Should metacognition be measured by logistic regression?

Is visual metacognition associated with autistic traits? A regression analysis shows no link between visual metacognition and Autism-Spectrum Quotient scores

Metacognition -the human ability to recognize correct decisions- is a key cognitive process linked to learning and development. Several recent studies investigated the relationship between metacognition and autism. However, the evidence is still inconsistent. While some studies reported autistic people having lower levels of metacognitive sensitivity, others did not. Leveraging the fact that autistic traits are present in the general population, our study investigated the relationship between visual metacognition and autistic traits in a sample of 360 neurotypical participants. We measured metacognition as the correspondence between confidence and accuracy in a visual two alternative forced choice task. Autistic-traits were assessed through the Autism-spectrum Quotient (AQ) score. A regression analysis revealed no statistically significant association between autistic traits and metacognition or confidence. Furthermore, we found no link between AQ sub-scales and metacognition. We do not find support for the hypothesis that autistic traits are associated with metacognition in the general population.

Confidence in consciousness research

To study (un)conscious perception and test hypotheses about consciousness, researchers need procedures for determining whether subjects consciously perceive stimuli or not. This article is an introduction to a family of procedures called "confidence-based procedures," which consist in interpreting metacognitive indicators as indicators of consciousness. I assess the validity and accuracy of these procedures, and answer a series of common objections to their use in consciousness research. I conclude that confidence-based procedures are valid for assessing consciousness, and, in most cases, accurate enough for our practical and scientific purposes. This article is categorized under: Psychology > Perception and Psychophysics Philosophy > Consciousness.

Continuous Theta Burst Stimulation to the left anterior medial prefrontal cortex influences metacognitive efficiency

The contribution of the prefrontal areas to visual awareness is critical for the Global Neuronal Workspace Theory and higher-order theories of consciousness. The goal of the present study was to test the potential engagement of the anterior medial prefrontal cortex (aMPFC) in visual awareness judgements. We aimed to temporarily influence the neuronal dynamics of the left aMPFC via neuroplasticity-like mechanisms. We used different Theta Burst Stimulation (TBS) protocols in combination with a visual identification task and visual awareness ratings. Either continuous TBS (cTBS), intermittent TBS (iTBS), or sham TBS was applied prior to the experimental paradigm in a within-participant design. Compared with sham TBS, we observed an increase in participants' ability to judge their perception adequately (metacognitive efficiency) following cTBS but not iTBS. The effect was accompanied by lower visual awareness ratings in incorrect responses. No significant differences in the identification task performance were observed. We interpret these results as evidence of the involvement of PFC in the brain network that underlies metacognition. Further, we discuss whether the results of TMS studies on perceptual metacognition can be taken as evidence for PFC involvement in awareness itself.

A window of subliminal perception

Under labels such as unconscious processing and subliminal perception, identification of stimuli falling below the subjective threshold (whether truly unconscious or not) has been found remarkably accurate in some experiments while completely at chance in others. Here, we first identify that an apparent window of subliminal perception arises in humans under specific stimulus conditions using different experimental paradigms and analysis methods. We then show that the standard signal detection theory (SDT) model is unable to account for this window and extend it until it is. We finally compare a range of models on empirical data. The models performing best are notable for their absence of hierarchical levels, indicating that the window could be a base property of any phenomenally conscious system. The models explain previously incompatible findings in the literature, and they allow for estimations of peaks in subthreshold perception across the spectrum of stimulus saliency, which may be used in further studies of subliminal perception.

Machine learning models effectively distinguish attention-deficit/hyperactivity disorder using event-related potentials

Accurate diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) is a significant challenge. Misdiagnosis has significant negative medical side effects. Due to the complex nature of this disorder, there is no computational expert system for diagnosis. Recently, automatic diagnosis of ADHD by machine learning analysis of brain signals has received an increased attention. This paper aimed to achieve an accurate model to discriminate between ADHD patients and healthy controls by pattern discovery. Event-Related Potentials (ERP) data were collected from ADHD patients and healthy controls. After pre-processing, ERP signals were decomposed and features were calculated for different frequency bands. The classification was carried out based on each feature using seven machine learning algorithms. Important features were then selected and combined. To find specific patterns for each model, the classification was repeated using the proposed patterns. Results indicated that the combination of complementary features can significantly improve the performance of the predictive models. The newly developed features, defined based on band power, were able to provide the best classification using the Generalized Linear Model, Logistic Regression, and Deep Learning with the average accuracy and Receiver operating characteristic curve > %99.85 and > 0.999, respectively. High and low frequencies (Beta, Delta) performed better than the mid, frequencies in the discrimination of ADHD from control. Altogether, this study developed a machine learning expert system that minimises misdiagnosis of ADHD and is beneficial for the evaluation of treatment efficacy.

Examining the robustness of the relationship between metacognitive efficiency and metacognitive bias

We recently found a positive relationship between estimates of metacognitive efficiency and metacognitive bias. However, this relationship was only examined on a within-subject level and required binarizing the confidence scale, a technique that introduces methodological difficulties. Here we examined the robustness of the positive relationship between estimates of metacognitive efficiency and metacognitive bias by conducting two different types of analyses. First, we developed a new within-subject analysis technique where the original n-point confidence scale is transformed into two different (n-1)-point scales in a way that mimics a naturalistic change in confidence. Second, we examined the across-subject correlation between metacognitive efficiency and metacognitive bias. Importantly, for both types of analyses, we not only established the direction of the effect but also computed effect sizes. We applied both techniques to the data from three tasks from the Confidence Database (N > 400 in each). We found that both approaches revealed a small to medium positive relationship between metacognitive efficiency and metacognitive bias. These results demonstrate that the positive relationship between metacognitive efficiency and metacognitive bias is robust across several analysis techniques and datasets, and have important implications for future research.

Modelling visibility judgments using models of decision confidence

How can we explain the regularities in subjective reports of human observers about their subjective visual experience of a stimulus? The present study tests whether a recent model of confidence in perceptual decisions, the weighted evidence and visibility model, can be generalized from confidence to subjective visibility. In a postmasked orientation identification task, observers reported the subjective visibility of the stimulus after each single identification response. Cognitive modelling revealed that the weighted evidence and visibility model provided a superior fit to the data compared with the standard signal detection model, the signal detection model with unsystematic noise superimposed on ratings, the postdecisional accumulation model, the two-channel model, the response-congruent evidence model, the two-dimensional Bayesian model, and the constant noise and decay model. A comparison between subjective visibility and decisional confidence revealed that visibility relied more on the strength of sensory evidence about features of the stimulus irrelevant to the identification judgment and less on evidence for the identification judgment. It is argued that at least two types of evidence are required to account for subjective visibility, one related to the identification judgment, and one related to the strength of stimulation.

Binomial Logistic Regression and Artificial Neural Network Methods to Classify Opioid-Dependent Subjects and Control Group Using Quantitative EEG Power Measures

Logistic regression (LR) and artificial neural networks (ANNs) are widely referred approaches in medical data classification studies. LR, a statistical fitting model, is suggested in medical problems because of its well-established methodology and coefficients contributing to the evaluation of clinical interpretations. ANNs are graphical models structured with node networks interconnected with arcs each of which is expressed in terms of weights discovered throughout the modeling process. Since ANNs have a complex structure with its layers and nodes in the layers, which provides ANNs the ability to model any data with complex relationships. Among the various models having origins in statistics and computer science, LR and ANNs have prevailed in the area of mass medical data classification. In this study, we introduce the 2 aforementioned approaches in order to generate a model dichotomizing 75 opioid-dependent patients and 59 control subjects from each other. Quantitative electroencephalography (QEEG) absolute power value of each electrode were calculated for 4 consecutive frequency bands namely delta, theta, alpha, and beta with the frequencies, 0.5 to 4, 4 to 8, 8 to 12, and 12 to 20 Hz, respectively. Significant independent variables contributing to the classification were underlined in LR while a feature selection (FS) method, genetic algorithm, is being applied to the ANN model to reveal more informative features. The performances of the classifiers were finally compared considering overall classification accuracies, area under receiver operating characteristic curve scores, and Gini coefficient. Although ANN-based classifier outperformed compared with LR, both models performed satisfactorily for absolute power measure in beta frequency band. Our results underline the potential benefit of the introduced methodology is promising and is to be treated as a clinical interface in dichotomizing substance use disorders subjects and for other medical data analysis studies.

Contextual cueing of visual search is associated with greater subjective experience of the search display configuration

Visual search is facilitated when display configurations are repeated over time, showing that memory of spatio-configural context can cue the location of the target. The present study investigates whether memory of the search target in relation to the configuration of distractors alters subjective experience of the visual search target and/or the subjective experience of the display configuration. Observers performed a masked localization task for targets embedded in repeated vs. non-repeated (baseline) arrays of distractors items. After the localization response, observers reported their subjective experience of either the target or the display configuration. Bayesian analysis revealed that repeated displays resulted in a stronger visual experience of both targets and display configurations. However, subsequent analysis showed that repeated search displays increased the correlation between the experience of the display configuration and localization accuracy, but there was no such effect on experience of the target stimulus. We suggest that memory of visual context enhances the representation of the current visual search display. This representation improves visual search and at the same time increases observers' subjective experience of the display configuration.