Pharmacological Elevation of Catecholamine Levels Improves Perceptual Decisions, But Not Metacognitive Insight.

Perceptual decisions are often accompanied by a feeling of decision confidence. Where the parietal cortex is known for its crucial role in shaping such perceptual decisions, metacognitive evaluations are thought to additionally rely on the (pre)frontal cortex. Because of this supposed neural differentiation between these processes, perceptual and metacognitive decisions may be divergently affected by changes in internal (e.g., attention, arousal) and external (e.g., task and environmental demands) factors. Although intriguing, causal evidence for this hypothesis remains scarce. Here, we investigated the causal effect of two neuromodulatory systems on behavioral and neural measures of perceptual and metacognitive decision-making. Specifically, we pharmacologically elevated levels of catecholamines (with atomoxetine) and acetylcholine (with donepezil) in healthy adult human participants performing a visual discrimination task in which we gauged decision confidence, while electroencephalography was measured. Where cholinergic effects were not robust, catecholaminergic enhancement improved perceptual sensitivity, while at the same time leaving metacognitive sensitivity unaffected. Neurally, catecholaminergic elevation did not affect sensory representations of task-relevant visual stimuli but instead enhanced well-known decision signals measured over the centroparietal cortex, reflecting the accumulation of sensory evidence over time. Crucially, catecholaminergic enhancement concurrently impoverished neural markers measured over the frontal cortex linked to the formation of metacognitive evaluations. Enhanced catecholaminergic neuromodulation thus improves perceptual but not metacognitive decision-making.

How (not) to demonstrate unconscious priming: Overcoming issues with post-hoc data selection, low power, and frequentist statistics.

One widely used scientific approach to studying consciousness involves contrasting conscious operations with unconscious ones. However, challenges in establishing the absence of conscious awareness have led to debates about the extent and existence of unconscious processes. We collected experimental data on unconscious semantic priming, manipulating prime presentation duration to highlight the critical role of the analysis approach in attributing priming effects to unconscious processing. We demonstrate that common practices like post-hoc data selection, low statistical power, and frequentist statistical testing can erroneously support claims of unconscious priming. Conversely, adopting best practices like direct performance-awareness contrasts, Bayesian tests, and increased statistical power can prevent such erroneous conclusions. Many past experiments, including our own, fail to meet these standards, casting doubt on previous claims about unconscious processing. Implementing these robust practices will enhance our understanding of unconscious processing and shed light on the functions and neural mechanisms of consciousness.

A disinhibitory circuit mechanism explains a general principle of peak performance during mid-level arousal.

Perceptual decision-making is highly dependent on the momentary arousal state of the brain, which fluctuates over time on a scale of hours, minutes, and even seconds. The textbook relationship between momentary arousal and task performance is captured by an inverted U-shape, as put forward in the Yerkes-Dodson law. This law suggests optimal performance at moderate levels of arousal and impaired performance at low or high arousal levels. However, despite its popularity, the evidence for this relationship in humans is mixed at best. Here, we use pupil-indexed arousal and performance data from various perceptual decision-making tasks to provide converging evidence for the inverted U-shaped relationship between spontaneous arousal fluctuations and performance across different decision types (discrimination, detection) and sensory modalities (visual, auditory). To further understand this relationship, we built a neurobiologically plausible mechanistic model and show that it is possible to reproduce our findings by incorporating two types of interneurons that are both modulated by an arousal signal. The model architecture produces two dynamical regimes under the influence of arousal: one regime in which performance increases with arousal and another regime in which performance decreases with arousal, together forming an inverted U-shaped arousal-performance relationship. We conclude that the inverted U-shaped arousal-performance relationship is a general and robust property of sensory processing. It might be brought about by the influence of arousal on two types of interneurons that together act as a disinhibitory pathway for the neural populations that encode the available sensory evidence used for the decision.

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.

Predictions and rewards affect decision-making but not subjective experience.

To survive, organisms constantly make decisions to avoid danger and maximize rewards in information-rich environments. As a result, decisions about sensory input are not only driven by sensory information but also by other factors, such as the expected rewards of a decision (known as the payoff matrix) or by information about temporal regularities in the environment (known as cognitive priors or predictions). However, it is unknown to what extent these different types of information affect subjective experience or whether they merely result in nonperceptual response criterion shifts. To investigate this question, we used three carefully matched manipulations that typically result in behavioral shifts in decision criteria: a visual illusion (Müller-Lyer condition), a punishment scheme (payoff condition), and a change in the ratio of relevant stimuli (base rate condition). To gauge shifts in subjective experience, we introduce a task in which participants not only make decisions about what they have just seen but are also asked to reproduce their experience of a target stimulus. Using Bayesian ordinal modeling, we show that each of these three manipulations affects the decision criterion as intended but that the visual illusion uniquely affects sensory experience as measured by reproduction. In a series of follow-up experiments, we use computational modeling to show that although the visual illusion results in a distinct drift-diffusion (DDM) parameter profile relative to nonsensory manipulations, reliance on DDM parameter estimates alone is not sufficient to ascertain whether a given manipulation is perceptual or nonperceptual.

A Silent Disco: Differential Effects of Beat-based and Pattern-based Temporal Expectations on Persistent Entrainment of Low-frequency Neural Oscillations.

The brain uses temporal structure in the environment, like rhythm in music and speech, to predict the timing of events, thereby optimizing their processing and perception. Temporal expectations can be grounded in different aspects of the input structure, such as a regular beat or a predictable pattern. One influential account posits that a generic mechanism underlies beat-based and pattern-based expectations, namely, entrainment of low-frequency neural oscillations to rhythmic input, whereas other accounts assume different underlying neural mechanisms. Here, we addressed this outstanding issue by examining EEG activity and behavioral responses during silent periods following rhythmic auditory sequences. We measured responses outlasting the rhythms both to avoid confounding the EEG analyses with evoked responses, and to directly test whether beat-based and pattern-based expectations persist beyond stimulation, as predicted by entrainment theories. To properly disentangle beat-based and pattern-based expectations, which often occur simultaneously, we used non-isochronous rhythms with a beat, a predictable pattern, or random timing. In Experiment 1 (n = 32), beat-based expectations affected behavioral ratings of probe events for two beat-cycles after the end of the rhythm. The effects of pattern-based expectations reflected expectations for one interval. In Experiment 2 (n = 27), using EEG, we found enhanced spectral power at the beat frequency for beat-based sequences both during listening and silence. For pattern-based sequences, enhanced power at a pattern-specific frequency was present during listening, but not silence. Moreover, we found a difference in the evoked signal following pattern-based and beat-based sequences. Finally, we show how multivariate pattern decoding and multiscale entropy-measures sensitive to non-oscillatory components of the signal-can be used to probe temporal expectations. Together, our results suggest that the input structure used to form temporal expectations may affect the associated neural mechanisms. We suggest climbing activity and low-frequency oscillations may be differentially associated with pattern-based and beat-based expectations.

An academic survey on theoretical foundations, common assumptions and the current state of consciousness science.

We report the results of an academic survey into the theoretical and methodological foundations, common assumptions, and the current state of the field of consciousness research. The survey consisted of 22 questions and was distributed on two different occasions of the annual meeting of the Association of the Scientific Study of Consciousness (2018 and 2019). We examined responses from 166 consciousness researchers with different backgrounds (e.g. philosophy, neuroscience, psychology, and computer science) and at various stages of their careers (e.g. junior/senior faculty and graduate/undergraduate students). The results reveal that there remains considerable discussion and debate between the surveyed researchers about the definition of consciousness and the way it should be studied. To highlight a few observations, a majority of respondents believe that machines could have consciousness, that consciousness is a gradual phenomenon in the animal kingdom, and that unconscious processing is extensive, encompassing both low-level and high-level cognitive functions. Further, we show which theories of consciousness are currently considered most promising by respondents and how supposedly different theories cluster together, which dependent measures are considered best to index the presence or absence of consciousness, and which neural measures are thought to be the most likely signatures of consciousness. These findings provide us with a snapshot of the current views of researchers in the field and may therefore help prioritize research and theoretical approaches to foster progress.

Broadband Dynamics Rather than Frequency-Specific Rhythms Underlie Prediction Error in the Primate Auditory Cortex.

Detection of statistical irregularities, measured as a prediction error response, is fundamental to the perceptual monitoring of the environment. We studied whether prediction error response is associated with neural oscillations or asynchronous broadband activity. Electrocorticography was conducted in three male monkeys, who passively listened to the auditory roving oddball stimuli. Local field potentials (LFPs) recorded over the auditory cortex underwent spectral principal component analysis, which decoupled broadband and rhythmic components of the LFP signal. We found that the broadband component captured the prediction error response, whereas none of the rhythmic components were associated with statistical irregularities of sounds. The broadband component displayed more stochastic, asymmetrical multifractal properties than the rhythmic components, which revealed more self-similar dynamics. We thus conclude that the prediction error response is captured by neuronal populations generating asynchronous broadband activity, defined by irregular dynamic states, which, unlike oscillatory rhythms, appear to enable the neural representation of auditory prediction error response.SIGNIFICANCE STATEMENT This study aimed to examine the contribution of oscillatory and asynchronous components of auditory local field potentials in the generation of prediction error responses to sensory irregularities, as this has not been directly addressed in the previous studies. Here, we show that mismatch negativity-an auditory prediction error response-is driven by the asynchronous broadband component of potentials recorded in the auditory cortex. This finding highlights the importance of nonoscillatory neural processes in the predictive monitoring of the environment. At a more general level, the study demonstrates that stochastic neural processes, which are often disregarded as neural noise, do have a functional role in the processing of sensory information.

The human visual system differentially represents subjectively and objectively invisible stimuli.

The study of unconscious processing requires a measure of conscious awareness. Awareness measures can be either subjective (based on participant's report) or objective (based on perceptual performance). The preferred awareness measure depends on the theoretical position about consciousness and may influence conclusions about the extent of unconscious processing and about the neural correlates of consciousness. We obtained functional magnetic resonance imaging (fMRI) measurements from 43 subjects while they viewed masked faces and houses that were either subjectively or objectively invisible. Even for objectively invisible (perceptually indiscriminable) stimuli, we found significant category information in both early, lower-level visual areas and in higher-level visual cortex, although representations in anterior, category-selective ventrotemporal areas were less robust. For subjectively invisible stimuli, similar to visible stimuli, there was a clear posterior-to-anterior gradient in visual cortex, with stronger category information in ventrotemporal cortex than in early visual cortex. For objectively invisible stimuli, however, category information remained virtually unchanged from early visual cortex to object- and category-selective visual areas. These results demonstrate that although both objectively and subjectively invisible stimuli are represented in visual cortex, the extent of unconscious information processing is influenced by the measurement approach. Furthermore, our data show that subjective and objective approaches are associated with different neural correlates of consciousness and thus have implications for neural theories of consciousness.

High-pass filtering artifacts in multivariate classification of neural time series data.

BACKGROUND: Traditionally, EEG/MEG data are high-pass filtered and baseline-corrected to remove slow drifts. Minor deleterious effects of high-pass filtering in traditional time-series analysis have been well-documented, including temporal displacements. However, its effects on time-resolved multivariate pattern classification analyses (MVPA) are largely unknown. NEW METHOD: To prevent potential displacement effects, we extend an alternative method of removing slow drift noise - robust detrending - with a procedure in which we mask out all cortical events from each trial. We refer to this method as trial-masked robust detrending. RESULTS: In both real and simulated EEG data of a working memory experiment, we show that both high-pass filtering and standard robust detrending create artifacts that result in the displacement of multivariate patterns into activity silent periods, particularly apparent in temporal generalization analyses, and especially in combination with baseline correction. We show that trial-masked robust detrending is free from such displacements. COMPARISON WITH EXISTING METHOD(S): Temporal displacement may emerge even with modest filter cut-off settings such as 0.05 Hz, and even in regular robust detrending. However, trial-masked robust detrending results in artifact-free decoding without displacements. Baseline correction may unwittingly obfuscate spurious decoding effects and displace them to the rest of the trial. CONCLUSIONS: Decoding analyses benefit from trial-masked robust detrending, without the unwanted side effects introduced by filtering or regular robust detrending. However, for sufficiently clean data sets and sufficiently strong signals, no filtering or detrending at all may work adequately. Implications for other types of data are discussed, followed by a number of recommendations.

Criteria for empirical theories of consciousness should focus on the explanatory power of mechanisms, not on functional equivalence.

Doerig and colleagues put forward the notion that we need hard and theory-neutral criteria by which to arbitrate between empirical (mechanistic) theories of consciousness. However, most of the criteria that they propose are not theory neutral because they focus on functional equivalence between systems. Because empirical theories of consciousness are mechanistic rather than functionalist, we think these criteria are not helpful when arbitrating between them.

Frontal cortex differentiates between free and imposed target selection in multiple-target search.

Cognitive control can involve proactive (preparatory) and reactive (corrective) mechanisms. Using a gaze-contingent eye tracking paradigm combined with fMRI, we investigated the involvement of these different modes of control and their underlying neural networks, when switching between different targets in multiple-target search. Participants simultaneously searched for two possible targets presented among distractors, and selected one of them. In one condition, only one of the targets was available in each display, so that the choice was imposed, and reactive control would be required. In the other condition, both targets were present, giving observers free choice over target selection, and allowing for proactive control. Switch costs emerged only when targets were imposed and not when target selection was free. We found differential levels of activity in the frontoparietal control network depending on whether target switches were free or imposed. Furthermore, we observed core regions of the default mode network to be active during target repetitions, indicating reduced control on these trials. Free and imposed switches jointly activated parietal and posterior frontal cortices, while free switches additionally activated anterior frontal cortices. These findings highlight unique contributions of proactive and reactive control during visual search.

Independent Neural Activity Patterns for Sensory- and Confidence-Based Information Maintenance during Category-Selective Visual Processing.

Several influential theories of consciousness attempt to explain how, when and where conscious perception arises in the brain. The extent of conscious perception of a stimulus is often probed by asking subjects to provide confidence estimations in their choices in challenging perceptual decision-making tasks. Here, we aimed to dissociate neural patterns of "cognitive" and "sensory" information maintenance by linking category selective visual processes to decision confidence using multivariate decoding techniques on human EEG data. Participants discriminated at-threshold masked face versus house stimuli and reported confidence in their discrimination performance. Three distinct types of category-selective neural activity patterns were observed, dissociable by their timing, scalp topography, relationship with decision confidence, and generalization profile. An early (∼150-200 ms) decoding profile was unrelated to confidence and quickly followed by two distinct decodable patterns of late neural activity (350-500 ms). One pattern was on-diagonal, global and highly related to decision confidence, likely indicating cognitive maintenance of consciously reportable stimulus representations. The other pattern however was off-diagonal, restricted to posterior electrode sites (local), and independent of decision confidence, and therefore may reflect sensory maintenance of category-specific information, possibly operating via recurrent processes within visual cortices. These results highlight that two functionally independent neural processes are operating in parallel, only one of which is related to decision confidence and conscious access.

Beta and Theta Oscillations Differentially Support Free Versus Forced Control over Multiple-Target Search.

Many important situations require human observers to simultaneously search for more than one object. Despite a long history of research into visual search, the behavioral and neural mechanisms associated with multiple-target search are poorly understood. Here we test the novel theory that the efficiency of looking for multiple targets critically depends on the mode of cognitive control the environment affords to the observer. We used an innovative combination of electroencephalogram (EEG) and eye tracking while participants searched for two targets, within two different contexts: either both targets were present in the search display and observers were free to prioritize either one of them, thus enabling proactive control over selection; or only one of the two targets would be present in each search display, which requires reactive control to reconfigure selection when the wrong target has been prioritized. During proactive control, both univariate and multivariate signals of beta-band (15-35 Hz) power suppression before display onset predicted switches between target selections. This signal originated over midfrontal and sensorimotor regions and has previously been associated with endogenous state changes. In contrast, imposed target selections requiring reactive control elicited prefrontal power enhancements in the delta/theta band (2-8 Hz), but only after display onset. This signal predicted individual differences in associated oculomotor switch costs, reflecting reactive reconfiguration of target selection. The results provide compelling evidence that multiple target representations are differentially prioritized during visual search, and for the first time reveal distinct neural mechanisms underlying proactive and reactive control over multiple-target search.SIGNIFICANCE STATEMENT Searching for more than one object in complex visual scenes can be detrimental for search performance. Although perhaps annoying in daily life, this can have severe consequences in professional settings such as medical and security screening. Previous research has not yet resolved whether multiple-target search involves changing priorities in what people attend to, and how such changes are controlled. We approached these questions by concurrently measuring cortical activity and eye movements using EEG and eye tracking while observers searched for multiple possible targets. Our findings provide the first unequivocal support for the existence of two modes of control during multiple-target search, which are expressed in qualitatively distinct time-frequency signatures of the EEG both before and after visual selection.

From ERPs to MVPA Using the Amsterdam Decoding and Modeling Toolbox (ADAM).

In recent years, time-resolved multivariate pattern analysis (MVPA) has gained much popularity in the analysis of electroencephalography (EEG) and magnetoencephalography (MEG) data. However, MVPA may appear daunting to those who have been applying traditional analyses using event-related potentials (ERPs) or event-related fields (ERFs). To ease this transition, we recently developed the Amsterdam Decoding and Modeling (ADAM) toolbox in MATLAB. ADAM is an entry-level toolbox that allows a direct comparison of ERP/ERF results to MVPA results using any dataset in standard EEGLAB or Fieldtrip format. The toolbox performs and visualizes multiple-comparison corrected group decoding and forward encoding results in a variety of ways, such as classifier performance across time, temporal generalization (time-by-time) matrices of classifier performance, channel tuning functions (CTFs) and topographical maps of (forward-transformed) classifier weights. All analyses can be performed directly on raw data or can be preceded by a time-frequency decomposition of the data in which case the analyses are performed separately on different frequency bands. The figures ADAM produces are publication-ready. In the current manuscript, we provide a cookbook in which we apply a decoding analysis to a publicly available MEG/EEG dataset involving the perception of famous, non-famous and scrambled faces. The manuscript covers the steps involved in single subject analysis and shows how to perform and visualize a subsequent group-level statistical analysis. The processing pipeline covers computation and visualization of group ERPs, ERP difference waves, as well as MVPA decoding results. It ends with a comparison of the differences and similarities between EEG and MEG decoding results. The manuscript has a level of description that allows application of these analyses to any dataset in EEGLAB or Fieldtrip format.

Lack of Free Choice Reveals the Cost of Having to Search for More Than One Object.

It is debated whether people can actively search for more than one object or whether this results in switch costs. Using a gaze-contingent eye-tracking paradigm, we revealed a crucial role for cognitive control in multiple-target search. We instructed participants to simultaneously search for two target objects presented among distractors. In one condition, both targets were available, which gave the observer free choice of what to search for and allowed for proactive control. In the other condition, only one of the two targets was available, so that the choice was imposed, and a reactive mechanism would be required. No switch costs emerged when target choice was free, but switch costs emerged reliably when targets were imposed. Bridging contradictory findings, the results are consistent with models of visual selection in which only one attentional template actively drives selection and in which the efficiency of switching targets depends on the type of cognitive control allowed for by the environment.

Perceptual integration without conscious access.

The visual system has the remarkable ability to integrate fragmentary visual input into a perceptually organized collection of surfaces and objects, a process we refer to as perceptual integration. Despite a long tradition of perception research, it is not known whether access to consciousness is required to complete perceptual integration. To investigate this question, we manipulated access to consciousness using the attentional blink. We show that, behaviorally, the attentional blink impairs conscious decisions about the presence of integrated surface structure from fragmented input. However, despite conscious access being impaired, the ability to decode the presence of integrated percepts remains intact, as shown through multivariate classification analyses of electroencephalogram (EEG) data. In contrast, when disrupting perception through masking, decisions about integrated percepts and decoding of integrated percepts are impaired in tandem, while leaving feedforward representations intact. Together, these data show that access consciousness and perceptual integration can be dissociated.

NMDA Receptor Antagonist Ketamine Distorts Object Recognition by Reducing Feedback to Early Visual Cortex.

It is a well-established fact that top-down processes influence neural representations in lower-level visual areas. Electrophysiological recordings in monkeys as well as theoretical models suggest that these top-down processes depend on NMDA receptor functioning. However, this underlying neural mechanism has not been tested in humans. We used fMRI multivoxel pattern analysis to compare the neural representations of ambiguous Mooney images before and after they were recognized with their unambiguous grayscale version. Additionally, we administered ketamine, an NMDA receptor antagonist, to interfere with this process. Our results demonstrate that after recognition, the pattern of brain activation elicited by a Mooney image is more similar to that of its easily recognizable grayscale version than to the pattern evoked by the identical Mooney image before recognition. Moreover, recognition of Mooney images decreased mean response; however, neural representations of separate images became more dissimilar. So from the neural perspective, unrecognizable Mooney images all "look the same", whereas recognized Mooneys look different. We observed these effects in posterior fusiform part of lateral occipital cortex and in early visual cortex. Ketamine distorted these effects of recognition, but in early visual cortex only. This suggests that top-down processes from higher- to lower-level visual areas might operate via an NMDA pathway.

Segmentation precedes face categorization under suboptimal conditions.

Both categorization and segmentation processes play a crucial role in face perception. However, the functional relation between these subprocesses is currently unclear. The present study investigates the temporal relation between segmentation-related and category-selective responses in the brain, using electroencephalography (EEG). Surface segmentation and category content were both manipulated using texture-defined objects, including faces. This allowed us to study brain activity related to segmentation and to categorization. In the main experiment, participants viewed texture-defined objects for a duration of 800 ms. EEG results revealed that segmentation-related responses precede category-selective responses. Three additional experiments revealed that the presence and timing of categorization depends on stimulus properties and presentation duration. Photographic objects were presented for a long and short (92 ms) duration and evoked fast category-selective responses in both cases. On the other hand, presentation of texture-defined objects for a short duration only evoked segmentation-related but no category-selective responses. Category-selective responses were much slower when evoked by texture-defined than by photographic objects. We suggest that in case of categorization of objects under suboptimal conditions, such as when low-level stimulus properties are not sufficient for fast object categorization, segmentation facilitates the slower categorization process.

Neural dynamics of social tie formation in economic decision-making.

The disposition for prosocial conduct, which contributes to cooperation as arising during social interaction, requires cortical network dynamics responsive to the development of social ties, or care about the interests of specific interaction partners. Here, we formulate a dynamic computational model that accurately predicted how tie formation, driven by the interaction history, influences decisions to contribute in a public good game. We used model-driven functional MRI to test the hypothesis that brain regions key to social interactions keep track of dynamics in tie strength. Activation in the medial prefrontal cortex (mPFC) and posterior cingulate cortex tracked the individual's public good contributions. Activation in the bilateral posterior superior temporal sulcus (pSTS), and temporo-parietal junction was modulated parametrically by the dynamically developing social tie-as estimated by our model-supporting a role of these regions in social tie formation. Activity in these two regions further reflected inter-individual differences in tie persistence and sensitivity to behavior of the interaction partner. Functional connectivity between pSTS and mPFC activations indicated that the representation of social ties is integrated in the decision process. These data reveal the brain mechanisms underlying the integration of interaction dynamics into a social tie representation which in turn influenced the individual's prosocial decisions.