Domain general mechanisms of perceptual decision making in human cortex

Flexible adaptation of task-positive brain networks predicts efficiency of evidence accumulation

Efficiency of evidence accumulation (EEA), an individual's ability to selectively gather goal-relevant information to make adaptive choices, is thought to be a key neurocomputational mechanism associated with cognitive functioning and transdiagnostic risk for psychopathology. However, the neural basis of individual differences in EEA is poorly understood, especially regarding the role of largescale brain network dynamics. We leverage data from 5198 participants from the Human Connectome Project and Adolescent Brain Cognitive Development Study to demonstrate a strong association between EEA and flexible adaptation to cognitive demand in the "task-positive" frontoparietal and dorsal attention networks. Notably, individuals with higher EEA displayed divergent task-positive network activation across n-back task conditions: higher activation under high cognitive demand (2-back) and lower activation under low demand (0-back). These findings suggest that brain networks' flexible adaptation to cognitive demands is a key neural underpinning of EEA.

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.

Encoding of continuous perceptual choices in human early visual cortex

Introduction

Research on the neural mechanisms of perceptual decision-making has typically focused on simple categorical choices, say between two alternative motion directions. Studies on such discrete alternatives have often suggested that choices are encoded either in a motor-based or in an abstract, categorical format in regions beyond sensory cortex.

Methods

In this study, we used motion stimuli that could vary anywhere between 0° and 360° to assess how the brain encodes choices for features that span the full sensory continuum. We employed a combination of neuroimaging and encoding models based on Gaussian process regression to assess how either stimuli or choices were encoded in brain responses.

Results

We found that single-voxel tuning patterns could be used to reconstruct the trial-by-trial physical direction of motion as well as the participants' continuous choices. Importantly, these continuous choice signals were primarily observed in early visual areas. The tuning properties in this region generalized between choice encoding and stimulus encoding, even for reports that reflected pure guessing.

Discussion

We found only little information related to the decision outcome in regions beyond visual cortex, such as parietal cortex, possibly because our task did not involve differential motor preparation. This could suggest that decisions for continuous stimuli take can place already in sensory brain regions, potentially using similar mechanisms to the sensory recruitment in visual working memory.

Dissociation and integration of outcome and state uncertainty signals in cognitive control

Signals related to uncertainty are frequently observed in regions of the cognitive control network, including anterior cingulate/medial prefrontal cortex (ACC/mPFC), dorsolateral prefrontal cortex (dlPFC), and anterior insular cortex. Uncertainty generally refers to conditions in which decision variables may assume multiple possible values and can arise at multiple points in the perception-action cycle, including sensory input, inferred states of the environment, and the consequences of actions. These sources of uncertainty are frequently correlated: noisy input can lead to unreliable estimates of the state of the environment, with consequential influences on action selection. Given this correlation amongst various sources of uncertainty, dissociating the neural structures underlying their estimation presents an ongoing issue: a region associated with uncertainty related to outcomes may estimate outcome uncertainty itself, or it may reflect a cascade effect of state uncertainty on outcome estimates. In this study, we derive signals of state and outcome uncertainty from mathematical models of risk and observe regions in the cognitive control network whose activity is best explained by signals related to state uncertainty (anterior insula), outcome uncertainty (dlPFC), as well as regions that appear to integrate the two (ACC/mPFC).

Association between somatosensory sensitivity and regional gray matter volume in healthy young volunteers: a voxel-based morphometry study

Two-point discrimination (2PD) test reflects somatosensory spatial discrimination ability, but evidence on the relationship between 2PD and cortical gray matter (GM) volume is limited. This study aimed to analyze the relationship between cortical GM volume and 2PD threshold in young healthy individuals and to clarify the characteristics of brain structure reflecting the individual differences in somatosensory function. 2PD was measured in 42 healthy (20 females) volunteers aged 20-32 years using a custom-made test system that can be controlled by a personal computer. The 2PD of the right index finger measured with this device has been confirmed to show good reproducibility. T1-weighted images were acquired using a 3-T magnetic resonance imaging scanner for voxel-based morphometry analysis. The mean 2PD threshold was 2.58 ± 0.54 mm. Whole-brain multiple regression analysis of the relationship between 2PD and GM volume showed that a lower 2PD threshold (i.e. better somatosensory function) significantly correlated with decreased GM volume from the middle temporal gyrus to the inferior parietal lobule (IPL) in the contralateral hemisphere. In conclusion, a lower GM volume in the middle temporal gyrus and IPL correlates with better somatosensory function. Thus, cortical GM volume may be a biomarker of somatosensory function.

Neural Mechanisms That Make Perceptual Decisions Flexible

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

Neural bases of loss aversion when choosing for oneself versus known or unknown others

Despite the ubiquitous interdependence between one's own decisions and others' welfare, and the controversial evidence on the behavioral effect of choosing for others, the neural bases of making decisions for another versus oneself remain unexplored. We investigated whether loss aversion (LA; the tendency to avoid losses over approaching equivalent gains) is modulated by (i) choosing for oneself, other individuals, or both; (ii) knowing or not knowing the other recipients; or (iii) an interaction between these factors. We used fMRI to assess the brain activations associated with choosing whether to accept or reject mixed gambles, either for oneself, for another player, or both, in 2 groups of 28 participants who had or had not briefly interacted with the other players before scanning. Participants displayed higher LA for choices involving their payoff compared with those affecting only the payoff of other, known, players. This "social" modulation of decision-making was found to engage the dorsomedial prefrontal cortex and its inhibitory connectivity to the middle cingulate cortex. This pattern might underpin decision-making for known others via self-other distinction processes associated with dorsomedial prefrontal areas, with this in turn promoting the inhibition of socially oriented responses through the downregulation of the midcingulate node of the empathy network.

Trial-by-trial fluctuations in amygdala activity track motivational enhancement of desirable sensory evidence during perceptual decision-making

People are biased toward seeing outcomes that they are motivated to see. For example, wanting their favored team to prevail biases sports fans to perceive an ambiguous foul in a manner that is favorable to the team they support. Here, we test the hypothesis that such motivational biases in perceptual decision-making are associated with amygdala activity. We used monetary incentives to experimentally manipulate participants to want to see one percept over another while they performed a categorization task involving ambiguous images. Participants were more likely to categorize an image as the category we motivated them to see, suggesting that wanting to see a particular percept biased their perceptual decisions. Heightened amygdala activity was associated with motivation consistent categorizations and tracked trial-by-trial enhancement of neural activity in sensory cortices encoding the desirable category. Analyses using a drift diffusion model further suggest that trial-by-trial amygdala activity was specifically associated with biases in the accumulation of sensory evidence. In contrast, frontoparietal regions commonly associated with biases in perceptual decision-making were not associated with motivational bias. Altogether, our results suggest that wanting to see an outcome biases perceptual decisions via distinct mechanisms and may depend on dynamic fluctuations in amygdala activity.

Saccadic landing positions reveal that eye movements are affected by distractor-based retrieval

Binding theories assume that stimulus and response features are integrated into short-lasting episodes and that upon repetition of any feature the whole episode is retrieved, thereby affecting performance. Such binding theories are nowadays the standard explanation for a wide range of action control tasks and aim to explain all simple actions, without making assumptions of effector specificity. Yet, it is unclear if eye movements are affected by integration and retrieval in the same way as manual responses. We asked participants to discriminate letters framed by irrelevant shapes. In Experiment 1, participants gave their responses with eye movements. Saccade landing positions showed a spatial error pattern consistent with predictions of binding theories. Saccadic latencies were not affected. In Experiment 2 with an increased interval between prime and probe, the error pattern diminished, again congruent with predictions of binding theories presuming quickly decaying retrieval effects. Experiment 3 used the same task as in Experiment 1, but participants executed their responses with manual key presses; again, we found a binding pattern in response accuracy. We conclude that eye movements and manual responses are affected by the same integration and retrieval processes, supporting the tacit assumption of binding theories to apply to any effector.

Task-general efficiency of evidence accumulation as a computationally-defined neurocognitive trait: Implications for clinical neuroscience

Quantifying individual differences in higher-order cognitive functions is a foundational area of cognitive science that also has profound implications for research on psychopathology. For the last two decades, the dominant approach in these fields has been to attempt to fractionate higher-order functions into hypothesized components (e.g., "inhibition", "updating") through a combination of experimental manipulation and factor analysis. However, the putative constructs obtained through this paradigm have recently been met with substantial criticism on both theoretical and empirical grounds. Concurrently, an alternative approach has emerged focusing on parameters of formal computational models of cognition that have been developed in mathematical psychology. These models posit biologically plausible and experimentally validated explanations of the data-generating process for cognitive tasks, allowing them to be used to measure the latent mechanisms that underlie performance. One of the primary insights provided by recent applications of such models is that individual and clinical differences in performance on a wide variety of cognitive tasks, ranging from simple choice tasks to complex executive paradigms, are largely driven by efficiency of evidence accumulation (EEA), a computational mechanism defined by sequential sampling models. This review assembles evidence for the hypothesis that EEA is a central individual difference dimension that explains neurocognitive deficits in multiple clinical disorders and identifies ways in which in this insight can advance clinical neuroscience research. We propose that recognition of EEA as a major driver of neurocognitive differences will allow the field to make clearer inferences about cognitive abnormalities in psychopathology and their links to neurobiology.

Targeting the Salience Network: A Mini-Review on a Novel Neuromodulation Approach for Treating Alcohol Use Disorder

Alcohol use disorder (AUD) continues to be challenging to treat despite the best available interventions, with two-thirds of individuals going on to relapse by 1 year after treatment. Recent advances in the brain-based conceptual framework of addiction have allowed the field to pivot into a neuromodulation approach to intervention for these devastative disorders. Small trials of repetitive transcranial magnetic stimulation (rTMS) have used protocols developed for other psychiatric conditions and applied them to those with addiction with modest efficacy. Recent evidence suggests that a TMS approach focused on modulating the salience network (SN), a circuit at the crossroads of large-scale networks associated with AUD, may be a fruitful therapeutic strategy. The anterior insula or dorsal anterior cingulate cortex may be particularly effective stimulation sites given emerging evidence of their roles in processes associated with relapse.

Functional localization and categorization of intentional decisions in humans: A meta-analysis of brain imaging studies

Brain-imaging research on intentional decision-making often employs a "free-choice" paradigm, in which participants choose among options with identical values or outcomes. Although the medial prefrontal cortex has commonly been associated with choices, there is no consensus on the wider network that underlies diverse intentional decisions and behaviours. Our systematic literature search identified 35 fMRI/PET experiments using various free-choice paradigms, with appropriate control conditions using external instructions. An Activation Likelihood Estimate (ALE) meta-analysis showed that, compared with external instructions, intentional decisions consistently activate the medial and dorsolateral prefrontal cortex, the left insula and the inferior parietal lobule. We then categorized the studies into four different types according to their experimental designs: reactive motor intention, perceptual intention, inhibitory intention, and cognitive intention. We conducted conjunction and contrast meta-analyses to identify consistent and selective spatial convergence of brain activation within each specific category of intentional decision. Finally, we used meta-analytic decoding to probe cognitive processes underlying free choices. Our findings suggest that the neurocognitive process underlying intentional decision incorporates anatomically separated components subserving distinct cognitive and computational roles.

Impaired sensory evidence accumulation and network function in Lewy body dementia

Deficits in attention underpin many of the cognitive and neuropsychiatric features of Lewy body dementia. These attention-related symptoms remain difficult to treat and there are many gaps in our understanding of their neurobiology. An improved understanding of attention-related impairments can be achieved via mathematical modelling approaches, which identify cognitive parameters to provide an intermediate level between observed behavioural data and its underlying neural correlate. Here, we apply this approach to identify the role of impaired sensory evidence accumulation in the attention deficits that characterize Lewy body dementia. In 31 people with Lewy body dementia (including 13 Parkinson's disease dementia and 18 dementia with Lewy bodies cases), 16 people with Alzheimer's disease, and 23 healthy controls, we administered an attention task whilst they underwent functional 3 T MRI. Using hierarchical Bayesian estimation of a drift-diffusion model, we decomposed task performance into drift rate and decision boundary parameters. We tested the hypothesis that the drift rate-a measure of the quality of sensory evidence accumulation-is specifically impaired in Lewy body dementia, compared to Alzheimer's disease. We further explored whether trial-by-trial variations in the drift rate related to activity within the default and dorsal attention networks, to determine whether altered activity in these networks was associated with slowed drift rates in Lewy body dementia. Our results revealed slower drift rates in the Lewy body dementia compared to the Alzheimer's disease group, whereas the patient groups were equivalent for their decision boundaries. The patient groups were reduced relative to controls for both parameters. This highlights sensory evidence accumulation deficits as a key feature that distinguishes attention impairments in Lewy body dementia, consistent with impaired ability to efficiently process information from the environment to guide behaviour. We also found that the drift rate was strongly related to activity in the dorsal attention network across all three groups, whereas the Lewy body dementia group showed a divergent relationship relative to the Alzheimer's disease and control groups for the default network, consistent with altered default network modulation being associated with impaired evidence accumulation. Together, our findings reveal impaired sensory evidence accumulation as a specific marker of attention problems in Lewy body dementia, which may relate to large-scale network abnormalities. By identifying impairments in a specific sub-process of attention, these findings will inform future exploratory and intervention studies that aim to understand and treat attention-related symptoms that are a key feature of Lewy body dementia.

Neural substrates underpinning intra-individual variability in children with ADHD: A voxel-based morphometry study

BACKGROUND/PURPOSE

Increased intra-individual variability (IIV) in reaction time (RT) is a key feature of attention-deficit/hyperactivity disorder (ADHD). However, little is known about neurobiology underpinnings of IIV in ADHD.

METHODS

We assessed 55 youths with ADHD, and 55 individually-matched typically developing control (TDC) with the MRI and Conners' Continuous Performance Test. The ex-Gaussian distribution of RT was estimated to capture IIV with the parameters σ (sigma) and τ (tau). The regional brain volumes, analyzed by voxel-based morphometry, were correlated with IIV parameters.

RESULTS

We found both distinct and shared correlations among ADHD and TDC. For grey matter, there were significant σ-by-group interactions in the cingulate cortex and thalamus and also a τ-by-group interaction in the right inferior frontal gyrus. There was also shared negative associations between σ and regional volumes of the right posterior cerebellum and a positive association between τ and the right anterior insula. For white matter, there was a significant σ-by-group interaction in the genu of the corpus callosum and significant τ-by-group interactions in the right anterior corona radiata, the left splenium of the corpus callosum, and bilateral posterior cerebellum. There were also shared patterns that increased τ was associated with increased regional volumes of the right anterior corona radiata and decreased regional volumes of the right posterior limb of the internal capsule.

CONCLUSION

This study highlights that brain regions responsible for the motor, salience processing and multimodal information integration are associated with increased IIV in youths with ADHD.

The role of right temporo-parietal junction in stimulus evaluation

A predominant model of the temporo-parietal junction (TPJ) claims that this region is critical for attentional orienting/reorienting toward an unexpected, but behaviorally significant stimulus. However, recent studies have suggested that the TPJ is also involved in the process of evaluating stimulus, especially matching between external sensory inputs and internal representations. While some studies provide evidence for the involvement of the TPJ in stimulus evaluation, the nature of the evaluative process mediated by the TPJ remains unclear. To address this issue, we tested whether the TPJ activation amplitude and its peak latency is proportional to the demand of the evaluative process. We found that when the amount of sensory evidence for the matching process was abundant, the TPJ was transiently activated. Importantly, the TPJ activation showed a greater and more sustained pattern while the sensory evidence was accumulating for a longer period of time. These findings suggest that the TPJ function is associated with the evaluative process of matching sensory inputs with internal representations, as well as attentional reorienting.

Time is body: Multimodal evidence of crosstalk between interoception and time estimation

Theoretical approaches propose a blending between interoception and time estimation. Interoception might constitute a neurophysiological mechanism for encoding duration. However, no study has assessed the convergence between interoception and time estimation using behavioral, neurophysiological, and functional anatomy signatures. We examined the multimodal convergence between interoception and time estimation using a two-fold approach. In study 1, we developed a dual design combining interoception (measuring heartbeat detection - HBD, and heartbeat evoked potential - HEP) with a time estimation paradigm (TEP, estimation of duration of a 120 s interval). In study 2, we performed a conjoint metanalysis (Multi-level Kernel Density Analysis, MKDA) of neuroimaging, including reports of interoception and time estimation. Both studies provide convergent evidence of time estimation's significant involvement in behavioral, electrophysiological (enhanced HEP), and neuroimaging (overlapping cluster in the right insula and operculum) signatures of interoception. Convergent results from both studies offer direct support for a shared mechanism of interoception and time estimation.

A distributed dynamic brain network mediates linguistic tone representation and categorization

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

Response modality-dependent categorical choice representations for vibrotactile comparisons

Previous electrophysiological studies in monkeys and humans suggest that premotor regions are the primary loci for the encoding of perceptual choices during vibrotactile comparisons. However, these studies employed paradigms wherein choices were inextricably linked with the stimulus order and selection of manual movements. It remains largely unknown how vibrotactile choices are represented when they are decoupled from these sensorimotor components of the task. To address this question, we used fMRI-MVPA and a variant of the vibrotactile frequency discrimination task which enabled the isolation of choice-related signals from those related to stimulus order and selection of the manual decision reports. We identified the left contralateral dorsal premotor cortex (PMd) and intraparietal sulcus (IPS) as carrying information about vibrotactile choices. Our finding provides empirical evidence for an involvement of the PMd and IPS in vibrotactile decisions that goes above and beyond the coding of stimulus order and specific action selection. Considering findings from recent studies in animals, we speculate that the premotor region likely serves as a temporary storage site for information necessary for the specification of concrete manual movements, while the IPS might be more directly involved in the computation of choice. Moreover, this finding replicates results from our previous work using an oculomotor variant of the task, with the important difference that the informative premotor cluster identified in the previous work was centered in the bilateral frontal eye fields rather than in the PMd. Evidence from these two studies indicates that categorical choices in human vibrotactile comparisons are represented in a response modality-dependent manner.

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.

The diffusion model visualizer: an interactive tool to understand the diffusion model parameters

Response time (RT) data play an important role in psychology. The diffusion model (DM) allows to analyze RT-data in a two-alternative-force-choice paradigm using a particle drift diffusion modeling approach. It accounts for right-skewed distributions in a natural way. However, the model incorporates seven parameters, the roles of which are difficult to comprehend from the model equation. Therefore, the present article introduces the diffusion model visualizer (DMV) allowing for interactive manipulation of each parameter and plotting the resulting RT densities. Thus, the DMV serves as a valuable tool for understanding the specific role of each model parameter. It may come in handy for didactical purposes and in research context. It allows for tracking down parameter estimation problems by delivering the model-based ideal densities, which can be juxtaposed to the data-based densities. It will also serve a valuable purpose in detecting outliers. The article describes the basics of the DM along with technical details of the DMV and gives several hints for its usage.

A response-locking protocol to boost sensitivity for fMRI-based neurochronometry

The timeline of brain‐wide neural activity relative to a behavioral event is crucial when decoding the neural implementation of a cognitive process. Yet, fMRI assesses neural processes indirectly via delayed and regionally variable hemodynamics. This method‐inherent temporal distortion impacts the interpretation of behavior‐linked neural timing. Here we describe a novel behavioral protocol that aims at disentangling the BOLD dynamics of the pre‐ and post‐response periods in response time tasks. We tested this response‐locking protocol in a perceptual decision‐making (random dot) task. Increasing perceptual difficulty produced expected activity increases over a broad network involving the lateral/medial prefrontal cortex and the anterior insula. However, response‐locking revealed a previously unreported functional dissociation within this network. preSMA and anterior premotor cortex (prePMV) showed post‐response activity modulations while anterior insula and anterior cingulate cortex did not. Furthermore, post‐response BOLD activity at preSMA showed a modulation in timing but not amplitude while this pattern was reversed at prePMV. These timeline dissociations with response‐locking thus revealed three functionally distinct sub‐networks in what was seemingly one shared distributed network modulated by perceptual difficulty. These findings suggest that our novel response‐locked protocol could boost the timing‐related sensitivity of fMRI.

Common and distinct brain activity associated with risky and ambiguous decision-making

Two often-studied forms of uncertain decision-making (DM) are risky-DM (outcome probabilities known) and ambiguous-DM (outcome probabilities unknown). While DM in general is associated with activation of several brain regions, previous neuroimaging efforts suggest a dissociation between activity linked with risky and ambiguous choices. However, the common and distinct neurobiological correlates associated with risky- and ambiguous-DM, as well as their specificity when compared to perceptual-DM (as a 'control condition'), remains to be clarified. We conducted multiple meta-analyses on neuroimaging results from 151 studies to characterize common and domain-specific brain activity during risky-, ambiguous-, and perceptual-DM. When considering all DM tasks, convergent activity was observed in brain regions considered to be consituents of the canonical salience, valuation, and executive control networks. When considering subgroups of studies, risky-DM (vs. perceptual-DM) was linked with convergent activity in the striatum and anterior cingulate cortex (ACC), regions associated with reward-related processes (determined by objective functional decoding). When considering ambiguous-DM (vs. perceptual-DM), activity convergence was observed in the lateral prefrontal cortex and insula, regions implicated in affectively-neutral mental processes (e.g., cognitive control and behavioral responding; determined by functional decoding). An exploratory meta-analysis comparing brain activity between substance users and non-users during risky-DM identified reduced convergent activity among users in the striatum, cingulate, and thalamus. Taken together, these findings suggest a dissociation of brain regions linked with risky- and ambiguous-DM reflecting possible differential functionality and highlight brain alterations potentially contributing to poor decision-making in the context of substance use disorders.

Model-based assessment and neural correlates of spatial memory deficits in mild cognitive impairment

Mild cognitive impairment (MCI) is characterized by subjective and objective memory impairments within the context of generally intact everyday functioning. Such memory deficits are typically thought to arise from medial temporal lobe dysfunction; however, differences in memory task performance can arise from a variety of altered processes (e.g., strategy adjustments) rather than, or in addition to, "pure" memory deficits. To address this problem, we applied the linear ballistic accumulator (LBA: Brown and Heathcote, 2008) model to data from individuals with MCI (n = 18) and healthy older adults (HOA; n = 16) who performed an object-location association memory retrieval task during functional magnetic resonance imaging (fMRI). The primary goals were to 1) assess between-group differences in model parameters indexing processes of interest (memory sensitivity, accumulation speed, caution and time spent on peripheral perceptual and motor processes) and 2) determine whether differences in model-based metrics were consistent with fMRI data. The LBA provided evidence that, relative to the HOA group, those with MCI displayed lower sensitivity (i.e., difficulty discriminating targets from lures), suggestive of memory impairment, and displayed higher evidence accumulation speed and greater caution, suggestive of increased arousal and strategic changes in this group, although these changes had little impact on MCI-related accuracy differences. Consistent with these findings, fMRI revealed reduced activation in brain regions previously linked to evidence accumulation and to the implementation of caution reductions in the MCI group. Findings suggest that multiple cognitive mechanisms differ during memory retrieval in MCI, and that these mechanisms may explain neuroimaging alterations outside of the medial temporal lobes.

The validity and consistency of continuous joystick response in perceptual decision-making

A computer joystick is an efficient and cost-effective response device for recording continuous movements in psychological experiments. Movement trajectories and other measures from continuous responses have expanded the insights gained from discrete responses (e.g., button presses) by providing unique information about how cognitive processes unfold over time. However, few studies have evaluated the validity of joystick responses with reference to conventional key presses, and how response modality can affect cognitive processes. Here we systematically compared human participants' behavioral performance of perceptual decision-making when they responded with either joystick movements or key presses in a four-alternative motion discrimination task. We found evidence that the response modality did not affect raw behavioral measures, including decision accuracy and mean response time, at the group level. Furthermore, to compare the underlying decision processes between the two response modalities, we fitted a drift-diffusion model of decision-making to individual participants' behavioral data. Bayesian analyses of the model parameters showed no evidence that switching from key presses to continuous joystick movements modulated the decision-making process. These results supported continuous joystick actions as a valid apparatus for continuous movements, although we highlight the need for caution when conducting experiments with continuous movement responses.

Cognitive and White-Matter Compartment Models Reveal Selective Relations between Corticospinal Tract Microstructure and Simple Reaction Time

The speed of motor reaction to an external stimulus varies substantially between individuals and is slowed in aging. However, the neuroanatomical origins of interindividual variability in reaction time (RT) remain unclear. Here, we combined a cognitive model of RT and a biophysical compartment model of diffusion-weighted MRI (DWI) to characterize the relationship between RT and microstructure of the corticospinal tract (CST) and the optic radiation (OR), the primary motor output and visual input pathways associated with visual-motor responses. We fitted an accumulator model of RT to 46 female human participants' behavioral performance in a simple reaction time task. The non-decision time parameter (T _er) derived from the model was used to account for the latencies of stimulus encoding and action initiation. From multi-shell DWI data, we quantified tissue microstructure of the CST and OR with the neurite orientation dispersion and density imaging (NODDI) model as well as the conventional diffusion tensor imaging model. Using novel skeletonization and segmentation approaches, we showed that DWI-based microstructure metrics varied substantially along CST and OR. The T _er of individual participants was negatively correlated with the NODDI measure of the neurite density in the bilateral superior CST. Further, we found no significant correlation between the microstructural measures and mean RT. Thus, our findings suggest a link between interindividual differences in sensorimotor speed and selective microstructural properties in white-matter tracts.SIGNIFICANCE STATEMENT How does our brain structure contribute to our speed to react? Here, we provided anatomically specific evidence that interindividual differences in response speed is associated with white-matter microstructure. Using a cognitive model of reaction time (RT), we estimated the non-decision time, as an index of the latencies of stimulus encoding and action initiation, during a simple reaction time task. Using an advanced microstructural model for diffusion MRI, we estimated the tissue properties and their variations along the corticospinal tract and optic radiation. We found significant location-specific correlations between the microstructural measures and the model-derived parameter of non-decision time but not mean RT. These results highlight the neuroanatomical signature of interindividual variability in response speed along the sensorimotor pathways.

Visual perceptual learning modulates decision network in the human brain: The evidence from psychophysics, modeling, and functional magnetic resonance imaging

Perceptual learning refers to improved perceptual performance after intensive training and was initially suggested to reflect long-term plasticity in early visual cortex. Recent behavioral and neurophysiological evidence further suggested that the plasticity in brain regions related to decision making could also contribute to the observed training effects. However, how perceptual learning modulates the responses of decision-related regions in the human brain remains largely unknown. In the present study, we combined psychophysics and functional magnetic resonance imaging (fMRI), and adopted a model-based approach to investigate this issue. We trained participants on a motion direction discrimination task and fitted their behavioral data using the linear ballistic accumulator model. The results from model fitting showed that behavioral improvement could be well explained by a specific improvement in sensory information accumulation. A critical model parameter, the drift rate of the information accumulation, was correlated with the fMRI responses derived from three spatial independent components: ventral premotor cortex (PMv), supplementary eye field (SEF), and the fronto-parietal network, including intraparietal sulcus (IPS) and frontal eye field (FEF). In this decision network, we found that the behavioral training effects were accompanied by signal enhancement specific to trained direction in PMv and FEF. Further, we also found direction-specific signal reduction in sensory areas (V3A and MT+), as well as the strengthened effective connectivity from V3A to PMv and from IPS to FEF. These findings provide evidence for the learning-induced decision refinement after perceptual learning and the brain regions that are involved in this process.

Characterizing the role of the pre-SMA in the control of speed/accuracy trade-off with directed functional connectivity mapping and multiple solution reduction

Several plausible theories of the neural implementation of speed/accuracy trade-off (SAT), the phenomenon in which individuals may alternately emphasize speed or accuracy during the performance of cognitive tasks, have been proposed, and multiple lines of evidence point to the involvement of the pre-supplemental motor area (pre-SMA). However, as the nature and directionality of the pre-SMA's functional connections to other regions involved in cognitive control and task processing are not known, its precise role in the top-down control of SAT remains unclear. Although recent advances in cross-sectional path modeling provide a promising way of characterizing these connections, such models are limited by their tendency to produce multiple equivalent solutions. In a sample of healthy adults (N = 18), the current study uses the novel approach of Group Iterative Multiple Model Estimation for Multiple Solutions (GIMME-MS) to assess directed functional connections between the pre-SMA, other regions previously linked to control of SAT, and regions putatively involved in evidence accumulation for the decision task. Results reveal a primary role of the pre-SMA for modulating activity in regions involved in the decision process but suggest that this region receives top-down input from the DLPFC. Findings also demonstrate the utility of GIMME-MS and solution-reduction methods for obtaining valid directional inferences from connectivity path models.

Perceptual load in decision making: The role of anterior insula and visual areas. An ERP study

The present study aimed at describing the effects of perceptual load on neurocognitive processes of decision-making. To this aim, we used a visual-motor discriminative task in which pairs of stimuli were assigned to either target or non-target categories. For each category, stimulus configuration was defined as simple or complex according to orientation and arrangement of the constituent segments. Analyses of prefrontal ERPs revealed that the pP1 component (at 180 ms) was larger for complex stimuli than simple for both categories, and the same result was found for the pP2 component (at 320 ms). Occipital ERPs revealed effects of perceptual load on the N1 component, but not on the mainly exogenous P1 component, indicating that amplitude modulations of prefrontal ERPs were not due to physical difference between simple and complex stimuli. Based on the recent literature, we discussed the pP1 activity as reflecting a process of sensory-motor integration, and the pP2 as a stimulus-response mapping process resulting from two gradients of activity: one category-based (larger for target than non-target stimuli), the other decision effort-based (enhanced when categorization implied a greater attentional load). Previous ERP-fMRI studies and present source analysis support the view that prefrontal ERPs were generated mainly by activity in the anterior Insula.

The Neurodynamic Decision Variable in Human Multi-alternative Perceptual Choice

The neural dynamics underpinning binary perceptual decisions and their transformation into actions are well studied, but real-world decisions typically offer more than two response alternatives. How does decision-related evidence accumulation dynamically influence multiple action representations in humans? The heightened conservatism required in multiple compared with binary choice scenarios suggests a mechanism that compensates for increased uncertainty when multiple choices are present by suppressing baseline activity. Here, we tracked action representations using corticospinal excitability during four- and two-choice perceptual decisions and modeled them using a sequential sampling framework. We found that the predictions made by leaky competing accumulator models to accommodate multiple choices (i.e., reduced baseline activity to compensate increased uncertainty) were borne out by dynamic changes in human action representations. This suggests a direct and continuous influence of interacting evidence accumulators, each favoring a different decision alternative, on downstream corticospinal excitability during complex choice.

Human VMPFC encodes early signatures of confidence in perceptual decisions

Choice confidence, an individual’s internal estimate of judgment accuracy, plays a critical role in adaptive behaviour, yet its neural representations during decision formation remain underexplored. Here, we recorded simultaneous EEG-fMRI while participants performed a direction discrimination task and rated their confidence on each trial. Using multivariate single-trial discriminant analysis of the EEG, we identified a stimulus-independent component encoding confidence, which appeared prior to subjects’ explicit choice and confidence report, and was consistent with a confidence measure predicted by an accumulation-to-bound model of decision-making. Importantly, trial-to-trial variability in this electrophysiologically-derived confidence signal was uniquely associated with fMRI responses in the ventromedial prefrontal cortex (VMPFC), a region not typically associated with confidence for perceptual decisions. Furthermore, activity in the VMPFC was functionally coupled with regions of the frontal cortex linked to perceptual decision-making and metacognition. Our results suggest that the VMPFC holds an early confidence representation arising from decision dynamics, preceding and potentially informing metacognitive evaluation.

While waiting to cross the road on a foggy morning, you see a shape in the distance that appears to be an approaching car. How do you decide if it is safe to cross? We often have to make important decisions about the world based on imperfect information. What guides our subsequent actions in these situations is a sense of accuracy, or confidence, that we associate with our initial judgments. You would not step off the kerb if you were only 10% confident the car was a safe distance away. But how, when, and where in the brain does such confidence emerge?

Gherman and Philiastides examined how brain activity relates to confidence during the early stages of decision-making, that is, before people have explicitly committed to a particular choice. Healthy volunteers were asked to judge the direction in which dots were moving across a screen. They then had to rate how confident they were in their decision. Two techniques – EEG and fMRI – tracked their brain activity during the task. EEG uses scalp electrodes to reveal when and how electrical activity is changing inside the brain, while fMRI, a type of brain scan, shows where these changes in brain activity occur. Used together, the two techniques provide a greater understanding of brain activity than either used alone.

Activity in multiple regions of the brain correlated with confidence at different stages of the task. Certain brain networks showed confidence-related activity while the volunteers tried to judge the direction of movement, and others were engaged when volunteers made their confidence ratings. However, activity in only one area reliably indicated how confident the volunteers felt before they had made their choice. This area, the ventromedial prefrontal cortex, also helps process rewards. This suggests that feelings of confidence early in the decision-making process could guide our behaviour by virtue of being rewarding.

Many brain disorders – including depression, schizophrenia and Parkinson's disease – compromise decision-making. Patients show changes in accuracy, response times, and in their ability to accurately evaluate their decisions. The methods used in the current study could help reveal the neural changes that cause these impairments. This could lead to new methods to diagnose and predict cognitive deficits, and new ways to treat them at an earlier stage.

Gain control explains the effect of distraction in human perceptual, cognitive, and economic decision making

When making decisions, humans are often distracted by irrelevant information. Distraction has a different impact on perceptual, cognitive, and value-guided choices, giving rise to well-described behavioral phenomena such as the tilt illusion, conflict adaptation, or economic decoy effects. However, a single, unified model that can account for all these phenomena has yet to emerge. Here, we offer one such account, based on adaptive gain control, and additionally show that it successfully predicts a range of counterintuitive new behavioral phenomena on variants of a classic cognitive paradigm, the Eriksen flanker task. We also report that blood oxygen level-dependent signals in a dorsal network prominently including the anterior cingulate cortex index a gain-modulated decision variable predicted by the model. This work unifies the study of distraction across perceptual, cognitive, and economic domains.

Oscillatory EEG signatures of postponed somatosensory decisions

In recent electroencephalography (EEG) studies, the vibrotactile frequency comparison task has been used to study oscillatory signatures of perceptual decision making in humans, revealing a choice-selective modulation of premotor upper beta band power shortly before decisions were reported. Importantly, these studies focused on decisions that were (1) indicated immediately after stimulus presentation, and (2) for which a direct motor mapping was provided. Here, we investigated whether the putative beta band choice signal also extends to postponed decisions, and how such a decision signal might be influenced by a response mapping that is dissociated from a specific motor command. We recorded EEG data in two separate experiments, both employing the vibrotactile frequency comparison task with delayed decision reports. In the first experiment, delayed choices were associated with a fixed motor mapping, whereas in the second experiment, choices were mapped onto a color code concealing a specific motor response until the end of the delay phase. In between stimulus presentations, as well as after the second stimulus, prefrontal beta band power indexed stimulus information held in working memory. Beta band power also encoded choices during the response delay, notably, in different cortical areas depending on the provided response mapping. In particular, when decisions were associated with a specific motor mapping, choices were represented in premotor cortices, whereas the color mapping resulted in a choice-selective modulation of beta band power in parietal cortices. Together, our findings imply that how a choice is expressed (i.e., the decision consequence) determines where in the cortical sensorimotor hierarchy an according decision signal is processed.

Cross-decoding supramodal information in the human brain

Perceptual decision making is the cognitive process wherein the brain classifies stimuli into abstract categories for more efficient downstream processing. A system that, during categorization, can process information regardless of the information's original sensory modality (i.e., a supramodal system) would have a substantial advantage over a system with dedicated processes for specific sensory modalities. While many studies have probed decision processes through the lens of one sensory modality, it remains unclear whether there are such supramodal brain areas that can flexibly process task-relevant information regardless of the original "format" of the information. To investigate supramodality, one must ensure that supramodal information exists somewhere within the functional architecture by rendering information from multiple sensory systems necessary but insufficient for categorization. To this aim, we tasked participants with categorizing auditory and tactile frequency-modulated sweeps according to learned, supramodal categories in a delayed match-to-category paradigm while we measured their blood-oxygen-level dependent signal with functional MRI. To detect supramodal information, we implemented a set of cross-modality pattern classification analyses, which demonstrated that the left caudate nucleus encodes category-level information but not stimulus-specific information (such as spatial directions and stimulus modalities), while the right inferior frontal gyrus, showing the opposite pattern, encodes stimulus-specific information but not category-level information. Given our paradigm, these results reveal abstract representations in the brain that are independent of motor, semantic, and sensory-specific processing, instead reflecting supramodal, categorical information, which points to the caudate nucleus as a locus of cognitive processes involved in complex behavior.

Awareness of perception and sensory-motor integration: ERPs from the anterior insula

The present work follows recent evidences of studies showing that visual stimuli evoke two early prefrontal event-related potentials (ERP) concomitant to the canonical occipital activities, but originating within the anterior insula (the pN1 and the pP1 components). To clarify the exogenous/endogenous nature of these components, we performed two experiments in which stimulus physical features (Experiment 1) and motor demands of the task (Experiment 2) were considered. In a simple response task (SRT), low-visibility stimuli evoked larger pN1 over the prefrontal areas (Experiment 1) with respect to high-visibility stimuli; in contrast, the occipital P1 component (concomitant to the pN1) had reduced amplitude in the low-visibility condition as expected. Furthermore, the latency of the P1, pN1 and pP1 was slower in the low-visibility condition (from 8 to 18 ms), and the motor response was slowed down as well (on average 14 ms). Pre-stimulus analysis showed that low-visibility stimuli were preceded by greater motor readiness. On the other hand, Experiment 2 showed that, compared with the SRT, the request to passively view the same stimuli was associated with smaller pP1. ERP source analysis confirmed the anterior insula source of the prefrontal ERPs; we interpreted these activities as the correlate of two top-down perceptual processing: the sensory awareness (the pN1) and the awareness of the sensory-motor integration (the pP1), associated with the subjective experience of the visual perception and the conscious experience of the sensory-motor coupling, respectively.

The neural system of metacognition accompanying decision-making in the prefrontal cortex

Decision-making is usually accompanied by metacognition, through which a decision maker monitors uncertainty regarding a decision and may then consequently revise the decision. These metacognitive processes can occur prior to or in the absence of feedback. However, the neural mechanisms of metacognition remain controversial. One theory proposes an independent neural system for metacognition in the prefrontal cortex (PFC); the other, that metacognitive processes coincide and overlap with the systems used for the decision-making process per se. In this study, we devised a novel “decision–redecision” paradigm to investigate the neural metacognitive processes involved in redecision as compared to the initial decision-making process. The participants underwent a perceptual decision-making task and a rule-based decision-making task during functional magnetic resonance imaging (fMRI). We found that the anterior PFC, including the dorsal anterior cingulate cortex (dACC) and lateral frontopolar cortex (lFPC), were more extensively activated after the initial decision. The dACC activity in redecision positively scaled with decision uncertainty and correlated with individual metacognitive uncertainty monitoring abilities—commonly occurring in both tasks—indicating that the dACC was specifically involved in decision uncertainty monitoring. In contrast, the lFPC activity seen in redecision processing was scaled with decision uncertainty reduction and correlated with individual accuracy changes—positively in the rule-based decision-making task and negatively in the perceptual decision-making task. Our results show that the lFPC was specifically involved in metacognitive control of decision adjustment and was subject to different control demands of the tasks. Therefore, our findings support that a separate neural system in the PFC is essentially involved in metacognition and further, that functions of the PFC in metacognition are dissociable.

Decision-making is often accompanied by a sense of uncertainty regarding the outcome. In many situations, there is no explicit feedback or cue to indicate whether the decision is correct or not. Fortunately, our brain can evaluate decision uncertainty using the internal signals and subsequently make appropriate adjustments to initial decisions. The process of considering the outcome of a decision and whether a decision should be adjusted is called metacognition, and it tends to be automatically induced. Thus, decision-making is usually accompanied by metacognition, and the two processes are inevitably coupled. However, the neural systems supporting metacognitive processing remain unclear and have often been misattributed to the neural system of the decision-making process per se. Here, we have analyzed this process in several volunteers by imaging the brain activity in specific regions while they performed Sudoku and random-dot motion (RDM) tasks. Our results suggest the existence of a neural system located in the prefrontal cortex (PFC) mainly involved in metacognition and independent from the neural system of decision-making.

Task-Relevant Information Modulates Primary Motor Cortex Activity Before Movement Onset

Monkey neurophysiology research supports the affordance competition hypothesis (ACH) proposing that cognitive information useful for action selection is integrated in sensorimotor areas. In this view, action selection would emerge from the simultaneous representation of competing action plans, in parallel biased by relevant task factors. This biased competition would take place up to primary motor cortex (M1). Although ACH is plausible in environments affording choices between actions, its relevance for human decision making is less clear. To address this issue, we designed an functional magnetic resonance imaging (fMRI) experiment modeled after monkey neurophysiology studies in which human participants processed cues conveying predictive information about upcoming button presses. Our results demonstrate that, as predicted by the ACH, predictive information (i.e., the relevant task factor) biases activity of primary motor regions. Specifically, first, activity before movement onset in contralateral M1 increases as the competition is biased in favor of a specific button press relative to activity in ipsilateral M1. Second, motor regions were more tightly coupled with fronto-parietal regions when competition between potential actions was high, again suggesting that motor regions are also part of the biased competition network. Our findings support the idea that action planning dynamics as proposed in the ACH are valid both in human and non-human primates.

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.

Brain waves from an "isolated" cortex: contribution of the anterior insula to cognitive functions

Using two independent electrical neuroimaging techniques (BESA and sLORETA), we tested a fMRI-seeded source modeling indicating that in visual discriminative tasks the anterior insula (aIns) participates in the generation of three prefrontal ERP components: the pN1 (at 115 ms), the pP1 (at 170 ms), and the pP2 (at 300 ms). This latter component represented the focus of the present study. Results showed that the pP2 had different activation profiles across hemispheres. The left aIns activity peaked at 420 ms (30 ms before the response) for both Go and No-go trials, that is independently from the ultimate choice (response or inhibition). The right aIns activity started at about 250 ms and progressively increased for a time interval extending after the motor response; its amplitude was larger in case of Go than No-go stimuli. We suggest that the activation of the left aIns reflected the timing of the decision, and the right aIns the categorization and the performance monitoring processes. A control experiment requiring simple (not discriminative) motor response revealed that the pP2 and the aIns activity were nearly absent after the 250 ms; this result confirmed that the aIns activity at this stage is associated with the decisional processes, and not with the motor response per se. The present investigation shed new lights on the insular contribution to perceptual decision-making, and opens to the possibility of assessing the aIns activity via ERP analysis.

The Attentional Drift Diffusion Model of Simple Perceptual Decision-Making

Perceptual decisions requiring the comparison of spatially distributed stimuli that are fixated sequentially might be influenced by fluctuations in visual attention. We used two psychophysical tasks with human subjects to investigate the extent to which visual attention influences simple perceptual choices, and to test the extent to which the attentional Drift Diffusion Model (aDDM) provides a good computational description of how attention affects the underlying decision processes. We find evidence for sizable attentional choice biases and that the aDDM provides a reasonable quantitative description of the relationship between fluctuations in visual attention, choices and reaction times. We also find that exogenous manipulations of attention induce choice biases consistent with the predictions of the model.

Self-evaluation of decision-making: A general Bayesian framework for metacognitive computation

People are often aware of their mistakes, and report levels of confidence in their choices that correlate with objective performance. These metacognitive assessments of decision quality are important for the guidance of behavior, particularly when external feedback is absent or sporadic. However, a computational framework that accounts for both confidence and error detection is lacking. In addition, accounts of dissociations between performance and metacognition have often relied on ad hoc assumptions, precluding a unified account of intact and impaired self-evaluation. Here we present a general Bayesian framework in which self-evaluation is cast as a "second-order" inference on a coupled but distinct decision system, computationally equivalent to inferring the performance of another actor. Second-order computation may ensue whenever there is a separation between internal states supporting decisions and confidence estimates over space and/or time. We contrast second-order computation against simpler first-order models in which the same internal state supports both decisions and confidence estimates. Through simulations we show that second-order computation provides a unified account of different types of self-evaluation often considered in separate literatures, such as confidence and error detection, and generates novel predictions about the contribution of one's own actions to metacognitive judgments. In addition, the model provides insight into why subjects' metacognition may sometimes be better or worse than task performance. We suggest that second-order computation may underpin self-evaluative judgments across a range of domains. (PsycINFO Database Record

Abstract and Effector-Selective Decision Signals Exhibit Qualitatively Distinct Dynamics before Delayed Perceptual Reports

Electrophysiological research has isolated neural signatures of decision formation in a variety of brain regions. Studies in rodents and monkeys have focused primarily on effector-selective signals that translate the emerging decision into a specific motor plan, but, more recently, research on the human brain has identified an abstract signature of evidence accumulation that does not appear to play any direct role in action preparation. The functional dissociations between these distinct signal types have only begun to be characterized, and their dynamics during decisions with deferred actions with or without foreknowledge of stimulus-effector mapping, a commonly studied task scenario in single-unit and functional imaging investigations, have not been established. Here we traced the dynamics of distinct abstract and effector-selective decision signals in the form of the broad-band centro-parietal positivity (CPP) and limb-selective β-band (8-16 and 18-30 Hz) EEG activity, respectively, during delayed-reported motion direction decisions with and without foreknowledge of direction-response mapping. With foreknowledge, the CPP and β-band signals exhibited a similar gradual build-up following evidence onset, but whereas choice-predictive β-band activity persisted up until the delayed response, the CPP dropped toward baseline after peaking. Without foreknowledge, the CPP exhibited identical dynamics, whereas choice-selective β-band activity was eliminated. These findings highlight qualitative functional distinctions between effector-selective and abstract decision signals and are of relevance to the assumptions founding functional neuroimaging investigations of decision-making.

SIGNIFICANCE STATEMENT

Neural signatures of evidence accumulation have been isolated in numerous brain regions. Although animal neurophysiology has largely concentrated on effector-selective decision signals that translate the emerging decision into a specific motor plan, recent research on the human brain has isolated abstract neural signatures of decision formation that are independent of specific sensory and motor requirements. Here, we examine the functional distinctions between the two distinct classes of decision variable signal during decisions with deferred actions with and without foreknowledge of stimulus-effector mapping. We find salient distinctions in the dynamics of abstract versus effector-selective decision signals in the human brain, in terms of sustainment through response delays and contingency on foreknowledge of stimulus-response mapping.

The contribution of the human posterior parietal cortex to episodic memory

The posterior parietal cortex (PPC) is traditionally associated with attention, perceptual decision making and sensorimotor transformations, but more recent human neuroimaging studies support an additional role in episodic memory retrieval. In this Opinion article, we present a functional-anatomical model of the involvement of the PPC in memory retrieval. Parietal regions involved in perceptual attention and episodic memory are largely segregated and often show a push-pull relationship, potentially mediated by prefrontal regions. Moreover, different PPC regions carry out specific functions during retrieval - for example, representing retrieved information, recoding this information based on task demands, or accumulating evidence for memory decisions.