Neurocomputational mechanisms of prior-informed perceptual decision-making in humans

Empirically Integrating the Evidence for Different Predictive Coding Components Using Auditory False Perception

Perception is a probabilistic estimation of the sensory information we receive at any given time and is shaped by an internal model generated by the brain by assimilating information over the life course. This predictive system in the brain has several components-(i) the internal model, (ii) the model-based prediction called priors, (iii) the weighted difference between the prior and sensory input called prediction error (PE) and (iv) the weighted sum of the prior and input called perceptual inference. Until now, different studies have explored the independent components of this predictive coding system, and we, for the first time to our knowledge, integrate them. To do this, we induce a conditioned hallucination (CH) illusion by means of a multisensory integration paradigm and use this as a model to study the behavioral and electrophysiological responses to this experience. Additionally, we also probe their predictive coding system using a well-established local-global auditory oddball paradigm. By comparing the behavioral and electrophysiological components of people more and less likely to perceive an illusion in the two paradigms, we observed that high perceivers place more confidence in their internal model and low perceivers in the sensory information. Furthermore, high perceivers were more sensitive than low perceivers to PEs that were generated by a change in the context of the sensory information, which served as a measure of a change in the internal model itself. As an exploratory analysis, we also observed that the objective likelihood of perceiving an illusion was corrected to the self-reported likelihood of perceiving an illusion in a day-to-day setting, which disappears when controlled for the perceptual threshold. These results taken together start to give us an idea as to how a person's innate bias-either towards a learned model or external information may-affect their perception in a sensory context.

Transcutaneous vagus nerve stimulation boosts accuracy during perceptual decision-making

BACKGROUND

The locus coeruleus-norepinephrine (LC-NE) system is a well-established regulator of behavior, yet its precise role remains unclear. Animal studies predominantly support a "gain" hypothesis, suggesting that the LC-NE system enhances sensory processing. In contrast, human studies have proposed an alternative "urgency" hypothesis, postulating that LC-NE primarily accelerates responses.

METHOD

To address this discrepancy, we administered transcutaneous vagus nerve stimulation (tVNS) in two experiments. In the first experiment (n = 22), we showed that 4-s tVNS trains reliably induced greater pupil dilation compared to SHAM condition, indicating increased LC-NE activity. In the second experiment (n = 21), we applied tVNS during a random dot motion task to assess its impact on perceptual decision-making.

RESULT

tVNS improved accuracy without affecting reaction times, which appears inconsistent with the "urgency" hypothesis. Exploratory drift-diffusion model analyses further support the "gain" hypothesis, revealing that tVNS increased the drift rate, indicative of enhanced evidence accumulation. Both accuracy and drift-rate improvements were most prominent following errors and especially pronounced in participants who exhibited post-error declines in these measures under SHAM.

CONCLUSION

Our findings align with the "gain" hypothesis, with tentative evidence suggesting that the impact of LC-NE activity adapts to task demands. Accordingly, tVNS showed the strongest effects in contexts prone to accuracy declines, possibly reflecting attentional disengagement, which points to a role of LC in mitigating lapses of attention.

Disentangling sources of variability in decision-making

Even the most highly-trained observers presented with identical choice-relevant stimuli will reliably exhibit substantial trial-to-trial variability in the timing and accuracy of their choices. Despite being a pervasive feature of choice behaviour and a prominent phenotype for numerous clinical disorders, the capability to disentangle the sources of such intra-individual variability (IIV) remains limited. In principle, computational models of decision-making offer a means of parsing and estimating these sources, but methodological limitations have prevented this potential from being fully realized in practice. In this Review, we first discuss current limitations of algorithmic models for understanding variability in decision-making behaviour. We then highlight recent advances in behavioural paradigm design, novel analyses of cross-trial behavioural and neural dynamics, and the development of neurally grounded computational models that are now making it possible to link distinct components of IIV to well-defined neural processes. Taken together, we demonstrate how these methods are opening up new avenues for systematically analysing the neural origins of IIV, paving the way for a more refined, holistic understanding of decision-making in health and disease.

Limited Evidence for Probabilistic Cueing Effects on Grating-Evoked Event-Related Potentials and Orientation Decoding Performance

We can rapidly learn recurring patterns that occur within our sensory environments. This knowledge allows us to form expectations about future sensory events. Several influential predictive coding models posit that, when a stimulus matches our expectations, the activity of feature-selective neurons in the visual cortex will be suppressed relative to when that stimulus is unexpected. However, after accounting for known critical confounds, there is currently scant evidence for these hypothesized effects from studies recording electrophysiological neural activity. To provide a strong test for expectation effects on stimulus-evoked responses in the visual cortex, we performed a probabilistic cueing experiment while recording electroencephalographic (EEG) data. Participants (n = 48) learned associations between visual cues and subsequently presented gratings. A given cue predicted the appearance of a certain grating orientation with 10%, 25%, 50%, 75%, or 90% validity. We did not observe any stimulus expectancy effects on grating-evoked event-related potentials. Multivariate classifiers trained to discriminate between grating orientations performed better when classifying 10% compared to 90% probability gratings. However, classification performance did not substantively differ across any other stimulus expectancy conditions. Our findings provide very limited evidence for modulations of prediction error signaling by probabilistic expectations as specified in contemporary predictive coding models.

Dissociable encoding of evolving beliefs and momentary belief updates in distinct neural decision signals

Making accurate decisions in noisy environments requires integrating evidence over time. Studies of simple perceptual decisions in static environments have identified two human neurophysiological signals that evolve with similar integration dynamics, with one - the centroparietal positivity - appearing to compute the running integral and continuously feed it to the other - motor beta lateralisation. However, it remains unknown whether and how these signals serve more distinct functional roles in more complex scenarios. Here, we use a volatile expanded judgement task that dissociates raw sensory information, belief updates, and the evolving belief itself. We find that motor beta lateralisation traces the evolving belief across stimuli, while the centroparietal positivity locally encodes the belief updates associated with each individual stimulus. These results suggest a flexible computational hierarchy where context-dependent belief updates can be computed sample-by-sample at an intermediate processing level to modify downstream belief representations for protracted decisions about discrete stimuli.

Basal ganglia components have distinct computational roles in decision-making dynamics under conflict and uncertainty

The basal ganglia (BG) play a key role in decision-making, preventing impulsive actions in some contexts while facilitating fast adaptations in others. The specific contributions of different BG structures to this nuanced behavior remain unclear, particularly under varying situations of noisy and conflicting information that necessitate ongoing adjustments in the balance between speed and accuracy. Theoretical accounts suggest that dynamic regulation of the amount of evidence required to commit to a decision (a dynamic "decision boundary") may be necessary to meet these competing demands. Through the application of novel computational modeling tools in tandem with direct neural recordings from human BG areas, we find that neural dynamics in the theta band manifest as variations in a collapsing decision boundary as a function of conflict and uncertainty. We collected intracranial recordings from patients diagnosed with either Parkinson's disease (PD) (n = 14) or dystonia (n = 3) in the subthalamic nucleus (STN), globus pallidus internus (GPi), and globus pallidus externus (GPe) during their performance of a novel perceptual discrimination task in which we independently manipulated uncertainty and conflict. To formally characterize whether these task and neural components influenced decision dynamics, we leveraged modified diffusion decision models (DDMs). Behavioral choices and response time distributions were best characterized by a modified DDM in which the decision boundary collapsed over time, but where the onset and shape of this collapse varied with conflict. Moreover, theta dynamics in BG structures modulated the onset and shape of this collapse but differentially across task conditions. In STN, theta activity was related to a prolonged decision boundary (indexed by slower collapse and therefore more deliberate choices) during high conflict situations. Conversely, rapid declines in GPe theta during low conflict conditions were related to rapidly collapsing boundaries and expedited choices, with additional complementary decision bound adjustments during high uncertainty situations. Finally, GPi theta effects were uniform across conditions, with increases in theta associated with a prolongation of decision bound collapses. Together, these findings provide a nuanced understanding of how our brain thwarts impulsive actions while nonetheless enabling behavioral adaptation amidst noisy and conflicting information.

Task goals shape the relationship between decision and movement speed

The speed at which we move is linked to the speed at which we decide to make these movements. Yet the principles guiding such relationship remain unclear: whereas some studies point toward a shared invigoration process boosting decision and movement speed jointly, others rather indicate a trade-off between both levels of control, with slower movements accompanying faster decisions. Here, we aimed 1) at further investigating the existence of a shared invigoration process linking decision and movement and 2) at testing the hypothesis that such a link is masked when detrimental to the reward rate. To this aim, we tested 62 subjects who performed the Tokens task in two experiments (separate sessions): experiment 1 evaluated how changing decision speed affects movement speed, whereas experiment 2 assessed how changing movement speed affects decision speed. In the latter experiment, subjects were encouraged to favor either decision speed (fast decision group) or decision accuracy (slow decision group). Various mixed model analyses revealed a coregulation of decision (urgency) and movement speed in experiment 1 and in the fast decision group of experiment 2 but not in the slow decision group, despite the fact that these same subjects displayed a coregulation effect in experiment 1. Altogether, our findings support the idea that coregulation occurs as a default mode but that this form of control is diminished or supplanted by a trade-off relationship, contingent on reward rate maximization. Drawing from these behavioral observations, we propose that multiple processes contribute to shaping the speed of decisions and movements.NEW & NOTEWORTHY The principles guiding the relationship between decision and movement speed are still unclear. In the present behavioral study involving two experiments conducted with 62 human subjects, we report findings indicating a relationship that varies as a function of the task goals. Coregulation emerges as a default mode of control that fades when detrimental to the reward rate, possibly because of the influence of other processes that can selectively shape the speed of our decisions or movements.

The Effect of Congruent versus Incongruent Distractor Positioning on Electrophysiological Signals during Perceptual Decision-Making

Key event-related potentials (ERPs) of perceptual decision-making such as centroparietal positivity (CPP) elucidate how evidence is accumulated toward a given choice. Furthermore, this accumulation can be impacted by visual target selection signals such as the N2 contralateral (N2c). How these underlying neural mechanisms of perceptual decision-making are influenced by the spatial congruence of distractors relative to target stimuli remains unclear. Here, we used electroencephalography (EEG) in humans of both sexes to investigate the effect of distractor spatial congruency (same vs different hemifield relative to targets) on perceptual decision-making. We confirmed that responses for perceptual decisions were slower for spatially incongruent versus congruent distractors of high salience. Similarly, markers of target selection (N2c peak amplitude) and evidence accumulation (CPP slope) were found to be lower when distractors were spatially incongruent versus congruent. To evaluate the effects of congruency further, we applied drift diffusion modeling to participant responses, which showed that larger amplitudes of both ERPs were correlated with shorter nondecision times when considering the effect of congruency. The modeling also suggested that congruency's effect on behavior occurred prior to and during evidence accumulation when considering the effects of the N2c peak and CPP slope. These findings point to spatially incongruent distractors, relative to congruent distractors, influencing decisions as early as the initial sensory processing phase and then continuing to exert an effect as evidence is accumulated throughout the decision-making process. Overall, our findings highlight how key electrophysiological signals of perceptual decision-making are influenced by the spatial congruence of target and distractor.

Common neural choice signals can emerge artefactually amid multiple distinct value signals

Previous work has identified characteristic neural signatures of value-based decision-making, including neural dynamics that closely resemble the ramping evidence accumulation process believed to underpin choice. Here we test whether these signatures of the choice process can be temporally dissociated from additional, choice-'independent' value signals. Indeed, EEG activity during value-based choice revealed distinct spatiotemporal clusters, with a stimulus-locked cluster reflecting affective reactions to choice sets and a response-locked cluster reflecting choice difficulty. Surprisingly, 'neither' of these clusters met the criteria for an evidence accumulation signal. Instead, we found that stimulus-locked activity can 'mimic' an evidence accumulation process when aligned to the response. Re-analysing four previous studies, including three perceptual decision-making studies, we show that response-locked signatures of evidence accumulation disappear when stimulus-locked and response-locked activity are modelled jointly. Collectively, our findings show that neural signatures of value can reflect choice-independent processes and look deceptively like evidence accumulation.

Human brain imaging with high-density electroencephalography: Techniques and applications

Electroencephalography (EEG) is a technique for non-invasively measuring neuronal activity in the human brain using electrodes placed on the participant's scalp. With the advancement of digital technologies, EEG analysis has evolved over time from the qualitative analysis of amplitude and frequency modulations to a comprehensive analysis of the complex spatiotemporal characteristics of the recorded signals. EEG is now considered a powerful tool for measuring neural processes in the same time frame in which they happen (i.e. the subsecond range). However, it is commonly argued that EEG suffers from low spatial resolution, which makes it difficult to localize the generators of EEG activity accurately and reliably. Today, the availability of high-density EEG (hdEEG) systems, combined with methods for incorporating information on head anatomy and sophisticated source-localization algorithms, has transformed EEG into an important neuroimaging tool. hdEEG offers researchers and clinicians a rich and varied range of applications. It can be used not only for investigating neural correlates in motor and cognitive neuroscience experiments, but also for clinical diagnosis, particularly in the detection of epilepsy and the characterization of neural impairments in a wide range of neurological disorders. Notably, the integration of hdEEG systems with other physiological recordings, such as kinematic and/or electromyography data, might be especially beneficial to better understand the neuromuscular mechanisms associated with deconditioning in ageing and neuromotor disorders, by mapping the neurokinematic and neuromuscular connectivity patterns directly in the brain.

Social and non-social feedback stimuli lead to comparable levels of reward learning and reward responsiveness in an online probabilistic reward task

Social stimuli seem to be processed more easily and efficiently than non-social stimuli. The current study tested whether social feedback stimuli improve reward learning in a probabilistic reward task (PRT), in which one response option is usually rewarded more often than the other via presentation of non-social reward stimuli. In a pre-registered online study with 305 participants, 75 participants were presented with a non-social feedback stimulus (a star) and information about gains, which is typically used in published PRT studies. Three other groups (with 73-82 participants each) were presented with one of three social feedback stimuli: verbal praise, an attractive happy face, or a "thumbs up"-picture. The data were analysed based on classical signal detection theory, drift diffusion modelling, and Bayesian analyses of null effects. All PRT variants yielded the expected behavioural preference for the more frequently rewarded response. There was no processing advantage of social over non-social feedback stimuli. Bayesian analyses further supported the observation that social feedback stimuli neither increased nor decreased behavioural preferences in the PRT. The current findings suggest that the PRT is a robust experimental paradigm independent of the applied feedback stimuli. They also suggest that the occurrence of a processing advantage for social feedback stimuli is dependent on the experimental task and design.

Decomposing loss aversion from a single neural signal

People often display stronger aversion to losses than appetite for equivalent gains, a widespread phenomenon known as loss aversion. The prevailing theory attributes loss aversion to a valuation bias that amplifies losses relative to gains. An alternative account attributes loss aversion to a response bias that avoids choices that might result in loss. By modeling the temporal dynamics of scalp electrical activity during decisions to accept or reject gambles within a sequential sampling framework, we decomposed valuation bias and response bias from a single event-related neural signal, the P3. Specifically, we found valuation bias manifested as larger sensitivity of P3 to losses than gains, which was localizable to reward-related brain regions. By contrast, response bias manifested as larger P3 preceding gamble acceptance than rejection and was localizable to motor cortex. Our study reveals the dissociable neural biomarkers of response bias and valuation bias underpinning loss-averse decisions.

Neural Signatures of Evidence Accumulation Encode Subjective Perceptual Confidence Independent of Performance

Confidence is an adaptive computation when environmental feedback is absent, yet there is little consensus regarding how perceptual confidence is computed in the brain. Difficulty arises because confidence correlates with other factors, such as accuracy, response time (RT), or evidence quality. We investigated whether neural signatures of evidence accumulation during a perceptual choice predict subjective confidence independently of these factors. Using motion stimuli, a central-parietal positive-going electroencephalogram component (CPP) behaves as an accumulating decision variable that predicts evidence quality, RT, accuracy, and confidence (Experiment 1, N = 25 adults). When we psychophysically varied confidence while holding accuracy constant (Experiment 2, N = 25 adults), the CPP still predicted confidence. Statistically controlling for RT, accuracy, and evidence quality (Experiment 3, N = 24 adults), the CPP still explained unique variance in confidence. The results indicate that a predecision neural signature of evidence accumulation, the CPP, encodes subjective perceptual confidence in decision-making independent of task performance.

Attention facilitates initiation of perceptual decision making: a combined psychophysical and electroencephalography study

Humans can selectively process information and make decisions by directing their attention to desired locations in their daily lives. Numerous studies have shown that attention increases the rate of correct responses and shortens reaction time, and it has been hypothesized that this phenomenon is caused by an increase in sensitivity of the sensory signals to which attention is directed. The present study employed psychophysical methods and electroencephalography (EEG) to test the hypothesis that attention accelerates the onset of information accumulation. Participants were asked to discriminate the motion direction of one of two random dot kinematograms presented on the left and right sides of the visual field, one of which was cued by an arrow in 80% of the trials. The drift-diffusion model was applied to the percentage of correct responses and reaction times in the attended and unattended fields of view. Attention primarily increased sensory sensitivity and shortened the time unrelated to decision making. Next, we measured centroparietal positivity (CPP), an EEG measure associated with decision making, and found that CPP latency was shorter in attended trials than in unattended trials. These results suggest that attention not only increases sensory sensitivity but also accelerates the initiation of decision making.

Proactive response preparation contributes to contingency learning: novel evidence from force-sensitive keyboards

Contingency learning can involve learning that the identity of one stimulus in a sequence predicts the identity of the next stimulus. It remains unclear, however, whether such learning speeds responses to the next stimulus only by reducing the threshold for triggering the expected response after stimulus onset or also by preparing the expected response before stimulus onset. To distinguish between these competing accounts, we manipulated the probabilities with which each of two prime arrows (Left and Right) were followed by each of two probe arrows (Up and Down) in a prime-probe task while using force-sensitive keyboards to monitor sub-threshold finger force. Consistent with the response preparation account, two experiments revealed greater force just before probe onset on the response key corresponding to the direction in which the probe was more (versus less) likely to point (e.g., Up vs. Down). Furthermore, mirroring sequential contingency effects in behavior, this pre-probe force effect vanished after a single low-probability trial. These findings favor the response preparation account over the threshold only account. They also suggest the possibility that contingency learning in our tasks indexes trial-by-trial expectations regarding the utility of the prime for predicting the upcoming probe.

Improving Efficiency: Automatic Intelligent Weighing System as a Replacement for Manual Pig Weighing

To verify the accuracy of AIWS, we weighed 106 pen growing-finishing pigs' weights using both the manual and AIWS methods, respectively. Accuracy was evaluated based on the values of MAE, MAPE, and RMSE. In the growth experiment, manual weighing was conducted every two weeks and AIWS predicted weight data was recorded daily, followed by fitting the growth curves. The results showed that MAE, MAPE, and RMSE values for 60 to 120 kg pigs were 3.48 kg, 3.71%, and 4.43 kg, respectively. The correlation coefficient r between the AIWS and manual method was 0.9410, and R2 was 0.8854. The two were extremely significant correlations (p < 0.001). In growth curve fitting, the AIWS method has lower AIC and BIC values than the manual method. The Logistic model by AIWS was the best-fit model. The age and body weight at the inflection point of the best-fit model were 164.46 d and 93.45 kg, respectively. The maximum growth rate was 831.66 g/d. In summary, AIWS can accurately predict pigs' body weights in actual production and has a better fitting effect on the growth curves of growing-finishing pigs. This study suggested that it was feasible for AIWS to replace manual weighing to measure the weight of 50 to 120 kg live pigs in large-scale farming.

Graded decisions in the human brain

Decision-makers objectively commit to a definitive choice, yet at the subjective level, human decisions appear to be associated with a degree of uncertainty. Whether decisions are definitive (i.e., concluding in all-or-none choices), or whether the underlying representations are graded, remains unclear. To answer this question, we recorded intracranial neural signals directly from the brain while human subjects made perceptual decisions. The recordings revealed that broadband gamma activity reflecting each individual's decision-making process, ramped up gradually while being graded by the accumulated decision evidence. Crucially, this grading effect persisted throughout the decision process without ever reaching a definite bound at the time of choice. This effect was most prominent in the parietal cortex, a brain region traditionally implicated in decision-making. These results provide neural evidence for a graded decision process in humans and an analog framework for flexible choice behavior.

Biological motion perception in the theoretical framework of perceptual decision-making: An event-related potential study

Biological motion perception plays a critical role in various decisions in daily life. Failure to decide accordingly in such a perceptual task could have life-threatening consequences. Neurophysiology and computational modeling studies suggest two processes mediating perceptual decision-making. One of these signals is associated with the accumulation of sensory evidence and the other with response selection. Recent EEG studies with humans have introduced an event-related potential called Centroparietal Positive Potential (CPP) as a neural marker aligned with the sensory evidence accumulation while effectively distinguishing it from motor-related lateralized readiness potential (LRP). The present study aims to investigate the neural mechanisms of biological motion perception in the framework of perceptual decision-making, which has been overlooked before. More specifically, we examine whether CPP would track the coherence of the biological motion stimuli and could be distinguished from the LRP signal. We recorded EEG from human participants while they performed a direction discrimination task of a point-light walker stimulus embedded in various levels of noise. Our behavioral findings revealed shorter reaction times and reduced miss rates as the coherence of the stimuli increased. In addition, CPP tracked the coherence of the biological motion stimuli with a tendency to reach a common level during the response, albeit with a later onset than the previously reported results in random-dot motion paradigms. Furthermore, CPP was distinguished from the LRP signal based on its temporal profile. Overall, our results suggest that the mechanisms underlying perceptual decision-making generalize to more complex and socially significant stimuli like biological motion.

Prior probability cues bias sensory encoding with increasing task exposure

When observers have prior knowledge about the likely outcome of their perceptual decisions, they exhibit robust behavioural biases in reaction time and choice accuracy. Computational modelling typically attributes these effects to strategic adjustments in the criterion amount of evidence required to commit to a choice alternative - usually implemented by a starting point shift - but recent work suggests that expectations may also fundamentally bias the encoding of the sensory evidence itself. Here, we recorded neural activity with EEG while participants performed a contrast discrimination task with valid, invalid, or neutral probabilistic cues across multiple testing sessions. We measured sensory evidence encoding via contrast-dependent steady-state visual-evoked potentials (SSVEP), while a read-out of criterion adjustments was provided by effector-selective mu-beta band activity over motor cortex. In keeping with prior modelling and neural recording studies, cues evoked substantial biases in motor preparation consistent with criterion adjustments, but we additionally found that the cues produced a significant modulation of the SSVEP during evidence presentation. While motor preparation adjustments were observed in the earliest trials, the sensory-level effects only emerged with extended task exposure. Our results suggest that, in addition to strategic adjustments to the decision process, probabilistic information can also induce subtle biases in the encoding of the evidence itself.

Intracranial electroencephalography reveals effector-independent evidence accumulation dynamics in multiple human brain regions

Neural representations of perceptual decision formation that are abstracted from specific motor requirements have previously been identified in humans using non-invasive electrophysiology; however, it is currently unclear where these originate in the brain. Here we capitalized on the high spatiotemporal precision of intracranial EEG to localize such abstract decision signals. Participants undergoing invasive electrophysiological monitoring for epilepsy were asked to judge the direction of random-dot stimuli and respond either with a speeded button press (N = 24), or vocally, after a randomized delay (N = 12). We found a widely distributed motor-independent network of regions where high-frequency activity exhibited key characteristics consistent with evidence accumulation, including a gradual buildup that was modulated by the strength of the sensory evidence, and an amplitude that predicted participants' choice accuracy and response time. Our findings offer a new view on the brain networks governing human decision-making.

Neural correlates of confidence during decision formation in a perceptual judgment task

When we make a decision, we also estimate the probability that our choice is correct or accurate. This probability estimate is termed our degree of decision confidence. Recent work has reported event-related potential (ERP) correlates of confidence both during decision formation (the centro-parietal positivity component; CPP) and after a decision has been made (the error positivity component; Pe). However, there are several measurement confounds that complicate the interpretation of these findings. More recent studies that overcome these issues have so far produced conflicting results. To better characterise the ERP correlates of confidence we presented participants with a comparative brightness judgment task while recording electroencephalography. Participants judged which of two flickering squares (varying in luminance over time) was brighter on average. Participants then gave confidence ratings ranging from "surely incorrect" to "surely correct". To elicit a range of confidence ratings we manipulated both the mean luminance difference between the brighter and darker squares (relative evidence) and the overall luminance of both squares (absolute evidence). We found larger CPP amplitudes in trials with higher confidence ratings. This association was not simply a by-product of differences in relative evidence (which covaries with confidence) across trials. We did not identify postdecisional ERP correlates of confidence, except when they were artificially produced by pre-response ERP baselines. These results provide further evidence for neural correlates of processes that inform confidence judgments during decision formation.

Cortical β Power Reflects a Neural Implementation of Decision Boundary Collapse in Speeded Decisions

A prominent account of decision-making assumes that information is accumulated until a fixed response threshold is crossed. However, many decisions require weighting of information appropriately against time. Collapsing response thresholds are a mathematically optimal solution to this decision problem. However, our understanding of the neurocomputational mechanisms underlying dynamic response thresholds remains significantly incomplete. To investigate this issue, we used a multistage drift-diffusion model (DDM) and also analyzed EEG β power lateralization (BPL). The latter served as a neural proxy for decision signals. We analyzed a large dataset (n = 863; 434 females and 429 males) from a speeded flanker task and data from an independent confirmation sample (n = 119; 70 females and 49 males). We showed that a DDM with collapsing decision thresholds, a process wherein the decision boundary reduces over time, captured participants' time-dependent decision policy more accurately than a model with fixed thresholds. Previous research suggests that BPL over motor cortices reflects features of a decision signal and that its peak, coinciding with the motor response, may serve as a neural proxy for the decision threshold. We show that BPL around the response decreased with increasing RTs. Together, our findings offer compelling evidence for the existence of collapsing decision thresholds in decision-making processes.

Metric error monitoring as a component of metacognitive processing

Metacognitive processing constitutes one of the contemporary target domains in consciousness research. Error monitoring (the ability to correctly report one's own errors without feedback) is considered one of the functional outcomes of metacognitive processing. Error monitoring is traditionally investigated as part of categorical decisions where choice accuracy is a binary construct (choice is either correct or incorrect). However, recent studies revealed that this ability is characterized by metric features (i.e., direction and magnitude) in temporal, spatial, and numerical domains. Here, we discuss methodological approaches to investigating metric error monitoring in both humans and non-human animals and review their findings. The potential neural substrates of metric error monitoring measures are also discussed. This new scope of metacognitive processing can help improve our current understanding of conscious processing from a new perspective. Thus, by summarizing and discussing the perspectives, findings, and common applications in the metric error monitoring literature, this paper aims to provide a guideline for future research.

Of Rodents and Primates: Time-Variant Gain in Drift-Diffusion Decision Models

Sequential sampling models of decision-making involve evidence accumulation over time and have been successful in capturing choice behaviour. A popular model is the drift-diffusion model (DDM). To capture the finer aspects of choice reaction times (RTs), time-variant gain features representing urgency signals have been implemented in DDM that can exhibit slower error RTs than correct RTs. However, time-variant gain is often implemented on both DDM's signal and noise features, with the assumption that increasing gain on the drift rate (due to urgency) is similar to DDM with collapsing decision bounds. Hence, it is unclear whether gain effects on just the signal or noise feature can lead to a different choice behaviour. This work presents an alternative DDM variant, focusing on the implications of time-variant gain mechanisms, constrained by model parsimony. Specifically, using computational modelling of choice behaviour of rats, monkeys, and humans, we systematically showed that time-variant gain only on the DDM's noise was sufficient to produce slower error RTs, as in monkeys, while time-variant gain only on drift rate leads to faster error RTs, as in rodents. We also found minimal effects of time-variant gain in humans. By highlighting these patterns, this study underscores the utility of group-level modelling in capturing general trends and effects consistent across species. Thus, time-variant gain on DDM's different components can lead to different choice behaviours, shed light on the underlying time-variant gain mechanisms for different species, and can be used for systematic data fitting.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42113-023-00194-1.

Quantifying decision-making in dynamic, continuously evolving environments

During perceptual decision-making tasks, centroparietal electroencephalographic (EEG) potentials report an evidence accumulation-to-bound process that is time locked to trial onset. However, decisions in real-world environments are rarely confined to discrete trials; they instead unfold continuously, with accumulation of time-varying evidence being recency-weighted towards its immediate past. The neural mechanisms supporting recency-weighted continuous decision-making remain unclear. Here, we use a novel continuous task design to study how the centroparietal positivity (CPP) adapts to different environments that place different constraints on evidence accumulation. We show that adaptations in evidence weighting to these different environments are reflected in changes in the CPP. The CPP becomes more sensitive to fluctuations in sensory evidence when large shifts in evidence are less frequent, and the potential is primarily sensitive to fluctuations in decision-relevant (not decision-irrelevant) sensory input. A complementary triphasic component over occipito-parietal cortex encodes the sum of recently accumulated sensory evidence, and its magnitude covaries with parameters describing how different individuals integrate sensory evidence over time. A computational model based on leaky evidence accumulation suggests that these findings can be accounted for by a shift in decision threshold between different environments, which is also reflected in the magnitude of pre-decision EEG activity. Our findings reveal how adaptations in EEG responses reflect flexibility in evidence accumulation to the statistics of dynamic sensory environments.

Modelling decision-making biases

Biases are a fundamental aspect of everyday life decision-making. A variety of modelling approaches have been suggested to capture decision-making biases. Statistical models are a means to describe the data, but the results are usually interpreted according to a verbal theory. This can lead to an ambiguous interpretation of the data. Mathematical cognitive models of decision-making outline the structure of the decision process with formal assumptions, providing advantages in terms of prediction, simulation, and interpretability compared to statistical models. We compare studies that used both signal detection theory and evidence accumulation models as models of decision-making biases, concluding that the latter provides a more comprehensive account of the decision-making phenomena by including response time behavior. We conclude by reviewing recent studies investigating attention and expectation biases with evidence accumulation models. Previous findings, reporting an exclusive influence of attention on the speed of evidence accumulation and prior probability on starting point, are challenged by novel results suggesting an additional effect of attention on non-decision time and prior probability on drift rate.

Stimulus expectations do not modulate visual event-related potentials in probabilistic cueing designs

Humans and other animals can learn and exploit repeating patterns that occur within their environments. These learned patterns can be used to form expectations about future sensory events. Several influential predictive coding models have been proposed to explain how learned expectations influence the activity of stimulus-selective neurons in the visual system. These models specify reductions in neural response measures when expectations are fulfilled (termed expectation suppression) and increases following surprising sensory events. However, there is currently scant evidence for expectation suppression in the visual system when confounding factors are taken into account. Effects of surprise have been observed in blood oxygen level dependent (BOLD) signals, but not when using electrophysiological measures. To provide a strong test for expectation suppression and surprise effects we performed a predictive cueing experiment while recording electroencephalographic (EEG) data. Participants (n=48) learned cue-face associations during a training session and were then exposed to these cue-face pairs in a subsequent experiment. Using univariate analyses of face-evoked event-related potentials (ERPs) we did not observe any differences across expected (90% probability), neutral (50%) and surprising (10%) face conditions. Across these comparisons, Bayes factors consistently favoured the null hypothesis throughout the time-course of the stimulus-evoked response. When using multivariate pattern analysis we did not observe above-chance classification of expected and surprising face-evoked ERPs. By contrast, we found robust within- and across-trial stimulus repetition effects. Our findings do not support predictive coding-based accounts that specify reduced prediction error signalling when perceptual expectations are fulfilled. They instead highlight the utility of other types of predictive processing models that describe expectation-related phenomena in the visual system without recourse to prediction error signalling.

Evidence Accumulation Rate Moderates the Relationship between Enriched Environment Exposure and Age-Related Response Speed Declines

Older adults exposed to enriched environments (EEs) maintain relatively higher levels of cognitive function, even in the face of compromised markers of brain health. Response speed (RS) is often used as a simple proxy to measure the preservation of global cognitive function in older adults. However, it is unknown which specific selection, decision, and/or motor processes provide the most specific indices of neurocognitive health. Here, using a simple decision task with electroencephalography (EEG), we found that the efficiency with which an individual accumulates sensory evidence was a critical determinant of the extent to which RS was preserved in older adults (63% female, 37% male). Moreover, the mitigating influence of EE on age-related RS declines was most pronounced when evidence accumulation rates were shallowest. These results suggest that the phenomenon of cognitive reserve, whereby high EE individuals can better tolerate suboptimal brain health to facilitate the preservation of cognitive function, is not just applicable to neuroanatomical indicators of brain aging but can be observed in markers of neurophysiology. Our results suggest that EEG metrics of evidence accumulation may index neurocognitive vulnerability of the aging brain.Significance Statement Response speed in older adults is closely linked with trajectories of cognitive aging. Here, by recording brain activity while individuals perform a simple computer task, we identify a neural metric that is a critical determinant of response speed. Older adults exposed to greater cognitive and social stimulation throughout a lifetime could maintain faster responding, even when this neural metric was impaired. This work suggests EEG is a useful technique for interrogating how a lifetime of stimulation benefits brain health in aging.

Balancing true and false detection of intermittent sensory targets by adjusting the inputs to the evidence accumulation process

Decisions about noisy stimuli are widely understood to be made by accumulating evidence up to a decision bound that can be adjusted according to task demands. However, relatively little is known about how such mechanisms operate in continuous monitoring contexts requiring intermittent target detection. Here, we examined neural decision processes underlying detection of 1 s coherence targets within continuous random dot motion, and how they are adjusted across contexts with weak, strong, or randomly mixed weak/strong targets. Our prediction was that decision bounds would be set lower when weak targets are more prevalent. Behavioural hit and false alarm rate patterns were consistent with this, and were well captured by a bound-adjustable leaky accumulator model. However, beta-band EEG signatures of motor preparation contradicted this, instead indicating lower bounds in the strong-target context. We thus tested two alternative models in which decision-bound dynamics were constrained directly by beta measurements, respectively, featuring leaky accumulation with adjustable leak, and non-leaky accumulation of evidence referenced to an adjustable sensory-level criterion. We found that the latter model best explained both behaviour and neural dynamics, highlighting novel means of decision policy regulation and the value of neurally informed modelling.

Confidence is predicted by pre- and post-choice decision signal dynamics

It is well established that one's confidence in a choice can be influenced by new evidence encountered after commitment has been reached, but the processes through which post-choice evidence is sampled remain unclear. To investigate this, we traced the pre- and post-choice dynamics of electrophysiological signatures of evidence accumulation (Centro-parietal Positivity, CPP) and motor preparation (mu/beta band) to determine their sensitivity to participants' confidence in their perceptual discriminations. Pre-choice CPP amplitudes scaled with confidence both when confidence was reported simultaneously with choice, and when reported 1 second after the initial direction decision with no intervening evidence. When additional evidence was presented during the post-choice delay period, the CPP exhibited sustained activation after the initial choice, with a more prolonged build-up on trials with lower certainty in the alternative that was finally endorsed, irrespective of whether this entailed a change-of-mind from the initial choice or not. Further investigation established that this pattern was accompanied by later lateralisation of motor preparation signals toward the ultimately chosen response and slower confidence reports when participants indicated low certainty in this response. These observations are consistent with certainty-dependent stopping theories according to which post-choice evidence accumulation ceases when a criterion level of certainty in a choice alternative has been reached, but continues otherwise. Our findings have implications for current models of choice confidence, and predictions they may make about EEG signatures.

"Reliable organisms from unreliable components" revisited: the linear drift, linear infinitesimal variance model of decision making

Diffusion models of decision making, in which successive samples of noisy evidence are accumulated to decision criteria, provide a theoretical solution to von Neumann's (1956) problem of how to increase the reliability of neural computation in the presence of noise. I introduce and evaluate a new neurally-inspired dual diffusion model, the linear drift, linear infinitesimal variance (LDLIV) model, which embodies three features often thought to characterize neural mechanisms of decision making. The accumulating evidence is intrinsically positively-valued, saturates at high intensities, and is accumulated for each alternative separately. I present explicit integral-equation predictions for the response time distribution and choice probabilities for the LDLIV model and compare its performance on two benchmark sets of data to three other models: the standard diffusion model and two dual diffusion model composed of racing Wiener processes, one between absorbing and reflecting boundaries and one with absorbing boundaries only. The LDLIV model and the standard diffusion model performed similarly to one another, although the standard diffusion model is more parsimonious, and both performed appreciably better than the other two dual diffusion models. I argue that accumulation of noisy evidence by a diffusion process and drift rate variability are both expressions of how the cognitive system solves von Neumann's problem, by aggregating noisy representations over time and over elements of a neural population. I also argue that models that do not solve von Neumann's problem do not address the main theoretical question that historically motivated research in this area.

Movement characteristics impact decision-making and vice versa

Previous studies suggest that humans are capable of coregulating the speed of decisions and movements if promoted by task incentives. It is unclear however whether such behavior is inherent to the process of translating decisional information into movements, beyond posing a valid strategy in some task contexts. Therefore, in a behavioral online study we imposed time constraints to either decision- or movement phases of a sensorimotor task, ensuring that coregulating decisions and movements was not promoted by task incentives. We found that participants indeed moved faster when fast decisions were promoted and decided faster when subsequent finger tapping movements had to be executed swiftly. These results were further supported by drift diffusion modelling and inspection of psychophysical kernels: Sensorimotor delays related to initiating the finger tapping sequence were shorter in fast-decision as compared to slow-decision blocks. Likewise, the decisional speed-accuracy tradeoff shifted in favor of faster decisions in fast-tapping as compared to slow-tapping blocks. These findings suggest that decisions not only impact movement characteristics, but that properties of movement impact the time taken to decide. We interpret these behavioral results in the context of embodied decision-making, whereby shared neural mechanisms may modulate decisions and movements in a joint fashion.

A unified neural account of contextual and individual differences in altruism

Altruism is critical for cooperation and productivity in human societies but is known to vary strongly across contexts and individuals. The origin of these differences is largely unknown, but may in principle reflect variations in different neurocognitive processes that temporally unfold during altruistic decision making (ranging from initial perceptual processing via value computations to final integrative choice mechanisms). Here, we elucidate the neural origins of individual and contextual differences in altruism by examining altruistic choices in different inequality contexts with computational modeling and electroencephalography (EEG). Our results show that across all contexts and individuals, wealth distribution choices recruit a similar late decision process evident in model-predicted evidence accumulation signals over parietal regions. Contextual and individual differences in behavior related instead to initial processing of stimulus-locked inequality-related value information in centroparietal and centrofrontal sensors, as well as to gamma-band synchronization of these value-related signals with parietal response-locked evidence-accumulation signals. Our findings suggest separable biological bases for individual and contextual differences in altruism that relate to differences in the initial processing of choice-relevant information.

Phantom controllers: Misspecified models create the false appearance of adaptive control during value-based choice

Decision scientists have grown increasingly interested in how people adaptively control their decision making. Researchers have demonstrated that parameters governing the accumulation of evidence towards a choice, such as the decision threshold, are shaped by information available prior to or in parallel with one's evaluation of an option set (e.g., recent outcomes or choice conflict). A recent account has taken a bold leap forward in this approach, suggesting that adjustments in decision parameters can be motivated by the value of the options under consideration. This motivated control account predicts that when faced with difficult choices (similarly valued options) under time pressure, people will adaptively lower their decision threshold to ensure that they make a choice in time. This account was supported by drift diffusion modeling of a deadlined choice task, demonstrating that decision thresholds decrease for difficult relative to easy choices. Here, we reanalyze the data from this experiment, and show that evidence for this novel account does not hold up to further scrutiny. Using a more systematic and comprehensive modeling approach, we show that this previously observed threshold adjustment disappears (or even reverses) under a more complete model of the data. Importantly, we further show how this and other apparent evidence for motivated control arises as an artifact of model (mis)specification, where one model's putatively controlled decision process (e.g., value-driven threshold adjustments) can mimic another model's stimulus-driven decision processes (e.g., accumulator competition or collapsing bounds). Collectively, this work reveals crucial insights and constraints in the pursuit of understanding how control guides decision-making, and when it doesn't.

Multiphasic value biases in fast-paced decisions

Perceptual decisions are biased toward higher-value options when overall gains can be improved. When stimuli demand immediate reactions, the neurophysiological decision process dynamically evolves through distinct phases of growing anticipation, detection, and discrimination, but how value biases are exerted through these phases remains unknown. Here, by parsing motor preparation dynamics in human electrophysiology, we uncovered a multiphasic pattern of countervailing biases operating in speeded decisions. Anticipatory preparation of higher-value actions began earlier, conferring a 'starting point' advantage at stimulus onset, but the delayed preparation of lower-value actions was steeper, conferring a value-opposed buildup-rate bias. This, in turn, was countered by a transient deflection toward the higher-value action evoked by stimulus detection. A neurally-constrained process model featuring anticipatory urgency, biased detection, and accumulation of growing stimulus-discriminating evidence, successfully captured both behavior and motor preparation dynamics. Thus, an intricate interplay of distinct biasing mechanisms serves to prioritise time-constrained perceptual decisions.

Motor cortical signals reflecting decision making and action preparation

Decision making often requires accumulating evidence in favour of a particular option. When choices are expressed with a motor response, these actions are preceded by reductions in the power of oscillations in the alpha and beta range in motor cortices. For unimanual movements, these reductions are greater over the hemisphere contralateral to the response side. Such lateralizations are hypothesized to be an online index of the neural state of decisions as they develop over time of processing. In contrast, the lateralized readiness potential (LRP) is considered to selectively activate a response and appears shortly before the motor output. We investigated to what extent these neural signals reflect integration of decision evidence or more motor-related action preparation. Using two different experiments, we found that lateralization of alpha and beta power (APL and BPL, respectively) rapidly emerged after stimulus presentation, even when making an overt response was not yet possible. In contrast, we show that even after prolonged stimulus presentation, no LRP was present. Instead, the LRP emerged only after an imperative cue, prompting participants to indicate their choice. Furthermore, we could show that variations in sensory evidence strength modulate APL and BPL onset times, suggesting that integration of evidence is represented in these motor cortical signals. We conclude that APL and BPL reflect higher cognitive processes rather than pure action preparation, whereas LRP is more closely tied to motor performance. APL and BPL potentially encode decision information in motor areas serving the later preparation of overt decision output.

Rule-based and stimulus-based cues bias auditory decisions via different computational and physiological mechanisms

Expectations, such as those arising from either learned rules or recent stimulus regularities, can bias subsequent auditory perception in diverse ways. However, it is not well understood if and how these diverse effects depend on the source of the expectations. Further, it is unknown whether different sources of bias use the same or different computational and physiological mechanisms. We examined how rule-based and stimulus-based expectations influenced behavior and pupil-linked arousal, a marker of certain forms of expectation-based processing, of human subjects performing an auditory frequency-discrimination task. Rule-based cues consistently biased choices and response times (RTs) toward the more-probable stimulus. In contrast, stimulus-based cues had a complex combination of effects, including choice and RT biases toward and away from the frequency of recently presented stimuli. These different behavioral patterns also had: 1) distinct computational signatures, including different modulations of key components of a novel form of a drift-diffusion decision model and 2) distinct physiological signatures, including substantial bias-dependent modulations of pupil size in response to rule-based but not stimulus-based cues. These results imply that different sources of expectations can modulate auditory processing via distinct mechanisms: one that uses arousal-linked, rule-based information and another that uses arousal-independent, stimulus-based information to bias the speed and accuracy of auditory perceptual decisions.

Prior information about upcoming stimuli can bias our perception of those stimuli. Whether different sources of prior information bias perception in similar or distinct ways is not well understood. We compared the influence of two kinds of prior information on tone-frequency discrimination: rule-based cues, in the form of explicit information about the most-likely identity of the upcoming tone; and stimulus-based cues, in the form of sequences of tones presented before the to-be-discriminated tone. Although both types of prior information biased auditory decision-making, they demonstrated distinct behavioral, computational, and physiological signatures. Our results suggest that the brain processes prior information in a form-specific manner rather than utilizing a general-purpose prior. Such form-specific processing has implications for understanding decision biases real-world contexts, in which prior information comes from many different sources and modalities.

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.

The Mental Maxwell Relations: A Thermodynamic Allegory for Higher Brain Functions

The theoretical framework of classical thermodynamics unifies vastly diverse natural phenomena and captures once-elusive effects in concrete terms. Neuroscience confronts equally varied, equally ineffable phenomena in the mental realm, but has yet to unite or to apprehend them rigorously, perhaps due to an insufficient theoretical framework. The terms for mental phenomena, the mental variables, typically used in neuroscience are overly numerous and imprecise. Unlike in thermodynamics or other branches of physics, in neuroscience, there are no core mental variables from which all others formally derive and it is unclear which variables are distinct and which overlap. This may be due to the nature of mental variables themselves. Unlike the variables of physics, perhaps they cannot be interpreted as composites of a small number of axioms. However, it is well worth exploring if they can, as that would allow more parsimonious theories of higher brain function. Here we offer a theoretical exercise in the spirit of the National Institutes of Health Research Domain Criteria (NIH RDoC) Initiative and the Cognitive Atlas Project, which aim to remedy this state of affairs. Imitating classical thermodynamics, we construct a formal framework for mental variables, an extended analogy - an allegory - between mental and thermodynamic quantities. Starting with mental correlates of the physical indefinables length, time, mass or force, and charge, we pursue the allegory up to mental versions of the thermodynamic Maxwell Relations. The Maxwell Relations interrelate the thermodynamic quantities volume, pressure, temperature, and entropy and were chosen since they are easy to derive, yet capable of generating nontrivial, nonobvious predictions. Our "Mental Maxwell Relations" interlink the mental variables consciousness, salience, arousal, and distraction and make nontrivial, nonobvious statements about mental phenomena. The mental system thus constructed is internally consistent, in harmony with introspection, and respects the RDoC criteria of employing only psychologically valid constructs with some evidence of a brain basis. We briefly apply these concepts to the problem of decision-making and sketch how some of them might be tested empirically.

Dissociating Value-Based Neurocomputation from Subsequent Selection-Related Activations in Human Decision-Making

Human decision-making requires the brain to fulfill neural computation of benefit and risk and therewith a selection between options. It remains unclear how value-based neural computation and subsequent brain activity evolve to achieve a final decision and which process is modulated by irrational factors. We adopted a sequential risk-taking task that asked participants to successively decide whether to open a box with potential reward/punishment in an eight-box trial, or not to open. With time-resolved multivariate pattern analyses, we decoded electroencephalography and magnetoencephalography responses to two successive low- and high-risk boxes before open-box action. Referencing the specificity of decoding-accuracy peak to a first-stage processing completion, we set it as the demarcation and dissociated the neural time course of decision-making into valuation and selection stages. The behavioral hierarchical drift diffusion modeling confirmed different information processing in two stages, that is, the valuation stage was related to the drift rate of evidence accumulation, while the selection stage was related to the nondecision time spent in response-producing. We further observed that medial orbitofrontal cortex participated in the valuation stage, while superior frontal gyrus engaged in the selection stage of irrational open-box decisions. Afterward, we revealed that irrational factors influenced decision-making through the selection stage rather than the valuation stage.

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.

Neurophysiology of Human Perceptual Decision-Making

The discovery of neural signals that reflect the dynamics of perceptual decision formation has had a considerable impact. Not only do such signals enable detailed investigations of the neural implementation of the decision-making process but they also can expose key elements of the brain's decision algorithms. For a long time, such signals were only accessible through direct animal brain recordings, and progress in human neuroscience was hampered by the limitations of noninvasive recording techniques. However, recent methodological advances are increasingly enabling the study of human brain signals that finely trace the dynamics of the unfolding decision process. In this review, we highlight how human neurophysiological data are now being leveraged to furnish new insights into the multiple processing levels involved in forming decisions, to inform the construction and evaluation of mathematical models that can explain intra- and interindividual differences, and to examine how key ancillary processes interact with core decision circuits.

Distractors Selectively Modulate Electrophysiological Markers of Perceptual Decisions

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

Predictive activation of sensory representations as a source of evidence in perceptual decision-making

Our brains can represent expected future states of our sensory environment. Recent work has shown that, when we expect a specific stimulus to appear at a specific time, we can predictively generate neural representations of that stimulus even before it is physically presented. These observations raise two exciting questions: Are pre-activated sensory representations used for perceptual decision-making? And, do we transiently perceive an expected stimulus that does not actually appear? To address these questions, we propose that pre-activated neural representations provide sensory evidence that is used for perceptual decision-making. This can be understood within the framework of the Diffusion Decision Model as an early accumulation of decision evidence in favour of the expected percept. Our proposal makes novel predictions relating to expectation effects on neural markers of decision evidence accumulation, and also provides an explanation for why we sometimes perceive stimuli that are expected, but do not appear.