cognitive states
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Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 535
Author(s):  
Mahsa Bagheri ◽  
Sarah D. Power

Research studies on EEG-based mental workload detection for a passive BCI generally focus on classifying cognitive states associated with the performance of tasks at different levels of difficulty, with no other aspects of the user’s mental state considered. However, in real-life situations, different aspects of the user’s state such as their cognitive (e.g., level of mental workload) and affective (e.g., level of stress/anxiety) states will often change simultaneously, and performance of a BCI system designed considering just one state may be unreliable. Moreover, multiple mental states may be relevant to the purposes of the BCI—for example both mental workload and stress level might be related to an aircraft pilot’s risk of error—and the simultaneous prediction of states may be critical in maximizing the practical effectiveness of real-life online BCI systems. In this study we investigated the feasibility of performing simultaneous classification of mental workload and stress level in an online passive BCI. We investigated both subject-specific and cross-subject classification approaches, the latter with and without the application of a transfer learning technique to align the distributions of data from the training and test subjects. Using cross-subject classification with transfer learning in a simulated online analysis, we obtained accuracies of 77.5 ± 6.9% and 84.1 ± 5.9%, across 18 participants for mental workload and stress level detection, respectively.


2022 ◽  
Vol 13 (1) ◽  
Author(s):  
Yan-Liang Shi ◽  
Nicholas A. Steinmetz ◽  
Tirin Moore ◽  
Kwabena Boahen ◽  
Tatiana A. Engel

AbstractCorrelated activity fluctuations in the neocortex influence sensory responses and behavior. Neural correlations reflect anatomical connectivity but also change dynamically with cognitive states such as attention. Yet, the network mechanisms defining the population structure of correlations remain unknown. We measured correlations within columns in the visual cortex. We show that the magnitude of correlations, their attentional modulation, and dependence on lateral distance are explained by columnar On-Off dynamics, which are synchronous activity fluctuations reflecting cortical state. We developed a network model in which the On-Off dynamics propagate across nearby columns generating spatial correlations with the extent controlled by attentional inputs. This mechanism, unlike previous proposals, predicts spatially non-uniform changes in correlations during attention. We confirm this prediction in our columnar recordings by showing that in superficial layers the largest changes in correlations occur at intermediate lateral distances. Our results reveal how spatially structured patterns of correlated variability emerge through interactions of cortical state dynamics, anatomical connectivity, and attention.


2022 ◽  
pp. 59-73
Author(s):  
Laurens R. Krol ◽  
Oliver W. Klaproth ◽  
Christoph Vernaleken ◽  
Nele Russwinkel ◽  
Thorsten O. Zander

2021 ◽  
Author(s):  
Joseph M. Saito ◽  
Matthew Kolisnyk ◽  
Keisuke Fukuda

Despite the massive capacity of visual long-term memory, individuals do not successfully encode all visual information they wish to remember. This variability in encoding success has been traditionally ascribed to fluctuations in individuals’ cognitive states (e.g., sustained attention) and differences in memory encoding processes (e.g., depth of encoding). However, recent work has shown that a considerable amount of variability in encoding success stems from intrinsic stimulus properties that determine the ease of encoding across individuals. While researchers have identified several perceptual and semantic properties that contribute to this stimulus memorability phenomenon, much remains unknown, including whether individuals are aware of the memorability of stimuli they encounter. In the present study, we investigated whether individuals have conscious access to the memorability of real-world stimuli while forming self-referential judgments of learning (JOL) during explicit memory encoding (Experiments 1A-B) and when asked about the perceived memorability of a stimulus in the absence of attempted encoding (Experiments 2A-B). We found that both JOLs and perceived memorability estimates were consistent across individuals and reliably predicted stimulus memorability. However, this apparent access to the properties that define memorability was not comprehensive. Individuals unexpectedly remembered and forgot consistent sets of stimuli as well. Thus, our findings demonstrate that individuals have conscious access to some—but not all—aspects of stimulus memorability and that this access exists regardless of the present demands on stimulus encoding.


PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261463
Author(s):  
Kyung Yoo ◽  
Jeongyeol Ahn ◽  
Sang-Hun Lee

Pupillometry, thanks to its strong relationship with cognitive factors and recent advancements in measuring techniques, has become popular among cognitive or neural scientists as a tool for studying the physiological processes involved in mental or neural processes. Despite this growing popularity of pupillometry, the methodological understanding of pupillometry is limited, especially regarding potential factors that may threaten pupillary measurements’ validity. Eye blinking can be a factor because it frequently occurs in a manner dependent on many cognitive components and induces a pulse-like pupillary change consisting of constriction and dilation with substantive magnitude and length. We set out to characterize the basic properties of this “blink-locked pupillary response (BPR),” including the shape and magnitude of BPR and their variability across subjects and blinks, as the first step of studying the confounding nature of eye blinking. Then, we demonstrated how the dependency of eye blinking on cognitive factors could confound, via BPR, the pupillary responses that are supposed to reflect the cognitive states of interest. By building a statistical model of how the confounding effects of eye blinking occur, we proposed a probabilistic-inference algorithm of de-confounding raw pupillary measurements and showed that the proposed algorithm selectively removed BPR and enhanced the statistical power of pupillometry experiments. Our findings call for attention to the presence and confounding nature of BPR in pupillometry. The algorithm we developed here can be used as an effective remedy for the confounding effects of BPR on pupillometry.


2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 975-975
Author(s):  
Jamie Knight ◽  
Tomiko Yoneda ◽  
Nathan Lewis ◽  
Graciela Muniz-Terrera ◽  
David Bennett ◽  
...  

Abstract Decreasing estrogen levels have been hypothesized to be associated with increased risk of dementia, yet the current literature reveals conflicting results. This study aimed to determine whether a longer reproductive period, as an indicator of longer exposure to endogenous estrogens, is associated with risk of transitioning to MCI and dementia. Women 65 and over (N=1507) from the Rush Memory and Aging Project met eligibility for the current analysis. The average length of reproductive period (menopause age minus menarche age) was 35 years (range=16-68 years), and 64% had natural menopause. Multistate survival modeling (MSM) was used to estimate the influence of reproductive period on risk of transitioning through cognitive states including mild cognitive impairment (MCI) and clinically diagnosed dementia, as well as death. Multinomial regression models estimated total and cognitively unimpaired life expectancies based on the transition probabilities estimated by the MSM. Results suggest that women with more reproductive years were less likely to transition from no cognitive impairment (NCI) to MCI, and were more likely to return to NCI from MCI. Analyses also suggest two additional years free of cognitive impairment for women with 45 vs 25 years of reproduction, though reproduction period did not significantly impact overall life expectancy. This study suggests that the number of years of reproductive duration is not associated with the transition to dementia, but is possibly associated with delayed cognitive decline, reduced risk of MCI, increased likelihood of returning to NCI from MCI, and increased lifespan free of cognitive impairment.


2021 ◽  
Author(s):  
Devarshi Mukherji ◽  
Manibrata Mukherji ◽  
Nivedita Mukherji

Abstract Alzheimer’s Disease (AD) is the most expensive and currently incurable disease that affects a large number of the elderly globally. One in five Medicare dollars is spent on AD-related tests and treatments. Accurate AD diagnosis is critical but often involves invasive and expensive tests that include brain scans and spinal taps. Recommending these tests for only patients who are likely to develop the disease will save families of cognitively normal individuals and hospitals from unnecessary expenditures. Moreover, many of the subjects chosen for clinical trials for AD therapies never develop any cognitive impairment and prove not to be ideal candidates for those trials. It is thereby critical to find inexpensive ways to first identify individuals who are likely to develop cognitive impairment and focus attention on them for in-depth testing, diagnosing, and clinical trial participation. Research shows that AD is a slowly progressing disease. This slow progression allows for early detection and treatment, but more importantly, gives the opportunity to predict the likelihood of disease development from early indications of memory lapses. Neuropsychological tests have been shown to be effective in identifying cognitive impairment. Relying exclusively on a set of longitudinal neuropsychological test data available from the ADNI database, this paper has developed Recurrent Neural Network (RNN) models to predict future neuropsychological test results and Multi-Level Perceptron (MLP) models to diagnose the future cognitive states of individuals based on those predicted results. The RNNs use sequence prediction techniques to predict test scores for two to four years in the future. The predicted scores and predictions of cognitive states based on them showed a high level of accuracy for a group of test subjects, when compared with their known future cognitive assessments conducted by ADNI. This shows that a battery of neuropsychological tests can be used to track the cognitive states of people above a certain age and identify those who are likely to develop cognitive impairment in the future. This ability to triage individuals into those who are likely to remain normal and those who will develop cognitive impairment in the future, advances the quest to find appropriate candidates for invasive tests like spinal taps for disease identification, and the ability to identify suitable candidates for clinical trials.


2021 ◽  
Vol 11 (21) ◽  
pp. 10255
Author(s):  
Boris M. Velichkovsky ◽  
Artemiy Kotov ◽  
Nikita Arinkin ◽  
Liudmila Zaidelman ◽  
Anna Zinina ◽  
...  

We implemented different modes of social gaze behavior in our companion robot, F-2, to evaluate the impression of the gaze behaviors on humans in three symmetric communicative situations: (a) the robot telling a story, (b) the person telling a story to the robot, and (c) both parties communicating about objects in the real world while solving a Tangram puzzle. In all the situations the robot localized the human’s eyes and directed its gaze between the human, the environment, and the object of interest in the problem space (if it existed). We examined the balance between different gaze directions as the novel key element to maintaining a feeling of social connection with the robot in humans. We extended the computer model of the robot in order to simulate realistic gaze behavior in the robot and create the impression of the robot changing its internal cognitive states. Other novel results include the implicit, rather than explicit, character of the robot gaze perception for many of our subjects and the role of individual differences, especially the level of emotional intelligence, in terms of human sensitivity to the robotic gaze. Therefore, in this study, we used an iterative approach, extending the applied cognitive architecture in order to simulate the balance between different behavioral reactions and to test it in the experiments. In such a way, we came to a description of the key behavioral cues that suggest to a person that the particular robot can be perceived as an emotional and even conscious creature.


2021 ◽  
Vol 2102 (1) ◽  
pp. 012004
Author(s):  
M Rojas-Contreras ◽  
C A Peña-Cortés ◽  
L A Moreno-Cuevas

Abstract The scope of this article is to analyze the behavior of children’s brain waves in response to interaction with an augmented reality application that aims to support the concept of measurement in physics teaching. In particular, the analysis of brain waves is carried out through a brain-computer interface that measures 6 cognitive states such as engagement, interest, stress, focus, excitation, and relaxation. The method used to perform the analysis is carried out by means of electroencephalography, which is an electrophysiological process to record the electrical activity of the brain, and which is captured by means of sensors located on the scalp. Once the signals are captured, they are amplified, digitized, and stored in a computer for processing and analysis. Initially, electrical signals are recorded in response to a measurement stimulus with traditional methods and later with an augmented reality application stimulus. Among the most relevant findings, it was possible to establish that interest and commitment increase in response to the teaching method supported with an augmented reality application for the measurement concept with respect to the traditional teaching method. The use of vision technologies in teaching the concept of measurement improves cognitive states of interest, commitment and reduces the level of stress.


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