Neural Mechanisms of Post-error Adjustments of Decision Policy in Parietal Cortex

Post-error slowing during motor sequence learning under extrinsic and intrinsic error feedback conditions

Post-error slowing, described as an error-corrective index of response binding during motor sequence learning, has only been demonstrated in the serial reaction time task under conditions where extrinsic error feedback is presented. The present experiment investigated whether post-error slowing is dependent on, or is influenced by, extrinsic error feedback. Thirty participants (14 females, Mage = 21.9 ± 1.8 years) completed the serial reaction time task with or without presentation of extrinsic error feedback. Post-error slowing was observed following response error whether feedback was presented or not. However, presentation of extrinsic error feedback increased post-error slowing across practice and extended the number of responses that were slowed following an error. There was no evidence of feedback effects on motor sequence learning or explicit awareness. Instead, feedback appeared to function as a performance factor that reduced response error rates relative to no feedback conditions. These findings illustrate that post-error slowing in motor sequence learning is not reliant on or a result of presentation of extrinsic error information. More specific to the serial reaction time task paradigm, the present findings demonstrate that the common practice of presenting error feedback is not necessary for investigating motor sequence learning unless the aim is to maintain low error rate. However, doing so might inflate reaction time in latter training blocks.

Subthalamic control of impulsive actions: insights from deep brain stimulation in Parkinson's disease

Control of actions allows adaptive, goal-directed behaviour. The basal ganglia, including the subthalamic nucleus, are thought to play a central role in dynamically controlling actions through recurrent negative feedback loops with the cerebral cortex. Here, we summarize recent translational studies that used deep brain stimulation to record neural activity from and apply electrical stimulation to the subthalamic nucleus in people with Parkinson's disease. These studies have elucidated spatial, spectral and temporal features of the neural mechanisms underlying the controlled delay of actions in cortico-subthalamic networks and demonstrated their causal effects on behaviour in distinct processing windows. While these mechanisms have been conceptualized as control signals for suppressing impulsive response tendencies in conflict tasks and as decision threshold adjustments in value-based and perceptual decisions, we propose a common framework linking decision-making, cognition and movement. Within this framework, subthalamic deep brain stimulation can lead to suboptimal choices by reducing the time that patients take for deliberation before committing to an action. However, clinical studies have consistently shown that the occurrence of impulse control disorders is reduced, not increased, after subthalamic deep brain stimulation surgery. This apparent contradiction can be reconciled when recognizing the multifaceted nature of impulsivity, its underlying mechanisms and modulation by treatment. While subthalamic deep brain stimulation renders patients susceptible to making decisions without proper forethought, this can be disentangled from effects related to dopamine comprising sensitivity to benefits versus costs, reward delay aversion and learning from outcomes. Alterations in these dopamine-mediated mechanisms are thought to underlie the development of impulse control disorders and can be relatively spared with reduced dopaminergic medication after subthalamic deep brain stimulation. Together, results from studies using deep brain stimulation as an experimental tool have improved our understanding of action control in the human brain and have important implications for treatment of patients with neurological disorders.

Decoding the task specificity of post-error adjustments: Features and determinants

Errors typically trigger post-error adjustments aimed at improving subsequent reactions within a single task, but little work has focused on whether these adjustments are task-general or task-specific across different tasks. We collected behavioral and electrophysiological (EEG) data when participants performed a psychological refractory period paradigm. This paradigm required them to complete Task 1 and Task 2 separated by a variable stimulus onset asynchrony (SOA). Behaviorally, post-error slowing and post-error accuracy exhibited task-general features at short SOAs but some task-specific features at long SOAs. EEG results manifest that task-general adjustments had a short-lived effect, whereas task-specific adjustments were long-lasting. Moreover, error awareness specifically conduced to the improvement of subsequent sensory processing and behavior performance in Task 1 (the task where errors occurred). These findings demonstrate that post-error adjustments rely on both transient, task-general interference and longer-lasting, task-specific control mechanisms simultaneously, with error awareness playing a crucial role in determining these mechanisms. We further discuss the contribution of central resources to the task specificity of post-error adjustments.

People are more error-prone after committing an error

Humans tend to slow down after making an error. A longstanding account of this post-error slowing is that people are simply more cautious. However, accuracy typically does not improve following an error, leading some researchers to suggest that an initial 'orienting' response may initially impair performance immediately following error. Unfortunately, characterizing the nature of this error-based impairment remains a challenge in standard tasks that use free response times. By exerting control over the timing of responses, we reveal the time course of stimulus-response processing. Participants are less accurate after an error even when given ample time to make a response. A computational model of response preparation rules out the possibility that errors lead to slower cognitive processing. Instead, we find that the efficacy of cognitive processing in producing an intended response is impaired following errors. Following an error, participants commit more slips of action that tend to be a repetition of the previous mistake. Rather than a strategic shift along a single speed-accuracy tradeoff function, post-error slowing observed in free response time tasks may be an adaptive response to impaired cognitive processing that reflects an altered relationship between the speed and accuracy of responses.

Neural and behavioral markers of inhibitory control predict symptom improvement during internet-delivered cognitive behavioral therapy for depression

Poor inhibitory control contributes to deficits in emotion regulation, which are often targeted by treatments for major depressive disorder (MDD), including cognitive behavioral therapy (CBT). Brain regions that contribute to inhibitory control and emotion regulation overlap; thus, inhibitory control might relate to response to CBT. In this study, we examined whether baseline inhibitory control and resting state functional connectivity (rsFC) within overlapping emotion regulation-inhibitory control regions predicted treatment response to internet-based CBT (iCBT). Participants with MDD were randomly assigned to iCBT (N = 30) or a monitored attention control (MAC) condition (N = 30). Elastic net regression was used to predict post-treatment Patient Health Questionnaire-9 (PHQ-9) scores from baseline variables, including demographic variables, PHQ-9 scores, Flanker effects (interference, sequential dependency, post-error slowing), and rsFC between the dorsal anterior cingulate cortex, bilateral anterior insula (AI), and right temporoparietal junction (TPJ). Essential prognostic predictor variables retained in the elastic net regression included treatment group, gender, Flanker interference response time (RT), right AI-TPJ rsFC, and left AI-right AI rsFC. Prescriptive predictor variables retained included interactions between treatment group and baseline PHQ-9 scores, age, gender, Flanker RT, sequential dependency effects on accuracy, post-error accuracy, right AI-TPJ rsFC, and left AI-right AI rsFC. Inhibitory control and rsFC within inhibitory control-emotion regulation regions predicted reduced symptom severity following iCBT, and these effects were stronger in the iCBT group than in the MAC group. These findings contribute to a growing literature indicating that stronger inhibitory control at baseline predicts better outcomes to psychotherapy, including iCBT.

Beyond peaks and troughs: Multiplexed performance monitoring signals in the EEG

With the discovery of event-related potentials elicited by errors more than 30 years ago, a new avenue of research on performance monitoring, cognitive control, and decision making emerged. Since then, the field has developed and expanded fulminantly. After a brief overview on the EEG correlates of performance monitoring, this article reviews recent advancements based on single-trial analyses using independent component analysis, multiple regression, and multivariate pattern classification. Given the close interconnection between performance monitoring and reinforcement learning, computational modeling and model-based EEG analyses have made a particularly strong impact. The reviewed findings demonstrate that error- and feedback-related EEG dynamics represent variables reflecting how performance-monitoring signals are weighted and transformed into an adaptation signal that guides future decisions and actions. The model-based single-trial analysis approach goes far beyond conventional peak-and-trough analyses of event-related potentials and enables testing mechanistic theories of performance monitoring, cognitive control, and decision making.

Task-general or specific: The alertness modulates post-error adjustment

Previous studies have shown that alertness is closely related to executive control function, but its impact on components of post-error adjustment is unknown. This study applied the Attentional Networks Test and the Four-choice Flanker task with three response stimulus intervals (RSIs) to explore the correlation between alertness and post-error adjustment. The linear mixed-effects model of alertness and RSI on the post-error processing indicators showed a significant negative correlation between the alertness and post-error slowing (PES) under 200 ms RSI , as well as between alertness and post-error improvement in accuracy (PIA) under both 700 ms RSI and 1200 ms RSI. Participants with lower alertness showed larger post-error slowing in the early stages, while those with higher alertness had smaller PIA in later stages. This study revealed the effects of alertness on different processing components of post-error adjustment. The control strategies utilized by individuals with high and low levels of alertness differed in preparation for performance monitoring. Alertness improved post-error response speed in a task-unspecific manner, but not post-error adaptation.

A fully spiking coupled model of a deep neural network and a recurrent attractor explains dynamics of decision making in an object recognition task

Objective.Object recognition and making a choice regarding the recognized object is pivotal for most animals. This process in the brain contains information representation and decision making steps which both take different amount of times for different objects. While dynamics of object recognition and decision making are usually ignored in object recognition models, here we proposed a fully spiking hierarchical model, explaining the process of object recognition from information representation to making decision.Approach.Coupling a deep neural network and a recurrent attractor based decision making model beside using spike time dependent plasticity learning rules in several convolutional and pooling layers, we proposed a model which can resemble brain behaviors during an object recognition task. We also measured human choices and reaction times in a psychophysical object recognition task and used it as a reference to evaluate the model.Main results.The proposed model explains not only the probability of making a correct decision but also the time that it takes to make a decision. Importantly, neural firing rates in both feature representation and decision making levels mimic the observed patterns in animal studies (number of spikes (p-value < 10-173) and the time of the peak response (p-value < 10-31) are significantly modulated with the strength of the stimulus). Moreover, the speed-accuracy trade-off as a well-known characteristic of decision making process in the brain is also observed in the model (changing the decision bound significantly affect the reaction time (p-value < 10-59) and accuracy (p-value < 10-165)).Significance.We proposed a fully spiking deep neural network which can explain dynamics of making decision about an object in both neural and behavioral level. Results showed that there is a strong and significant correlation (r= 0.57) between the reaction time of the model and of human participants in the psychophysical object recognition task.

Incorporation of a cost of deliberation time in perceptual decision making

Many decisions benefit from the accumulation of evidence obtained sequentially over time. In such circumstances, the decision maker must balance speed against accuracy, and the nature of this tradeoff mediates competing desiderata and costs, especially those associated with the passage of time. A neural mechanism to achieve this balance is to accumulate evidence in suitable units and to terminate the deliberation when enough evidence has accrued. To accommodate time costs, it has been hypothesized that the criterion to terminate a decision may become lax as a function of time. Here we tested this hypothesis by manipulating the cost of time in a perceptual choice-reaction time task. Participants discriminated the direction of motion in a dynamic random-dot display, which varied in difficulty across trials. After each trial, they received feedback in the form of points based on whether they made a correct or erroneous choice. They were instructed to maximize their points per unit of time. Unbeknownst to the participants, halfway through the experiment, we increased the time pressure by canceling a small fraction of trials if they had not made a decision by a provisional deadline. Although the manipulation canceled less than 5% of trials, it induced the participants to make faster decisions while lowering their decision accuracy. The pattern of choices and reaction times were explained by bounded drift-diffusion. In all phases of the experiment, stopping bounds were found to decline as a function of time, consistent with the optimal solution, and this decline was exaggerated in response to the time-cost manipulation.

Neural Representations of Post-Decision Accuracy and Reward Expectation in the Caudate Nucleus and Frontal Eye Field

Performance monitoring that supports ongoing behavioral adjustments is often examined in the context of either choice confidence for perceptual decisions (i.e., "did I get it right?") or reward expectation for reward-based decisions (i.e., "what reward will I receive?"). However, our understanding of how the brain encodes these distinct evaluative signals remains limited because they are easily conflated, particularly in commonly used two-alternative tasks with symmetric rewards for correct choices. Previously we used a motion-discrimination task with asymmetric rewards to identify neural substrates of forming reward-biased perceptual decisions in the caudate nucleus (part of the striatum in the basal ganglia) and the frontal eye field (FEF, in prefrontal cortex). Here we leveraged this task design to partially decouple estimates of accuracy and reward expectation and examine their impacts on subsequent decisions and their representations in those two brain areas. We identified distinguishable representations of these two evaluative signals in individual caudate and FEF neurons, with regional differences in their distribution patterns and time courses. We observed that well-trained monkeys (both sexes) used both evaluative signals, infrequently but consistently, to adjust their subsequent decisions. We found further that these behavioral adjustments had reliable relationships with the neural representations of both evaluative signals in caudate, but not FEF. These results suggest that the cortico-striatal decision network may use diverse evaluative signals to monitor and adjust decision-making behaviors, adding to our understanding of the different roles that the FEF and caudate nucleus play in a diversity of decision-related computations.

Early Action Error Processing Is Due to Domain-General Surprise, Whereas Later Processing Is Error Specific

The ability to adapt behavior after erroneous actions is one of the key aspects of cognitive control. Error commission typically causes people to slow down their subsequent actions [post-error slowing (PES)]. Recent work has challenged the notion that PES reflects adaptive, controlled processing and instead suggests that it is a side effect of the surprising nature of errors. Indeed, human neuroimaging suggests that the brain networks involved in processing errors overlap with those processing error-unrelated surprise, calling into question whether there is a specific system for error processing in the brain at all. In the current study, we used EEG decoding and a novel behavioral paradigm to test whether there are indeed unique, error-specific processes that contribute to PES beyond domain-general surprise. Across two experiments in male and female humans (N = 76), we found that both errors and error-unrelated surprise were followed by slower responses when response-stimulus intervals were short. Furthermore, the early neural processes following error-specific and domain-general surprise showed significant cross-decoding. However, at longer intervals, which provided additional processing time, only errors were still followed by post-trial slowing. Furthermore, this error-specific PES effect was reflected in sustained neural activity that could be decoded from that associated with domain-general surprise, with the strongest contributions found at lateral frontal, occipital, and sensorimotor scalp sites. These findings suggest that errors and surprise initially share common processes, but that after additional processing time, unique, genuinely error-specific processes take over and contribute to behavioral adaptation.SIGNIFICANCE STATEMENT Humans typically slow their actions after errors (PES). Some suggest that PES is a side effect of the unexpected, surprising nature of errors, challenging the notion of a genuine error processing system in the human brain. Here, we used multivariate EEG decoding to identify behavioral and neural processes uniquely related to error processing. Action slowing occurred following both action errors and error-unrelated surprise when time to prepare the next response was short. However, when there was more time to react, only errors were followed by slowing, further reflected in sustained neural activity. This suggests that errors and surprise initially share common processing, but that after additional time, error-specific, adaptive processes take over.

Unraveling the influence of trial-based motivational changes on performance monitoring stages in a flanker task

Performance monitoring (PM) is a vital component of adaptive behavior and known to be influenced by motivation. We examined effects of potential gain (PG) and loss avoidance (LA) on neural correlates of PM at different processing stages, using a task with trial-based changes in these motivational contexts. Findings suggest more attention is allocated to the PG context, with higher amplitudes for respective correlates of stimulus and feedback processing. The PG context favored rapid responses, while the LA context emphasized accurate responses. Lower response thresholds in the PG context after correct responses derived from a drift-diffusion model also indicate a more approach-oriented response style in the PG context. This cognitive shift is mirrored in neural correlates: negative feedback in the PG context elicited a higher feedback-related negativity (FRN) and higher theta power, whereas positive feedback in the LA context elicited higher P3a and P3b amplitudes, as well as higher theta power. There was no effect of motivational context on response-locked brain activity. Given the similar frequency of negative feedback in both contexts, the elevated FRN and theta power in PG trials cannot be attributed to variations in reward prediction error. The observed variations in the FRN indicate that the effect of outcome valence is modulated by motivational salience.

Initial conditions combine with sensory evidence to induce decision-related dynamics in premotor cortex

We used a dynamical systems perspective to understand decision-related neural activity, a fundamentally unresolved problem. This perspective posits that time-varying neural activity is described by a state equation with an initial condition and evolves in time by combining at each time step, recurrent activity and inputs. We hypothesized various dynamical mechanisms of decisions, simulated them in models to derive predictions, and evaluated these predictions by examining firing rates of neurons in the dorsal premotor cortex (PMd) of monkeys performing a perceptual decision-making task. Prestimulus neural activity (i.e., the initial condition) predicted poststimulus neural trajectories, covaried with RT and the outcome of the previous trial, but not with choice. Poststimulus dynamics depended on both the sensory evidence and initial condition, with easier stimuli and fast initial conditions leading to the fastest choice-related dynamics. Together, these results suggest that initial conditions combine with sensory evidence to induce decision-related dynamics in PMd.

Self-motion perception and sequential decision-making: where are we heading?

To navigate and guide adaptive behaviour in a dynamic environment, animals must accurately estimate their own motion relative to the external world. This is a fundamentally multisensory process involving integration of visual, vestibular and kinesthetic inputs. Ideal observer models, paired with careful neurophysiological investigation, helped to reveal how visual and vestibular signals are combined to support perception of linear self-motion direction, or heading. Recent work has extended these findings by emphasizing the dimension of time, both with regard to stimulus dynamics and the trade-off between speed and accuracy. Both time and certainty-i.e. the degree of confidence in a multisensory decision-are essential to the ecological goals of the system: terminating a decision process is necessary for timely action, and predicting one's accuracy is critical for making multiple decisions in a sequence, as in navigation. Here, we summarize a leading model for multisensory decision-making, then show how the model can be extended to study confidence in heading discrimination. Lastly, we preview ongoing efforts to bridge self-motion perception and navigation per se, including closed-loop virtual reality and active self-motion. The design of unconstrained, ethologically inspired tasks, accompanied by large-scale neural recordings, raise promise for a deeper understanding of spatial perception and decision-making in the behaving animal. This article is part of the theme issue 'Decision and control processes in multisensory perception'.

EEG Dynamics of Error Processing and Associated Behavioral Adjustments in Preschool Children

Preschool children show neural responses and make behavioral adjustments immediately following an error. However, there is a lack of evidence regarding how neural responses to error predict subsequent behavioral adjustments during childhood. The aim of our study was to explore the neural dynamics of error processing and associated behavioral adjustments in preschool children from unsatisfied basic needs (UBN) homes. Using EEG recordings during a go/no-go task, we examined within-subject associations between the error-related negativity (ERN), frontal theta power, post-error slowing, and post-error accuracy. Post-error accuracy increased linearly with post-error slowing, and there was no association between the neural activity of error processing and post-error accuracy. However, during successful error recovery, the frontal theta power, but not the ERN amplitude, was associated positively with post-error slowing. These findings indicated that preschool children from UBN homes adjusted their behavior following an error in an adaptive form and that the error-related theta activity may be associated with the adaptive forms of post-error behavior. Furthermore, our data support the adaptive theory of post-error slowing and point to some degree of separation between the neural mechanisms represented by the ERN and theta.

Attentional fluctuations and the temporal organization of memory

Event boundaries and temporal context shape the organization of episodic memories. We hypothesized that attentional fluctuations during encoding serve as "events" that affect temporal context representations and recall organization. Individuals encoded trial-unique objects during a modified sustained attention task. Memory was tested with free recall. Response time variability during the encoding tasks was used to characterize "in the zone" and "out of the zone" attentional states. We predicted that: 1) "in the zone", vs. "out of the zone", attentional states should be more conducive to maintaining temporal context representations that can cue temporally organized recall; and 2) temporally distant "in the zone" states may enable more recall "leaps" across intervening items. We replicated several important findings in the sustained attention and memory fields, including more online errors during "out of the zone" vs. "in the zone" attentional states and recall that was temporally structured. Yet, across four studies, we found no evidence for either of our main hypotheses. Recall was robustly temporally organized, and there was no difference in recall organization for items encoded "in the zone" vs. "out of the zone". We conclude that temporal context serves as a strong scaffold for episodic memory, one that can support organized recall even for items encoded during relatively poor attentional states. We also highlight the numerous challenges in striking a balance between sustained attention tasks (long blocks of a repetitive task) and memory recall tasks (short lists of unique items) and describe strategies for researchers interested in uniting these two fields.

Recurrent networks endowed with structural priors explain suboptimal animal behavior

The strategies found by animals facing a new task are determined both by individual experience and by structural priors evolved to leverage the statistics of natural environments. Rats quickly learn to capitalize on the trial sequence correlations of two-alternative forced choice (2AFC) tasks after correct trials but consistently deviate from optimal behavior after error trials. To understand this outcome-dependent gating, we first show that recurrent neural networks (RNNs) trained in the same 2AFC task outperform rats as they can readily learn to use across-trial information both after correct and error trials. We hypothesize that, although RNNs can optimize their behavior in the 2AFC task without any a priori restrictions, rats' strategy is constrained by a structural prior adapted to a natural environment in which rewarded and non-rewarded actions provide largely asymmetric information. When pre-training RNNs in a more ecological task with more than two possible choices, networks develop a strategy by which they gate off the across-trial evidence after errors, mimicking rats' behavior. Population analyses show that the pre-trained networks form an accurate representation of the sequence statistics independently of the outcome in the previous trial. After error trials, gating is implemented by a change in the network dynamics that temporarily decouple the categorization of the stimulus from the across-trial accumulated evidence. Our results suggest that the rats' suboptimal behavior reflects the influence of a structural prior that reacts to errors by isolating the network decision dynamics from the context, ultimately constraining the performance in a 2AFC laboratory task.

Neural Mechanisms That Make Perceptual Decisions Flexible

Neural mechanisms of perceptual decision making have been extensively studied in experimental settings that mimic stable environments with repeating stimuli, fixed rules, and payoffs. In contrast, we live in an ever-changing environment and have varying goals and behavioral demands. To accommodate variability, our brain flexibly adjusts decision-making processes depending on context. Here, we review a growing body of research that explores the neural mechanisms underlying this flexibility. We highlight diverse forms of context dependency in decision making implemented through a variety of neural computations. Context-dependent neural activity is observed in a distributed network of brain structures, including posterior parietal, sensory, motor, and subcortical regions, as well as the prefrontal areas classically implicated in cognitive control. We propose that investigating the distributed network underlying flexible decisions is key to advancing our understanding and discuss a path forward for experimental and theoretical investigations.

Stress-induced impairment reveals the stage and features of post-error adaptive adjustment

An increased reaction time often occurs after error responses (post-error slowing, PES). However, the role of top-down regulation in post-error processing remains to be debated. Impairing cognitive control function through acute stress would help to investigate the role and stage of top-down adaptive regulation in post-error processing. Here, we recruited 50 healthy male participants who were randomly assigned to either a stress condition (Trier Social Stress Task, TSST) or a control condition (control version of the TSST). A color-word Stroop task with different response stimulus intervals (RSIs) was used to investigate the effects of acute stress on different stages of post-error processing. The results showed that cortisol, heart rate, perceived stress level, and negative affect were higher in the stress group (n = 24) than in the control group (n = 26), indicating successful stress induction. The accuracy of post-error response in the control group increased with the extension of RSI, and the reaction time decreased. However, the accuracy of 1,200 ms RSI was close to that of 700 ms RSI in the stress group but was significantly lower than that in the control group. The results suggested that acute stress caused the impairment of top-down adaptive regulation after error. Furthermore, our study manifested adaptive adjustment only in the late stages of post-error processing, indicating the phasic and adaptive features of post-error adjustment.

Prefrontal Cortical to Mediodorsal Thalamus Projection Neurons Regulate Posterror Adaptive Control of Behavior

Adaptive control is the online adjustment of behavior to guide and optimize responses after errors or conflict. The neural circuits involved in monitoring and adapting behavioral performance following error are poorly understood. The prefrontal cortex (PFC) plays a critical role in this form of control. However, these brain areas are densely connected with many other regions, and it is unknown which projections are critical for adaptive behavior. Here, we tested the involvement of four distinct dorsal and ventral prefrontal cortical projections to striatal and thalamic target areas in adaptive control. We re-analyzed data from published experiments, using trial-by-trial analyses of behavior in an operant task for attention and impulsivity. We find that male rats slow their responses and perform worse following errors. Moreover, by combining retrograde labeling and chemogenetic silencing, we find that dorsomedial prefrontal pyramidal neurons that project to the lateral nucleus of the mediodorsal thalamus (MDL) are involved in posterror performance and timing of responses, specifically with unpredictable delays until stimulus presentation. Together, these data show that dorsal medial PFC (mPFC) projection neurons targeting the lateral MDT regulate adaptive control to flexibly optimize behavioral responses in goal-directed behavior.

Consensus Goals in the Field of Visual Metacognition

Despite the tangible progress in psychological and cognitive sciences over the last several years, these disciplines still trail other more mature sciences in identifying the most important questions that need to be solved. Reaching such consensus could lead to greater synergy across different laboratories, faster progress, and increased focus on solving important problems rather than pursuing isolated, niche efforts. Here, 26 researchers from the field of visual metacognition reached consensus on four long-term and two medium-term common goals. We describe the process that we followed, the goals themselves, and our plans for accomplishing these goals. If this effort proves successful within the next few years, such consensus building around common goals could be adopted more widely in psychological science.

Persistent activity in human parietal cortex mediates perceptual choice repetition bias

Humans and other animals tend to repeat or alternate their previous choices, even when judging sensory stimuli presented in a random sequence. It is unclear if and how sensory, associative, and motor cortical circuits produce these idiosyncratic behavioral biases. Here, we combined behavioral modeling of a visual perceptual decision with magnetoencephalographic (MEG) analyses of neural dynamics, across multiple regions of the human cerebral cortex. We identified distinct history-dependent neural signals in motor and posterior parietal cortex. Gamma-band activity in parietal cortex tracked previous choices in a sustained fashion, and biased evidence accumulation toward choice repetition; sustained beta-band activity in motor cortex inversely reflected the previous motor action, and biased the accumulation starting point toward alternation. The parietal, not motor, signal mediated the impact of previous on current choice and reflected individual differences in choice repetition. In sum, parietal cortical signals seem to play a key role in shaping choice sequences.

Post-error adjustments depend causally on executive attention: Evidence from an intervention study

Detecting and correcting execution errors is crucial for safe and efficient goal-directed behavior. Despite intensive investigations on error processing, the cognitive foundations of this process remain unclear. Based on the presumed relation between executive attention (EA) and error processing, we implemented a seven-day EA intervention by adopting the Posner cueing paradigm to test the potential causal link from EA to error processing in healthy adults. The experimental group (high EA, HEA) was trained on the Posner cueing paradigm, with a ratio of invalid cue (IC) trials to valid cue (VC) trials of 5:1 and a corresponding ratio of 1:1 in the active control group (low EA, LEA). We found that the EA intervention improved EA across intervention sessions. Critically, after the EA intervention, the HEA group showed that post-error accuracy (PEA) was restored to the same level as the post-correct accuracy (in comparison with the LEA group). However, post-error slowing and the flanker effect were not modulated by the EA intervention. Furthermore, we observed that the changes in the accuracy of VC trials positively predicted the changes in PEA and that the two groups were classified according to the changes in PEA with a 61.3% accuracy. Based on these results, we propose that EA causally drives error processing. And the capabilities of the “actively catch” more attention resources and the automatic mismatch processing developed after EA intervention is transferable to error processing, thereby directly resulting in the gains in post-error adjustments. Our work informs the potential cognitive mechanisms underlying this causal link.

Two Types of Motor Inhibition after Action Errors in Humans

Adaptive behavior requires the ability to appropriately react to action errors. Post-error slowing (PES) of response times is one of the most reliable phenomena in human behavior. It has been proposed that PES is partially achieved through inhibition of the motor system. However, there is no direct evidence for this link, or indeed, that the motor system is physiologically inhibited after errors altogether. Here, we used transcranial magnetic stimulation and electromyography to measure corticospinal excitability (CSE) across four experiments using a Simon task, in which female and male human participants sometimes committed errors. Errors were followed by reduced CSE at two different time points and in two different modes. Shortly after error commission (250 ms), CSE was broadly suppressed (i.e., even task-unrelated motor effectors were inhibited). During the preparation of the subsequent response, CSE was specifically reduced at task-relevant effectors only. This latter effect was directly related to PES, with stronger CSE suppression accompanying greater PES. This suggests that PES is achieved through increased inhibitory control during post-error responses. To provide converging evidence, we then reanalyzed an openly available EEG dataset that contained both Simon- and Stop-signal tasks using independent component analysis. We found that the same neural source component that indexed action cancellation in the stop-signal task also showed clear PES-related activity during post-error responses in the Simon task. Together, these findings provide evidence that post-error adaptation is partially achieved through motor inhibition. Moreover, inhibition is engaged in two modes (first nonselective, then selective), aligning with recent multistage theories of error processing.SIGNIFICANCE STATEMENT It is a common observation that humans implement a higher degree of caution when repeating an action during which they just committed a mistake. In the laboratory, such increased "caution" is reflected in post-error slowing of response latencies. Many competing theories exist regarding the precise neural mechanisms underlying post-error slowing. Using transcranial magnetic stimulation, we show that, after error commission, the human corticomotor system is momentarily inhibited, both immediately after an error and during the preparation of the next action. Moreover, motor inhibition during the latter time period is directly predictive of post-error slowing. This shows that inhibitory control is a key mechanism humans engage to regulate their own behavior in the aftermath of error commission.

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.

Decoding the Specificity of Post-Error Adjustments Using EEG-Based Multivariate Pattern Analysis

Errors can elicit post-error adjustments that serve to optimize performance by preventing further errors. An essential but unsolved issue is that whether post-error adjustments are domain-general or domain-specific, which was investigated in the present study through eliciting different types of errors. Behavioral and electrophysiological data were recorded when male and female subjects performed the Eriksen flanker task. For this study, we examined the aforementioned issue by combining event-related potential and multivariate pattern analysis. The results indicated that post-error slowing, error-related negativity, and error positivity were comparable between congruent and incongruent errors, indicating that errors triggered domain-general interference mechanisms, whereas post-error accuracy and late positive potential elicited by incongruent errors were larger than those elicited by congruent errors, exhibiting domain-specific control adjustment mechanisms. Importantly, no successful decoding soon after errors was found between congruent and incongruent errors, but above-chance decoding was observed between these two types of errors with increasing time, which further support that domain-general adjustments occurred in the early stage, whereas domain-specific adjustments appeared in the late stage. Furthermore, brain-behavior correlation results suggested that the late post-error adjustments predicted subsequent behavior performance. Together, this study revealed that early domain-general interference adjustments induced by errors are reflected in error detection and error awareness, which are independent of error types; on the contrary, late domain-specific control adjustments are reflected in attentional adjustments, which are modulated by error types.SIGNIFICANCE STATEMENT To date, clear evidence on the specificity of post-error adjustments is lacking. The present study provides neurophysiological evidence that post-error adjustments simultaneously rely on both domain-general and domain-specific mechanisms. Event-related potential results indicated that domain-general adjustments were accompanied by the interference of error detection and error awareness. In contrast, domain-specific adjustments were associated with attentional adjustments. Multivariate pattern analysis further decoded the two features of post-error adjustments in the early stage matching the time patterns of error-related negativity and error positivity and in the late stage corresponding to the late positive potential. Temporal generalization analysis showed that domain-specific processing appeared stably in late post-error adjustments. Hence, we propose that post-error different stages may determine the specificity of post-error adjustments.

Dynamic task-belief is an integral part of decision-making

Natural decisions involve two seemingly separable processes: inferring the relevant task (task-belief) and performing the believed-relevant task. The assumed separability has led to the traditional practice of studying task-switching and perceptual decision-making individually. Here, we used a novel paradigm to manipulate and measure macaque monkeys' task-belief and demonstrated inextricable neuronal links between flexible task-belief and perceptual decision-making. We showed that in animals, but not in artificial networks that performed as well or better than the animals, stronger task-belief is associated with better perception. Correspondingly, recordings from neuronal populations in cortical areas 7a and V1 revealed that stronger task-belief is associated with better discriminability of the believed-relevant, but not the believed-irrelevant, feature. Perception also impacts belief updating; noise fluctuations in V1 help explain how task-belief is updated. Our results demonstrate that complex tasks and multi-area recordings can reveal fundamentally new principles of how biology affects behavior in health and disease.

Post-error Slowing Reflects the Joint Impact of Adaptive and Maladaptive Processes During Decision Making

Errors and their consequences are typically studied by investigating changes in decision speed and accuracy in trials that follow an error, commonly referred to as “post-error adjustments”. Many studies have reported that subjects slow down following an error, a phenomenon called “post-error slowing” (PES). However, the functional significance of PES is still a matter of debate as it is not always adaptive. That is, it is not always associated with a gain in performance and can even occur with a decline in accuracy. Here, we hypothesized that the nature of PES is influenced by one’s speed-accuracy tradeoff policy, which determines the overall level of choice accuracy in the task at hand. To test this hypothesis, we had subjects performing a task in two distinct contexts (separate days), which either promoted speed (hasty context) or cautiousness (cautious context), allowing us to consider post-error adjustments according to whether subjects performed choices with a low or high accuracy level, respectively. Accordingly, our data indicate that post-error adjustments varied according to the context in which subjects performed the task, with PES being solely significant in the hasty context (low accuracy). In addition, we only observed a gain in performance after errors in a specific trial type, suggesting that post-error adjustments depend on a complex combination of processes that affect the speed of ensuing actions as well as the degree to which such PES comes with a gain in performance.

Action errors impair active working memory maintenance

The ability to detect and correct action errors is paramount to safe and efficient behavior. Its underlying processes are subject of intense scientific debate. The recent adaptive orienting theory of error processing (AOT) proposes that errors trigger a cascade of processes that purportedly begins with a broad suppression of active motoric and-crucially-cognitive processes. While the motoric effects of errors are well established, an empirical test of their purported suppressive effects on active cognitive processes is still missing. Here, we provide data from seven experiments that clearly demonstrate such effects. Participants maintained information in working memory (WM) and performed different response conflict tasks during the delay period. Motor error commission during the delay period consistently reduced accuracy on the WM probe, demonstrating an error-related impairment of WM maintenance. We discuss the broad theoretical and practical implications of this finding, both for the AOT and beyond. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The geometry of domain-general performance monitoring in the human medial frontal cortex

Controlling behavior to flexibly achieve desired goals depends on the ability to monitor one's own performance. It is unknown how performance monitoring can be both flexible, to support different tasks, and specialized, to perform each task well. We recorded single neurons in the human medial frontal cortex while subjects performed two tasks that involve three types of cognitive conflict. Neurons encoding conflict probability, conflict, and error in one or both tasks were intermixed, forming a representational geometry that simultaneously allowed task specialization and generalization. Neurons encoding conflict retrospectively served to update internal estimates of conflict probability. Population representations of conflict were compositional. These findings reveal how representations of evaluative signals can be both abstract and task-specific and suggest a neuronal mechanism for estimating control demand.

Neural Sources of Prediction Errors Detect Unrealistic VR Interactions

Objective Neural interfaces hold significant promise to implicitly track user experience. Their application in VR/AR simulations is especially favorable as it allows user assessment without breaking the immersive experience. In VR, designing immersion is one key challenge. Subjective questionnaires are the established metrics to assess the effectiveness of immersive VR simulations. However, administering such questionnaires requires breaking the immersive experience they are supposed to assess. Approach We present a complimentary metric based on a ERPs. For the metric to be robust, the neural signal employed must be reliable. Hence, it is beneficial to target the neural signal's cortical origin directly, efficiently separating signal from noise. To test this new complementary metric, we designed a reach-to-tap paradigm in VR to probe EEG and movement adaptation to visuo-haptic glitches. Our working hypothesis was, that these glitches, or violations of the predicted action outcome, may indicate a disrupted user experience. Main Results Using prediction error negativity features, we classified VR glitches with ~77\% accuracy. We localized the EEG sources driving the classification and found midline cingulate EEG sources and a distributed network of parieto-occipital EEG sources to enable the classification success. Significance Prediction error signatures from these sources reflect violations of user's predictions during interaction with AR/VR, promising a robust and targeted marker for adaptive user interfaces.

Proactive and reactive accumulation-to-bound processes compete during perceptual decisions

Standard models of perceptual decision-making postulate that a response is triggered in reaction to stimulus presentation when the accumulated stimulus evidence reaches a decision threshold. This framework excludes however the possibility that informed responses are generated proactively at a time independent of stimulus. Here, we find that, in a free reaction time auditory task in rats, reactive and proactive responses coexist, suggesting that choice selection and motor initiation, commonly viewed as serial processes, are decoupled in general. We capture this behavior by a novel model in which proactive and reactive responses are triggered whenever either of two competing processes, respectively Action Initiation or Evidence Accumulation, reaches a bound. In both types of response, the choice is ultimately informed by the Evidence Accumulation process. The Action Initiation process readily explains premature responses, contributes to urgency effects at long reaction times and mediates the slowing of the responses as animals get satiated and tired during sessions. Moreover, it successfully predicts reaction time distributions when the stimulus was either delayed, advanced or omitted. Overall, these results fundamentally extend standard models of evidence accumulation in decision making by showing that proactive and reactive processes compete for the generation of responses.

Post-error Slowing During Instrumental Learning is Shaped by Working Memory-based Choice Strategies

Post-error slowing (PES) - a relative increase in response time for a decision on trialtgiven an error on trialt - 1 - is a well-known effect in studies of human decision-making. Post-error processing is reflected in neural signatures such as reduced activity in sensorimotor regions and increased activity in medial prefrontal cortex. PES is thought to reflect the deployment of executive resources to get task performance back on track. This provides a general account of PES that cuts across perceptual decision-making, memory, and learning tasks. With respect to PES and learning, things are complicated by the fact that learning often reflectsmultiple qualitatively different processes with distinct neural correlates. It is unclear if multiple processes shape PES during learning, or if PES reflects a policy for reacting to errors generated by one particular process (e.g., cortico-striatal reinforcement learning). Here we provide behavioral and computational evidence that PES is influenced by the operation of multiple distinct processes. Human subjects learned a simple visuomotor skill (arbitrary visuomotor association learning) under low load conditionsmore amenable to simple working memory-based strategies, and high load conditions that were putatively more reliant on trial-by-trial reinforcement learning. PES decreased withload, even when the progress of learning (i.e., reinforcement history) was accounted for. This result suggested that PES during learning is influenced by the recruitment of working memory. Indeed, observed PES effects were approximated by a computational model with parallel working memory and reinforcement learning systems that are differentially recruited according to cognitive load.

Bayesian inference with incomplete knowledge explains perceptual confidence and its deviations from accuracy

In perceptual decisions, subjects infer hidden states of the environment based on noisy sensory information. Here we show that both choice and its associated confidence are explained by a Bayesian framework based on partially observable Markov decision processes (POMDPs). We test our model on monkeys performing a direction-discrimination task with post-decision wagering, demonstrating that the model explains objective accuracy and predicts subjective confidence. Further, we show that the model replicates well-known discrepancies of confidence and accuracy, including the hard-easy effect, opposing effects of stimulus variability on confidence and accuracy, dependence of confidence ratings on simultaneous or sequential reports of choice and confidence, apparent difference between choice and confidence sensitivity, and seemingly disproportionate influence of choice-congruent evidence on confidence. These effects may not be signatures of sub-optimal inference or discrepant computational processes for choice and confidence. Rather, they arise in Bayesian inference with incomplete knowledge of the environment.

Contrasting time and frequency domains: ERN and induced theta oscillations differentially predict post-error behavior

The present study investigated the neural dynamics of error processing in both the time and frequency domains, as well as associated behavioral phenomena, at the single-trial level. We used a technique that enabled us to separately investigate the evoked and induced aspects of the EEG signal (Cohen & Donner, 2013, Journal of Neurophysiology, 110[12], 2752-2763). We found that at the single-trial level, while the (evoked) error-related negativity (ERN) predicted only post-error slowing (PES)-and only when errors occurred on incongruent trials-induced frontal midline theta power served as a robust predictor of both PES and post-error accuracy (PEA) regardless of stimulus congruency. Mediation models of both electrophysiological indices demonstrated that although the relationship between theta and PEA was mediated by PES, there was not a relationship between the ERN and PEA. Our data suggest that although the ERN and frontal midline theta index functionally related underlying cognitive processes, they are not simply the same process manifested in different domains. In addition, our findings are consistent with the adaptive theory of post-error slowing, as PES was positively associated with post-error accuracy at the single-trial level. More generally, our study provides additional support for the inclusion of a time-frequency approach to better understand the role of medial frontal cortex in action monitoring.

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.

Timing-dependent differential effects of unexpected events on error processing reveal the interactive dynamics of surprise and error processing

When unexpected events occur during goal-directed behavior, they automatically trigger an orienting-related cascade of psychological and neural processes through which they influence behavior and cognition. If the unexpected event was caused by an action error, additional error-specific, strategic-related processes have been proposed to follow the initial orienting period. Little is known about the neural interactions between action errors and unexpected perceptual events, two instantiations of unexpected events, in these two putative stages of post-error processing. Here, we aimed to address this by investigating the electrophysiological dynamics associated with action errors and unexpected perceptual events using scalp EEG with a focus on the frontal midline (FM) delta-to-theta oscillations (1-8 Hz) indicative of the performance-monitoring system. Specifically, we examined how the timing of unexpected sounds would influence behavior and neural oscillations after action errors, depending on the length of the intertrial interval (ITI). Our data showed that unexpected sounds aggravated post-error decreases in accuracy when they occurred (1) immediately after errors (i.e., post-error orienting period), regardless of ITI and (2) immediately after the post-error stimulus (i.e., post-error strategic period), at short ITIs. Meanwhile, action errors and unexpected sounds independently produced increased FM delta-to-theta power during the post-error orienting period, regardless of ITIs. However, when unexpected sounds occurred during the post-error strategic period, action errors produced lower FM delta-to-theta power than correct responses, at short ITIs. These differential effects of unexpected events on behavior and FM delta-to-theta dynamics support the notion of the two post-error periods during which different processes are implemented.

Representational geometry of perceptual decisions in the monkey parietal cortex

Lateral intraparietal (LIP) neurons represent formation of perceptual decisions involving eye movements. In circuit models for these decisions, neural ensembles that encode actions compete to form decisions. Consequently, representation and readout of the decision variables (DVs) are implemented similarly for decisions with identical competing actions, irrespective of input and task context differences. Further, DVs are encoded as partially potentiated action plans through balance of activity of action-selective ensembles. Here, we test those core principles. We show that in a novel face-discrimination task, LIP firing rates decrease with supporting evidence, contrary to conventional motion-discrimination tasks. These opposite response patterns arise from similar mechanisms in which decisions form along curved population-response manifolds misaligned with action representations. These manifolds rotate in state space based on context, indicating distinct optimal readouts for different tasks. We show similar manifolds in lateral and medial prefrontal cortices, suggesting similar representational geometry across decision-making circuits.

Trading accuracy for speed over the course of a decision

Humans and other animals often need to balance the desire to gather sensory information (to make the best choice) with the urgency to act, facing a speed-accuracy tradeoff (SAT). Given the ubiquity of SAT across species, extensive research has been devoted to understanding the computational mechanisms allowing its regulation at different timescales, including from one context to another, and from one decision to another. However, animals must frequently change their SAT on even shorter timescales-that is, over the course of an ongoing decision-and little is known about the mechanisms that allow such rapid adaptations. The present study aimed at addressing this issue. Human subjects performed a decision task with changing evidence. In this task, subjects received rewards for correct answers but incurred penalties for mistakes. An increase or a decrease in penalty occurring halfway through the trial promoted rapid SAT shifts, favoring speeded decisions either in the early or in the late stage of the trial. Importantly, these shifts were associated with stage-specific adjustments in the accuracy criterion exploited for committing to a choice. Those subjects who decreased the most their accuracy criterion at a given decision stage exhibited the highest gain in speed, but also the highest cost in terms of performance accuracy at that time. Altogether, the current findings offer a unique extension of previous work, by suggesting that dynamic cha*nges in accuracy criterion allow the regulation of the SAT within the timescale of a single decision.NEW & NOTEWORTHY Extensive research has been devoted to understanding the mechanisms allowing the regulation of the speed-accuracy tradeoff (SAT) from one context to another and from one decision to another. Here, we show that humans can voluntarily change their SAT on even shorter timescales-that is, over the course of a decision. These rapid SAT shifts are associated with dynamic adjustments in the accuracy criterion exploited for committing to a choice.

Post-error adjustment among children aged 7 years with a familial high risk of schizophrenia or bipolar disorder: A population-based cohort study

The cognitive control system matures gradually with age and shows age-related sex differences. To gain knowledge concerning error adaptation in familial high-risk groups, investigating error adaptation among the offspring of parents with severe mental disorders is important and may contribute to the understanding of cognitive functioning in at-risk individuals. We identified an observational cohort through Danish registries and measured error adaptation using an Eriksen flanker paradigm. We tested 497 7-year-old children with a familial high risk of schizophrenia (N = 192) or bipolar disorder (N = 116) for deficits in error adaptation compared with a control group (N = 189). We investigated whether error adaptation differed between high-risk groups compared with controls and sex differences in the adaptation to errors, irrespective of high-risk status. Overall, children exhibited post-error slowing (PES), but the slowing of responses did not translate to significant improvements in accuracy. No differences were detected between either high-risk group compared with the controls. Boys showed less PES and PES after incongruent trials than girls. Our results suggest that familial high risk of severe mental disorders does not influence error adaptation at this early stage of cognitive control development. Error adaptation behavior at age 7 years shows specific sex differences.

Choices change the temporal weighting of decision evidence

Many decisions result from the accumulation of decision-relevant information (evidence) over time. Even when maximizing decision accuracy requires weighting all the evidence equally, decision-makers often give stronger weight to evidence occurring early or late in the evidence stream. Here, we show changes in such temporal biases within participants as a function of intermittent judgments about parts of the evidence stream. Human participants performed a decision task that required a continuous estimation of the mean evidence at the end of the stream. The evidence was either perceptual (noisy random dot motion) or symbolic (variable sequences of numbers). Participants also reported a categorical judgment of the preceding evidence half-way through the stream in one condition or executed an evidence-independent motor response in another condition. The relative impact of early versus late evidence on the final estimation flipped between these two conditions. In particular, participants' sensitivity to late evidence after the intermittent judgment, but not the simple motor response, was decreased. Both the intermittent response as well as the final estimation reports were accompanied by nonluminance-mediated increases of pupil diameter. These pupil dilations were bigger during intermittent judgments than simple motor responses and bigger during estimation when the late evidence was consistent than inconsistent with the initial judgment. In sum, decisions activate pupil-linked arousal systems and alter the temporal weighting of decision evidence. Our results are consistent with the idea that categorical choices in the face of uncertainty induce a change in the state of the neural circuits underlying decision-making.NEW & NOTEWORTHY The psychology and neuroscience of decision-making have extensively studied the accumulation of decision-relevant information toward a categorical choice. Much fewer studies have assessed the impact of a choice on the processing of subsequent information. Here, we show that intermittent choices during a protracted stream of input reduce the sensitivity to subsequent decision information and transiently boost arousal. Choices might trigger a state change in the neural machinery for decision-making.

Brain potentials reveal reduced attention and error-processing during a monetary Go/No-Go task in procrastination

Procrastination is a self-regulatory problem of voluntarily and destructively delaying intended and necessary or personally important tasks. Previous studies showed that procrastination is associated with executive dysfunctions that seem to be particularly strong in punishing contexts. In the present event-related potential (ERP) study a monetary version of the parametric Go/No-Go task was performed by high and low academic procrastinators to verify the influence of motivational context (reward vs. punishment expectation) and task difficulty (easy vs. hard) on procrastination-related executive dysfunctions. The results revealed increased post-error slowing along with reduced P300 and error-related negativity (ERN) amplitudes in high (vs. low) procrastination participants—effects that indicate impaired attention and error-related processing in this group. This pattern of results did not differ as a function of task difficulty and motivation condition. However, when the task got more difficult executive attention deficits became even more apparent at the behavioral level in high procrastinators, as indexed by increased reaction time variability. The findings substantiate prior preliminary evidence that procrastinators show difficulties in certain aspects of executive functioning (in attention and error processing) during execution of task-relevant behavior, which may be more apparent in highly demanding situations.

Neural and behavioral traces of error awareness

Monitoring for errors and behavioral adjustments after errors are essential for daily life. A question that has not been addressed systematically yet, is whether consciously perceived errors lead to different behavioral adjustments compared to unperceived errors. Our goal was to develop a task that would enable us to study different commonly observed neural correlates of error processing and post-error adjustments in their relation to error awareness and accuracy confidence in a single experiment. We assessed performance in a new number judgement error awareness task in 70 participants. We used multiple, robust, single-trial EEG regressions to investigate the link between neural correlates of error processing (e.g., error-related negativity (ERN) and error positivity (Pe)) and error awareness. We found that only aware errors had a slowing effect on reaction times in consecutive trials, but this slowing was not accompanied by post-error increases in accuracy. On a neural level, error awareness and confidence had a modulating effect on both the ERN and Pe, whereby the Pe was most predictive of participants' error awareness. Additionally, we found partial support for a mediating role of error awareness on the coupling between the ERN and behavioral adjustments in the following trial. Our results corroborate previous findings that show both an ERN/Pe and a post-error behavioral adaptation modulation by error awareness. This suggests that conscious error perception can support meta-control processes balancing the recruitment of proactive and reactive control. Furthermore, this study strengthens the role of the Pe as a robust neural index of error awareness.

Simultaneous EEG and pupillary evidence for post-error arousal during a speeded performance task

Arousal evoked by detecting a performance error may provide a mechanism by which error detection leads to either adaptive or maladaptive changes in attention and performance. By pairing EEG data acquisition with simultaneous measurements of pupil diameter, which is thought to reflect norepinephrinergic arousal, this study tested whether transient changes in EEG oscillations in the alpha frequency range (8-12 Hz) following performance mistakes may reflect error-evoked arousal. In the inter-trial interval following performance mistakes (approximately 8% of trials), pupil diameter increased and EEG alpha power decreased, compared to the inter-trial interval following correct responses. Moreover when trials were binned based on pupil diameter on a within-subjects basis, trials with greater pupil diameter were associated with lower EEG alpha power during the inter-trial interval. This pattern of association suggests that error-related alpha suppression, like pupil dilation, reflects arousal in response to error commission. Errors were also followed by worse next-trial performance, implying that error-evoked arousal may not always be beneficial for adaptive control.

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.

Nonlinear neural network dynamics accounts for human confidence in a sequence of perceptual decisions

Electrophysiological recordings during perceptual decision tasks in monkeys suggest that the degree of confidence in a decision is based on a simple neural signal produced by the neural decision process. Attractor neural networks provide an appropriate biophysical modeling framework, and account for the experimental results very well. However, it remains unclear whether attractor neural networks can account for confidence reports in humans. We present the results from an experiment in which participants are asked to perform an orientation discrimination task, followed by a confidence judgment. Here we show that an attractor neural network model quantitatively reproduces, for each participant, the relations between accuracy, response times and confidence. We show that the attractor neural network also accounts for confidence-specific sequential effects observed in the experiment (participants are faster on trials following high confidence trials). Remarkably, this is obtained as an inevitable outcome of the network dynamics, without any feedback specific to the previous decision (that would result in, e.g., a change in the model parameters before the onset of the next trial). Our results thus suggest that a metacognitive process such as confidence in one's decision is linked to the intrinsically nonlinear dynamics of the decision-making neural network.

Reinforcement biases subsequent perceptual decisions when confidence is low, a widespread behavioral phenomenon

Learning from successes and failures often improves the quality of subsequent decisions. Past outcomes, however, should not influence purely perceptual decisions after task acquisition is complete since these are designed so that only sensory evidence determines the correct choice. Yet, numerous studies report that outcomes can bias perceptual decisions, causing spurious changes in choice behavior without improving accuracy. Here we show that the effects of reward on perceptual decisions are principled: past rewards bias future choices specifically when previous choice was difficult and hence decision confidence was low. We identified this phenomenon in six datasets from four laboratories, across mice, rats, and humans, and sensory modalities from olfaction and audition to vision. We show that this choice-updating strategy can be explained by reinforcement learning models incorporating statistical decision confidence into their teaching signals. Thus, reinforcement learning mechanisms are continually engaged to produce systematic adjustments of choices even in well-learned perceptual decisions in order to optimize behavior in an uncertain world.