Investigating the different mechanisms in related neural activities: a focus on auditory perception and imagery.

Neuroimaging studies have shown that the neural representation of imagery is closely related to the perception modality; however, the undeniable different experiences between perception and imagery indicate that there are obvious neural mechanism differences between them, which cannot be explained by the simple theory that imagery is a form of weak perception. Considering the importance of functional integration of brain regions in neural activities, we conducted correlation analysis of neural activity in brain regions jointly activated by auditory imagery and perception, and then brain functional connectivity (FC) networks were obtained with a consistent structure. However, the connection values between the areas in the superior temporal gyrus and the right precentral cortex were significantly higher in auditory perception than in the imagery modality. In addition, the modality decoding based on FC patterns showed that the FC network of auditory imagery and perception can be significantly distinguishable. Subsequently, voxel-level FC analysis further verified the distribution regions of voxels with significant connectivity differences between the 2 modalities. This study complemented the correlation and difference between auditory imagery and perception in terms of brain information interaction, and it provided a new perspective for investigating the neural mechanisms of different modal information representations.

The Self-Narrative Role in the Diagnosis of Motivation to Achieve Personal Success.

This study aims to investigate the efficacy of self-narrative as a tool for identifying personality traits conducive to motivation for success. The research employs several methodologies, including the "Readiness for Self-Development" test by V.L. Pavlov, the Achievement Motivation Diagnosis test, and the katathym imaginative psychotherapy motive "Mountain" as a form of self-narrative. Psycholinguistic analysis tools such as the verbosity coefficient, embolism coefficient, and correlation coefficient are utilized. Through empirical analysis of self-narratives, criteria for evaluating motivational potential are established. Quantitative and qualitative analyses of respondent narratives reveal distinct patterns. Coefficients derived from psycholinguistic analysis indicate a correlation between self-narratives and motivation for achievement. Respondents demonstrating motivation for success and readiness for self-development exhibit lower verbosity and embolism coefficients. Conversely, those with a tendency to avoid failure and low readiness for self-development tend to employ narratives rich in superficial language elements. The findings suggest that self-narrative analysis can effectively gauge an individual's motivational tendencies and readiness for personal development. This method holds promise as a tool for identifying and nurturing talent within organizations, particularly in the context of forming a vertical personnel reserve. By understanding the motivational drivers revealed through self-narratives, organizations can better tailor their approaches to talent management and development.

Beyond imagination: Sorting out and treating psychosis in the context of autism spectrum disorder.

In the last decades, growing caseness for Autism Spectrum Disorder (ASD) has been observed, owing to the diagnostic accretion of low-impairment forms, over and above other possible causes. Unrecognized ASD is likely to be mislabeled as a psychotic disorder (PD), as people in the spectrum may show 'pseudopsychotic' symptoms, resembling both negative and positive symptoms. On the other hand, PDs are likely to be overlooked when they arise in people with ASD, due to the 'diagnostic overshadowing' of new-onset conditions by lifelong core autistic symptoms. The three available metanalyses on the occurrence of psychosis in adults with ASD convergently reported a rate of PDs that is at least ten times higher than in the general population. Therefore, the lack of literature addressing risk factors, outcomes, and treatment options for psychosis in the context of ASD is utterly concerning. The present review aims to summarize up-to-date knowledge of PDs with comorbid ASD in terms of clinical features, course, and treatment.

No clear evidence of a difference between individuals who self-report an absence of auditory imagery and typical imagers on auditory imagery tasks.

Aphantasia is characterised by the inability to create mental images in one's mind. Studies investigating impairments in imagery typically focus on the visual domain. However, it is possible to generate many different forms of imagery including imagined auditory, kinesthetic, tactile, motor, taste and other experiences. Recent studies show that individuals with aphantasia report a lack of imagery in modalities, other than vision, including audition. However, to date, no research has examined whether these reductions in self-reported auditory imagery are associated with decrements in tasks that require auditory imagery. Understanding the extent to which visual and auditory imagery deficits co-occur can help to better characterise the core deficits of aphantasia and provide an alternative perspective on theoretical debates on the extent to which imagery draws on modality-specific or modality-general processes. In the current study, individuals that self-identified as being aphantasic and matched control participants with typical imagery performed two tasks: a musical pitch-based imagery and voice-based categorisation task. The majority of participants with aphantasia self-reported significant deficits in both auditory and visual imagery. However, we did not find a concomitant decrease in performance on tasks which require auditory imagery, either in the full sample or only when considering those participants that reported significant deficits in both domains. These findings are discussed in relation to the mechanisms that might obscure observation of imagery deficits in auditory imagery tasks in people that report reduced auditory imagery.

Aphantasia and involuntary imagery.

Aphantasia is a condition that is often characterized as the impaired ability to create voluntary mental images. Aphantasia is assumed to selectively affect voluntary imagery mainly because even though aphantasics report being unable to visualize something at will, many report having visual dreams. We argue that this common characterization of aphantasia is incorrect. Studies on aphantasia are often not clear about whether they are assessing voluntary or involuntary imagery, but some studies show that several forms of involuntary imagery are also affected in aphantasia (including imagery in dreams). We also raise problems for two attempts to show that involuntary images are preserved in aphantasia. In addition, we report the results of a study about afterimages in aphantasia, which suggest that these tend to be less intense in aphantasics than in controls. Involuntary imagery is often treated as a unitary kind that is either present or absent in aphantasia. We suggest that this approach is mistaken and that we should look at different types of involuntary imagery case by case. Doing so reveals no evidence of preserved involuntary imagery in aphantasia. We suggest that a broader characterization of aphantasia, as a deficit in forming mental imagery, whether voluntary or not, is more appropriate. Characterizing aphantasia as a volitional deficit is likely to lead researchers to give incorrect explanations for aphantasia, and to look for the wrong mechanisms underlying it.

Ideas of the university.

When intellectual values are no longer paramount, other commitments-say to the professionalized disciplinary pathways that have congealed as the default means of university organization and governance-not only fill the vacuum but seriously limit our imagination.

Effects of finger pinch motor imagery on short-latency afferent inhibition and corticospinal excitability.

Motor imagery is a cognitive process involving the simulation of motor actions without actual movements. Despite the reported positive effects of motor imagery training on motor function, the underlying neurophysiological mechanisms have yet to be fully elucidated. Therefore, the purpose of the present study was to investigate how sustained tonic finger-pinching motor imagery modulates sensorimotor integration and corticospinal excitability using short-latency afferent inhibition (SAI) and single-pulse transcranial magnetic stimulation (TMS) assessments, respectively. Able-bodied individuals participated in the study and assessments were conducted under two experimental conditions in a randomized order between participants: (1) participants performed motor imagery of a pinch task while observing a visual image displayed on a monitor (Motor Imagery), and (2) participants remained at rest with their eyes fixed on the monitor displaying a cross mark (Control). For each condition, sensorimotor integration and corticospinal excitability were evaluated during sustained tonic motor imagery in separate sessions. Sensorimotor integration was assessed by SAI responses, representing inhibition of motor-evoked potentials (MEPs) in the first dorsal interosseous muscle elicited by TMS following median nerve stimulation. Corticospinal excitability was assessed by MEP responses elicited by single-pulse TMS. There was no significant difference in the magnitude of SAI responses between motor imagery and Control conditions, while MEP responses were significantly facilitated during the Motor Imagery condition compared to the Control condition. These findings suggest that motor imagery facilitates corticospinal excitability, without altering sensorimotor integration, possibly due to insufficient activation of the somatosensory circuits or lack of afferent feedback during sustained tonic motor imagery.

The positive dimension of schizotypy is associated with self-report measures of autobiographical memory and future thinking but not experimenter-scored indices.

ABSTRACTThe ability to remember our past and to imagine the future are critical to our sense of self. Previous research has indicated that they are disrupted in schizophrenia. However, it is unclear (i) whether this is found when examining experimenter-scored indices of content and/or participants' self-report of phenomenological characteristics, and (ii) how these abilities might be related to symptoms. This study sought to address these questions by taking a dimensional approach and measuring positive and negative schizotypal experiences in healthy people (n = 90). Participants were given cue words. For some, they remembered an event from the past and for others they generated an event in the future. No significant relationships were found with any aspect of schizotypy when participants' descriptions were scored by the experimenter according to a standardised episodic content measure. In contrast, several significant positive correlations were observed for past memory and future thinking when examining the positive dimension of schizotypy and participants' ratings, particularly to sensory characteristics of the experience and mental pre- or reliving. These results indicate enhanced subjective experiences of autobiographical memory and future thinking in those who report delusional and hallucinatory-like occurrences, which might be linked to mental imagery or metacognitive alterations.

EISATC-Fusion: Inception Self-Attention Temporal Convolutional Network Fusion for Motor Imagery EEG Decoding.

The motor imagery brain-computer interface (MI-BCI) based on electroencephalography (EEG) is a widely used human-machine interface paradigm. However, due to the non-stationarity and individual differences among subjects in EEG signals, the decoding accuracy is limited, affecting the application of the MI-BCI. In this paper, we propose the EISATC-Fusion model for MI EEG decoding, consisting of inception block, multi-head self-attention (MSA), temporal convolutional network (TCN), and layer fusion. Specifically, we design a DS Inception block to extract multi-scale frequency band information. And design a new cnnCosMSA module based on CNN and cos attention to solve the attention collapse and improve the interpretability of the model. The TCN module is improved by the depthwise separable convolution to reduces the parameters of the model. The layer fusion consists of feature fusion and decision fusion, fully utilizing the features output by the model and enhances the robustness of the model. We improve the two-stage training strategy for model training. Early stopping is used to prevent model overfitting, and the accuracy and loss of the validation set are used as indicators for early stopping. The proposed model achieves within-subject classification accuracies of 84.57% and 87.58% on BCI Competition IV Datasets 2a and 2b, respectively. And the model achieves cross-subject classification accuracies of 67.42% and 71.23% (by transfer learning) when training the model with two sessions and one session of Dataset 2a, respectively. The interpretability of the model is demonstrated through weight visualization method.

Adaptive Time-Frequency Segment Optimization for Motor Imagery Classification.

Motor imagery (MI)-based brain-computer interface (BCI) has emerged as a crucial method for rehabilitating stroke patients. However, the variability in the time-frequency distribution of MI-electroencephalography (EEG) among individuals limits the generalizability of algorithms that rely on non-customized time-frequency segments. In this study, we propose a novel method for optimizing time-frequency segments of MI-EEG using the sparrow search algorithm (SSA). Additionally, we apply a correlation-based channel selection (CCS) method that considers the correlation coefficient of features between each pair of EEG channels. Subsequently, we utilize a regularized common spatial pattern method to extract effective features. Finally, a support vector machine is employed for signal classification. The results on three BCI datasets confirmed that our algorithm achieved better accuracy (99.11% vs. 94.00% for BCI Competition III Dataset IIIa, 87.70% vs. 81.10% for Chinese Academy of Medical Sciences dataset, and 87.94% vs. 81.97% for BCI Competition IV Dataset 1) compared to algorithms with non-customized time-frequency segments. Our proposed algorithm enables adaptive optimization of EEG time-frequency segments, which is crucial for the development of clinically effective motor rehabilitation.

On the relationship between foveal mask interference and mental imagery in peripheral object recognition.

A delayed foveal mask affects perception of peripheral stimuli. The effect is determined by the timing of the mask and by the similarity with the peripheral stimulus. A congruent mask enhances performance, while an incongruent one impairs it. It is hypothesized that foveal masks disrupt a feedback mechanism reaching the foveal cortex. This mechanism could be part of a broader circuit associated with mental imagery, but this hypothesis has not as yet been tested. We investigated the link between mental imagery and foveal feedback. We tested the relationship between performance fluctuations caused by the foveal mask-measured in terms of discriminability (d') and criterion (C)-and the scores from two questionnaires designed to assess mental imagery vividness (VVIQ) and another exploring object imagery, spatial imagery and verbal cognitive styles (OSIVQ). Contrary to our hypotheses, no significant correlations were found between VVIQ and the mask's impact on d' and C. Neither the object nor spatial subscales of OSIVQ correlated with the mask's impact. In conclusion, our findings do not substantiate the existence of a link between foveal feedback and mental imagery. Further investigation is needed to determine whether mask interference might occur with more implicit measures of imagery.

Individual-finger motor imagery classification: a data-driven approach with Shapley-informed augmentation.

Objective. Classifying motor imagery (MI) tasks that involve fine motor control of the individual five fingers presents unique challenges when utilizing electroencephalography (EEG) data. In this paper, we systematically assess the classification of MI functions for the individual five fingers using single-trial time-domain EEG signals. This assessment encompasses both within-subject and cross-subject scenarios, supported by data-driven analysis that provides statistical validation of the neural correlate that could potentially discriminate between the five fingers.Approach. We present Shapley-informed augmentation, an informed approach to enhance within-subject classification accuracy. This method is rooted in insights gained from our data-driven analysis, which revealed inconsistent temporal features encoding the five fingers MI across sessions of the same subject. To evaluate its impact, we compare within-subject classification performance both before and after implementing this augmentation technique.Main results. Both the data-driven approach and the model explainability analysis revealed that the parietal cortex contains neural information that helps discriminate the individual five fingers' MI apart. Shapley-informed augmentation successfully improved classification accuracy in sessions severely affected by inconsistent temporal features. The accuracy for sessions impacted by inconsistency in their temporal features increased by an average of26.3%±6.70, thereby enabling a broader range of subjects to benefit from brain-computer interaction (BCI) applications involving five-fingers MI classification. Conversely, non-impacted sessions experienced only a negligible average accuracy decrease of2.01±5.44%. The average classification accuracy achieved is around 60.0% (within-session), 50.0% (within-subject) and 40.0% (leave-one-subject-out).Significance. This research offers data-driven evidence of neural correlates that could discriminate between the individual five fingers MI and introduces a novel Shapley-informed augmentation method to address temporal variability of features, ultimately contributing to the development of personalized systems.

Peering into the future: Eye movements predict neural repetition effects during episodic simulation.

Imagining future scenarios involves recombining different elements of past experiences into a coherent event, a process broadly supported by the brain's default network. Prior work suggests that distinct brain regions may contribute to the inclusion of different simulation features. Here we examine how activity in these brain regions relates to the vividness of future simulations. Thirty-four healthy young adults imagined future events with familiar people and locations in a two-part study involving a repetition suppression paradigm. First, participants imagined events while their eyes were tracked during a behavioral session. Immediately after, participants imagined events during MRI scanning. The events to be imagined were manipulated such that some were identical to those imagined in the behavioral session while others involved new locations, new people, or both. In this way, we could examine how self-report ratings and eye movements predict brain activity during simulation along with specific simulation features. Vividness ratings were negatively correlated with eye movements, in contrast to an often-observed positive relationship with past recollection. Moreover, fewer eye movements predicted greater involvement of the hippocampus during simulation, an effect specific to location features. Our findings suggest that eye movements may facilitate scene construction for future thinking, lending support to frameworks that spatial information forms the foundation of episodic simulation.

Demystifying wine expertise through the lens of imagination: Descriptions and imagery vividness across sensory modalities.

For most untrained novices, talking about wine or imagining the smells and flavours of wine is difficult. Wine experts, on the other hand, have been found to have better imagery for wine, and are also more proficient in describing wine. Some scholars have suggested that imagery and language are based on similar underlying processes, but no conclusive evidence has been found regarding mental imagery and language production. In this study, we examined the relationship between imagery and language use in both novices and experts. In an online experiment, wine experts and novices were asked to imagine the colour, smell, taste and mouthfeel of wines in different situations, and were asked to rate the vividness of the imagined experience as well as describe it with words. The results show that experts differ from novices on a number of linguistic measures when describing wine, including the number of words used, the type of words used, the concreteness of those words, and the adjective to noun ratio. Similarly, imagery for wine was more vivid in wine experts compared to novices in the modalities of smell, taste, and mouthfeel, in alignment with previous work. Surprisingly, we found that no single linguistic variable significantly predicted the reported vividness of wine imagery, neither in experts nor in novices. However, the linguistic model predicted imagery vividness better using data from experts compared to novices. Taken together, these findings underscore that imagery and language are different facets of wine cognition.

A matter of precision? Scene imagery in individuals with high-functioning autism spectrum disorder.

The ability to create mental representations of scenes is essential for remembering, predicting, and imagining. In individuals with autism spectrum disorders (ASD) this ability may be impaired. Considering that autistic characteristics such as weak central coherence or reduced communication abilities may disadvantage autistic participants in traditional imagery tasks, this study attempted to use a novel task design to measure the ability of scene imagery. Thirty high-functioning adults with ASD and 27 non-autistic matched control adults were asked to describe imagined fictitious scenes using two types of scene imagery tasks. In a free imagery task, participants were asked to imagine a scene based on a given keyword. In a guided imagery task, participants had to imagine a scene based on a detailed description of the scene. Additionally, narrative abilities were assessed using the Narrative Scoring Scheme. Statistical analyses revealed no group effects in the free and guided imagery of fictional scenes. Participants with ASD performed worse than control participants in the narrative task. Narrative abilities correlated positively with performance in both imagery tasks in the ASD group only. Hence, individuals with ASD seem to show as good imagery abilities as non-autistic individuals. The results are discussed in the light of the differences between imagery and imagination and possible gender differences.

TBEEG: A Two-Branch Manifold Domain Enhanced Transformer Algorithm for Learning EEG Decoding.

The electroencephalogram-based (EEG) brain-computer interface (BCI) has garnered significant attention in recent research. However, the practicality of EEG remains constrained by the lack of efficient EEG decoding technology. The challenge lies in effectively translating intricate EEG into meaningful, generalizable information. EEG signal decoding primarily relies on either time domain or frequency domain information. There lacks a method capable of simultaneously and effectively extracting both time and frequency domain features, as well as efficiently fuse these features. Addressing these limitations, a two-branch Manifold Domain enhanced transformer algorithm is designed to holistically capture EEG's spatio-temporal information. Our method projects the time-domain information of EEG signals into the Riemannian spaces to fully decode the time dependence of EEG signals. Using wavelet transform, the time domain information is converted into frequency domain information, and the spatial information contained in the frequency domain information of EEG signal is mined through the spectrogram. The effectiveness of the proposed TBEEG algorithm is validated on BCIC-IV-2a dataset and MAMEM-SSVEP-II datasets.

Unraveling motor imagery brain patterns using explainable artificial intelligence based on Shapley values.

BACKGROUND AND OBJECTIVE: Motor imagery (MI) based brain-computer interfaces (BCIs) are widely used in rehabilitation due to the close relationship that exists between MI and motor execution (ME). However, the underlying brain mechanisms of MI remain not well understood. Most MI-BCIs use the sensorimotor rhythms elicited in the primary motor cortex (M1) and somatosensory cortex (S1), which consist of an event-related desynchronization followed by an event-related synchronization. Consequently, this has resulted in systems that only record signals around M1 and S1. However, MI could involve a more complex network including sensory, association, and motor areas. In this study, we hypothesize that the superior accuracies achieved by new deep learning (DL) models applied to MI decoding rely on focusing on a broader MI activation of the brain. Parallel to the success of DL, the field of explainable artificial intelligence (XAI) has seen continuous development to provide explanations for DL networks success. The goal of this study is to use XAI in combination with DL to extract information about MI brain activation patterns from non-invasive electroencephalography (EEG) signals. METHODS: We applied an adaptation of Shapley additive explanations (SHAP) to EEGSym, a state-of-the-art DL network with exceptional transfer learning capabilities for inter-subject MI classification. We obtained the SHAP values from two public databases comprising 171 users generating left and right hand MI instances with and without real-time feedback. RESULTS: We found that EEGSym based most of its prediction on the signal of the frontal electrodes, i.e. F7 and F8, and on the first 1500 ms of the analyzed imagination period. We also found that MI involves a broad network not only based on M1 and S1, but also on the prefrontal cortex (PFC) and the posterior parietal cortex (PPC). We further applied this knowledge to select a 8-electrode configuration that reached inter-subject accuracies of 86.5% ± 10.6% on the Physionet dataset and 88.7% ± 7.0% on the Carnegie Mellon University's dataset. CONCLUSION: Our results demonstrate the potential of combining DL and SHAP-based XAI to unravel the brain network involved in producing MI. Furthermore, SHAP values can optimize the requirements for out-of-laboratory BCI applications involving real users.

Health anxiety amplifies fearful responses to illness-related imagery.

Severe health anxiety (HA) is characterized by excessive worry and anxiety about one's health, often accompanied by distressing intrusive imagery of signs of a serious illness or potentially receiving bad news about having a life-threatening disease. However, the emotional responses to these illness-related mental images in relation to HA have not been fully elucidated. Emotional responses to mental imagery of 142 participants were assessed in a well-controlled script-driven imagery task, systematically comparing emotional responses to illness-related imagery with neutral and standard fear imagery. The results revealed that participants reported higher anxiety, aversion, emotional arousal, and a stronger avoidance tendency during imagery of fear and illness-related scenes compared to neutral scenes. Importantly, the emotional modulation varied by the level of HA, indicating that individuals with higher HA experienced stronger emotional responses to illness-related imagery. This association between HA and fearful imagery could not be better accounted for by other psychological factors such as trait anxiety, anxiety sensitivity, somatic symptom severity, or symptoms of depression and anxiety. Fearful responding to standard threat material was not associated with HA. The present findings highlight the importance of considering fear responding to mental imagery in understanding and addressing HA.

Goal characteristics predict the occurrence of goal-related events through belief in future occurrence.

While previous studies have highlighted the role of episodic future thinking in goal pursuit, the underlying cognitive mechanisms remain unexplored. Episodic future thinking may promote goal pursuit by shaping the feeling that imagined events will (or will not) happen in the future - referred to as belief in future occurrence. We investigated whether goal self-concordance (Experiment 1) and other goal characteristics identified as influential in goal pursuit (Experiment 2) modulate belief in the future occurrence of goal-related events and predict the actual occurrence of these events. Results showed that goal self-concordance, engagement, and expectancy had an indirect effect on the actual occurrence of events, which was (partially) mediated by belief in future occurrence. The mediating role of belief supports the view that belief in future occurrence when imagining events conveys useful information, allowing us to make informed decisions and undertake adaptive actions in the process of goal pursuit.