numbers of flashes presented to the left and right visual hemifields and indicated the side that had the greater number of flashes. Using a signal-detection theory-based model, we found that the standard deviation in their internal estimate of flash number scaled linearly with the number of flashes. This indicates a major source of noise that, surprisingly, is not consistent with the widely used 'drift-diffusion modeling' (DDM) approach but is instead closely related to proposed models of numerical cognition and counting. We speculate that this form of noise could be important in accumulation of evidence tasks generally.

DOI:http://dx.doi.org/10.7554/eLife.11308.001

Perceptual decision-making, i.e. making choices based on observed evidence, is rarely perfect. Humans and other animals tend to respond correctly on some trials and incorrectly on others. For over a century, this variability has been used to study the basis of decision-making. Most behavioral models assume that random fluctuations or 'noise' in the decision-making process is the primary source of variability and errors. However, the nature of this noise is unclear and the subject of intense scrutiny.

To investigate the sources of the behavioral variability during decision-making, Scott, Constantinople et al. trained rats to perform a visual 'accumulation of evidence' task. The animals counted flashes of light that appeared on either their left or their right. Up to 15 flashes occurred on each side, in a random order, and the rats then received a reward if they selected the side that the greatest number of flashes had occurred on. The rats chose correctly on many occasions but not on every single one.

Using a computer-controlled rat training facility or 'rat academy', Scott, Constantinople et al. collected hundreds of thousands of behavioral trials from over a dozen rats. This large dataset provided the statistical power necessary to test the assumptions of leading models of behavioral variability during decision-making, and revealed that noise grew more rapidly with the number of flashes than previously predicted. This finding explained patterns of behavior that previous models struggled with, most notably the fact that individuals make errors even on the easiest trials. The analysis also revealed that animals maintain two separate running totals – one of stimuli on the left and another of stimuli on the right – rather than a single tally of the difference between the two.

Scott, Constantinople et al. further demonstrated that rats could be trained to perform this task using a new system that enables functional brain imaging. The next step is to repeat these experiments while simultaneously recording brain activity to study the neural circuits that underlie decision-making and its variability.

DOI:http://dx.doi.org/10.7554/eLife.11308.002

The classic P300 encodes a build-to-threshold decision variable

The P300 component of the human event-related potential has been the subject of intensive experimental investigation across a five-decade period, owing to its apparent relevance to a wide range of cognitive functions and its sensitivity to numerous brain disorders, yet its exact contribution to cognition remains unresolved. Here, we carry out key analyses of the P300 elicited by transient auditory and visual targets to examine its potential role as a 'decision variable' signal that accumulates evidence to a decision bound. Consistent with the latter, we find that the P300 reaches a stereotyped amplitude immediately prior to response execution and that its rate of rise scales with target detection difficulty and accounts for trial-to-trial variance in RT. Computational simulations of an accumulation-to-bound decision process faithfully captured P300 dynamics when its parameters were set by model fits to the RT distributions. Thus, where the dominant explanatory accounts have conceived of the P300 as a unitary neural event, our data reveal it to be a dynamically evolving neural signature of decision formation. These findings place the P300 at the heart of a mechanistically principled framework for understanding decision-making in both the typical and atypical human brain.