Imagination Task Research Articles

A novel scoring protocol reveals age-related differences in abstract compared to concrete thinking in cued autobiographical remembering

Human imagination has garnered growing interest in many fields. However, it remains unclear how to characterize different forms of imaginative thinking and how imagination differs between young and older adults. Here, we introduce a novel scoring protocol based on recent theoretical developments in the cognitive neuroscience of imagination to provide a broad tool with which to characterize imaginative thinking. The scoring protocol distinguishes between concrete/perceptual forms of imagination termed the “mind’s eye” and abstract/reflective forms of imagination termed the “mind’s mind.” The protocol also captures whether thoughts pertain to the self, others, or both. We applied this scoring protocol with high inter-rater reliability across two studies involving distinct participants and narrative-based imagination tasks. When compared to young adults, older adults showed a bias toward general content, which is a feature of the mind’s mind form of thinking while describing aloud their memories of specific, past events (Study 1). Further, older adults made fewer references to the self. In a separate study of only older adults (Study 2), increasing age was not associated with a bias toward the mind’s mind while describing specific past or future events. These results reveal that imaginative thinking can be characterized within the Mind’s Eye Mind’s Mind framework, with implications for understanding cognitively normal older age.

Science teachers’ conceptual perspectives on scientific experiments: a metaphorical representation

ABSTRACT Background Despite the valuable learning opportunities experimentation offers, it poses significant challenges for teachers and learners. The effectiveness of instructional scaffolding in students’ experiments is intricately linked to science teachers’ conceptual foundations and practical abilities to impart science concepts and epistemic practices. Purpose Considering their metaphorical representations, this research unveiled science teachers’ imaginations regarding core epistemic practical work, specifically scientific experiments. Methods The present study engaged 340 Turkish science teachers. The participants used a metaphor imagination task to express their perceptions of scientific experiments. Results The study identified metaphorical conceptualizations organized around structural themes (action, tool, and place). This indicates that the participants perceived the generation of scientific knowledge through experiments as a dynamic process akin to science-as-activity. Furthermore, three predominant conceptual orientations were highlighted: hypothesis testing, knowledge production, and discovery. Teachers viewed scientific experiments as avenues for knowledge generation through discovery, with hypothesis testing playing a significant role. Conclusion The findings suggest that the metaphors conveyed a science-as-logic and science-as-theory perspective rather than a science-as-practice understanding. The study advocates for reconsidering and redesigning teacher training programs to instill the understanding that scientific experiments extend beyond isolated activities. These developmental programs should stress that experimenting is an epistemic activity involving collaborative knowledge generation within social, cultural, contextual, and institutional contexts.

Mind's eye in the Alzheimer's disease spectrum continuum.

Visual mental imagery (VMI) is the ability to represent stimuli in the mind without sensory visual input. Previous studies have shown alterations in VMI in Alzheimer's disease (AD). However, VMI has not been investigated in the AD prodromal stage, mild cognitive impairment (MCI). We investigated VMI ability in mild AD and MCI patients, hypothesizing that VMI ability could be compromised since early disease stage. We enrolled 14 patients with mild AD, 19 amnestic MCI (aMCI), and 23 healthy control subjects (HC), matched for sex, age, and education. VMI assessment included: 1) the O'clock test that allows disentangling the possible role of visuo-perceptual difficulties in the VMI task's performance; 2) a modified version of The Complete Visual Mental Imagery Battery (CVMIB), including tasks evaluating the different VMI processes (generation, maintenance, inspection and transformation). Results indicated that AD patients performed worse than HC in both perceptual and imaginal tasks of the O'clock test and in all CVMIB's tasks but maintenance. On the contrary, aMCI patients showed difficulties in the generation process and in the imaginal task of the O'clock test. Visual images generation, inspection and transformation processes are impaired in mild AD. Moreover, the generation process is selectively impaired in aMCI patients, suggesting that VMI deficits are already present in the prodromal stage of AD.

A Reconsideration of Imaginative Points of Resistance

In this short piece, I want to explore the idea that limits of imagination, that I refer to here as to ‘points of resistance’, can play an essential role in certain imaginative tasks. To show how points of resistance can be carriers of crucial information, I focus here on the analysis of 9/11. Leaving aside personal and political implications, I investigate the possible plausibility of some statements of the US Secretary of the Defense at the time of the attacks, attributing the cause of 9/11 to imagination. I propose that, despite being dismissed as an outrageous analysis of responsibilities involved in the success of the terroristic attacks, there could be a role that a failure of imagination might have played.

EEG oscillations in recent suicide attempters: Assessing responses to positive and negative future imagination tasks

BackgroundOur goal is to uncover Electroencephalography (EEG) power spectrum patterns during imagination tasks in individuals who attempted suicide within the past 1–4 weeks, addressing gaps in understanding the neural correlates of future imagination in suicidal behavior. MethodsThis case-control study comprised a total of 60 participants, consisting of 47 females and 13 males. The sample comprised three groups: Attempted Suicide + Major Depressive Disorder (SA+MDD), Major Depressive Disorder (MDD), and Healthy Controls (HC). To assess participants' future imagination, a unique approach called the Positive and Negative Future Imagination Task was designed, which involved scenario visualization concurrent with EEG recording. Statistical analyses included ANOVA with post-hoc pairwise comparisons to compare EEG power spectrum between three groups. ResultsIt was observed that the SA+MDD group experienced significantly increased theta frequency band in the right frontocentral regions when compared to the MDD group during both positive and negative imagination tasks (P-value < 0.05). Furthermore, increased gamma activity was observed in the SA+MDD group compared to the HC group, predominantly in the right frontocentral areas during both imagination tasks. ConclusionThe results of this study indicate that individuals who have recently attempted suicide exhibit heightened neural activity in the frontocentral regions of the right hemisphere of the brain, specifically in theta and gamma band frequencies, when contemplating both positive and negative aspects of the future. These findings, in the context of behavioral tasks may indicate a decrease in the ability to envision a positive future and an increase in visualizing a negative future.

Influence of a robotic companion on women's food choices: Evidence from an imaginary task.

Previous research has demonstrated the influence of commensal dining between humans on food choices, whereas we conducted two studies to examine how the presence of a robot might influence people's choices between meat-heavy and vegetable-forward meals in imaginary scenarios. In Study 1, participants were instructed to choose three desirable dishes from a set of two meat and two vegetable dishes while they imagined eating alone, with a human, or with a robot. Although the meat dishes were rated as more palatable and pleasant, the female participants chose fewer meat-heavy meals when eating alone or with a robot than when eating with a human, whereas no such effect was observed for the male participants. We also replicated these patterns in Study 2, as the female participants chose fewer meat-heavy meals when eating with a robot and a human than when eating with two humans. Collectively, these findings provide empirical evidence regarding how the presence of a certain robot can influence female consumers' food choices in imaginary scenarios, which has direct implications for the practice of promoting sustainable food choices.

Effects of Exercise Training on EEG Activity Patterns during Cognitive Tasks

To explore the effective method to detect the effect of exercise training on EEG activity, this paper compared the distinguishability of EEG changes relevanted to exercise training in different cognitive states. EEG signals of college students who have undergone long-term exercise training (exercise training group) and college students who have not undergone such training (control group) were collected under specific cognitive tasks (Motor imagination tasks). Then the power characteristics of alpha wave and beta wave were obtained by wavelet transform method. The machine learning method was used to distinguish the difference of brain electrical activity between the exercise-trained subjects and the control group during the exercise-related cognitive tasks. Results showed that EEG patterns between two groups were not distinguished in rest status (the accuracy is lower than 59%), but they were separable in the completion of the motor cognitive tasks. The accuracy was over 85%. The pattern of brain electrical activity during cognitive tasks can better reflect the effect of exercise training.

Hybrid Brain-Computer Interface Controlled Soft Robotic Glove for Stroke Rehabilitation.

Soft robotic glove controlled by a brain-computer interface (BCI) have demonstrated effectiveness in hand rehabilitation for stroke patients. Current systems rely on static visual representations for patients to perform motor imagination (MI) tasks, resulting in lower BCI performance. Therefore, this study innovatively used MI and high-frequency steady-state visual evoked potential (SSVEP) to construct a friendly and natural hybrid BCI paradigm. Specifically, the stimulation interface sequentially presented decomposed action pictures of the left and right hands gripping a ball, with the pictures flashing at specific stimulation frequencies (left: 34 Hz, right: 35 Hz). Integrating soft robotic glove as feedback, we established a comprehensive "peripheral - central - peripheral" hand rehabilitation system to facilitate the hand rehabilitation of patients. Filter bank common spatial pattern (FBCSP) and filter bank canonical correlation analysis (FBCCA) algorithms were used to identify MI and SSVEP signals, respectively. Additionally, we proposed a novel fusion algorithm to decide the final output of the system. The feasibility of the proposed system was validated through online experiments involving 12 healthy subjects and 9 stroke patients, achieving accuracy rates of 95.83 ± 6.83% and 63.33 ± 10.38, respectively. The accuracy of MI and SSVEP in 12 healthy subjects reached 81.67 ± 15.63% and 95.14 ± 7.47%, both lower than the accuracy after fusion, these results confirmed the effectiveness of the proposed algorithm. The accuracy rate was more than 50% in both healthy subjects and patients, confirming the effectiveness of the proposed system.

Neuronal activation patterns during self-referential pain imagination

In clinical assessments and pain therapy, patients are asked to imagine themselves in pain. However, the underlying neuronal processes remain poorly understood. Prior research has focused on empathy for pain or reported small sample sizes. Thus, the present study aimed to promote the neurobiological understanding of self-referential pain imagination. We hypothesised to find activation contrasts (pain vs. no pain) across pain-related areas and expected two of the most prominent predictors of chronic pain, pain sensitivity (PS) and locus of control (LoC), to be moderators.In an fMRI study, N = 82 participants completed a pain imagination task, in which they were asked to imagine themselves in painful and non-painful situations presented in the form of pictures and texts. After each trial, they were instructed to give painfulness ratings. As a laboratory measure of PS, electrical pain thresholds were assessed. A questionnaire was completed to measure LoC.Across presentation modes we found activity contrasts in previously pain-related regions, such as the prefrontal, supplementary motor, primary motor, somatosensory and posterior parietal cortices, and the cerebellum. We found positive associations of PS and external LoC with painfulness ratings, and a negative correlation between PS and internal LoC. Despite our hypotheses, neither PS nor internal LoC were significant predictors of the BOLD-signal contrasts.Though future studies are needed to draw further conclusions, our results provide preliminary evidence of a potential neuronal imagination-perception overlap in pain.

“The future will be green, or not at all”: How positive (utopian) and negative (dystopian) thoughts about the future shape collective climate action

The global movement to combat climate change is focused on pressuring governments, industry and other key decision-makers to take urgent action to mitigate the causes and impacts of climate change. The movement has played an important role in global transformation and change. What motivates people to engage in collective climate action? The current study examines the role of prospection, that is, thoughts and emotions about the future, in shaping collective climate action. Two studies (Study 1: N = 413; Study 2: N = 440) test experimentally the effects of positive (utopian) and negative (dystopian) future-oriented thinking on collective climate action via future-oriented emotions (hope and fear). Participants were assigned to engage in one of two imagination tasks focussing either on a utopian society that has adapted to climate change, or a dystopian society, or a control condition (passive or active). Across both studies, utopian thinking was found to indirectly affect collective climate action by evoking feelings of hope. Additionally, an indirect effect of dystopian thinking on collective climate action through fear was found in Study 2. These results suggest that both forms of future-oriented thinking may have the potential to increase collective climate action intentions by evoking an emotional response.

과학 관련 미래 상상하기 과정에서 나타나는 뇌활동성 분석

We investigated the neural bases of future imagination in science using functional magnetic resonance imaging (fMRI). Participants were 10 science and engineering undergraduate students. Seven of them (70%) received sciencegifted education when they went through primary, middle, or high school, and all participants got good grade in science and engineering class. In the future imagination task, participants were asked to imagine the science relatedfuture based on the sentence. In the control task, participants were asked to read the science related sentence by eye. Analysis showed that future imagination task produced greater brain activation than control task in bilateralparahippocampal gyrus and fusiform gyrus (BA 37), bilateral temporal cortex (BA 39), left superior frontal cortex (BA 6), right prefrontal cortex (BA 46), bilateral cerebellum, and so on. These areas are involved in long term memory, generating visual mental image, integrating sensory information, spatial working memory, self-initiated retrieval and so on. Thus, our result suggest that future imagination process in science involves complex brain network. It could provide the brain scientific explanation for the meaning of the future imagination activities in science gifted education.

Analysis of EEG Signals Between Motor Imaginary Tasks and Rest Condition for Biometric Application

Biometric technology has gained immense popularity as an effective solution for enhancing cybersecurity, specifically in countering financial fraud and security threats. EEG-based authentication is unique among biometric authentication methods due to its unparalleled confidentiality and non-replicability. This study explores the feasibility of using motor imagery tasks and rest conditions for human authentication. Ten physically fit subjects, aged between 20 and 28 years, participated voluntarily in the study. The subjects perform imaginary tasks involving their left and right-hand movement. Each task lasted for two minutes, separated by a one-minute break, and EEG data were collected using the EPOC+ device, which features 14-channel electrodes. The sampling frequency was set at 128 Hz. To extract relevant frequency information, Butterworth bandpass filters were employed to extract the alpha (8-13Hz), beta (14-30Hz), and gamma (30-42Hz) frequency bands. Linear features, such as power spectral density (PSD), were obtained using the Welch Method and the Burg Method, while spectral entropy was used to extract non-linear features. Statistical features mean, median, standard deviation, minimum, and maximum were derived from the PSD, and spectral entropy was used as input for the classifiers. Multiple classifiers, including k-nearest neighbor (KNN), support vector machine (SVM), decision tree and Naive Bayes, were employed for the classification task. The Welch method combined with the support vector machine classifier achieved a higher classification accuracy of 96.83% for the beta waves from channels C3, C4, O1, and O2, corresponding to the frontal and occipital lobes. Interestingly, the rest conditions exhibited a higher classification accuracy of 96.83% compared to the motor-imagery tasks, which achieved 96.04%. The utilization of motor imagery tasks and rest conditions, along with the application of advanced classification techniques, holds promise for the development of robust and reliable biometric systems in cybersecurity.

We both wanted it! The impact of contact meta-perceptions on the effectiveness of an imagined contact task

The present study examined if and how contact meta-perceptions might affect the impact of imagined contact on a wide range of intergroup outcomes. 260 Italian university students were randomly allocated to imagine an encounter with a Chinese immigrant (experimental group) or an ingroup member (control group). We then examined if believing that the encounter had been sought only by themselves or by both themselves and the outgroup member (i.e., contact meta-perceptions) could affect the effectiveness of the imagination task, considering also the role of previous direct contact. A significant condition x contact meta-perceptions interaction effect emerged on many intergroup outcomes (i.e., emotions, empathy, trust, outgroup norms, stereotypes, and self-disclosure). Imagined contact was more effective when participants perceived that both themselves and the outgroup member desired contact, with previous direct contact having limited influence. These findings highlight the importance of mutual desire for contact in improving intergroup relations.

The design and implementation of multi-character classification scheme based on EEG signals of visual imagery.

In visual-imagery-based brain-computer interface (VI-BCI), there are problems of singleness of imagination task and insufficient description of feature information, which seriously hinder the development and application of VI-BCI technology in the field of restoring communication. In this paper, we design and optimize a multi-character classification scheme based on electroencephalogram (EEG) signals of visual imagery (VI), which is used to classify 29 characters including 26 lowercase English letters and three punctuation marks. Firstly, a new paradigm of randomly presenting characters and including preparation stage is designed to acquire EEG signals and construct a multi-character dataset, which can eliminate the influence between VI tasks. Secondly, tensor data is obtained by the Morlet wavelet transform, and a feature extraction algorithm based on tensor-uncorrelated multilinear principal component analysis is used to extract high-quality features. Finally, three classifiers, namely support vector machine, K-nearest neighbor, and extreme learning machine, are employed for classifying multi-character, and the results are compared. The experimental results demonstrate that, the proposed scheme effectively extracts character features with minimal redundancy, weak correlation, and strong representation capability, and successfully achieves an average classification accuracy 97.59% for 29 characters, surpassing existing research in terms of both accuracy and quantity of classification. The present study designs a new paradigm for acquiring EEG signals of VI, and combines the Morlet wavelet transform and UMPCA algorithm to extract the character features, enabling multi-character classification in various classifiers. This research paves a novel pathway for establishing direct brain-to-world communication.

Trauma-Informed Evangelism: Cultivating Communities of Wounded Healers

Trauma-Informed Evangelism: Cultivating Communities of Wounded Healers

Trauma-Informed Evangelism: Cultivating Communities of Wounded Healers

Trauma-Informed Evangelism: Cultivating Communities of Wounded Healers

Imagine Being Humble: Integrating Imagined Intergroup Contact and Cultural Humility to Foster Inclusive Intergroup Relations.

To reduce prejudice and to promote intergroup harmony and equality, the imagined intergroup contact technique, based on the mental simulation of an encounter with an outgroup member, has been proposed. Though a substantial body of research has provided support for the efficacy of imagined intergroup contact in prejudice reduction, an alternative strand of research has raised questions about its effectiveness. In this experiment, we combined imagined intergroup contact with cultural humility, that is, an other-oriented, humble approach toward people with different cultural backgrounds, recognizing status and power imbalances and privileges. Specifically, we tested whether instructions aimed at eliciting cultural humility during imagined contact boosted its effectiveness in reducing prejudice and promoting future contact intentions, compared to a standard imagined contact condition and to a control imagination task. Intergroup anxiety was tested as a mediator of the effects of culturally humble imagined contact on reduced prejudice and on future contact intentions. We found that culturally humble imagined contact, compared to the two other conditions, reduced intergroup anxiety and yielded indirect effects on reduced prejudice and increased future contact intentions. The findings will be discussed by focusing on the integration of cultural humility in prejudice reduction techniques based on intergroup contact.

Effects of transcranial magnetic stimulation on cortical structures during motor imagination performance in the brain-computer interface

The aftermath of a stroke can frequently result in impaired motor functions causing problems performing habitual limb movements. These movement disorders stem from damage to the cerebral cortex and disruptions to neuronal connections in the central pyramidal pathways [1]. Restoring motor skills following a stroke is a time-consuming and challenging process, requiring resources from both external medical sources and the patient. Despite these challenges, it is still feasible for patients to regain control over their limb movements. The common approach for stroke neurorehabilitation consists of therapeutic physical activities and kinesiotherapy. These methods rely on afferent information during motor tasks to repair connections between intact brain areas [2]. Through exercising the impacted limbs, synaptic rearrangement happens in the cortex, awakening dormant neurons and increasing cortical areas adjacent to inactive regions. Although these techniques can partially restore movement, a significant number of stroke patients still face impairments. It is crucial to acknowledge the limitations of the current interventions and explore new approaches for better outcomes. Traditional rehabilitation methods often do not fully restore movement control, prompting researchers to explore alternative approaches. Motor-imagery-based brain-computer interfaces (BCIs) have gained attention recently [3, 4]. They allow for integrating various feedback mechanisms, which can be combined with upper and lower limb exoskeletons. The level of control a subject has over a BCI system directly affects the recovery process [5]. Introducing transcranial magnetic stimulation (TMS) into motor-imagery BCIs shows promise for developing a unified and highly efficient approach to post-stroke rehabilitation. Twenty-nine healthy adult participants (21 females) with a mean age of 20.93±2.14 years were recruited for this experiment. All participants were right-handed and had no prior experience with brain-computer interfaces. Ethical approval was obtained from the local ethics committee of Lobachevsky State University of Nizhny Novgorod (ethical approval No. 2, dated 03/19/2021), and written informed consent was obtained from all participants. Subjects were randomly assigned to receive either a sham intervention (n=15) or active TMS (n=14). The experiment displayed tasks on a 24-inch LCD screen placed 2 meters away. Participants sat in a reclining chair with hands on adjustable armrests for EEG signal recording. Motor-imagery-based BCI training took place over two days with four daily tasks: motor performance, quasi-motor, and two motor imaginations of the dominant hand. EEG activity was recorded prior to and after completing the tasks. Each task comprised of 20 trials, each lasting 10 seconds. TMS was administered in between two motor imagination tasks, and a rest period of 2 minutes followed the stimulation. The researcher objectively monitored electromyography (EMG) in real-time during motor tasks. Certified NVX 52 amplifiers with 32 Cl/Ag electrodes positioned according to the international 10-10 system recorded electroencephalography (EEG) signals. The EEG was digitized at a sampling rate of 1000 Hz and filtered with a 50 Hz Notch filter. Disposable electrodes were used to record EMG data from musculus flexor digitorum superficialis on the right hand. EMG signals were digitized at 1000 Hz and filtered using a 50 Hz Notch filter. TMS was applied to the dorsolateral prefrontal cortex (dlPFC) using a figure-8 coil connected to a Neuro MS/D magnetic stimulator. Sham stimulation was performed with the same parameters, except the coil was tilted 90 degrees to simulate the sound of real stimulation. A nonparametric permutation test was conducted to assess the statistically significant dissimilarities between rest periods and periods subsequent to conducting motor imagery after TMS. Significant clusters of the highest event-related desynchronization (ERD) were observed. The analysis demonstrated a significant negative cluster in the theta rhythm (0–6 Hz) that was distinct from the magnetic stimulation site. Similar significant ERD clusters were identified during the first and second series of imaginary movements. The study examined alterations in motor cortex function following targeted rTMS. The results demonstrated that TMS using particular parameters resulted in the pre-activation of brain regions comparable to those stimulated during motor imagery. The implementation of this stimulation protocol increased activity in cortical regions related to motor imagery. The outcomes propose the probable effectiveness of TMS in elevating motor cortex activation during the rehabilitation process.

Scheduling independent tasks on multiple cloud-assisted edge servers with energy constraint

In this paper, we study task scheduling with or without energy constraint in mobile edge computing with multiple cloud-assisted edge servers as combinatorial optimization problems within the framework of classical scheduling theory. The first problem is to schedule a list of independent tasks on a mobile device and several heterogeneous edge servers and cloud servers, such that the makespan is minimized. The second problem is to schedule a list of independent tasks and to determine the computation and communication speeds of a mobile device, such that the makespan is minimized and the energy consumption of the mobile device does not exceed certain energy budget. The paper makes the following tangible contributions. We design heuristic task scheduling algorithms for both problems by considering the heterogeneity of computation and communication speeds. We derive a lower bound for the optimal schedule and prove an asymptotic performance bound for our heuristic algorithms. We experimentally evaluate the performance of our heuristic algorithms and show that their performance is very close to that of an optimal algorithm. Our analysis employs three key techniques, namely, the method of communication unification (i.e., all tasks have the same communication to computation ratio), the concept of effective speed of an edge server or a cloud server (i.e., the perceived speed of a server by ignoring the details and differences of communication speed and computation speed, wireless communication time and wired communication time, a regular edge server and a cloud-assisted edge server, execution time and waiting time), and the construction of virtual tasks (i.e., imaginary tasks which do not exist). Such unique techniques make it possible to derive a lower bound for the optimal solution, to derive an upper bound for the heuristic solution, to prove an asymptotic performance bound, and to find the best edge server order. To the best of the author's knowledge, this is the first paper in the literature which optimizes the makespan of task scheduling with or without energy constraint in mobile edge computing with multiple cloud-assisted edge servers.

Brain Computer Interface Based on Motor Imagery for Mechanical Arm Grasp Control

This paper puts forward a brain computer interface (BCI) system to realize the hand and wrist control using the ABB Mechanical Arm. This BCI system gathers four kinds of motor imaginary (MI) tasks (hand grasp, hand spread, wrist flexion and wrist extension) electroencephalogram (EEG) signals from 30 electrodes. It utilizes two fifth-order Butterworth Band-Pass Filter (BPF) with different bandwidths and normalization method to achieve the raw MI tasks EEG signals preprocessing. The main challenge of feature extraction is to extract enough representative features from MI tasks to classify them. This proposed BCI system extracts eleven kinds of features in time domain and time-frequency domain and uses mutual information method to reduce the large dimension of the extracted features. In addition, the BCI system applies a single convolutional layer Convolutional neural networks (CNN) with 30 filters to implement the quaternary classification of MI tasks. Compared with early researches, the classification accuracy of this BCI system is increased by about 35%. The actual mechanical arm grasping control experiments verifies that this BCI system has good adaptability.