Terms of debate: Consensus definitions to guide the scientific discourse on visual distraction.

Hypothesis-driven research rests on clearly articulated scientific theories. The building blocks for communicating these theories are scientific terms. Obviously, communication - and thus, scientific progress - is hampered if the meaning of these terms varies idiosyncratically across (sub)fields and even across individual researchers within the same subfield. We have formed an international group of experts representing various theoretical stances with the goal to homogenize the use of the terms that are most relevant to fundamental research on visual distraction in visual search. Our discussions revealed striking heterogeneity and we had to invest much time and effort to increase our mutual understanding of each other's use of central terms, which turned out to be strongly related to our respective theoretical positions. We present the outcomes of these discussions in a glossary and provide some context in several essays. Specifically, we explicate how central terms are used in the distraction literature and consensually sharpen their definitions in order to enable communication across theoretical standpoints. Where applicable, we also explain how the respective constructs can be measured. We believe that this novel type of adversarial collaboration can serve as a model for other fields of psychological research that strive to build a solid groundwork for theorizing and communicating by establishing a common language. For the field of visual distraction, the present paper should facilitate communication across theoretical standpoints and may serve as an introduction and reference text for newcomers.

Perceptual learning in humans: An active, top-down-guided process.

Deep neural network (DNN) models of human-like vision are typically built by feeding blank slate DNN visual images as training data. However, the literature on human perception and perceptual learning suggests that developing DNNs that truly model human vision requires a shift in approach in which perception is not treated as a largely bottom-up process, but as an active, top-down-guided process.

The (In)flexible self: Psychopathology, mindfulness, and neuroscience

Clinical and neuroscientific evidence indicates that transdiagnostic processes contribute to the generation and maintenance of psychopathological symptoms and disorders. Rigidity (inflexibility) appears a core feature of most transdiagnostic pathological processes. Decreasing rigidity may prove important to restore and maintain mental health. One of the primary domains in which rigidity and flexibility plays a role concerns the self. We adopt the pattern theory of self (PTS) for a working definition of self. This incorporates the pluralist view on self as constituted by multiple aspects or processes, understood to constitute a self-pattern, i.e. processes organized in non-linear dynamical relations across a number of time scales. The use of mindfulness meditation in the format of Mindfulness Based Interventions (MBIs) has been developed over four decades in Clinical Psychology. MBIs are promising as evidence-based treatments, shown to be equivalent to gold-standard treatments and superior to specific active controls in several randomized controlled trials. Notably, MBIs have been shown to target transdiagnostic symptoms. Given the hypothesized central role of rigid, habitual self-patterns in psychopathology, PTS offers a useful frame to understand how mindfulness may be beneficial in decreasing inflexibility. We discuss the evidence that mindfulness can alter the psychological and behavioral expression of individual aspects of the self-pattern, as well as favour change in the self-pattern as a whole gestalt. We discuss neuroscientific research on how the phenomenology of the self (pattern) is reflected in associated cortical networks and meditation-related alterations in cortical networks. Creating a synergy between these two aspects can increase understanding of psychopathological processes and improve diagnostic and therapeutic options. (AU)

The Distractor Positivity Component and the Inhibition of Distracting Stimuli.

There has been a long-lasting debate about whether salient stimuli, such as uniquely colored objects, have the ability to automatically distract us. To resolve this debate, it has been suggested that salient stimuli do attract attention but that they can be suppressed to prevent distraction. Some research supporting this viewpoint has focused on a newly discovered ERP component called the distractor positivity (PD), which is thought to measure an inhibitory attentional process. This collaborative review summarizes previous research relying on this component with a specific emphasis on how the PD has been used to understand the ability to ignore distracting stimuli. In particular, we outline how the PD component has been used to gain theoretical insights about how search strategy and learning can influence distraction. We also review alternative accounts of the cognitive processes indexed by the PD component. Ultimately, we conclude that the PD component is a useful tool for understanding inhibitory processes related to distraction and may prove to be useful in other areas of study related to cognitive control.

Cessations of consciousness in meditation: Advancing a scientific understanding of nirodha samapatti.

Absence of consciousness can occur due to a concussion, anesthetization, intoxication, epileptic seizure, or other fainting/syncope episode caused by lack of blood flow to the brain. However, some meditation practitioners also report that it is possible to undergo a total absence of consciousness during meditation, lasting up to 7 days, and that these "cessations" can be consistently induced. One form of extended cessation (i.e., nirodha samapatti) is thought to be different from sleep because practitioners are said to be completely impervious to external stimulation. That is, they cannot be 'woken up' from the cessation state as one might be from a dream. Cessations are also associated with the absence of any time experience or tiredness, and are said to involve a stiff rather than a relaxed body. Emergence from meditation-induced cessations is said to have profound effects on subsequent cognition and experience (e.g., resulting in a sudden sense of clarity, openness, and possibly insights). In this paper, we briefly outline the historical context for cessation events, present preliminary data from two labs, set a research agenda for their study, and provide an initial framework for understanding what meditation induced cessation may reveal about the mind and brain. We conclude by integrating these so-called nirodha and nirodha samapatti experiences-as they are known in classical Buddhism-into current cognitive-neurocomputational and active inference frameworks of meditation.

Investigation of advanced mindfulness meditation "cessation" experiences using EEG spectral analysis in an intensively sampled case study.

Mindfulness meditation is a contemplative practice informed by Buddhism that targets the development of present-focused awareness and non-judgment of experience. Interest in mindfulness is burgeoning, and it has been shown to be effective in improving mental and physical health in clinical and non-clinical contexts. In this report, for the first time, we used electroencephalography (EEG) combined with a neurophenomenological approach to examine the neural signature of "cessation" events, which are dramatic experiences of complete discontinuation in awareness similar to the loss of consciousness, which are reported to be experienced by very experienced meditators, and are proposed to be evidence of mastery of mindfulness meditation. We intensively sampled these cessations as experienced by a single advanced meditator (with over 23,000 h of meditation training) and analyzed 37 cessation events collected in 29 EEG sessions between November 12, 2019, and March 11, 2020. Spectral analyses of the EEG data surrounding cessations showed that these events were marked by a large-scale alpha-power decrease starting around 40 s before their onset, and that this alpha-power was lowest immediately following a cessation. Region-of-interest (ROI) based examination of this finding revealed that this alpha-suppression showed a linear decrease in the occipital and parietal regions of the brain during the pre-cessation time period. Additionally, there were modest increases in theta power for the central, parietal, and right temporal ROIs during the pre-cessation timeframe, whereas power in the Delta and Beta frequency bands were not significantly different surrounding cessations. By relating cessations to objective and intrinsic measures of brain activity (i.e., EEG power) that are related to consciousness and high-level psychological functioning, these results provide evidence for the ability of experienced meditators to voluntarily modulate their state of consciousness and lay the foundation for studying these unique states using a neuroscientific approach.

Insight and the selection of ideas.

Perhaps it is no accident that insight moments accompany some of humanity's most important discoveries in science, medicine, and art. Here we propose that feelings of insight play a central role in (heuristically) selecting an idea from the stream of consciousness by capturing attention and eliciting a sense of intuitive confidence permitting fast action under uncertainty. The mechanisms underlying this Eureka heuristic are explained within an active inference framework. First, implicit restructuring via Bayesian reduction leads to a higher-order prediction error (i.e., the content of insight). Second, dopaminergic precision-weighting of the prediction error accounts for the intuitive confidence, pleasure, and attentional capture (i.e., the feeling of insight). This insight as precision account is consistent with the phenomenology, accuracy, and neural unfolding of insight, as well as its effects on belief and decision-making. We conclude by reflecting on dangers of the Eureka Heuristic, including the arising and entrenchment of false beliefs and the vulnerability of insights under psychoactive substances and misinformation.

Mechanisms of human dynamic object recognition revealed by sequential deep neural networks.

Humans can quickly recognize objects in a dynamically changing world. This ability is showcased by the fact that observers succeed at recognizing objects in rapidly changing image sequences, at up to 13 ms/image. To date, the mechanisms that govern dynamic object recognition remain poorly understood. Here, we developed deep learning models for dynamic recognition and compared different computational mechanisms, contrasting feedforward and recurrent, single-image and sequential processing as well as different forms of adaptation. We found that only models that integrate images sequentially via lateral recurrence mirrored human performance (N = 36) and were predictive of trial-by-trial responses across image durations (13-80 ms/image). Importantly, models with sequential lateral-recurrent integration also captured how human performance changes as a function of image presentation durations, with models processing images for a few time steps capturing human object recognition at shorter presentation durations and models processing images for more time steps capturing human object recognition at longer presentation durations. Furthermore, augmenting such a recurrent model with adaptation markedly improved dynamic recognition performance and accelerated its representational dynamics, thereby predicting human trial-by-trial responses using fewer processing resources. Together, these findings provide new insights into the mechanisms rendering object recognition so fast and effective in a dynamic visual world.

Illusory object recognition is either perceptual or cognitive in origin depending on decision confidence.

We occasionally misinterpret ambiguous sensory input or report a stimulus when none is presented. It is unknown whether such errors have a sensory origin and reflect true perceptual illusions, or whether they have a more cognitive origin (e.g., are due to guessing), or both. When participants performed an error-prone and challenging face/house discrimination task, multivariate electroencephalography (EEG) analyses revealed that during decision errors (e.g., mistaking a face for a house), sensory stages of visual information processing initially represent the presented stimulus category. Crucially however, when participants were confident in their erroneous decision, so when the illusion was strongest, this neural representation flipped later in time and reflected the incorrectly reported percept. This flip in neural pattern was absent for decisions that were made with low confidence. This work demonstrates that decision confidence arbitrates between perceptual decision errors, which reflect true illusions of perception, and cognitive decision errors, which do not.

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.

The (In)flexible self: Psychopathology, mindfulness, and neuroscience.

Clinical and neuroscientific evidence indicates that transdiagnostic processes contribute to the generation and maintenance of psychopathological symptoms and disorders. Rigidity (inflexibility) appears a core feature of most transdiagnostic pathological processes. Decreasing rigidity may prove important to restore and maintain mental health. One of the primary domains in which rigidity and flexibility plays a role concerns the self. We adopt the pattern theory of self (PTS) for a working definition of self. This incorporates the pluralist view on self as constituted by multiple aspects or processes, understood to constitute a self-pattern, i.e. processes organized in non-linear dynamical relations across a number of time scales. The use of mindfulness meditation in the format of Mindfulness Based Interventions (MBIs) has been developed over four decades in Clinical Psychology. MBIs are promising as evidence-based treatments, shown to be equivalent to gold-standard treatments and superior to specific active controls in several randomized controlled trials. Notably, MBIs have been shown to target transdiagnostic symptoms. Given the hypothesized central role of rigid, habitual self-patterns in psychopathology, PTS offers a useful frame to understand how mindfulness may be beneficial in decreasing inflexibility. We discuss the evidence that mindfulness can alter the psychological and behavioral expression of individual aspects of the self-pattern, as well as favour change in the self-pattern as a whole gestalt. We discuss neuroscientific research on how the phenomenology of the self (pattern) is reflected in associated cortical networks and meditation-related alterations in cortical networks. Creating a synergy between these two aspects can increase understanding of psychopathological processes and improve diagnostic and therapeutic options.

Visual working memory representations bias attention more when they are the target of an action plan.

Attention has frequently been regarded as an emergent property of linking sensory representations to action plans. It has recently been proposed that similar mechanisms may operate within visual working memory (VWM), such that linking an object in VWM to an action plan strengthens its sensory memory representation, which then expresses as an attentional bias. Here we directly tested this hypothesis by comparing attentional biases induced by VWM representations which were the target of a future action, to those induced by VWM representations that were equally task-relevant, but not the direct target of action. We predicted that the first condition would result in a more prioritized memory state and hence stronger attentional biases. Specifically, participants memorized a geometric shape for a subsequent memory test. At test, in case of a match, participants either had to perform a grip movement on the matching object (action condition), or perform the same movement, but on an unrelated object (control condition). To assess any attentional biases, during the delay period between memorandum and test, participants performed a visual selection task in which either the target was surrounded by the memorized shape (congruent trials) or a distractor (incongruent trials). Eye movements were measured as a proxy for attentional priority. We found a significant interaction for saccade latencies between action condition and shape congruency, reflecting more pronounced VWM-based attentional biases in the action condition. Our results are consistent with the idea that action plans prioritize sensory representations in VWM.

Ten simple rules to study distractor suppression.

Distractor suppression refers to the ability to filter out distracting and task-irrelevant information. Distractor suppression is essential for survival and considered a key aspect of selective attention. Despite the recent and rapidly evolving literature on distractor suppression, we still know little about how the brain suppresses distracting information. What limits progress is that we lack mutually agreed upon principles of how to study the neural basis of distractor suppression and its manifestation in behavior. Here, we offer ten simple rules that we believe are fundamental when investigating distractor suppression. We provide guidelines on how to design conclusive experiments on distractor suppression (Rules 1-3), discuss different types of distractor suppression that need to be distinguished (Rules 4-6), and provide an overview of models of distractor suppression and considerations of how to evaluate distractor suppression statistically (Rules 7-10). Together, these rules provide a concise and comprehensive synopsis of promising advances in the field of distractor suppression. Following these rules will propel research on distractor suppression in important ways, not only by highlighting prominent issues to both new and more advanced researchers in the field, but also by facilitating communication between sub-disciplines.

Arousal state affects perceptual decision-making by modulating hierarchical sensory processing in a large-scale visual system model.

Arousal levels strongly affect task performance. Yet, what arousal level is optimal for a task depends on its difficulty. Easy task performance peaks at higher arousal levels, whereas performance on difficult tasks displays an inverted U-shape relationship with arousal, peaking at medium arousal levels, an observation first made by Yerkes and Dodson in 1908. It is commonly proposed that the noradrenergic locus coeruleus system regulates these effects on performance through a widespread release of noradrenaline resulting in changes of cortical gain. This account, however, does not explain why performance decays with high arousal levels only in difficult, but not in simple tasks. Here, we present a mechanistic model that revisits the Yerkes-Dodson effect from a sensory perspective: a deep convolutional neural network augmented with a global gain mechanism reproduced the same interaction between arousal state and task difficulty in its performance. Investigating this model revealed that global gain states differentially modulated sensory information encoding across the processing hierarchy, which explained their differential effects on performance on simple versus difficult tasks. These findings offer a novel hierarchical sensory processing account of how, and why, arousal state affects task performance.

Leveraging Spiking Deep Neural Networks to Understand the Neural Mechanisms Underlying Selective Attention.

Spatial attention enhances sensory processing of goal-relevant information and improves perceptual sensitivity. Yet, the specific neural mechanisms underlying the effects of spatial attention on performance are still contested. Here, we examine different attention mechanisms in spiking deep convolutional neural networks. We directly contrast effects of precision (internal noise suppression) and two different gain modulation mechanisms on performance on a visual search task with complex real-world images. Unlike standard artificial neurons, biological neurons have saturating activation functions, permitting implementation of attentional gain as gain on a neuron's input or on its outgoing connection. We show that modulating the connection is most effective in selectively enhancing information processing by redistributing spiking activity and by introducing additional task-relevant information, as shown by representational similarity analyses. Precision only produced minor attentional effects in performance. Our results, which mirror empirical findings, show that it is possible to adjudicate between attention mechanisms using more biologically realistic models and natural stimuli.

Effects of tDCS on the attentional blink revisited: A statistical evaluation of a replication attempt.

The attentional blink (AB) phenomenon reveals a bottleneck of human information processing: the second of two targets is often missed when they are presented in rapid succession among distractors. In our previous work, we showed that the size of the AB can be changed by applying transcranial direct current stimulation (tDCS) over the left dorsolateral prefrontal cortex (lDLPFC) (London & Slagter, Journal of Cognitive Neuroscience, 33, 756-68, 2021). Although AB size at the group level remained unchanged, the effects of anodal and cathodal tDCS were negatively correlated: if a given individual's AB size decreased from baseline during anodal tDCS, their AB size would increase during cathodal tDCS, and vice versa. Here, we attempted to replicate this finding. We found no group effects of tDCS, as in the original study, but we no longer found a significant negative correlation. We present a series of statistical measures of replication success, all of which confirm that both studies are not in agreement. First, the correlation here is significantly smaller than a conservative estimate of the original correlation. Second, the difference between the correlations is greater than expected due to sampling error, and our data are more consistent with a zero-effect than with the original estimate. Finally, the overall effect when combining both studies is small and not significant. Our findings thus indicate that the effects of lDPLFC-tDCS on the AB are less substantial than observed in our initial study. Although this should be quite a common scenario, null findings can be difficult to interpret and are still under-represented in the brain stimulation and cognitive neuroscience literatures. An important auxiliary goal of this paper is therefore to provide a tutorial for other researchers, to maximize the evidential value from null findings.

Attention and distraction in the predictive brain.

Whether it is possible to ignore a physically salient distractor has been a topic of active debate over the past 25 years, with empirical evidence for and against each of the theoretical stances. We put forward that predictive processing may provide a unified theoretical perspective that can account reasonably well for the empirical literature on attentional capture. In this perspective, capture is a logical consequence of the overall imperative of the brain to predict what sensory signals provide precise information to achieve goal-directed behaviour.

Qualitative Versus Quantitative Individual Differences in Cognitive Neuroscience.

Individual differences in cognitive performance can be quantitative or qualitative in nature. Accounting for qualitative as well as quantitative individual differences is of importance for cognitive neuroscience, where a central goal is not only to relate brain function to behavior generally, but also to understand and predict individual behavior from neural data. In turn, cognitive neuroscience can help determine the nature of individual differences by revealing the underlying neural mechanisms and uncover qualitative individual differences that are not immediately apparent from behavioral data, enhancing our understanding of why and how people behave the way they do.

From many to (n)one: Meditation and the plasticity of the predictive mind.

How profoundly can humans change their own minds? In this paper we offer a unifying account of deconstructive meditation under the predictive processing view. We start from simple axioms. First, the brain makes predictions based on past experience, both phylogenetic and ontogenetic. Second, deconstructive meditation brings one closer to the here and now by disengaging anticipatory processes. We propose that practicing meditation therefore gradually reduces counterfactual temporally deep cognition, until all conceptual processing falls away, unveiling a state of pure awareness. Our account also places three main styles of meditation (focused attention, open monitoring, and non-dual) on a single continuum, where each technique relinquishes increasingly engrained habits of prediction, including the predicted self. This deconstruction can also permit certain insights by making the above processes available to introspection. Our framework is consistent with the state of empirical and (neuro)phenomenological evidence and illuminates the top-down plasticity of the predictive mind. Experimental rigor, neurophenomenology, and no-report paradigms are needed to further understanding of how meditation affects predictive processing and the self.

Neural mechanisms underlying distractor inhibition on the basis of feature and/or spatial expectations.

A rapidly growing body of research indicates that inhibition of distracting information may not be under flexible, top-down control, but instead heavily relies on expectations derived from past experience about the likelihood of events. Yet, how expectations about distracting information influence distractor inhibition at the neural level remains unclear. To determine how expectations induced by distractor features and/or location regularities modulate distractor processing, we measured EEG while participants performed two variants of the additional singleton paradigm. Critically, in these different variants, target and distractor features either randomly swapped across trials, or were fixed, allowing for the development of distractor feature-based expectations. Moreover, the task was initially performed without any spatial regularity, after which a high probability distractor location was introduced. Our results show that both distractor feature- and location regularities contributed to distractor inhibition, as indicated by corresponding reductions in distractor costs during visual search and an earlier distractor-evoked Pd component. Yet, control analyses showed that while observers were sensitive to regularities across longer time scales, the observed effects to a large extent reflected intertrial repetition. Large individual differences further suggest a functional dissociation between early and late Pd components, with the former reflecting early sensory suppression related to intertrial priming and the latter reflecting suppression sensitive to expectations derived over a longer time scale. Also, counter to some previous findings, no increase in anticipatory alpha-band activity was observed over visual regions representing the expected distractor location, although this effect should be interpreted with caution as the effect of spatial statistical learning was also less pronounced than in other studies. Together, these findings suggest that intertrial priming and statistical learning may both contribute to distractor suppression and reveal the underlying neural mechanisms.