Dissociating Cognitive Processes During Ambiguous Information Processing in Perceptual Decision-Making

Conspiracy beliefs are associated with a reduction in frontal beta power and biases in categorizing ambiguous stimuli

Prior beliefs, such as conspiracy beliefs, significantly influence our perception of the natural world. However, the brain activity associated with perceptual decision-making in conspiracy beliefs is not well understood. To shed light on this topic, we conducted a study examining the EEG activity of believers, and skeptics during resting state with perceptual decision-making task. Our study shows that conspiracy beliefs are related to the reduced power of beta frequency band. Furthermore, skeptics tended to misclassify ambiguous face stimuli as houses more frequently than believers. These results help to explain the differences in brain activity between believers and skeptics, especially in how conspiracy beliefs impact the categorization of ambiguous stimuli.

Studying perceptual bias in favor of the from-above Necker cube perspective in a goal-directed behavior

When viewing a completely ambiguous image, different interpretations can switch involuntarily due to internal top-down processing. In the case of the Necker cube, an entirely ambiguous stimulus, observers often display a bias in perceptual switching between two interpretations based on their perspectives: one with a from-above perspective (FA) and the other with a from-below perspective (FB). Typically, observers exhibit a priori top-down bias in favor of the FA interpretation, which may stem from a statistical tendency in everyday life where we more frequently observe objects from above. However, it remains unclear whether this perceptual bias persists when individuals voluntarily decide on the Necker cube's interpretation in goal-directed behavior, and the impact of ambiguity in this context is not well-understood. In our study, we instructed observers to voluntarily identify the orientation of a Necker cube while manipulating its ambiguity from low (LA) to high (HA). Our investigation aimed to test two hypotheses: (i) whether the perspective (FA or FB) would result in a bias in response time, and (ii) whether this bias would depend on the level of stimulus ambiguity. Additionally, we analyzed electroencephalogram (EEG) signals to identify potential biomarkers that could explain the observed perceptual bias. The behavioral results confirmed a perceptual bias in favor of the from-above perspective, as indicated by shorter response times. However, this bias diminished for stimuli with high ambiguity. For the LA stimuli, the occipital theta-band power consistently exceeded the frontal theta-band power throughout most of the decision-making time. In contrast, for the HA stimuli, the frontal theta-band power started to exceed the occipital theta-band power during the 0.3-s period preceding the decision. We propose that occipital theta-band power reflects evidence accumulation, while frontal theta-band power reflects its evaluation and decision-making processes. For the FB perspective, occipital theta-band power exhibited higher values and dominated over a longer duration, leading to an overall increase in response time. These results suggest that more information and more time are needed to encode stimuli with a FB perspective, as this template is less common for the observers compared to the template for a cube with a FA perspective.

Influence of nuclear power plant interface complexity on user decision-making: an ERP study

ABSTRACUser decision-making concerning critical operations is very important to nuclear power plant (NPP) safety. The NPP interface is the main information source that guides decision-making; thus, a good interface design is essential. Among the interface design factors such as interface complexity, layout and colour, interface complexity (the amount of information in the interface) has the greatest impact on NPP operator decision-making. This paper used the event-related potential (ERP) to evaluate the impact of interface complexity on user decision-making and found interface complexity has a specific range suitable for decision-making. Based on this important finding, a fast and economical method of evaluating NPP interfaces in all design phases was proposed. This method compensates for the shortcomings of traditional methods, such as heuristic evaluation and experimental evaluation, which are inconvenient for evaluating interfaces in initial design phase; it can also be applied to interfaces with similar features in other industrial fields. Practitioner summary: Evaluation of the impact of NPP interface complexity on user decision-making through an ERP experiment revealed a specific range of interface complexity that facilitates user decision-making. Based on this finding, a new, fast and inexpensive interface evaluation method was proposed. Abbreviations: NPP: nuclear power plant, it is a thermal power station in which the heat source is a nuclear reactor; ERP: event-related potential, it is the measured brain response that is the direct result of a specific cognitive, or motor event.

Verifying unfamiliar identities: Effects of processing name and face information in the same identity-matching task

Matching the identity of unfamiliar faces is important in applied identity verification tasks, for example when verifying photo ID at border crossings, in secure access areas, or when issuing identity credentials. In these settings, other biographical details-such as name or date of birth on an identity document-are also often compared to existing records, but the impact of these concurrent checks on decisions has not been examined. Here, we asked participants to sequentially compare name, then face information between an ID card and digital records to detect errors. Across four experiments (combined n = 274), despite being told that mismatches between written name pairs and face image pairs were independent, participants were more likely to say that face images matched when names also matched. Across all experiments, we found that this bias was unaffected by the image quality, suggesting that the source of the bias is somewhat independent of perceptual processes. In a final experiment, we show that this decisional bias was found only for name checks, but not when participants were asked to check ID card expiration dates or unrelated object names. We conclude that the bias arises from processing identity information and propose that it operates at the level of unfamiliar person identity representations. Results are interpreted in the context of theoretical models of face processing, and we discuss applied implications.

Combining Statistical Analysis and Machine Learning for EEG Scalp Topograms Classification

Incorporating brain-computer interfaces (BCIs) into daily life requires reducing the reliance of decoding algorithms on the calibration or enabling calibration with the minimal burden on the user. A potential solution could be a pre-trained decoder demonstrating a reasonable accuracy on the naive operators. Addressing this issue, we considered ambiguous stimuli classification tasks and trained an artificial neural network to classify brain responses to the stimuli of low and high ambiguity. We built a pre-trained classifier utilizing time-frequency features corresponding to the fundamental neurophysiological processes shared between subjects. To extract these features, we statistically contrasted electroencephalographic (EEG) spectral power between the classes in the representative group of subjects. As a result, the pre-trained classifier achieved 74% accuracy on the data of newly recruited subjects. Analysis of the literature suggested that a pre-trained classifier could help naive users to start using BCI bypassing training and further increased accuracy during the feedback session. Thus, our results contribute to using BCI during paralysis or limb amputation when there is no explicit user-generated kinematic output to properly train a decoder. In machine learning, our approach may facilitate the development of transfer learning (TL) methods for addressing the cross-subject problem. It allows extracting the interpretable feature subspace from the source data (the representative group of subjects) related to the target data (a naive user), preventing the negative transfer in the cross-subject tasks.

Machine learning evaluates changes in functional connectivity under a prolonged cognitive load

One must be aware of the black-box problem by applying machine learning models to analyze high-dimensional neuroimaging data. It is due to a lack of understanding of the internal algorithms or the input features upon which most models make decisions despite outstanding performance in classification, pattern recognition, and prediction. Here, we approach the fundamentally high-dimensional problem of classifying cognitive brain states based on functional connectivity by selecting and interpreting the most relevant input features. Specifically, we consider the alterations in the cortical synchrony under a prolonged cognitive load. Our study highlights the advances of this machine learning method in building a robust classification model and percept-related prestimulus connectivity changes over the conventional trial-averaged statistical analysis.

Cross-modal auditory priors drive the perception of bistable visual stimuli with reliable differences between individuals

It is a widely held assumption that the brain performs perceptual inference by combining sensory information with prior expectations, weighted by their uncertainty. A distinction can be made between higher- and lower-level priors, which can be manipulated with associative learning and sensory priming, respectively. Here, we simultaneously investigate priming and the differential effect of auditory vs. visual associative cues on visual perception, and we also examine the reliability of individual differences. Healthy individuals (N = 29) performed a perceptual inference task twice with a one-week delay. They reported the perceived direction of motion of dot pairs, which were preceded by a probabilistic visuo-acoustic cue. In 30% of the trials, motion direction was ambiguous, and in half of these trials, the auditory versus the visual cue predicted opposing directions. Cue-stimulus contingency could change every 40 trials. On ambiguous trials where the visual and the auditory cue predicted conflicting directions of motion, participants made more decisions consistent with the prediction of the acoustic cue. Increased predictive processing under stimulus uncertainty was indicated by slower responses to ambiguous (vs. non-ambiguous) stimuli. Furthermore, priming effects were also observed in that perception of ambiguous stimuli was influenced by perceptual decisions on the previous ambiguous and unambiguous trials as well. Critically, behavioural effects had substantial inter-individual variability which showed high test-retest reliability (intraclass correlation coefficient (ICC) > 0.78). Overall, higher-level priors based on auditory (vs. visual) information had greater influence on visual perception, and lower-level priors were also in action. Importantly, we observed large and stable differences in various aspects of task performance. Computational modelling combined with neuroimaging could allow testing hypotheses regarding the potential mechanisms causing these behavioral effects. The reliability of the behavioural differences implicates that such perceptual inference tasks could be valuable tools during large-scale biomarker and neuroimaging studies.

Sensor-Level Wavelet Analysis Reveals EEG Biomarkers of Perceptual Decision-Making

Perceptual decision-making requires transforming sensory information into decisions. An ambiguity of sensory input affects perceptual decisions inducing specific time-frequency patterns on EEG (electroencephalogram) signals. This paper uses a wavelet-based method to analyze how ambiguity affects EEG features during a perceptual decision-making task. We observe that parietal and temporal beta-band wavelet power monotonically increases throughout the perceptual process. Ambiguity induces high frontal beta-band power at 0.3–0.6 s post-stimulus onset. It may reflect the increasing reliance on the top-down mechanisms to facilitate accumulating decision-relevant sensory features. Finally, this study analyzes the perceptual process using mixed within-trial and within-subject design. First, we found significant percept-related changes in each subject and then test their significance at the group level. Thus, observed beta-band biomarkers are pronounced in single EEG trials and may serve as control commands for brain-computer interface (BCI).

Effect of repetition on the behavioral and neuronal responses to ambiguous Necker cube images

A repeated presentation of an item facilitates its subsequent detection or identification, a phenomenon of priming. Priming may involve different types of memory and attention and affects neural activity in various brain regions. Here we instructed participants to report on the orientation of repeatedly presented Necker cubes with high (HA) and low (LA) ambiguity. Manipulating the contrast of internal edges, we varied the ambiguity and orientation of the cube. We tested how both the repeated orientation (referred to as a stimulus factor) and the repeated ambiguity (referred to as a top-down factor) modulated neuronal and behavioral response. On the behavioral level, we observed higher speed and correctness of the response to the HA stimulus following the HA stimulus and a faster response to the right-oriented LA stimulus following the right-oriented stimulus. On the neuronal level, the prestimulus theta-band power grew for the repeated HA stimulus, indicating activation of the neural networks related to attention and uncertainty processing. The repeated HA stimulus enhanced hippocampal activation after stimulus onset. The right-oriented LA stimulus following the right-oriented stimulus enhanced activity in the precuneus and the left frontal gyri before the behavioral response. During the repeated HA stimulus processing, enhanced hippocampal activation may evidence retrieving information to disambiguate the stimulus and define its orientation. Increased activation of the precuneus and the left prefrontal cortex before responding to the right-oriented LA stimulus following the right-oriented stimulus may indicate a match between their orientations. Finally, we observed increased hippocampal activation after responding to the stimuli, reflecting the encoding stimulus features in memory. In line with the large body of works relating the hippocampal activity with episodic memory, we suppose that this type of memory may subserve the priming effect during the repeated presentation of ambiguous images.

Revealing a multiplex brain network through the analysis of recurrences

A multilayer approach has recently received particular attention in network neuroscience as a suitable model to describe brain dynamics by adjusting its activity in different frequency bands, time scales, modalities, or ages to different layers of a multiplex graph. In this paper, we demonstrate an approach to a frequency-based multilayer functional network constructed from nonstationary multivariate data by analyzing recurrences in application to electroencephalography. Using the recurrence-based index of synchronization, we construct intralayer (within-frequency) and interlayer (cross-frequency) graph edges to model the evolution of a whole-head functional connectivity network during a prolonged stimuli classification task. We demonstrate that the graph edges' weights increase during the experiment and negatively correlate with the response time. We also show that while high-frequency activity evolves toward synchronization of remote local areas, low-frequency connectivity tends to establish large-scale coupling between them.