Neural Suppression Elicited During Motor Imagery Following the Observation of Biological Motion From Point-Light Walker Stimuli

Introduction: Advantageous effects of biological motion (BM) detection, a low-perceptual mechanism that allows the rapid recognition and understanding of spatiotemporal characteristics of movement via salient kinematics information, can be amplified when combined with motor imagery (MI), i.e., the mental simulation of motor acts. According to Jeannerod’s neurostimulation theory, asynchronous firing and reduction of mu and beta rhythm oscillations, referred to as suppression over the sensorimotor area, are sensitive to both MI and action observation (AO) of BM. Yet, not many studies investigated the use of BM stimuli using combined AO-MI tasks. In this study, we assessed the neural response in the form of event-related synchronization and desynchronization (ERD/S) patterns following the observation of point-light-walkers and concordant MI, as compared to MI alone.Methods: Twenty right-handed healthy participants accomplished the experimental task by observing BM stimuli and subsequently performing the same movement using kinesthetic MI (walking, cycling, and jumping conditions). We recorded an electroencephalogram (EEG) with 32 channels and performed time-frequency analysis on alpha (8–13 Hz) and beta (18–24 Hz) frequency bands during the MI task. A two-way repeated-measures ANOVA was performed to test statistical significance among conditions and electrodes of interest.Results: The results revealed significant ERD/S patterns in the alpha frequency band between conditions and electrode positions. Post hoc comparisons showed significant differences between condition 1 (walking) and condition 3 (jumping) over the left primary motor cortex. For the beta band, a significantly less difference in ERD patterns (p < 0.01) was detected only between condition 3 (jumping) and condition 4 (reference).Discussion: Our results confirmed that the observation of BM combined with MI elicits a neural suppression, although just in the case of jumping. This is in line with previous findings of AO and MI (AOMI) eliciting a neural suppression for simulated whole-body movements. In the last years, increasing evidence started to support the integration of AOMI training as an adjuvant neurorehabilitation tool in Parkinson’s disease (PD).Conclusion: We concluded that using BM stimuli in AOMI training could be promising, as it promotes attention to kinematic features and imitative motor learning.

The Efficacy of Downward Counterfactual Thinking for Regulating Emotional Memories in Anxious Individuals

Aversive autobiographical memories sometimes prompt maladaptive emotional responses and contribute to affective dysfunction in anxiety and depression. One way to regulate the impact of such memories is to create a downward counterfactual thought–a mental simulation of how the event could have been worse–to put what occurred in a more positive light. Despite its intuitive appeal, counterfactual thinking has not been systematically studied for its regulatory efficacy. In the current study, we compared the regulatory impact of downward counterfactual thinking, temporal distancing, and memory rehearsal in 54 adult participants representing a spectrum of trait anxiety. Participants recalled regretful experiences and rated them on valence, arousal, regret, and episodic detail. Two to six days later, they created a downward counterfactual of the remembered event, thought of how they might feel about it 10 years from now, or simply rehearsed it. A day later, participants re-rated the phenomenological characteristics of the events. Across all participants, downward counterfactual thinking, temporal distancing, and memory rehearsal were equally effective at reducing negative affect associated with a memory. However, in individuals with higher trait anxiety, downward counterfactual thinking was more effective than rehearsal for reducing regret, and it was as effective as distancing in reducing arousal. We discuss these results in light of the functional theory of counterfactual thinking and suggest that they motivate further investigation into downward counterfactual thinking as a means to intentionally regulate emotional memories in affective disorders.

Oversampled and undersolved: Depressive rumination from a predictive coding perspective

Rumination is a widely recognized cognitive deviation in depression. An integrative view that combines clinical findings on rumination with theories of mental simulation and cognitive problem-solving could help explain the development and maintenance of rumination in a computationally and biologically plausible framework. In this review, we connect insights from neuroscience and computational psychiatry to elucidate rumination as repetitive but unsuccessful attempts at mental problem-solving. Appealing to a predictive processing account, we suggest that problem-solving is based on an algorithm that generates candidate behavior (policy primitives for problem solutions) using a Bayesian sampling approach, evaluates resulting policies for action, and then engages in instrumental learning to reduce prediction errors. We present evidence suggesting that this problem-solving algorithm is distorted in depression: Specifically, depressive rumination is regarded as excessive Bayesian sampling of candidates that is associated with high prediction errors without activation of the successive steps (policy evaluation, instrumental learning) of the algorithm. Thus, prediction errors cannot be decreased, and excessive resampling of the same problems occur. This then leads to reduced precision weighting attributed to external, “online” stimuli, low behavioral output and high opportunity costs due to the time-consuming nature of the sampling process itself. We review different computational reasons that make the proposed Bayesian sampling algorithm vulnerable to a ruminative „halting problem”. We also identify neurophysiological correlates of these deviations in pathological connectivity patterns of different brain networks. We conclude by suggesting future directions for research into behavioral and neurophysiological features of the model and point to clinical implications.

Partial Mental Simulation Explains Fallacies in Physical Reasoning

People can reason intuitively, efficiently, and accurately about everyday physical events. Recent accounts suggest that people use mental simulation to make such intuitive physical judgments. But mental simulation models are computationally expensive; how is physical reasoning relatively accurate, while maintaining computational tractability? We suggest that people make use of partial simulation, mentally moving forward in time only parts of the world deemed relevant. We propose a novel partial simulation model, and test it on the physical conjunction fallacy, a recently observed phenomenon (Ludwin-Peery, Bramley, Davis, & Gureckis, 2020) that poses a challenge for full simulation models. We find an excellent fit between our model's predictions and human performance on a set of scenarios that build on and extend those used by Ludwin-Peery et al. (2020), quantitatively and qualitatively accounting for a deviation from optimal performance. Our results suggest more generally how we allocate cognitive resources to efficiently represent and simulate physical scenes.

Effectiveness of mental simulations on the early mobilization of patients after cesarean section: a randomized controlled trial

We aimed to investigate whether psychological intervention (single mental simulation) among women after cesarean surgery (CC) can affect their willingness to verticalize, actual verticalization, and the duration of the first mobilization. In this prospective randomised, controlled study, 150 women after CC were divided into 3 groups: experimental group with process-simulation with elements of relaxation, experimental group with outcome-simulation with elements of relaxation and control group with elements of relaxation only. After a 5-h stay in the post-operative room, women listened to a recording with a stimulation. Pain and anxiety of verticalization were measured before and after listening to the recording and after verticalization. Almost 12% more patients verticalized in the process-simulation group than in the control group. Percentages of mobilized patients were: 39.4% the process-simulation group; 32.8% in the outcome-simulation group; 27.7% controls (p = 0.073). Mobilization was 5 min longer in the process-simulation group then in control (p < 0.01). Anxiety after the simulation was a significant covariate of the willingness to verticalize, actual verticalization and time spent in mobilization. We conclude that a single mental simulation can effectively motivate patients for their first verticalization after CC. Perceived anxiety before verticalization may affect the effectiveness of interventions, so we recommend to check it at the postoperative care.ClinicalTrials.gov Identifier: NCT04829266.

Autosuggestion: a cognitive process that empowers your brain?

Autosuggestion is a cognitive process that is believed to enable control over one’s own cognitive and physiological states. Despite its potential importance for basic science and clinical applications, such as in rehabilitation, stress reduction, or pain therapy, the neurocognitive mechanisms and psychological concepts that underlie autosuggestion are poorly defined. Here, by reviewing empirical data on autosuggestion and related phenomena such as mental imagery, mental simulation, and suggestion, we offer a neurocognitive concept of autosuggestion. We argue that autosuggestion is characterized by three major factors: reinstantiation, reiteration, and volitional, active control over one’s own physiological states. We also propose that autosuggestion might involve the ‘overwriting’ of existing predictions or brain states that expect the most common (but not desired) outcome. We discuss potential experimental paradigms that could be used to study autosuggestion in the future, and discuss the strengths and weaknesses of current evidence. This review provides a first overview on how to define, experimentally induce, and study autosuggestion, which may facilitate its use in basic science and clinical practice.

Mental Simulation to Promote Exercise Intentions and Behaviors

Mental simulation, which employs specific patterns of imagery, can increase the intention to exercise as well as actual engagement in exercise. The present studies explored the effects of mental simulation on the intention to engage in exercise while regulating emotions. The first study confirmed that mental simulation did promote intentions of participants. The second found that video-primed mental simulation was a more effective method of exercise intention promotion than semantic-primed and image-primed mental simulation. In the third study, it was found that combining process-based and outcome-based mental simulations increased exercise intentions. Positive emotions mediated imagery ability and intention to exercise. The final study found that the mental simulation interventions most effective for exercise adherence were those that balanced the valence of process and outcome components in such a way that a challenging process results in a positive outcome, or a smooth process results in a negative outcome. Each of these results has practical implications for exercise interventions that will be discussed.

Human-Centered Explainable Artificial Intelligence for Marine Autonomous Surface Vehicles

Explainable Artificial Intelligence (XAI) for Autonomous Surface Vehicles (ASVs) addresses developers’ needs for model interpretation, understandability, and trust. As ASVs approach wide-scale deployment, these needs are expanded to include end user interactions in real-world contexts. Despite recent successes of technology-centered XAI for enhancing the explainability of AI techniques to expert users, these approaches do not necessarily carry over to non-expert end users. Passengers, other vessels, and remote operators will have XAI needs distinct from those of expert users targeted in a traditional technology-centered approach. We formulate a concept called ‘human-centered XAI’ to address emerging end user interaction needs for ASVs. To structure the concept, we adopt a model-based reasoning method for concept formation consisting of three processes: analogy, visualization, and mental simulation, drawing from examples of recent ASV research at the Norwegian University of Science and Technology (NTNU). The examples show how current research activities point to novel ways of addressing XAI needs for distinct end user interactions and underpin the human-centered XAI approach. Findings show how representations of (1) usability, (2) trust, and (3) safety make up the main processes in human-centered XAI. The contribution is the formation of human-centered XAI to help advance the research community’s efforts to expand the agenda of interpretability, understandability, and trust to include end user ASV interactions.