Imagery-Focused Therapy for Visual Hallucinations: A Case Series.

INTRODUCTION: Visual hallucinations (VH) are more common than previously thought and are linked to higher levels of distress and disability in people with a psychotic illness. Despite this, scant attention has been given to VHs in the clinical literature, and the few therapy case series of cognitive behavioural therapy (CBT) published to date have not demonstrated reliable change. In other areas of clinical research, problematic mental imagery has been found to be more strongly related to negative affect in psychological disorders than negative linguistic thinking, and imagery focused techniques have commonly been found to improve the outcomes in CBT trials. Given VHs have many similarities with visual mental imagery and many of the distressing beliefs associated with VHs targeted in CBT are maintained by accompanying mental imagery (i.e., imaging a hallucinated figure attacking them), it seems plausible that an imagery-focused approach to treating VHs may be most effective. METHODS: The current study is a multiple baseline case series (N = 11) of a 10-session imagery-focused therapy for VH in a transdiagnostic sample. RESULTS: The study had good attendance and feedback, no adverse events and only one [seemly unrelated] drop-out, suggesting good feasibility, safety and acceptability. The majority of clients reported reduction on both full-scale measures (administered at 3 baselines, midtherapy, posttherapy and 3-month follow-up) and weekly measures of VH severity and distress, ranging from medium to large effect sizes. CONCLUSIONS: The case series suggests that an imagery-focused approach to treating VHs may be beneficial, with a recommendation for more rigorous clinical trials to follow.

Working mechanisms of imagery rescripting (ImRs) in adult patients with childhood-related PTSD: a pilot study.

Background: Imagery rescripting (ImRs) has shown to be an effective treatment for posttraumatic stress disorders (PTSD) resulting from childhood-related trauma. The current theory is that the change of meaning of the trauma memory is central to the treatment. Several authors have suggested that the expression of needs, feelings and actions may act as potential healing factors, but little specific research aimed at (in)validating this hypothesis has been done so far.Objective: In this study we investigated to what extent the expression of inhibited action tendencies and the fulfilling of needs lead to the reduction of PTSD symptoms in clients with early childhood trauma.Method: Recordings of 249 therapy sessions of 24 ImRs treatments were rated with an observation instrument developed for this purpose, after which the scores were related to pre and posttreatment symptoms, assessed with the Impact of Events Scale-Revised (IES-R).Results: Scores on the IES-R decreased from pretreatment to posttreatment. The two subscales of the NATS (At-scale and N-scale)significantly predicted the posttreatment scores on the IES-R after controlling for the influence of pretreatment IES-R scores: the better the expression of inhibited action tendencies and the better the fulfilling of needs, the lower the symptoms after treatment.Conclusions: This pilot study on the underlying mechanisms of ImRs in PTSD treatment has shown that the expression of action tendencies and fulfilling basic needs during ImRs are associated with a decrease in PTSD symptoms after treatment, and that actions and basic needs cannot be viewed separately. Follow-up research could focus on which of the six domains of the Needs and Action tendencies Scale (NATS) has the greatest effect on the reduction of PTSD symptoms. With this information we can further improve the ImRs protocol.

The pilot study of working mechanisms of imaginary rescripting shows that the NATS is a reliable research tool for observing expressed action tendencies and fulfilled needs.The better the action tendencies are expressed during treatment and the better the needs are fulfilled, the lower posttreatment symptoms.It seems useful if practitioners are specifically trained during the ImRs training in performing actions that lead to the fulfilment of basic needs.

Land-use classification based on high-resolution remote sensing imagery and deep learning models.

High-resolution imagery and deep learning models have gained increasing importance in land-use mapping. In recent years, several new deep learning network modeling methods have surfaced. However, there has been a lack of a clear understanding of the performance of these models. In this study, we applied four well-established and robust deep learning models (FCN-8s, SegNet, U-Net, and Swin-UNet) to an open benchmark high-resolution remote sensing dataset to compare their performance in land-use mapping. The results indicate that FCN-8s, SegNet, U-Net, and Swin-UNet achieved overall accuracies of 80.73%, 89.86%, 91.90%, and 96.01%, respectively, on the test set. Furthermore, we assessed the generalization ability of these models using two measures: intersection of union and F1 score, which highlight Swin-UNet's superior robustness compared to the other three models. In summary, our study provides a systematic analysis of the classification differences among these four deep learning models through experiments. It serves as a valuable reference for selecting models in future research, particularly in scenarios such as land-use mapping, urban functional area recognition, and natural resource management.

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.

Self-supervised contrastive learning for EEG-based cross-subject motor imagery recognition.

Objective. The extensive application of electroencephalography (EEG) in brain-computer interfaces (BCIs) can be attributed to its non-invasive nature and capability to offer high-resolution data. The acquisition of EEG signals is a straightforward process, but the datasets associated with these signals frequently exhibit data scarcity and require substantial resources for proper labeling. Furthermore, there is a significant limitation in the generalization performance of EEG models due to the substantial inter-individual variability observed in EEG signals.Approach. To address these issues, we propose a novel self-supervised contrastive learning framework for decoding motor imagery (MI) signals in cross-subject scenarios. Specifically, we design an encoder combining convolutional neural network and attention mechanism. In the contrastive learning training stage, the network undergoes training with the pretext task of data augmentation to minimize the distance between pairs of homologous transformations while simultaneously maximizing the distance between pairs of heterologous transformations. It enhances the amount of data utilized for training and improves the network's ability to extract deep features from original signals without relying on the true labels of the data.Main results. To evaluate our framework's efficacy, we conduct extensive experiments on three public MI datasets: BCI IV IIa, BCI IV IIb, and HGD datasets. The proposed method achieves cross-subject classification accuracies of 67.32%, 82.34%, and 81.13%on the three datasets, demonstrating superior performance compared to existing methods.Significance. Therefore, this method has great promise for improving the performance of cross-subject transfer learning in MI-based BCI systems.

Effect of PTSD treatment on cardiovascular reactivity during trauma memory recall and correspondence with symptom improvement.

ABSTRACTBackground: Prior research has shown PTSD treatment leads to reductions in cardiovascular reactivity during trauma recall, but the extent to which such reductions are associated with changes in PTSD symptoms is less clear. Moreover, such relationships have not been investigated in a cognitively focused PTSD treatment.Objective: To examine changes in cardiovascular reactivity to the trauma memory in patients receiving cognitive processing therapy (CPT), CPT with a written trauma account, and a written account only condition. We also examined the association of such changes with symptom improvement.Method: 118 women with PTSD secondary to interpersonal violence completed pre- and post-treatment assessments of PTSD symptoms and cardiovascular reactivity during a script-driven imagery task.Results: Results indicated a significant but modest reduction in cardiovascular reactivity in CPT conditions. Changes in cardiovascular reactivity and reexperiencing symptoms were significantly associated among the whole sample. Among individuals with the greatest reactivity to the trauma memory at pretreatment, associations were also seen with changes in total PTSD, numbing, and trauma-related guilt.Conclusions: Results indicate that previous findings on the effect of PTSD treatment on cardiovascular reactivity during trauma recall extend to cognitively oriented treatment. Baseline cardiovascular reactivity may influence the extent to which reductions in PTSD symptoms and reactivity during trauma recall are related.

Cognitive Processing Therapy leads to reduced heart rate reactivity when recalling a trauma memory.Decreases in heart rate reactivity are associated with reduced reexperiencing symptoms.Changes in heart rate reactivity and PTSD symptoms are more closely related among patients with greater pretreatment reactivity.

Multimodal brain-controlled system for rehabilitation training: Combining asynchronous online brain-computer interface and exoskeleton.

BACKGROUND: Traditional therapist-based rehabilitation training for patients with movement impairment is laborious and expensive. In order to reduce the cost and improve the treatment effect of rehabilitation, many methods based on human-computer interaction (HCI) technology have been proposed, such as robot-assisted therapy and functional electrical stimulation (FES). However, due to the lack of active participation of brain, these methods have limited effects on the promotion of damaged nerve remodeling. NEW METHOD: Based on the neurofeedback training provided by the combination of brain-computer interface (BCI) and exoskeleton, this paper proposes a multimodal brain-controlled active rehabilitation system to help improve limb function. The joint control mode of steady-state visual evoked potential (SSVEP) and motor imagery (MI) is adopted to achieve self-paced control and thus maximize the degree of brain involvement, and a requirement selection function based on SSVEP design is added to facilitate communication with aphasia patients. COMPARISON WITH EXISTING METHODS: In addition, the Transformer is introduced as the MI decoder in the asynchronous online BCI to improve the global perception of electroencephalogram (EEG) signals and maintain the sensitivity and efficiency of the system. RESULTS: In two multi-task online experiments for left hand, right hand, foot and idle states, subject achieves 91.25% and 92.50% best accuracy, respectively. CONCLUSION: Compared with previous studies, this paper aims to establish a high-performance and low-latency brain-controlled rehabilitation system, and provide an independent and autonomous control mode of the brain, so as to improve the effect of neural remodeling. The performance of the proposed method is evaluated through offline and online experiments.

Coping with test anxiety using imagery rescripting: A two-session randomized controlled trial.

BACKGROUND: Up to 55 % of students experience test anxiety (TA), which is characterized by intense physiological and psychological symptoms before or during exams, such as anxiety, fear of failure, sweating, or increased heart rate. Furthermore, TA increases graduation times and can result in discontinuance of the graduate program all together. Previous research demonstrated the beneficial effects of combining cognitive behavioral therapy with imagery rescripting, however, treatment programs are comparably long. Hence, they do not account for the students´ time-sensitive schedules. Therefore, the present study investigates a two-session short-intervention using imagery rescripting to treat TA. METHODS: 44 students and pupils were randomly assigned to either the two-session imagery rescripting intervention (22 participants) or the waitlist-control condition (22 participants). One week before the intervention clinical interviews were conducted and self-report questionnaires on TA, self-efficacy, symptoms of depression, and intrusive prospective images were completed (T1). The same questionnaires were completed one week (T2) and six months after the intervention (T3). RESULTS: Test anxiety significantly decreased from T1 to T2, as well as from T1 to T3 within the intervention group. Furthermore, there were medium to large within and between group effects for situational test anxiety, self-efficacy, symptoms of depression, as well as prospective intrusive images, showing significant improvements for the intervention group at six months follow-up. LIMITATIONS: The study is limited to the comparably small sample size, as well as the sole usage of self-report measurements. CONCLUSIONS: The presented short-intervention provides a feasible treatment technique, which can be easily applied within school and university counseling centers.

Multi-Task Heterogeneous Ensemble Learning-Based Cross-Subject EEG Classification Under Stroke Patients.

Robot-assisted motor training is applied for neurorehabilitation in stroke patients, using motor imagery (MI) as a representative paradigm of brain-computer interfaces to offer real-life assistance to individuals facing movement challenges. However, the effectiveness of training with MI may vary depending on the location of the stroke lesion, which should be considered. This paper introduces a multi-task electroencephalogram-based heterogeneous ensemble learning (MEEG-HEL) specifically designed for cross-subject training. In the proposed framework, common spatial patterns were used for feature extraction, and the features according to stroke lesions are shared and selected through sequential forward floating selection. The heterogeneous ensembles were used as classifiers. Nine patients with chronic ischemic stroke participated, engaging in MI and motor execution (ME) paradigms involving finger tapping. The classification criteria for the multi-task were established in two ways, taking into account the characteristics of stroke patients. In the cross-subject session, the first involved a direction recognition task for two-handed classification, achieving a performance of 0.7419 (±0.0811) in MI and 0.7061 (±0.1270) in ME. The second task focused on motor assessment for lesion location, resulting in a performance of 0.7457 (±0.1317) in MI and 0.6791 (±0.1253) in ME. Comparing the specific-subject session, except for ME on the motor assessment task, performance on both tasks was significantly higher than the cross-subject session. Furthermore, classification performance was similar to or statistically higher in cross-subject sessions compared to baseline models. The proposed MEEG-HEL holds promise in improving the practicality of neurorehabilitation in clinical settings and facilitating the detection of lesions.

Mechanisms of therapeutic change after psychedelic treatment in OCD.

Novel treatments are required for the 30-50% of individuals with obsessive-compulsive disorder (OCD) who remain resistant to first-line pharmacological and psychotherapeutic treatments. Recent pilot data suggest benefit from psilocybin-assisted psychotherapy (PAP) and from imagery rescripting (ImRs). We explore psychological mechanisms of change underpinning both interventions that appear to allow for reprocessing of negative emotions and core beliefs associated with past aversive events. A next critical step in PAP is the development of psychotherapeutic frameworks grounded in theory. We propose that basing PAP on an ImRs framework may provide synergistic benefits in symptom reduction, modification of core beliefs, and value-based living.

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.

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.

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.

Classification of Motor Imagery EEG signals using high resolution time-frequency representations and convolutional neural network.

A Motor Imagery (MI) based Brain Computer Interface (BCI) system aims to provide neuro-rehabilitation for the motor disabled people and patients with brain injuries (e.g., stroke patients) etc. The aim of this work is to classify the left and right hand MI tasks by utilizing the occurrence of event related desynchronization and synchronization (ERD\ERS) in the Electroencephalogram (EEG) during these tasks. This study proposes to use a set of Complex Morlet Wavelets (CMW) having frequency dependent widths to generate high-resolution time-frequency representations (TFR) of the MI EEG signals present in the channels C3 and C4. A novel method for the selection of the value of number of cycles relative to the center frequency of the CMW is studied here for extracting the MI task features. The generated TFRs are given as input to a Convolutional neural network (CNN) for classifying them into left or right hand MI tasks. The proposed framework attains a classification accuracy of 82.2% on the BCI Competition IV dataset 2a, showing that the TFRs generated in this work give a higher classification accuracy than the baseline methods and other existing algorithms.

Upper Limb Electromyographic Responses to Motor Imagery and Action Observation in Acquired Brain Injury.

Acquired Brain Injuries are one of the leading causes of mortality and disability worldwide. One of the most frequent sequelae is motor impairment of the upper limbs, which affects people's functionality and quality of life. Following the discovery of mirror neurons, new techniques were developed based on the mechanisms of neuronal plasticity, such as motor imagery (MI) and action observation (AO). We propose a protocol using electromyographic recordings of forearm muscles in people who have suffered a stroke during an MI task and an AO task. Three different experimental conditions will be studied during the electromyographic recordings: control recording, recording during MI, and recording during AO. Understanding the muscle activation in each technique will allow us to develop future protocols and intervention plans, improving the quality of care for people who have suffered a stroke.

Stimulus-elicited involuntary autobiographical memories.

The reflexive imagery task (RIT) has been used to investigate stimulus-elicited involuntary mental processes. The task has been successful in eliciting involuntary perceptual experiences, urges, and even higher-order cognitions, but it has never been used to elicit autobiographical memories, even though in everyday life these memories are often activated involuntarily by external stimuli. These memories are different in interesting ways from the kinds of mental representations that have been activated involuntarily in the RIT. The memories have properties which might make them insusceptible to such a form of external influence. Perhaps substantive effects will not arise because the mental representations associated with autobiographical memories are complex, poly-sensory, and rich in terms of content. To investigate this matter, we developed a variant of the RIT in which participants were presented with external stimuli (line drawings of everyday objects) and instructed not to recall any autobiographical memories. We investigated whether the nature of the involuntary memories was influenced by the nature of the stimulus. In two experiments, the involuntary memories were associated to the stimulus on a majority of the trials (∼80%). We discuss theoretical implications of this finding and of identifying the conditions in which such involuntary effects will not arise. The boundary conditions of the RIT effect illuminate the limits of unconscious processing and also the role of conscious processing in nervous function.

Psychophysiological strategies for enhancing performance through imagery-skin conductance level analysis in guided vs. self-produced imagery.

Athletes need to achieve their optimal level of arousal for peak performance. Visualization or mental rehearsal (i.e., Imagery) often helps to obtain an appropriate level of activation, which can be detected by monitoring Skin Conductance Level (SCL). However, different types of imagery could elicit different amount of physiological arousal. Therefore, this study aims: (1) to investigate differences in SCL associated with two instructional modalities of imagery (guided vs. self-produced) and six different scripts; (2) to check if SCL could differentiate respondents according to their sport expertise. Thirty participants, aged between 14 and 42 years (M = 22.93; SD = 5.24), with different sport levels took part in the study. Participants listened to each previously recorded script and then were asked to imagine the scene for a minute. During the task, SCL was monitored. We analysed the mean value, variance, slope and number of fluctuations per minute of the electrodermal signal. Unsupervised machine learning models were used for measuring the resemblance of the signal. The Wilcoxon signed-rank test was used for distinguishing guided and self-produced imagery, and The Mann-Whitney U test was used for distinguishing results of different level athletes. We discovered that among others, self-produced imagery generates lower SCL, higher variance, and a higher number of fluctuations compared to guided imagery. Moreover, we found similarities of the SCL signal among the groups of athletes (i.e. expertise level). From a practical point of view, our findings suggest that different imagery instructional modalities can be implemented for specific purposes of mental preparation.

Association between visuo-spatial working memory and gait motor imagery.

Although motor imagery and working memory (WM) appear to be closely linked, no previous studies have demonstrated direct evidence for the relationship between motor imagery and WM abilities. This study investigated the association between WM and gait motor imagery and focused on the individual differences in young adults. This study included 33 participants (mean age: 22.2 ± 0.9 years). We used two methods to measure the ability of different WM domains: verbal and visuo-spatial WM. Gait motor imagery accuracy was assessed via the mental chronometry paradigm. We measured the times participants took to complete an actual and imagined walk along a 5 m walkway, with three different path widths. The linear mixed effects model analysis revealed that visuo-spatial WM ability was a significant predictor of the accuracy of gait motor imagery, but not of verbal WM ability. Specifically, individuals with lower visuo-spatial WM ability demonstrated more inaccuracies in the difficult path-width conditions. However, gait motor imagery was not as accurate as actual walking even in the easiest path width or in participants with high visuo-spatial WM ability. Further, visuo-spatial WM ability was significantly correlated with mental walking but not with actual walking. These results suggest that visuo-spatial WM is related to motor imagery rather than actual movement.

Revisiting the blind mind: Still no evidence for sensory visual imagery in individuals with aphantasia.

The inability to visualise was given the name aphantasia in 2015 by Zeman and colleagues. In 2018 we published research showing that fifteen individuals who self-identified as having aphantasia also demonstrated a lack of sensory visual imagery when undergoing the binocular rivalry imagery paradigm, suggesting more than just a metacognitive difference. Here we update these findings with over fifty participants with aphantasia and show that there is evidence for a lack of sensory imagery in aphantasia. How the binocular rivalry paradigm scores relate to the vividness of visual imagery questionnaire (VVIQ) and how aphantasia can be confirmed is discussed.