The neural processes underlying perceptual decision making in humans: Recent progress and future directions

Camouflage patterning modulates neural signatures of attention and decision-making

Many animals rely on visual camouflage to avoid detection and increase their chances of survival. Edge disruption is commonly seen in the natural world, with animals evolving high-contrast markings that are incongruent with their real body outline in order to avoid recognition. While many studies have investigated how camouflage properties influence viewer performance and eye movement in predation search tasks, researchers in the field have yet to consider how camouflage may directly modulate visual attention and object processing. To examine how disruptive coloration modulates attention, we use a visual object recognition model to quantify object saliency. We determine if object saliency is predictive of human behavioural performance and subjective certainty, as well as neural signatures of attention and decision-making. We show that increasing edge disruption not only reduces detection and identification performance but is also associated with a dampening of neurophysiological signatures of attentional filtering. Increased self-reported certainty regarding decisions corresponds with neurophysiological signatures of evidence accumulation and decision-making. In summary, we have demonstrated a potential mechanism by which edge disruption increases the evolutionary fitness of animals by reducing the brain's ability to distinguish signal from noise, and hence to detect and identify the camouflaged animal.

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.

Investigating the Effect of Contextual Cueing with Face Stimuli on Electrophysiological Measures in Younger and Older Adults

Extracting repeated patterns from our surroundings plays a crucial role in contextualizing information, making predictions, and guiding our behavior implicitly. Previous research showed that contextual cueing enhances visual search performance in younger adults. In this study, we investigated whether contextual cueing could also improve older adults' performance and whether age-related differences in the neural processes underlying implicit contextual learning could be detected. Twenty-four younger and 25 older participants performed a visual search task with contextual cueing. Contextual information was generated using repeated face configurations alongside random new configurations. We measured RT difference between new and repeated configurations; ERPs to uncover the neural processes underlying contextual cueing for early (N2pc), intermediate (P3b), and late (r-LRP) processes; and multiscale entropy and spectral power density analyses to examine neural dynamics. Both younger and older adults showed similar contextual cueing benefits in their visual search efficiency at the behavioral level. In addition, they showed similar patterns regarding contextual information processing: Repeated face configurations evoked decreased finer timescale entropy (1-20 msec) and higher frequency band power (13-30 Hz) compared with new configurations. However, we detected age-related differences in ERPs: Younger, but not older adults, had larger N2pc and P3b components for repeated compared with new configurations. These results suggest that contextual cueing remains intact with aging. Although attention- and target-evaluation-related ERPs differed between the age groups, the neural dynamics of contextual learning were preserved with aging, as both age groups increasingly utilized more globally grouped representations for repeated face configurations during the learning process.

Catecholaminergic neuromodulation and selective attention jointly shape perceptual decision-making

Perceptual decisions about sensory input are influenced by fluctuations in ongoing neural activity, most prominently driven by attention and neuromodulator systems. It is currently unknown if neuromodulator activity and attention differentially modulate perceptual decision-making and/or whether neuromodulatory systems in fact control attentional processes. To investigate the effects of two distinct neuromodulatory systems and spatial attention on perceptual decisions, we pharmacologically elevated cholinergic (through donepezil) and catecholaminergic (through atomoxetine) levels in humans performing a visuo-spatial attention task, while we measured electroencephalography (EEG). Both attention and catecholaminergic enhancement improved decision-making at the behavioral and algorithmic level, as reflected in increased perceptual sensitivity and the modulation of the drift rate parameter derived from drift diffusion modeling. Univariate analyses of EEG data time-locked to the attentional cue, the target stimulus, and the motor response further revealed that attention and catecholaminergic enhancement both modulated pre-stimulus cortical excitability, cue- and stimulus-evoked sensory activity, as well as parietal evidence accumulation signals. Interestingly, we observed both similar, unique, and interactive effects of attention and catecholaminergic neuromodulation on these behavioral, algorithmic, and neural markers of the decision-making process. Thereby, this study reveals an intricate relationship between attentional and catecholaminergic systems and advances our understanding about how these systems jointly shape various stages of perceptual decision-making.

Mechanisms of spatial contextual cueing in younger and older adults

The contextual cueing effect is the phenomenon observed when response time (RT) becomes faster in visual search in repeated context compared with a new one. In the present study, we explored whether the mechanisms involved in the effect are age dependent. We investigated it in younger (N = 20, 12 women, 21.2 ± 1.75 years) and older (N = 19, nine women, 67.05 ± 3.94 years) adults. We found a faster target identification in the repeated configurations with similar magnitude in the two age groups, which indicates that this contextual cueing effect remained intact even in the older participants. To shed light on the underlying mechanisms, we measured and compared the amplitude of three event-related potentials: N2pc, P3, and response-locked LRP. In the younger group, the larger contextual cueing effect (novel-minus-repeated RT difference) correlated positively with a larger difference in amplitude for repeated compared with novel configurations for both the N2pc and the P3 components, but there was no correlation with the response-locked lateralized readiness potential (rLRP) amplitude difference. However, in the older group, only the rLRP amplitude difference between novel and repeated configurations showed an enhancement with larger contextual cueing. These results suggest that different mechanisms are responsible for the contextual effect in the two age groups. It has both an early and an intermediate locus in younger adults: effective attentional allocation and successful stimulus categorization, or decision-making confidence are involved; while in older adults, a late locus was identified: a more efficient response organization led to a faster reaction.

Spatiotemporal neural dynamics of object recognition under uncertainty in humans

While there is a wealth of knowledge about core object recognition-our ability to recognize clear, high-contrast object images-how the brain accomplishes object recognition tasks under increased uncertainty remains poorly understood. We investigated the spatiotemporal neural dynamics underlying object recognition under increased uncertainty by combining MEG and 7 Tesla (7T) fMRI in humans during a threshold-level object recognition task. We observed an early, parallel rise of recognition-related signals across ventral visual and frontoparietal regions that preceded the emergence of category-related information. Recognition-related signals in ventral visual regions were best explained by a two-state representational format whereby brain activity bifurcated for recognized and unrecognized images. By contrast, recognition-related signals in frontoparietal regions exhibited a reduced representational space for recognized images, yet with sharper category information. These results provide a spatiotemporally resolved view of neural activity supporting object recognition under uncertainty, revealing a pattern distinct from that underlying core object recognition.

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.

Electrophysiological correlates of self-prioritization

Personally relevant stimuli exert a powerful influence on social cognition. What is not yet fully understood, however, is how early in the processing stream self-relevance influences decisional operations. Here we used a shape-label matching task in conjunction with electroencephalography and computational modeling to explore this issue. A theoretically important pattern of results was observed. First, a standard self-prioritization effect emerged indicating that responses to self-related items were faster and more accurate than responses to other-related stimuli. Second, a hierarchical drift diffusion model analysis revealed that this effect was underpinned by the enhanced uptake of evidence from self-related stimuli. Third, self-other discrimination during matching trials was observed at both early posterior N1 and late centro-parietal P3 components. Fourth, whereas the N1 was associated with the rate of information accumulation during decisional processing, P3 activity was linked with the evidential requirements of response selection. These findings elucidate the electrophysiological correlates of self-prioritization.

Informational Entropy Threshold as a Physical Mechanism for Explaining Tree-like Decision Making in Humans

While approaches based on physical grounds (such as the drift-diffusion model-DDM) have been exhaustively used in psychology and neuroscience to describe perceptual decision making in humans, similar approaches to complex situations, such as sequential (tree-like) decisions, are still scarce. For such scenarios that involve a reflective prospection of future options, we offer a plausible mechanism based on the idea that subjects can carry out an internal computation of the uncertainty about the different options available, which is computed through the corresponding Shannon entropy. When the amount of information gathered through sensory evidence is enough to reach a given threshold in the entropy, this will trigger the decision. Experimental evidence in favor of this entropy-based mechanism was provided by exploring human performance during navigation through a maze on a computer screen monitored with the help of eye trackers. In particular, our analysis allows us to prove that (i) prospection is effectively used by humans during such navigation tasks, and an indirect quantification of the level of prospection used is attainable; in addition, (ii) the distribution of decision times during the task exhibits power-law tails, a feature that our entropy-based mechanism is able to explain, unlike traditional (DDM-like) frameworks.

Hasty sensorimotor decisions rely on an overlap of broad and selective changes in motor activity

Humans and other animals are able to adjust their speed–accuracy trade-off (SAT) at will depending on the urge to act, favoring either cautious or hasty decision policies in different contexts. An emerging view is that SAT regulation relies on influences exerting broad changes on the motor system, tuning its activity up globally when hastiness is at premium. The present study aimed to test this hypothesis. A total of 50 participants performed a task involving choices between left and right index fingers, in which incorrect choices led either to a high or to a low penalty in 2 contexts, inciting them to emphasize either cautious or hasty policies. We applied transcranial magnetic stimulation (TMS) on multiple motor representations, eliciting motor-evoked potentials (MEPs) in 9 finger and leg muscles. MEP amplitudes allowed us to probe activity changes in the corresponding finger and leg representations, while participants were deliberating about which index to choose. Our data indicate that hastiness entails a broad amplification of motor activity, although this amplification was limited to the chosen side. On top of this effect, we identified a local suppression of motor activity, surrounding the chosen index representation. Hence, a decision policy favoring speed over accuracy appears to rely on overlapping processes producing a broad (but not global) amplification and a surround suppression of motor activity. The latter effect may help to increase the signal-to-noise ratio of the chosen representation, as supported by single-trial correlation analyses indicating a stronger differentiation of activity changes in finger representations in the hasty context.

Many have argued that the regulation of the speed-accuracy tradeoff relies on an urgency signal, which implements "collapsing decision thresholds" by tuning neural activity in a global manner in decision-related structures. This study indicates that the reality is more subtle, with several aspects of "urgency" being specifically targeted to particular corticospinal populations within the motor system.

Detection of tACS Entrainment Critically Depends on Epoch Length

Neural entrainment is the phase synchronization of a population of neurons to an external rhythmic stimulus such as applied in the context of transcranial alternating current stimulation (tACS). tACS can cause profound effects on human behavior. However, there remain a significant number of studies that find no behavioral effect when tACS is applied to human subjects. To investigate this discrepancy, we applied time sensitive phase lock value (PLV) based analysis to single unit data from the rat motor cortex. The analysis revealed that detection of neural entrainment depends critically on the epoch length within which spiking information is accumulated. Increasing the epoch length allowed for detection of progressively weaker levels of neural entrainment. Based on this single unit analysis, we hypothesized that tACS effects on human behavior would be more easily detected in a behavior paradigm which utilizes longer epoch lengths. We tested this by using tACS to entrain tremor in patients and healthy volunteers. When the behavioral data were analyzed using short duration epochs tremor entrainment effects were not detectable. However, as the epoch length was progressively increased, weak tremor entrainment became detectable. These results suggest that tACS behavioral paradigms that rely on the accumulation of information over long epoch lengths will tend to be successful at detecting behavior effects. However, tACS paradigms that rely on short epoch lengths are less likely to detect effects.

An initial 'snapshot' of sensory information biases the likelihood and speed of subsequent changes of mind

We often need to rapidly change our mind about perceptual decisions in order to account for new information and correct mistakes. One fundamental, unresolved question is whether information processed prior to a decision being made ('pre-decisional information') has any influence on the likelihood and speed with which that decision is reversed. We investigated this using a luminance discrimination task in which participants indicated which of two flickering greyscale squares was brightest. Following an initial decision, the stimuli briefly remained on screen, and participants could change their response. Using psychophysical reverse correlation, we examined how moment-to-moment fluctuations in stimulus luminance affected participants' decisions. This revealed that the strength of even the very earliest (pre-decisional) evidence was associated with the likelihood and speed of later changes of mind. To account for this effect, we propose an extended diffusion model in which an initial 'snapshot' of sensory information biases ongoing evidence accumulation.

Dynamic large-scale connectivity of intrinsic cortical oscillations supports adaptive listening in challenging conditions

In multi-talker situations, individuals adapt behaviorally to this listening challenge mostly with ease, but how do brain neural networks shape this adaptation? We here establish a long-sought link between large-scale neural communications in electrophysiology and behavioral success in the control of attention in difficult listening situations. In an age-varying sample of N = 154 individuals, we find that connectivity between intrinsic neural oscillations extracted from source-reconstructed electroencephalography is regulated according to the listener's goal during a challenging dual-talker task. These dynamics occur as spatially organized modulations in power-envelope correlations of alpha and low-beta neural oscillations during approximately 2-s intervals most critical for listening behavior relative to resting-state baseline. First, left frontoparietal low-beta connectivity (16 to 24 Hz) increased during anticipation and processing of a spatial-attention cue before speech presentation. Second, posterior alpha connectivity (7 to 11 Hz) decreased during comprehension of competing speech, particularly around target-word presentation. Connectivity dynamics of these networks were predictive of individual differences in the speed and accuracy of target-word identification, respectively, but proved unconfounded by changes in neural oscillatory activity strength. Successful adaptation to a listening challenge thus latches onto two distinct yet complementary neural systems: a beta-tuned frontoparietal network enabling the flexible adaptation to attentive listening state and an alpha-tuned posterior network supporting attention to speech.

Model-Based Planning Deficits in Compulsivity Are Linked to Faulty Neural Representations of Task Structure

Compulsive individuals have deficits in model-based planning, but the mechanisms that drive this have not been established. We examined two candidates-that compulsivity is linked to (1) an impaired model of the task environment and/or (2) an inability to engage cognitive control when making choices. To test this, 192 participants performed a two-step reinforcement learning task with concurrent EEG recordings, and we related the neural and behavioral data to their scores on a self-reported transdiagnostic dimension of compulsivity. To examine subjects' internal model of the task, we used established behavioral and neural responses to unexpected events [reaction time (RT) slowing, P300 wave, and parietal-occipital alpha band power] measured when an unexpected transition occurred. To assess cognitive control, we probed theta power at the time of initial choice. As expected, model-based planning was linked to greater behavioral (RT) and neural (alpha power, but not P300) sensitivity to rare transitions. Critically, the sensitivities of both RT and alpha to task structure were weaker in those high in compulsivity. This RT-compulsivity effect was tested and replicated in an independent pre-existing dataset (N = 1413). We also found that mid-frontal theta power at the time of choice was reduced in highly compulsive individuals though its relation to model-based planning was less pronounced. These data suggest that model-based planning deficits in compulsive individuals may arise, at least in part, from having an impaired representation of the environment, specifically how actions lead to future states.SIGNIFICANCE STATEMENT Compulsivity is linked to poorer performance on tasks that require model-based planning, but it is unclear what precise mechanisms underlie this deficit. Do compulsive individuals fail to engage cognitive control at the time of choice? Or do they have difficulty in building and maintaining an accurate representation of their environment, the foundation needed to behave in a goal-directed manner? With reaction time and EEG measures in 192 individuals who performed a two-step decision-making task, we found that compulsive individuals are less sensitive to surprising action-state transitions, where they slow down less and show less alpha band suppression following a rare transition. These findings implicate failures in maintaining an accurate model of the world in model-based planning deficits in compulsivity.

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.

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

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

The state of the onion: Grammatical aspect modulates object representation during event comprehension

The present ERP study assessed whether grammatical aspect is used as a cue in online event comprehension, in particular when reading about events in which an object is visually changed. While perfective aspect cues holistic event representations, including an event's endpoint, progressive aspect highlights intermediate phases of an event. In a 2 × 3 design, participants read SVO sentences describing a change-of-state event (e.g., to chop an onion), with grammatical Aspect manipulated (perfective "chopped" vs progressive "was chopping"). Thereafter, they saw a Picture of an object either having undergone substantial state-change (SC; a chopped onion), no state-change (NSC; an onion in its original state) or an unrelated object (U; a cactus, acting as control condition). Their task was to decide whether the object in the Picture was mentioned in the sentence. We focused on N400 modulation, with ERPs time-locked to picture onset. U pictures elicited an N400 response as expected, suggesting detection of categorical mismatches in object type. For SC and NSC pictures, a whole-head follow-up analysis revealed a P300, implying people were engaged in detailed evaluation of pictures of matching objects. SC pictures received most positive responses overall. Crucially, there was an interaction of Aspect and Picture: SC pictures resulted in a higher amplitude P300 after sentences in the perfective compared to the progressive. Thus, while the perfective cued for a holistic event representation, including the resultant state of the affected object (i.e., the chopped onion) constraining object representations online, the progressive defocused event completion and object-state change. Grammatical aspect thus guided online event comprehension by cueing the visual representation(s) of an object's state.

How Native Background Affects Human Performance in Real-World Visual Object Detection: An Event-Related Potential Study

Visual processing refers to the process of perceiving, analyzing, synthesizing, manipulating, transforming, and thinking of visual objects. It is modulated by both stimulus-driven and goal-directed factors and manifested in neural activities that extend from visual cortex to high-level cognitive areas. Extensive body of studies have investigated the neural mechanisms of visual object processing using synthetic or curated visual stimuli. However, synthetic or curated images generally do not accurately reflect the semantic links between objects and their backgrounds, and previous studies have not provided answers to the question of how the native background affects visual target detection. The current study bridged this gap by constructing a stimulus set of natural scenes with two levels of complexity and modulating participants' attention to actively or passively attend to the background contents. Behaviorally, the decision time was elongated when the background was complex or when the participants' attention was distracted from the detection task, and the object detection accuracy was decreased when the background was complex. The results of event-related potentials (ERP) analysis explicated the effects of scene complexity and attentional state on the brain responses in occipital and centro-parietal areas, which were suggested to be associated with varied attentional cueing and sensory evidence accumulation effects in different experimental conditions. Our results implied that efficient visual processing of real-world objects may involve a competition process between context and distractors that co-exist in the native background, and extensive attentional cues and fine-grained but semantically irrelevant scene information were perhaps detrimental to real-world object detection.

Similar EEG Activity Patterns During Experimentally-Induced Auditory Illusions and Veridical Perceptions

Hallucinations and illusions are two instances of perceptual experiences illustrating how perception might diverge from external sensory stimulations and be generated or altered based on internal brain states. The occurrence of these phenomena is not constrained to patient populations. Similar experiences can be elicited in healthy subjects by means of suitable experimental procedures. Studying the neural mechanisms underlying these experiences not only has the potential to expand our understanding of the brain's perceptual machinery but also of how it might get impaired. In the current study, we employed an auditory signal detection task to induce auditory illusions by presenting speech snippets at near detection threshold intensity embedded in noise. We investigated the neural correlates of auditory false perceptions by examining the EEG activity preceding the responses in speech absent (false alarm, FA) trials and comparing them to speech present (hit) trials. The results of the comparison of event-related potentials (ERPs) in the activation period vs. baseline revealed the presence of an early negativity (EN) and a late positivity (LP) similar in both hits and FAs, which were absent in misses, correct rejections (CR) and control button presses (BPs). We postulate that the EN and the LP might represent the auditory awareness negativity (AAN) and centro-parietal positivity (CPP) or P300, respectively. The event-related spectral perturbations (ERSPs) exhibited a common power enhancement in low frequencies (<4 Hz) in hits and FAs. The low-frequency power enhancement has been frequently shown to be accompanied with P300 as well as separately being a marker of perceptual awareness, referred to as slow cortical potentials (SCP). Furthermore, the comparison of hits vs. FAs showed a significantly higher LP amplitude and low frequency power in hits compared to FAs. Generally, the observed patterns in the present results resembled some of the major neural correlates associated with perceptual awareness in previous studies. Our findings provide evidence that the neural correlates associated with conscious perception, can be elicited in similar ways in both presence and absence of externally presented sensory stimuli. The present findings did not reveal any pre-stimulus alpha and beta modulations distinguishing conscious vs. unconscious perceptions.

Implicit anticipation of probabilistic regularities: Larger CNV emerges for unpredictable events

Anticipation of upcoming events plays a crucial role in automatic behaviors. It is, however, still unclear whether the event-related brain potential (ERP) markers of anticipation could track the implicit acquisition of probabilistic regularities that can be considered as building blocks of automatic behaviors. Therefore, in a four-choice reaction time (RT) task performed by young adults (N = 36), the contingent negative variation (CNV) as an ERP marker of anticipation was measured from the onset of a cue stimulus until the presentation of a target stimulus. Due to the probability structure of the task, target stimuli were either predictable or unpredictable, but this was unknown to participants. The cue did not contain predictive information on the upcoming target. Results showed that the CNV amplitude during response preparation was larger before the unpredictable than before the predictable target stimuli. In addition, although RTs increased, the P3 amplitude decreased for the unpredictable as compared with the predictable target stimuli, possibly due to the stronger response preparation that preceded stimulus presentation. These results suggest that enhanced attentional resources are allocated to the implicit anticipation and processing of unpredictable events. This might originate from the formation of internal models on the probabilistic regularities of the stimulus stream, which primarily facilitates the processing of predictable events. Overall, we provide ERP evidence that supports the role of implicit anticipation and predictive processes in the acquisition of probabilistic regularities.

Neural surprise in somatosensory Bayesian learning

Tracking statistical regularities of the environment is important for shaping human behavior and perception. Evidence suggests that the brain learns environmental dependencies using Bayesian principles. However, much remains unknown about the employed algorithms, for somesthesis in particular. Here, we describe the cortical dynamics of the somatosensory learning system to investigate both the form of the generative model as well as its neural surprise signatures. Specifically, we recorded EEG data from 40 participants subjected to a somatosensory roving-stimulus paradigm and performed single-trial modeling across peri-stimulus time in both sensor and source space. Our Bayesian model selection procedure indicates that evoked potentials are best described by a non-hierarchical learning model that tracks transitions between observations using leaky integration. From around 70ms post-stimulus onset, secondary somatosensory cortices are found to represent confidence-corrected surprise as a measure of model inadequacy. Indications of Bayesian surprise encoding, reflecting model updating, are found in primary somatosensory cortex from around 140ms. This dissociation is compatible with the idea that early surprise signals may control subsequent model update rates. In sum, our findings support the hypothesis that early somatosensory processing reflects Bayesian perceptual learning and contribute to an understanding of its underlying mechanisms.

Our environment features statistical regularities, such as a drop of rain predicting imminent rainfall. Despite the importance for behavior and survival, much remains unknown about how these dependencies are learned, particularly for somatosensation. As surprise signalling about novel observations indicates a mismatch between one’s beliefs and the world, it has been hypothesized that surprise computation plays an important role in perceptual learning. By analyzing EEG data from human participants receiving sequences of tactile stimulation, we compare different formulations of surprise and investigate the employed underlying learning model. Our results indicate that the brain estimates transitions between observations. Furthermore, we identified different signatures of surprise computation and thereby provide a dissociation of the neural correlates of belief inadequacy and belief updating. Specifically, early surprise responses from around 70ms were found to signal the need for changes to the model, with encoding of its subsequent updating occurring from around 140ms. These results provide insights into how somatosensory surprise signals may contribute to the learning of environmental statistics.

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

To interact successfully with diverse sensory environments, we must adapt our decision processes to account for time constraints and prior probabilities. The full set of decision-process parameters that undergo such flexible adaptation has proven to be difficult to establish using simplified models that are based on behaviour alone. Here, we utilize well-characterized human neurophysiological signatures of decision formation to construct and constrain a build-to-threshold decision model with multiple build-up (evidence accumulation and urgency) and delay components (pre- and post-decisional). The model indicates that all of these components were adapted in distinct ways and, in several instances, fundamentally differ from the conclusions of conventional diffusion modelling. The neurally informed model outcomes were corroborated by independent neural decision signal observations that were not used in the model's construction. These findings highlight the breadth of decision-process parameters that are amenable to strategic adjustment and the value in leveraging neurophysiological measurements to quantify these adjustments.

Semantic P600-but not N400-effects index crosslinguistic variability in speakers' expectancies for expression of motion

The expression of motion shows strong crosslinguistic variability; however, less is known about speakers' expectancies for lexicalizations of motion at the neural level. We examined event-related brain potentials (ERPs) in native English or Spanish speakers while they read grammatical sentences describing animations involving manner and path components of motion that did or did not violate language-specific patterns of expression. ERPs demonstrated different expectancies between speakers: Spanish speakers showed higher expectancies for motion verbs to encode path and English speakers showed higher expectancies for motion verbs to encode manner followed by a secondary path expression. Interestingly, grammatical but infrequent motion expressions (manner verbs in Spanish, path verbs and secondary manner expressions in English) elicited semantic P600 rather than the expected N400 effects-with or without post-N400 positivities-that are typically associated with semantic processing. Overall, our findings provide the first empirical evidence for the effect of crosslinguistic variation in processing motion event descriptions at the neural level.

Exploring the Effects of an Acute Dose of Antipsychotic Medication on Motivation-mediated BOLD Activity Using fMRI and a Perceptual Decision-making Task

The left inferior frontal gyrus and the bilateral ventral striatum are thought to be involved in motivation-mediated decision-making. Antipsychotics may influence this relationship, and atypical antipsychotics improve secondary negative symptoms in schizophrenia, such as loss of motivation, although the acute effects of pharmacological medication on motivation are not fully understood. In this single-blinded, randomized controlled trial, 49 healthy volunteers were randomized into three groups to receive a single dose of haloperidol, aripiprazole or placebo. Between 4.0 and 5.6 h later, participant's brain blood-oxygen-level dependent (BOLD) activity was recorded using functional magnetic resonance imaging (fMRI) while completing a perceptual decision-making fMRI task consisting of one neutral and one motivated condition. Response bias, reflecting the participant's willingness to say that the target stimulus is present, was calculated using signal detection theory. Concurrent with widespread changes in BOLD signal in the motivated vs. neutral condition, a less conservative, mathematically optimal response bias was observed in the motivated condition across the whole sample. Within-group differences in BOLD signal in the left inferior frontal gyrus and bilateral ventral striatum were observed between conditions in the aripiprazole and haloperidol groups, but not in the placebo group. No robust between-group differences in brain activity in the left inferior frontal gyrus or the bilateral ventral striatum were found. Overall, we found no robust evidence for an effect of either aripiprazole or haloperidol on motivationally mediated behavior. An interesting pattern of correlations possibly related to pharmacologically induced alterations in the dopamine system was observed, although findings remain inconclusive and must be replicated in larger samples.

Perceptual Decision-Making in Children: Age-Related Differences and EEG Correlates

Children make faster and more accurate decisions about perceptual information as they get older, but it is unclear how different aspects of the decision-making process change with age. Here, we used hierarchical Bayesian diffusion models to decompose performance in a perceptual task into separate processing components, testing age-related differences in model parameters and links to neural data. We collected behavioural and EEG data from 96 6- to 12-year-old children and 20 adults completing a motion discrimination task. We used a component decomposition technique to identify two response-locked EEG components with ramping activity preceding the response in children and adults: one with activity that was maximal over centro-parietal electrodes and one that was maximal over occipital electrodes. Younger children had lower drift rates (reduced sensitivity), wider boundary separation (increased response caution) and longer non-decision times than older children and adults. Yet, model comparisons suggested that the best model of children's data included age effects only on drift rate and boundary separation (not non-decision time). Next, we extracted the slope of ramping activity in our EEG components and covaried these with drift rate. The slopes of both EEG components related positively to drift rate, but the best model with EEG covariates included only the centro-parietal component. By decomposing performance into distinct components and relating them to neural markers, diffusion models have the potential to identify the reasons why children with developmental conditions perform differently to typically developing children and to uncover processing differences inapparent in the response time and accuracy data alone.

Human stereoEEG recordings reveal network dynamics of decision-making in a rule-switching task

The processing steps that lead up to a decision, i.e., the transformation of sensory evidence into motor output, are not fully understood. Here, we combine stereoEEG recordings from the human cortex, with single-lead and time-resolved decoding, using a wide range of temporal frequencies, to characterize decision processing during a rule-switching task. Our data reveal the contribution of rostral inferior parietal lobule (IPL) regions, in particular PFt, and the parietal opercular regions in decision processing and demonstrate that the network representing the decision is common to both task rules. We reconstruct the sequence in which regions engage in decision processing on single trials, thereby providing a detailed picture of the network dynamics involved in decision-making. The reconstructed timeline suggests that the supramarginal gyrus in IPL links decision regions in prefrontal cortex with premotor regions, where the motor plan for the response is elaborated.

Confidence drives a neural confirmation bias

A prominent source of polarised and entrenched beliefs is confirmation bias, where evidence against one's position is selectively disregarded. This effect is most starkly evident when opposing parties are highly confident in their decisions. Here we combine human magnetoencephalography (MEG) with behavioural and neural modelling to identify alterations in post-decisional processing that contribute to the phenomenon of confirmation bias. We show that holding high confidence in a decision leads to a striking modulation of post-decision neural processing, such that integration of confirmatory evidence is amplified while disconfirmatory evidence processing is abolished. We conclude that confidence shapes a selective neural gating for choice-consistent information, reducing the likelihood of changes of mind on the basis of new information. A central role for confidence in shaping the fidelity of evidence accumulation indicates that metacognitive interventions may help ameliorate this pervasive cognitive bias.

Using Transcranial Magnetic Stimulation to Test a Network Model of Perceptual Decision Making in the Human Brain

Previous research has suggested that the lateral occipital cortex (LOC) is involved with visual decision making, and specifically with the accumulation of information leading to a decision. In humans, this research has been primarily based on imaging and electroencephalography (EEG), and as such only correlational. One line of such research has led to a model of three spatially distributed brain networks that activate in temporal sequence to enable visual decision-making. The model predicted that disturbing neural processing in the LOC at a specific latency would slow object decision-making, increasing reaction time (RT) in a difficult discrimination task. We utilized transcranial magnetic stimulation (TMS) to test this prediction, perturbing LOC beginning at 400 ms post-stimulus onset, a time in the model corresponding to LOC activation at a particular difficulty level, with the expectation of increased RT. Thirteen healthy adults participated in two TMS sessions in which left and right LOC were stimulated separately utilizing neuronavigation and robotic coil guidance. Participants performed a two-alternative forced-choice task selecting whether a car or face was present on each trial amidst visual noise pre-tested to approximate a 75% accuracy level. In an effort to disrupt processing, pairs of TMS pulses separated by 50 ms were presented at one of five stimulus onset asynchronies (SOAs): -200, 200, 400, 450, or 500 ms. Behavioral performance differed systematically across SOAs for RT and accuracy measures. As predicted, TMS at 400 ms resulted in a significant slowing of RT. TMS delivered at -200 ms resulted in faster RT, indicating early stimulation may result in priming and performance enhancement. Use of TMS thus causally demonstrated the involvement of LOC in this task, and more broadly with perceptual decision-making; additionally, it demonstrated the role of TMS in testing well-developed neural models of perceptual processing.

The role of premature evidence accumulation in making difficult perceptual decisions under temporal uncertainty

The computations and neural processes underpinning decision making have primarily been investigated using highly simplified tasks in which stimulus onsets cue observers to start accumulating choice-relevant information. Yet, in daily life we are rarely afforded the luxury of knowing precisely when choice-relevant information will appear. Here, we examined neural indices of decision formation while subjects discriminated subtle stimulus feature changes whose timing relative to stimulus onset ('foreperiod') was uncertain. Joint analysis of behavioural error patterns and neural decision signal dynamics indicated that subjects systematically began the accumulation process before any informative evidence was presented, and further, that accumulation onset timing varied systematically as a function of the foreperiod of the preceding trial. These results suggest that the brain can adjust to temporal uncertainty by strategically modulating accumulation onset timing according to statistical regularities in the temporal structure of the sensory environment with particular emphasis on recent experience.

Tracking the implicit acquisition of nonadjacent transitional probabilities by ERPs

The implicit acquisition of complex probabilistic regularities has been found to be crucial in numerous automatized cognitive abilities, including language processing and associative learning. However, it has not been completely elucidated how the implicit extraction of second-order nonadjacent transitional probabilities is reflected by neurophysiological processes. Therefore, this study investigated the sensitivity of event-related brain potentials (ERPs) to these probabilistic regularities embedded in a sequence of visual stimuli without providing explicit information on the structure of the stimulus stream. Healthy young adults (N = 32) performed a four-choice RT task that included a sequential regularity between nonadjacent trials yielding a complex transitional probability structure. ERPs were measured relative to both stimulus and response onset. RTs indicated the rapid acquisition of the sequential regularity and the transitional probabilities. The acquisition process was also tracked by the stimulus-locked and response-locked P3 component: The P3 peak was larger for the sequence than for the random stimuli, while the late P3 was larger for less probable than for more probable short-range relations among the random stimuli. According to the RT and P3 effects, sensitivity to the sequential regularity is assumed to be supported by the initial sensitivity to the transitional probabilities. These results suggest that stimulus-response contingencies on the probabilistic regularities of the ongoing stimulus context are implicitly mapped and constantly revised. Overall, this study (1) highlights the role of predictive processes during implicit memory formation, and (2) delineates a potential to gain further insight into the dynamics of implicit acquisition processes.

Revisiting the relationship between the P3b and working memory updating

The P3b is an extensively studied neurophysiological phenomenon that is predominantly explained in the cognitive neuroscience literature as reflecting context updating, presumably in working memory (WM). Despite the prevalence and influence of the context updating hypothesis, direct empirical support for the role of WM updating in eliciting the P3b is still missing. The present study was designed to address the empirical gap in understanding the functional role of P3b in general, and specifically in relation to WM updating. A mass-univariate approach was used to test the unique contribution of WM updating, categorization, and stimulus probability to the P3b. The results indicated that the P3b is only modulated by the categorization process, a finding that challenges the WM updating hypothesis. Taken together these results, we suggest that the P3b reflects a WM guided target identification mechanism, which operates as part of a goal-directed learning strategy.

Speed-Accuracy Tradeoffs in Brain and Behavior: Testing the Independence of P300 and N400 Related Processes in Behavioral Responses to Sentence Categorization

Although the N400 was originally discovered in a paradigm designed to elicit a P300 (Kutas and Hillyard, 1980), its relationship with the P300 and how both overlapping event-related potentials (ERPs) determine behavioral profiles is still elusive. Here we conducted an ERP (N = 20) and a multiple-response speed-accuracy tradeoff (SAT) experiment (N = 16) on distinct participant samples using an antonym paradigm (The opposite of black is white/nice/yellow with acceptability judgment). We hypothesized that SAT profiles incorporate processes of task-related decision-making (P300) and stimulus-related expectation violation (N400). We replicated previous ERP results (Roehm et al., 2007): in the correct condition (white), the expected target elicits a P300, while both expectation violations engender an N400 [reduced for related (yellow) vs. unrelated targets (nice)]. Using multivariate Bayesian mixed-effects models, we modeled the P300 and N400 responses simultaneously and found that correlation between residuals and subject-level random effects of each response window was minimal, suggesting that the components are largely independent. For the SAT data, we found that antonyms and unrelated targets had a similar slope (rate of increase in accuracy over time) and an asymptote at ceiling, while related targets showed both a lower slope and a lower asymptote, reaching only approximately 80% accuracy. Using a GLMM-based approach (Davidson and Martin, 2013), we modeled these dynamics using response time and condition as predictors. Replacing the predictor for condition with the averaged P300 and N400 amplitudes from the ERP experiment, we achieved identical model performance. We then examined the piecewise contribution of the P300 and N400 amplitudes with partial effects (see Hohenstein and Kliegl, 2015). Unsurprisingly, the P300 amplitude was the strongest contributor to the SAT-curve in the antonym condition and the N400 was the strongest contributor in the unrelated condition. In brief, this is the first demonstration of how overlapping ERP responses in one sample of participants predict behavioral SAT profiles of another sample. The P300 and N400 reflect two independent but interacting processes and the competition between these processes is reflected differently in behavioral parameters of speed and accuracy.

Centro-parietal EEG potentials index subjective evidence and confidence during perceptual decision making

Recent studies suggest that a centro-parietal positivity (CPP) in the EEG signal tracks the absolute (unsigned) strength of accumulated evidence for choices that require the integration of noisy sensory input. Here, we investigated whether the CPP might also reflect the evidence for decisions based on a quantitative comparison between two sequentially presented stimuli (a signed quantity). We recorded EEG while participants decided whether the latter of two vibrotactile frequencies was higher or lower than the former in six variants of this task (n = 116). To account for biases in sequential comparisons, we applied a behavioral model based on Bayesian inference that estimated subjectively perceived frequency differences. Immediately after the second stimulus, parietal ERPs reflected the signed value of subjectively perceived differences and afterwards their absolute value. Strikingly, the modulation by signed difference was evident in trials without any objective evidence for either choice and correlated with choice-selective premotor beta band amplitudes. Modulations by the absolute strength of subjectively perceived evidence - a direct indicator of task difficulty - exhibited all features of statistical decision confidence. Together, our data suggest that parietal EEG signals first index subjective evidence, and later include a measure of confidence in the context of perceptual decision making.

Catecholamine Modulation of Evidence Accumulation during Perceptual Decision Formation: A Randomized Trial

Recent behavioral modeling and pupillometry studies suggest that neuromodulatory arousal systems play a role in regulating decision formation but neurophysiological support for these observations is lacking. We employed a randomized, double-blinded, placebo-controlled, crossover design to probe the impact of pharmacological enhancement of catecholamine levels on perceptual decision-making. Catecholamine levels were manipulated using the clinically relevant drugs methylphenidate and atomoxetine, and their effects were compared with those of citalopram and placebo. Participants performed a classic EEG oddball paradigm that elicits the P3b, a centro-parietal potential that has been shown to trace evidence accumulation, under each of the four drug conditions. We found that methylphenidate and atomoxetine administration shortened RTs to the oddball targets. The neural basis of this behavioral effect was an earlier P3b peak latency, driven specifically by an increase in its buildup rate without any change in its time of onset or peak amplitude. This study provides neurophysiological evidence for the catecholaminergic enhancement of a discrete aspect of human decision-making, that is, evidence accumulation. Our results also support theoretical accounts suggesting that catecholamines may enhance cognition via increases in neural gain.

Risk-taking bias in human decision-making is encoded via a right-left brain push-pull system

A person's decisions vary even when options stay the same, like when a gambler changes bets despite constant odds of winning. Internal bias (e.g., emotion) contributes to this variability and is shaped by past outcomes, yet its neurobiology during decision-making is not well understood. To map neural circuits encoding bias, we administered a gambling task to 10 participants implanted with intracerebral depth electrodes in cortical and subcortical structures. We predicted the variability in betting behavior within and across patients by individual bias, which is estimated through a dynamical model of choice. Our analysis further revealed that high-frequency activity increased in the right hemisphere when participants were biased toward risky bets, while it increased in the left hemisphere when participants were biased away from risky bets. Our findings provide electrophysiological evidence that risk-taking bias is a lateralized push-pull neural system governing counterintuitive and highly variable decision-making in humans.

Decoding vibrotactile choice independent of stimulus order and saccade selection during sequential comparisons

Decision-making in the somatosensory domain has been intensively studied using vibrotactile frequency discrimination tasks. Results from human and monkey electrophysiological studies from this line of research suggest that perceptual choices are encoded within a sensorimotor network. These findings, however, rely on experimental settings in which perceptual choices are inextricably linked to sensory and motor components of the task. Here, we devised a novel version of the vibrotactile frequency discrimination task with saccade responses which has the crucial advantage of decoupling perceptual choices from sensory and motor processes. We recorded human fMRI data from 32 participants while they performed the task. Using a whole-brain searchlight multivariate classification technique, we identify the left lateral prefrontal cortex and the oculomotor system, including the bilateral frontal eye fields (FEF) and intraparietal sulci, as representing vibrotactile choices. Moreover, we show that the decoding accuracy of choice information in the right FEF correlates with behavioral performance. Not only are these findings in remarkable agreement with previous work, they also provide novel fMRI evidence for choice coding in human oculomotor regions, which is not limited to saccadic decisions, but pertains to contexts where choices are made in a more abstract form.

Reconciling age-related changes in behavioural and neural indices of human perceptual decision-making

Ageing impacts on decision-making behaviour across a range of cognitive tasks and scenarios. Computational modelling has proved valuable in providing mechanistic interpretations of these age-related differences; however, the extent to which model parameter differences accurately reflect changes to the underlying neural computations remains unclear. Here, we report that age-related effects on neural signatures of decision formation are inconsistent with behavioural fits derived from a prominent accumulation-to-bound model. Most notably, model-predicted bound differences were absent neurophysiologically. However, constraining the model to match the decision-predictive elements of the brain signals provided more parsimonious fits to behaviour and generated predictions regarding the neural data that were empirically validated. These included a task-dependent slowing of evidence accumulation among older adults and reduced between-trial accumulation rate variability, which was linked to enhanced attentional engagement. Our findings highlight how combining neurophysiological measurements with computational modelling can yield unique insights into group differences in neural decision mechanisms.

Bridging Neural and Computational Viewpoints on Perceptual Decision-Making

Sequential sampling models have provided a dominant theoretical framework guiding computational and neurophysiological investigations of perceptual decision-making. While these models share the basic principle that decisions are formed by accumulating sensory evidence to a bound, they come in many forms that can make similar predictions of choice behaviour despite invoking fundamentally different mechanisms. The identification of neural signals that reflect some of the core computations underpinning decision formation offers new avenues for empirically testing and refining key model assumptions. Here, we highlight recent efforts to explore these avenues and, in so doing, consider the conceptual and methodological challenges that arise when seeking to infer decision computations from complex neural data.

Decisions are expedited through multiple neural adjustments spanning the sensorimotor hierarchy

When decisions are made under speed pressure, "urgency" signals elevate neural activity toward action-triggering thresholds independent of the sensory evidence, thus incurring a cost to choice accuracy. While urgency signals have been observed in brain circuits involved in preparing actions, their influence at other levels of the sensorimotor pathway remains unknown. We used a novel contrast-comparison paradigm to simultaneously trace the dynamics of sensory evidence encoding, evidence accumulation, motor preparation, and muscle activation in humans. Results indicate speed pressure impacts multiple sensorimotor levels but in crucially distinct ways. Evidence-independent urgency was applied to cortical action-preparation signals and downstream muscle activation, but not directly to upstream levels. Instead, differential sensory evidence encoding was enhanced in a way that partially countered the negative impact of motor-level urgency on accuracy, and these opposing sensory-boost and motor-urgency effects had knock-on effects on the buildup and pre-response amplitude of a motor-independent representation of cumulative evidence.

Single Trial Plasticity in Evidence Accumulation Underlies Rapid Recalibration to Asynchronous Audiovisual Speech

Asynchronous arrival of audiovisual information at the peripheral sensory organs is a ubiquitous property of signals in the natural environment due to differences in the propagation time of light and sound. As these cues are constantly changing their distance from the observer, rapid adaptation to asynchronies is crucial for their appropriate integration. We investigated the neural basis of rapid recalibration to asynchronous audiovisual speech in humans using a combination of psychophysics, drift diffusion modeling, and electroencephalography (EEG). Consistent with previous reports, we found that perception of audiovisual temporal synchrony depends on the temporal ordering of the previous trial. Drift diffusion modelling indicated that this recalibration effect is well accounted for by changes in the rate of evidence accumulation (i.e. drift rate). Neural responses as indexed via evoked potentials were similarly found to vary based on the temporal ordering of the previous trial. Within and across subject correlations indicated that the observed changes in drift rate and the modulation of evoked potential magnitude were related. These results indicate that the rate and direction of evidence accumulation are affected by immediate sensory history and that these changes contribute to single trial recalibration to audiovisual temporal asynchrony.

Dynamic Interplay of Value and Sensory Information in High-Speed Decision Making

In dynamic environments, split-second sensorimotor decisions must be prioritized according to potential payoffs to maximize overall rewards. The impact of relative value on deliberative perceptual judgments has been examined extensively [1-6], but relatively little is known about value-biasing mechanisms in the common situation where physical evidence is strong but the time to act is severely limited. In prominent decision models, a noisy but statistically stationary representation of sensory evidence is integrated over time to an action-triggering bound, and value-biases are affected by starting the integrator closer to the more valuable bound. Here, we show significant departures from this account for humans making rapid sensory-instructed action choices. Behavior was best explained by a simple model in which the evidence representation-and hence, rate of accumulation-is itself biased by value and is non-stationary, increasing over the short decision time frame. Because the value bias initially dominates, the model uniquely predicts a dynamic "turn-around" effect on low-value cues, where the accumulator first launches toward the incorrect action but is then re-routed to the correct one. This was clearly exhibited in electrophysiological signals reflecting motor preparation and evidence accumulation. Finally, we construct an extended model that implements this dynamic effect through plausible sensory neural response modulations and demonstrate the correspondence between decision signal dynamics simulated from a behavioral fit of that model and the empirical decision signals. Our findings suggest that value and sensory information can exert simultaneous and dynamically countervailing influences on the trajectory of the accumulation-to-bound process, driving rapid, sensory-guided actions.

Antagonistic Interactions Between Microsaccades and Evidence Accumulation Processes During Decision Formation

Despite their small size, microsaccades can impede stimulus detections if executed at inopportune times. Although it has been shown that microsaccades evoke both inhibitory and excitatory responses across different visual regions, their impact on the higher-level neural decision processes that bridge sensory responses to action selection has yet to be examined. Here, we show that when human observers monitor stimuli for subtle feature changes, the occurrence of microsaccades long after (up to 800 ms) change onset predicts slower reaction times and this is accounted for by momentary suppression of neural signals at each key stage of decision formation: visual evidence encoding, evidence accumulation, and motor preparation. Our data further reveal that, independent of the timing of the change events, the onset of neural decision formation coincides with a systematic inhibition of microsaccade production, persisting until the perceptual report is executed. Our combined behavioral and neural measures highlight antagonistic interactions between microsaccade occurrence and evidence accumulation during visual decision-making tasks.SIGNIFICANCE STATEMENT When fixating on a location in space, we frequently make tiny eye movements called microsaccades. In the present study, we show that these microsaccades impede our ability to make perceptual decisions about visual stimuli and this impediment specifically occurs via the disruption of several processing levels of the sensorimotor network: the encoding of visual evidence itself, the accumulation of visual evidence toward a response, and effector-selective motor preparation. Furthermore, we show that the production of microsaccades is inhibited during the perceptual decision, possibly as a counteractive measure to mitigate their negative effect on behavior in this context. The combined behavioral and neural measures used in this study provide strong and novel evidence for the interaction of fixational eye movements and the perceptual decision-making process.

The impact of natural aging on computational and neural indices of perceptual decision making: A review

It is well established that natural aging negatively impacts on a wide variety of cognitive functions and research has sought to identify core neural mechanisms that may account for these disparate changes. A central feature of any cognitive task is the requirement to translate sensory information into an appropriate action - a process commonly known as perceptual decision making. While computational, psychophysical, and neurophysiological research has made substantial progress in establishing the key computations and neural mechanisms underpinning decision making, it is only relatively recently that this knowledge has begun to be applied to research on aging. The purpose of this review is to provide an overview of this work which is beginning to offer new insights into the core psychological processes that mediate age-related cognitive decline in adults aged 65 years and over. Mathematical modelling studies have consistently reported that older adults display longer non-decisional processing times and implement more conservative decision policies than their younger counterparts. However, there are limits on what we can learn from behavioural modeling alone and neurophysiological analyses can play an essential role in empirically validating model predictions and in pinpointing the precise neural mechanisms that are impacted by aging. Although few studies to date have explicitly examined correspondences between computational models and neural data with respect to cognitive aging, neurophysiological studies have already highlighted age-related changes at multiple levels of the sensorimotor hierarchy that are likely to be consequential for decision making behaviour. Here, we provide an overview of this literature and suggest some future directions for the field.

Adaptive History Biases Result from Confidence-Weighted Accumulation of past Choices

Perceptual decision-making is biased by previous events, including the history of preceding choices: observers tend to repeat (or alternate) their judgments of the sensory environment more often than expected by chance. Computational models postulate that these so-called choice history biases result from the accumulation of internal decision signals across trials. Here, we provide psychophysical evidence for such a mechanism and its adaptive utility. Male and female human observers performed different variants of a challenging visual motion discrimination task near psychophysical threshold. In a first experiment, we decoupled categorical perceptual choices and motor responses on a trial-by-trial basis. Choice history bias was explained by previous perceptual choices, not motor responses, highlighting the importance of internal decision signals in action-independent formats. In a second experiment, observers performed the task in stimulus environments containing different levels of autocorrelation and providing no external feedback about choice correctness. Despite performing under overall high levels of uncertainty, observers adjusted both the strength and the sign of their choice history biases to these environments. When stimulus sequences were dominated by either repetitions or alternations, the individual degree of this adjustment of history bias was about as good a predictor of individual performance as individual perceptual sensitivity. The history bias adjustment scaled with two proxies for observers' confidence about their previous choices (accuracy and reaction time). Together, our results are consistent with the idea that action-independent, confidence-modulated decision variables are accumulated across choices in a flexible manner that depends on decision-makers' model of their environment.

SIGNIFICANCE STATEMENT Decisions based on sensory input are often influenced by the history of one's preceding choices, manifesting as a bias to systematically repeat (or alternate) choices. We here provide support for the idea that such choice history biases arise from the context-dependent accumulation of a quantity referred to as the decision variable: the variable's sign dictates the choice and its magnitude the confidence about choice correctness. We show that choices are accumulated in an action-independent format and a context-dependent manner, weighted by the confidence about their correctness. This confidence-weighted accumulation of choices enables decision-makers to flexibly adjust their behavior to different sensory environments. The bias adjustment can be as important for optimizing performance as one's sensitivity to the momentary sensory input.

Simple reaction times to cyclopean stimuli reveal that the binocular system is tuned to react faster to near than to far objects

Binocular depth perception is an important mechanism to segregate the visual scene for mapping relevant objects in our environment. Convergent evidence from psychophysical and neurophysiological studies have revealed asymmetries between the processing of near (crossed) and far (uncrossed) binocular disparities. The aim of the present study was to test if near or far objects are processed faster and with higher contrast sensitivity in the visual system. We therefore measured the relationship between binocular disparity and simple reaction time (RT) as well as contrast gain based on the contrast-RT function in young healthy adults. RTs were measured to suddenly appearing cyclopean target stimuli, which were checkerboard patterns encoded by depth in dynamic random dot stereograms (DRDS). The DRDS technique allowed us to selectively study the stereoscopic processing system by eliminating all monocular cues. The results showed that disparity and contrast had significant effects on RTs. RTs as a function of disparity followed a U-shaped tuning curve indicating an optimum at around 15 arc min, where RTs were minimal. Surprisingly, the disparity tuning of RT was much less pronounced for far disparities. At the optimal disparity, we measured advantages of about 80 ms and 30 ms for near disparities at low (10%) and high (90%) contrasts, respectively. High contrast always reduced RTs as well as the disparity dependent differences. Furthermore, RT-based contrast gains were higher for near disparities in the range of disparities where RTs were the shortest. These results show that the sensitivity of the human visual system is biased for near versus far disparities and near stimuli can result in faster motor responses, probably because they bear higher biological relevance.

Biased and unbiased perceptual decision-making on vocal emotions

Perceptual decision-making on emotions involves gathering sensory information about the affective state of another person and forming a decision on the likelihood of a particular state. These perceptual decisions can be of varying complexity as determined by different contexts. We used functional magnetic resonance imaging and a region of interest approach to investigate the brain activation and functional connectivity behind two forms of perceptual decision-making. More complex unbiased decisions on affective voices recruited an extended bilateral network consisting of the posterior inferior frontal cortex, the orbitofrontal cortex, the amygdala, and voice-sensitive areas in the auditory cortex. Less complex biased decisions on affective voices distinctly recruited the right mid inferior frontal cortex, pointing to a functional distinction in this region following decisional requirements. Furthermore, task-induced neural connectivity revealed stronger connections between these frontal, auditory, and limbic regions during unbiased relative to biased decision-making on affective voices. Together, the data shows that different types of perceptual decision-making on auditory emotions have distinct patterns of activations and functional coupling that follow the decisional strategies and cognitive mechanisms involved during these perceptual decisions.