Altered grid-like coding in early blind people.

Cognitive maps in the hippocampal-entorhinal system are central for the representation of both spatial and non-spatial relationships. Although this system, especially in humans, heavily relies on vision, the role of visual experience in shaping the development of cognitive maps remains largely unknown. Here, we test sighted and early blind individuals in both imagined navigation in fMRI and real-world navigation. During imagined navigation, the Human Navigation Network, constituted by frontal, medial temporal, and parietal cortices, is reliably activated in both groups, showing resilience to visual deprivation. However, neural geometry analyses highlight crucial differences between groups. A 60° rotational symmetry, characteristic of a hexagonal grid-like coding, emerges in the entorhinal cortex of sighted but not blind people, who instead show a 90° (4-fold) symmetry, indicative of a square grid. Moreover, higher parietal cortex activity during navigation in blind people correlates with the magnitude of 4-fold symmetry. In sum, early blindness can alter the geometry of entorhinal cognitive maps, possibly as a consequence of higher reliance on parietal egocentric coding during navigation.

Fatigue in children using motor imagery and P300 brain-computer interfaces.

BACKGROUND: Brain-computer interface (BCI) technology offers children with quadriplegic cerebral palsy unique opportunities for communication, environmental exploration, learning, and game play. Research in adults demonstrates a negative impact of fatigue on BCI enjoyment, while effects on BCI performance are variable. To date, there have been no pediatric studies of BCI fatigue. The purpose of this study was to assess the effects of two different BCI paradigms, motor imagery and visual P300, on the development of self-reported fatigue and an electroencephalography (EEG) biomarker of fatigue in typically developing children. METHODS: Thirty-seven typically-developing school-aged children were recruited to a prospective, crossover study. Participants attended three sessions: (A) motor imagery-BCI, (B) visual P300-BCI, and (C) video viewing (control). The motor imagery task involved an imagined left- or right-hand squeeze. The P300 task involved attending to one square on a 3 × 3 grid during a random single flash sequence. Each paradigm had respective calibration periods and a similar visual counting game. Primary outcomes were self-reported fatigue and the power of the EEG alpha band both collected during resting-state periods pre- and post-task. Self-reported fatigue was measured using a 10-point visual analog scale. EEG alpha band power was calculated as the integrated power spectral density from 8 to 12 Hz of the EEG spectrum. RESULTS: Thirty-two children completed the protocol (age range 7-16, 63% female). Self-reported fatigue and EEG alpha band power increased across all sessions (F(1,155) = 33.9, p < 0.001; F = 5.0(1,149), p = 0.027 respectively). No differences in fatigue development were observed between session types. There was no correlation between self-reported fatigue and EEG alpha band power change. BCI performance varied between participants and paradigms as expected but was not associated with self-reported fatigue or EEG alpha band power. CONCLUSION: Short periods (30-mintues) of BCI use can increase self-reported fatigue and EEG alpha band power to a similar degree in children performing motor imagery and P300 BCI paradigms. Performance was not associated with our measures of fatigue; the impact of fatigue on useability and enjoyment is unclear. Our results reflect the variability of fatigue and the BCI experience more broadly in children and warrant further investigation.

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.

Rapid-IAF: Rapid Identification of Individual Alpha Frequency in EEG Data Using Sequential Bayesian Estimation.

Rapid and robust identification of the individual alpha frequency (IAF) in electroencephalogram (EEG) is an essential factor for successful brain-computer interface (BCI) use. Here we demonstrate an algorithm to determine the IAF from short-term resting-state scalp EEG data. First, we outlined the algorithm to determine IAF from short-term resting scalp EEG data and evaluated its reliability using a large-scale dataset of scalp EEG during motor imagery-based BCI use and independent dataset for generalizability confirmation (N = 147). Next, we characterized the relationship between IAF and responsive frequency band of sensorimotor rhythm, which exhibits prominent event-related desynchronization (SMR-ERD) while attempting unilateral and movement. The proposed sequential Bayesian estimation algorithm (Rapid-IAF) determined IAF from less than 26-second resting EEG data among 95% of participants, indicating a clear advance over the conventional methods, which uses 2-15 minutes of data in previous literatures. We confirmed that the determined IAF corresponded to the frequency of SMR, which exhibits the most prominent event-related desynchronization during BCI use (individual SMR-ERD frequency, ISF). Moreover, intraclass correlation revealed that the estimated IAF was more stable than ISF across sessions, suggesting its reliability and utility for robust BCI use without intermittent recalibration. In summary, our method rapidly and reliably determined IAF compared to the conventional method using the spectral power change based on task-related response. The method can be utilized to quick BCI initialization. The demonstration of rapid, task-free parametrization of individual variability of neural responses would be of importance for future BCI systems including neural communication via a cursor, an avatar or robots, and closed-loop neurofeedback training.

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.

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.

No verbal overshadowing in aphantasia: The role of visual imagery for the verbal overshadowing effect.

The verbal overshadowing effect refers to the phenomenon that the verbal description of a past complex stimulus impairs its subsequent recognition. Theoretical explanations range from interference between different mental representations to the activation of different processing orientations or a provoked shift in the recognition criterion. In our study, 61 participants with aphantasia (= lack of mental imagery) and 70 controls participated in a verbal overshadowing paradigm. The verbal overshadowing effect did not occur in people with aphantasia, although the effect was replicated in controls. We speculate that this is either due to the lack of visual representations in people with aphantasia that verbal descriptions could interfere with, or to the absence of a shift in processing orientation during verbalisation. To rule out criterion-based explanations, further research is needed to distinguish between discriminability and response bias in people with aphantasia. Finally, data indicated that the verbal overshadowing effect may even be reversed in individuals with aphantasia, partly due to a lower memory performance in the no verbalisation condition. Effects of further variables are discussed, such as mental strategies, memory confidence, and difficulty, quantity and quality of verbalisation.

Self-monitoring deficits in schizophrenia: A cross-sectional study of the underlying cognitive mechanisms.

BACKGROUND: People diagnosed with schizophrenia share underlying cognitive deficits in self-monitoring (i.e., identifying the source of self-generated behaviours). This study aimed to investigate whether self-monitoring deficits in schizophrenia are due to a cognitive response bias towards external perceptions or a reduced discriminability of imagined and performed actions. We hypothesised that self-monitoring deficits in individuals with schizophrenia are primarily driven by bottom-up processes, leading to a compromised ability to discriminate between internally generated behaviours as opposed to a cognitive response bias towards performed actions. METHODS: We recruited 333 participants, including 192 with schizophrenia and 141 healthy controls. As part of the Action-Memory Task, participants were instructed to either imagine or physically perform 36 different actions, half of which were presented as pictograms and half as text. In the test phase, participants indicated whether they had performed or imagined each action, whether it appeared in text or pictogram, or whether it was a new action. Using Signal Detection Theory, the study primarily analysed group differences in discriminability and response-bias. RESULTS: Participants with schizophrenia made significantly more self-monitoring errors than healthy controls. This was primarily due to significantly lower sensitivity, but not a response bias. Whereas recognition memory errors were driven by both lower sensitivity and a response bias. CONCLUSIONS: The findings suggest that self-monitoring in schizophrenia was specifically impaired by a compromised discriminability of imagined and performed events and an inability to appropriately compensate by adjusting decision-thresholds. Implications on the role of bottom-up and top-down cognitive mechanisms are discussed.

Observing an expert's action swapped with an observer's face increases corticospinal excitability during combined action observation and motor imagery.

This study aimed to examine whether observing an expert's action swapped with an observer's face increases corticospinal excitability during combined action observation and motor imagery (AOMI). Twelve young males performed motor imagery of motor tasks with different difficulties while observing the actions of an expert performer and an expert performer with a swapped face. Motor tasks included bilateral wrist dorsiflexion (EASY) and unilateral two-ball rotating motions (DIFF). During the AOMI of EASY and DIFF, single-pulse transcranial magnetic stimulation was delivered to the left primary motor cortex, and motor-evoked potentials (MEPs) were obtained from the extensor carpi ulnaris and first dorsal interosseous muscles of the right upper limb, respectively. Visual analogue scale (VAS) assessed the subjective similarity of the expert performer with the swapped face in the EASY and DIFF to the participants themselves. The MEP amplitude in DIFF was larger in the observation of the expert performer with the swapped face than that of the expert performer (P = 0.012); however, the corresponding difference was not observed in EASY (P = 1.000). The relative change in the MEP amplitude from observing the action of the expert performer to that of the expert performer with the swapped face was positively correlated with VAS only in DIFF (r = 0.644, P = 0.024). These results indicate that observing the action of an expert performer with the observer's face enhances corticospinal excitability during AOMI, depending on the task difficulty and subjective similarity between the expert performer being observed and the observer.

Boosting lower-limb motor imagery performance through an ensemble method for gait rehabilitation.

Lower-limb exoskeletons have been used extensively in many rehabilitation applications to assist disabled people with their therapies. Brain-machine interfaces (BMIs) further provide effective and natural control schemes. However, the limited performance of brain signal decoding from lower-limb kinematics restricts the broad growth of both BMI and rehabilitation industry. To address these challenges, we propose an ensemble method for lower-limb motor imagery (MI) classification. The proposed model employs multiple techniques to boost performance, including deep and shallow parts. Traditional wavelet transformation followed by filter-bank common spatial pattern (CSP) employs neurophysiologically reasonable patterns, while multi-head self-attention (MSA) followed by temporal convolutional network (TCN) extracts deeper encoded generalized patterns. Experimental results in a customized lower-limb exoskeleton on 8 subjects in 3 consecutive sessions showed that the proposed method achieved 60.27% and 64.20% for three (MI of left leg, MI of right leg, and rest) and two classes (lower-limb MI vs. rest), respectively. Besides, the proposed model achieves improvements of up to 4% and 2% accuracy for the subject-specific and subject-independent modes compared to the current state-of-the-art (SOTA) techniques, respectively. Finally, feature analysis was conducted to show discriminative brain patterns in each MI task and sessions with different feedback modalities. The proposed models integrated in the brain-actuated lower-limb exoskeleton established a potential BMI for gait training and neuroprosthesis.

Cross-Subject Motor Imagery Decoding by Transfer Learning of Tactile ERD.

For Brain-Computer Interface (BCI) based on motor imagery (MI), the MI task is abstract and spontaneous, presenting challenges in measurement and control and resulting in a lower signal-to-noise ratio. The quality of the collected MI data significantly impacts the cross-subject calibration results. To address this challenge, we introduce a novel cross-subject calibration method based on passive tactile afferent stimulation, in which data induced by tactile stimulation is utilized to calibrate transfer learning models for cross-subject decoding. During the experiments, tactile stimulation was applied to either the left or right hand, with subjects only required to sense tactile stimulation. Data from these tactile tasks were used to train or fine-tune models and subsequently applied to decode pure MI data. We evaluated BCI performance using both the classical Common Spatial Pattern (CSP) combined with the Linear Discriminant Analysis (LDA) algorithm and a state-of-the-art deep transfer learning model. The results demonstrate that the proposed calibration method achieved decoding performance at an equivalent level to traditional MI calibration, with the added benefit of outperforming traditional MI calibration with fewer trials. The simplicity and effectiveness of the proposed cross-subject tactile calibration method make it valuable for practical applications of BCI, especially in clinical settings.

Can the prosocial benefits of episodic simulation transfer to different people and situational contexts?

Previous research has found that episodic simulation of events of helping others can effectively enhance intentions to help the same person involved and the identical situational context as the imagined scenarios. This 'prosocial simulation effect' is argued to reflect, at least in part, associative memory mechanisms whereby the simulation is reactivated when in the same situation as that imagined. However, to date, no study has examined systematically whether this 'prosocial simulation effect' can be transferred to response scenarios involving different people and/or situational contexts to the imagined scenarios, and if so, whether the degree of overlap with the imagined helping episode modulated the transfer effect. Across two experiments, we systematically varied the overlap of the simulated and response scenarios, both in terms of the persons in need and/or the situational contexts, and whether would influence the magnitude of prosocial simulation effect. Results from both experiments showed that the prosocial simulation effect can be transferred to response scenarios involving different people and situational contexts to the simulated scenarios. However, this finding was primarily driven by response scenarios that had a high degree of overlap to the simulated scenarios. The application of our findings to the practical implementation of simulation to promote prosociality in the real world is discussed.

Cortical activation and BCI performance during brief tactile imagery: A comparative study with motor imagery.

Brain-computer interfaces (BCIs) rely heavily on motor imagery (MI) for operation, yet tactile imagery (TI) presents a novel approach that may be advantageous in situations where visual feedback is impractical. The current study aimed to compare the cortical activity and digit classification performance induced by TI and MI to assess the viability of TI for use in BCIs. Twelve right-handed participants engaged in trials of TI and MI, focusing on their left and right index digits. Event-related desynchronization (ERD) in the mu and beta bands was analyzed, and classification accuracy was determined through an artificial neural network (ANN). Comparable ERD patterns were observed in both TI and MI, with significant decreases in ERD during imagery tasks. The ANN demonstrated high classification accuracy, with TI achieving a mean±SD of 79.30 ± 3.91 % and MI achieving 81.10 ± 2.96 %, with no significant difference between the two (p = 0.11). The study found that TI induces substantial ERD comparable to MI and maintains high classification accuracy, supporting its potential as an effective mental strategy for BCIs. This suggests that TI could be a valuable alternative in BCI applications, particularly for individuals unable to rely on visual cues.

Colors in the mind's eye.

The famous "Piazza del Duomo" paper, published in Cortex in 1978, inspired a considerable amount of research on visual mental imagery in brain-damaged patients. As a consequence, single-case reports featuring dissociations between perceptual and imagery abilities challenged the prevailing model of visual mental imagery. Here we focus on mental imagery for colors. A case study published in Cortex showed perfectly preserved color imagery in a patient with acquired achromatopsia after bilateral lesions at the borders between the occipital and temporal cortex. Subsequent neuroimaging findings in healthy participants extended and specified this result; color imagery elicited activation in both a domain-general region located in the left fusiform gyrus and the anterior color-biased patch within the ventral temporal cortex, but not in more posterior color-biased patches. Detailed studies of individual neurological patients, as those often published in Cortex, are still critical to inspire and constrain neurocognitive research and its theoretical models.

"The mere imagination scares me"-evidence for fear responses during mental imagery of pain-associated interoceptive sensations in adolescents with chronic pain.

ABSTRACT: According to the bio-informational theory of emotion by Lang, mental imagery of fearful stimuli activates physiological and behavioural response systems, even in the absence of sensory input. We investigated whether instructed mental imagery of pain-associated (not painful) interoceptive sensations entails a threat value and elicits increased startle response, skin conductance level (SCL), and heart rate (HR) indicative of defensive mobilization in adolescents with chronic pain. Additionally, self-reported measures (fear, fear of pain, desire to avoid) were assessed. Adolescents (11-18 years) with chronic headache (CH, n = 46) or chronic abdominal pain (CAP, n = 29) and a control group (n = 28) were asked to imagine individualized pain-associated, neutral and standardized fear scripts. During pain-associated compared with neutral imagery, both pain groups showed higher mean HR, with CH also showing higher HR reactivity, while HR acceleration was not observed within control group. In contrast, during pain-associated compared with neutral imagery, startle response magnitude and SCL remained unchanged in all groups. Additionally, overall levels in self-reports were higher during pain-associated compared with neutral imagery, but significantly more pronounced in the pain groups compared with the control group. Results suggest that the mere imagination of pain-associated sensations elicits specific autonomic fear responses accompanied by increased self-reported fear in adolescents with chronic pain. The specific modulation of heart rate shed new light on our understanding of multimodal fear responses in adolescents with chronic pain and may help to refine paradigms to decrease fear of interoceptive sensations in chronic pain.

Fractal Dimension as a discriminative feature for high accuracy classification in motor imagery EEG-based brain-computer interface.

BACKGROUND AND OBJECTIVE: The brain-computer interface (BCI) technology acquires human brain electrical signals, which can be effectively and successfully used to control external devices, potentially supporting subjects suffering from motor impairments in the interaction with the environment. To this aim, BCI systems must correctly decode and interpret neurophysiological signals reflecting the intention of the subjects to move. Therefore, the accurate classification of single events in motor tasks represents a fundamental challenge in ensuring efficient communication and control between users and BCIs. Movement-associated changes in electroencephalographic (EEG) sensorimotor rhythms, such as event-related desynchronization (ERD), are well-known features of discriminating motor tasks. Fractal dimension (FD) can be used to evaluate the complexity and self-similarity in brain signals, potentially providing complementary information to frequency-based signal features. METHODS: In the present work, we introduce FD as a novel feature for subject-independent event classification, and we test several machine learning (ML) models in behavioural tasks of motor imagery (MI) and motor execution (ME). RESULTS: Our results show that FD improves the classification accuracy of ML compared to ERD. Furthermore, unilateral hand movements have higher classification accuracy than bilateral movements in both MI and ME tasks. CONCLUSIONS: These results provide further insights into subject-independent event classification in BCI systems and demonstrate the potential of FD as a discriminative feature for EEG signals.

Motor cortex activation during visuomotor transformations: evoked potentials during overt and imagined movements.

Despite the prevalence of visuomotor transformations in our motor skills, their mechanisms remain incompletely understood, especially when imagery actions are considered such as mentally picking up a cup or pressing a button. Here, we used a stimulus-response task to directly compare the visuomotor transformation underlying overt and imagined button presses. Electroencephalographic activity was recorded while participants responded to highlights of the target button while ignoring the second, non-target button. Movement-related potentials (MRPs) and event-related desynchronization occurred for both overt movements and motor imagery (MI), with responses present even for non-target stimuli. Consistent with the activity accumulation model where visual stimuli are evaluated and transformed into the eventual motor response, the timing of MRPs matched the response time on individual trials. Activity-accumulation patterns were observed for MI, as well. Yet, unlike overt movements, MI-related MRPs were not lateralized, which appears to be a neural marker for the distinction between generating a mental image and transforming it into an overt action. Top-down response strategies governing this hemispheric specificity should be accounted for in future research on MI, including basic studies and medical practice.

Frequency Domain Channel-Wise Attack to CNN Classifiers in Motor Imagery Brain-Computer Interfaces.

OBJECTIVE: Convolutional neural network (CNN), a classical structure in deep learning, has been commonly deployed in the motor imagery brain-computer interface (MIBCI). Many methods have been proposed to evaluate the vulnerability of such CNN models, primarily by attacking them using direct temporal perturbations. In this work, we propose a novel attacking approach based on perturbations in the frequency domain instead. METHODS: For a given natural MI trial in the frequency domain, the proposed approach, called frequency domain channel-wise attack (FDCA), generates perturbations at each channel one after another to fool the CNN classifiers. The advances of this strategy are two-fold. First, instead of focusing on the temporal domain, perturbations are generated in the frequency domain where discriminative patterns can be extracted for motor imagery (MI) classification tasks. Second, the perturbing optimization is performed based on differential evolution algorithm in a black-box scenario where detailed model knowledge is not required. RESULTS: Experimental results demonstrate the effectiveness of the proposed FDCA which achieves a significantly higher success rate than the baselines and existing methods in attacking three major CNN classifiers on four public MI benchmarks. CONCLUSION: Perturbations generated in the frequency domain yield highly competitive results in attacking MIBCI deployed by CNN models even in a black-box setting, where the model information is well-protected. SIGNIFICANCE: To our best knowledge, existing MIBCI attack approaches are all gradient-based methods and require details about the victim model, e.g., the parameters and objective function. We provide a more flexible strategy that does not require model details but still produces an effective attack outcome.

Learning beyond sensations: How dreams organize neuronal representations.

Semantic representations in higher sensory cortices form the basis for robust, yet flexible behavior. These representations are acquired over the course of development in an unsupervised fashion and continuously maintained over an organism's lifespan. Predictive processing theories propose that these representations emerge from predicting or reconstructing sensory inputs. However, brains are known to generate virtual experiences, such as during imagination and dreaming, that go beyond previously experienced inputs. Here, we suggest that virtual experiences may be just as relevant as actual sensory inputs in shaping cortical representations. In particular, we discuss two complementary learning principles that organize representations through the generation of virtual experiences. First, "adversarial dreaming" proposes that creative dreams support a cortical implementation of adversarial learning in which feedback and feedforward pathways engage in a productive game of trying to fool each other. Second, "contrastive dreaming" proposes that the invariance of neuronal representations to irrelevant factors of variation is acquired by trying to map similar virtual experiences together via a contrastive learning process. These principles are compatible with known cortical structure and dynamics and the phenomenology of sleep thus providing promising directions to explain cortical learning beyond the classical predictive processing paradigm.

Physiological and subjective arousal to prospective mental imagery: A mechanism for behavioral change?

Emotional prospective mental imagery, in which we simulate possible future events within our minds, have a pronounced impact on behavior. For example, repeated engagement in positive prospective imagery can lead to behavioral activation, while negative prospective imagery can lead to catastrophizing and avoidance. Physiological arousal boosts memory consolidation, creating emotional memories. Thus, if emotional prospective imagery produces an arousal response, the memory consolidation of these simulations of the future may be boosted, offering a possible underlying mechanism for the impact of emotional prospective imagery on behavior. In order to examine the feasibility of arousal as a possible mechanism behind the impact of emotional prospective imagery on behavior, sixty participants produced autobiographical prospective imagery of 30 scenes (10 positive, 10 neutral, and 10 negative), during which arousal responses (skin conductance) were measured, and ratings for subjective arousal, valence, and imagery vividness were collected. Moreover, because vividness of prospective imagery has been related to anxiety and depression, the study examined this relation also for event-related autobiographical prospective imagery. The results showed that emotional prospective imagery were associated with higher subjective arousal ratings as compared to neutral imagery. Physiological arousal responses showed a similar pattern, but further data is needed for a firm conclusion. Nevertheless, arousal-boosted consolidation remains a possible contributing mechanism for the impact of emotional prospective imagery on behavior. Moreover, results suggest both anxiety and depression may entail a reduced ability to invent prospective life situations. However, only anxiety was associated with less vivid imaginations, unless the imaginations were of negative content. Hence, anxious individuals may experience negative prospective imagery more vividly than imagery with neutral and positive content.