High-level Cognitive Tasks Research Articles

Extracting Geoscientific Dataset Names from the Literature Based on the Hierarchical Temporal Memory Model

Extracting geoscientific dataset names from the literature is crucial for building a literature–data association network, which can help readers access the data quickly through the Internet. However, the existing named-entity extraction methods have low accuracy in extracting geoscientific dataset names from unstructured text because geoscientific dataset names are a complex combination of multiple elements, such as geospatial coverage, temporal coverage, scale or resolution, theme content, and version. This paper proposes a new method based on the hierarchical temporal memory (HTM) model, a brain-inspired neural network with superior performance in high-level cognitive tasks, to accurately extract geoscientific dataset names from unstructured text. First, a word-encoding method based on the Unicode values of characters for the HTM model was proposed. Then, over 12,000 dataset names were collected from geoscience data-sharing websites and encoded into binary vectors to train the HTM model. We conceived a new classifier scheme for the HTM model that decodes the predictive vector for the encoder of the next word so that the similarity of the encoders of the predictive next word and the real next word can be computed. If the similarity is greater than a specified threshold, the real next word can be regarded as part of the name, and a successive word set forms the full geoscientific dataset name. We used the trained HTM model to extract geoscientific dataset names from 100 papers. Our method achieved an F1-score of 0.727, outperforming the GPT-4- and Claude-3-based few-shot learning (FSL) method, with F1-scores of 0.698 and 0.72, respectively.

Enhancing specialization of attention-related EEG power and phase synchronism brain patterns by meditation.

Meditation, mental training that aims to improve one's ability to regulate their cognition, has been widely applied in clinical medicine. However, the mechanism by which meditation affects brain activity is still unclear. To explore this question, electroencephalogram data were recorded in 20 long-term meditators and 20 nonmeditators during 2 high-level cognitive tasks (meditation and mental calculation) and a relaxed resting state (control). Then, the power spectral density and phase synchronization of the electroencephalogram were extracted and compared between these 2 groups. In addition, machine learning was used to discriminate the states within each group. We found that the meditation group showed significantly higher classification accuracy and calculation efficiency than the control group. Then, during the calculation task, both the power and global phase synchronism of the gamma response decreased in meditators compared to their relaxation state; yet, no such change was observed in the control group. A potential explanation for our observations is that meditation improved the flexibility of the brain through neural plastic mechanism. In conclusion, we provided robust evidence that long-term meditation experience could produce detectable neurophysiological changes in brain activity, which possibly enhance the functional segregation and/or specialization in the brain.

Extracting reproducible subject-specific MEG evoked responses with independent component analysis

Abstract Reliable individual-level measures of neural activity are essential for capturing interindividual variability in brain activity recorded by magnetoencephalography (MEG). While conventional group-level analyses highlight shared features in the data, individual-level specificity is often lost. Current methods for assessing reproducibility of brain responses focus on group-level statistics and neglect subject-specific temporal and spatial characteristics. This study proposes a combined ICA algorithm (comICA), aimed at extracting within-individual consistent MEG evoked responses. The proposed hypotheses behind comICA are based on the temporal profiles of the evoked responses, the corresponding spatial information, as well as independence and linearity. ComICA is presented and tested against simulated data and test–retest recordings of a high-level cognitive task (picture naming). The results show high reliability in extracting the shared activations in the simulations (success rate >93%) and the ability to successfully reproduce group-level results on reproducibility for the test–retest MEG recordings. Our model offers means for noise reduction, targeted extraction of specific activation components in experimental designs, and potential integration across different recordings.

An EEG Dataset of Neural Signatures in a Competitive Two-Player Game Encouraging Deceptive Behavior

Studying deception is vital for understanding decision-making and social dynamics. Recent EEG research has deepened insights into the brain mechanisms behind deception. Standard methods in this field often rely on memory, are vulnerable to countermeasures, yield false positives, and lack real-world relevance. Here, we present a comprehensive dataset from an EEG-monitored competitive, two-player card game designed to elicit authentic deception behavior. Our extensive dataset contains EEG data from 12 pairs (N = 24 participants with role switching), controlled for age, gender, and risk-taking, with detailed labels and annotations. The dataset combines standard event-related potential and microstate analyses with state-of-the-art decoding approaches of four scenarios: spontaneous/instructed truth-telling and lying. This demonstrates game-based methods’ efficacy in studying deception and sets a benchmark for future research. Overall, our dataset represents a unique resource with applications in cognitive neuroscience and related fields for studying deception, competitive behavior, decision-making, inter-brain synchrony, and benchmarking of decoding frameworks in a difficult, high-level cognitive task.

Task-Dependent Visual Behavior in Immersive Environments: A Comparative Study of Free Exploration, Memory and Visual Search.

Visual behavior depends on both bottom-up mechanisms, where gaze is driven by the visual conspicuity of the stimuli, and top-down mechanisms, guiding attention towards relevant areas based on the task or goal of the viewer. While this is well-known, visual attention models often focus on bottom-up mechanisms. Existing works have analyzed the effect of high-level cognitive tasks like memory or visual search on visual behavior; however, they haveoften done so with different stimuli, methodology, metrics and participants, which makes drawing conclusions and comparisons between tasks particularly difficult. In this work we present a systematic study of how different cognitive tasks affect visual behavior in a novel within-subjects design scheme. Participants performed free exploration, memory and visual search tasks in three different scenes while their eye and head movements were being recorded. We found significant, consistent differences between tasks in the distributions of fixations, saccades and head movements. Our findings can provide insights for practitioners and content creators designing task-oriented immersive applications.

Ancilia: Scalable Intelligent Video Surveillance for the Artificial Intelligence of Things

With the advancement of vision-based artificial intelligence, the proliferation of the Internet of Things connected cameras, and the increasing societal need for rapid and equitable security, the demand for accurate real-time intelligent surveillance has never been higher. This article presents Ancilia, an end-to-end scalable, intelligent video surveillance system for the Artificial Intelligence of Things. Ancilia brings state-of-theart artificial intelligence to real-world surveillance applications while respecting ethical concerns and performing high-level cognitive tasks in real-time. Ancilia aims to revolutionize the surveillance landscape, to bring more effective, intelligent, and equitable security to the field, resulting in safer and more secure communities without requiring people to compromise their right to privacy.

I know that I know. But do I know that I do not know?

Metacognition-the ability of individuals to monitor one's own cognitive performance and decisions-is often studied empirically based on the retrospective confidence ratings. In experimental research, participants are asked to report how sure they are in their response, or to report how well their performance in high-level cognitive or low-level perceptual tasks is. These retrospective confidence ratings are used as a measure of monitoring effectiveness: larger difference in confidence ratings assigned to correct and incorrect responses reflects better ability to estimate the likelihood of making an error by an experiment participant, or better metacognitive monitoring ability. We discuss this underlying assumption and provide some methodological consideration that might interfere with interpretation of results, depending on what is being asked to evaluate, how the confidence response is elicited, and the overall proportion of different trial types within one experimental session. We conclude that mixing trials on which decision confidence is assigned when positive evidence needs to be evaluated and the trials on which absence of positive evidence needs to be evaluated should be avoided. These considerations might be important when designing experimental work to explore metacognitive efficiency using retrospective confidence ratings.

Older adults get masked emotion priming for happy but not angry faces: evidence for a positivity effect in early perceptual processing of emotional signals

ABSTRACT In higher-level cognitive tasks, older compared to younger adults show a bias towards positive emotion information and away from negative information (a positivity effect). It is unclear whether this effect occurs in early perceptual processing. This issue is important for determining if the positivity effect is due to automatic rather than controlled processing. We tested this with older and younger adults on a positive/negative face emotion valence classification task using masked priming. Positive (happy) and negative (angry) face targets were preceded by masked repetition or valence primes with neutral face baselines. In Experiment 1, 30 younger and 30 older adults were tested with 50 ms primes. Younger adults showed repetition priming for both positive and negative targets. Older adults showed repetition priming for positive but not negative targets. Neither group showed valence priming. In Experiment 2, 30 older and 29 younger adults were tested with longer duration primes. Younger adults showed repetition priming for both positive and negative emotions, and no valence priming. Older adults only showed repetition and valence priming for positive targets. We proposed older adults’ lack of angry face priming was due to an early attention orienting strategy favouring happy expressions at the expense of angry ones.

Noise exposure in early adulthood causes age-dependent and brain region-specific impairments in cognitive function.

Hearing loss is a chronic health condition that affects millions of people worldwide. In addition to age-related hearing impairment, excessive noise exposure is a leading cause of hearing loss. Beyond the devastating effects of hearing impairment itself, epidemiological studies have identified hearing loss as a major risk factor for age-related cognitive decline, including dementia. At present, we currently lack a full understanding of the brain regions and underlying molecular changes that are responsible for mediating the link between hearing loss and cognitive impairment across aging. In the present study, we exposed 6-month-old rats to an occupational-like noise (100 dB SPL, 4 h/day × 30 days) or sham exposure and investigated both hippocampal-dependent (i.e., spatial learning and memory, assessed using the Morris water maze) and striatal-dependent (i.e., visuomotor associative learning, assessed using an operant-conditioning task) cognitive function across aging at 7, 10, and 13 months of age. We also investigated brain region-specific changes in microglial expression following noise/sham exposure in order to assess the potential contribution of this cell type to noise-induced cognitive impairments. Consistent with human studies, the occupational-like noise exposure resulted in high-frequency hearing loss, evidenced by a significant increase in hearing thresholds at 20 kHz. Ultimately, our results suggest that not all higher-level cognitive tasks or their associated brain regions appear to be equally susceptible to noise-induced deficits during aging, as the occupational-like noise exposure caused an age-dependent deficit in spatial but not visuomotor associative learning, as well as altered microglial expression in the hippocampus but not the striatum. Interestingly, we found no significant relationships between spatial learning ability and the level of hearing loss or altered microglial density in the hippocampus following noise exposure, suggesting that other changes in the brain likely contribute to hippocampal-dependent cognitive dysfunction following noise exposure. Lastly, we found that a subset of younger animals also showed noise-induced deficits in spatial learning; findings which suggest that noise exposure may represent an increased risk for cognitive impairment in vulnerable subjects. Overall, our findings highlight that even a mild occupational-like noise exposure earlier in adulthood can have long lasting implications for cognitive function later in life.

A generic neural factor linking resting-state neural dynamics and the brain's response to unexpectedness in multilevel cognition.

The brain's response to change is fundamental to learning and adaptation; this implies the presence of a universal neural mechanism under various contexts. We hypothesized that this mechanism manifests in neural activity patterns across low and high levels of cognition during task processing as well as in resting-state neural dynamics, because both these elements are different facets of the same dynamical system. We tested our hypothesis by (i) characterizing (a) the neural response to changes in low-level continuous information stream and unexpectedness at different cognitive levels and (b) the spontaneous neural dynamics in resting state, and (ii) examining the associations among the dynamics according to cross-individual variability (n= 200). Our results showed that the brain's response magnitude was monotonically correlated with the magnitude of information fluctuation in a low-level task, forming a simple psychophysical function; moreover, this effect was found to be associated with the brain's response to unexpectedness in high-level cognitive tasks (including language processing). These coherent multilevel neural effects in task processing were also shown to be strongly associated with resting-state neural dynamics characterized by the waxing and waning of Alpha oscillation. Taken together, our results revealed large-scale consistency between the neural dynamic system and multilevel cognition.

Bicultural Minds: A Cultural Priming Approach to the Self-Bias Effect.

Recent research has discovered a robust bias towards the processing of self-relevant information in perceptual matching. Self-associated stimuli are processed faster and more accurately than other-associated stimuli. Priming of independent or interdependent self-construal can dynamically modulate self-biases in high-level cognitive tasks. This study explored whether priming of independent/interdependent mindsets can modulate the self-bias effect in perceptual matching. In two experiments, British participants performed a priming task (Experiment 1 using a word-search task—an implicit priming approach, Experiment 2 with a reflective thinking task—an explicit priming method) immediately followed by a perceptual matching task, where they first learned to associate geometric shapes with labels (e.g., circle is you, square is friend, triangle is stranger) and then made judgments on whether shape-label pairs displayed on-screen were the correct associations or not. The analysis in Experiment 1 revealed that priming the interdependent self-construal led to a reduced self-bias effect in perceptual matching in participants who had low bias compared to those with high bias in the neutral/non-priming condition. In contrast, priming the independent self-construal did not modulate the self-bias in perceptual matching. The effects were replicated in Experiment 2. The results indicate that the self is a dynamic concept that can modulate perceptual processing by accessing different cultural contexts.

Analyzing Complex Problem Solving by Dynamic Brain Networks.

Complex problem solving is a high level cognitive task of the human brain, which has been studied over the last decade. Tower of London (TOL) is a game that has been widely used to study complex problem solving. In this paper, we aim to explore the underlying cognitive network structure among anatomical regions of complex problem solving and its subtasks, namely planning and execution. A new computational model for estimating a brain network at each time instant of fMRI recordings is proposed. The suggested method models the brain network as an Artificial Neural Network, where the weights correspond to the relationships among the brain anatomic regions. The first step of the model is preprocessing that manages to decrease the spatial redundancy while increasing the temporal resolution of the fMRI recordings. Then, dynamic brain networks are estimated using the preprocessed fMRI signal to train the Artificial Neural Network. The properties of the estimated brain networks are studied in order to identify regions of interest, such as hubs and subgroups of densely connected brain regions. The representation power of the suggested brain network is shown by decoding the planning and execution subtasks of complex problem solving. Our findings are consistent with the previous results of experimental psychology. Furthermore, it is observed that there are more hubs during the planning phase compared to the execution phase, and the clusters are more strongly connected during planning compared to execution.

Time Latency-Centric Signal Processing: A Perspective of Smart Manufacturing.

Smart manufacturing employs embedded systems such as CNC machine tools, programable logic controllers, automated guided vehicles, robots, digital measuring instruments, cyber-physical systems, and digital twins. These systems collectively perform high-level cognitive tasks (monitoring, understanding, deciding, and adapting) by making sense of sensor signals. When sensor signals are exchanged through the abovementioned embedded systems, a phenomenon called time latency or delay occurs. As a result, the signal at its origin (e.g., machine tools) and signal received at the receiver end (e.g., digital twin) differ. The time and frequency domain-based conventional signal processing cannot adequately address the delay-centric issues. Instead, these issues can be addressed by the delay domain, as suggested in the literature. Based on this consideration, this study first processes arbitrary signals in time, frequency, and delay domains and elucidates the significance of delay domain over time and frequency domains. Afterward, real-life signals collected while machining different materials are analyzed using frequency and delay domains to reconfirm its (the delay domain’s) significance in real-life settings. In both cases, it is found that the delay domain is more informative and reliable than the time and frequency domains when the delay is unavoidable. Moreover, the delay domain can act as a signature of a machining situation, distinguishing it (the situation) from others. Therefore, computational arrangements enabling delay domain-based signal processing must be enacted to effectively functionalize the smart manufacturing-centric embedded systems.

Using Expectations to Drive Cognitive Behavior

Generating future states of the world is an essential component of high-level cognitive tasks such as planning. We explore the notion that such future-state generation is more widespread and forms an integral part of cognition. We call these generated states expectations, and propose that cognitive systems constantly generate expectations, match them to observed behavior and react when a difference exists between the two. We describe an ACT-R model that performs expectation-driven cognition on two tasks – pedestrian tracking and behavior classification. The model generates expectations of pedestrian movements to track them. The model also uses differences in expectations to identify distinctive features that differentiate these tracks. During learning, the model learns the association between these features and the various behaviors. During testing, it classifies pedestrian tracks by recalling the behavior associated with the features of each track. We tested the model on both single and multiple behavior datasets and compared the results against a k-NN classifier. The k-NN classifier outperformed the model in correct classifications, but the model had fewer incorrect classifications in the multiple behavior case, and both systems had about equal incorrect classifications in the single behavior case.

Color Modulates Feature Integration.

Bayesian models of object recognition propose the resolution of ambiguity through probabilistic integration of prior experience with available sensory information. Color, even when task-irrelevant, has been shown to modulate high-level cognitive control tasks. However, it remains unclear how color modulations affect lower-level perceptual processing. We investigated whether color affects feature integration using the flash-jump illusion. This illusion occurs when an apparent motion stimulus, a rectangular bar appearing at different locations along a motion trajectory, changes color at a single position. Observers misperceive this color change as occurring farther along the trajectory of motion. This mislocalization error is proposed to be produced by a Bayesian perceptual framework dependent on responses in area V4. Our results demonstrated that the color of the flash modulated the magnitude of the flash-jump illusion such that participants reported less of a shift, i.e., a more veridical flash location, for both red and blue flashes, as compared to green and yellow. Our findings extend color-dependent modulation effects found in higher-order executive functions into lower-level Bayesian perceptual processes. Our results also support the theory that feature integration is a Bayesian process. In this framework, color modulations play an inherent and automatic role as different colors have different weights in Bayesian perceptual processing.

Awake intraoperative mapping to identify cortical regions related to music performance: Technical note

The aim of this manuscript is to present our intraoperative technique assessing the ability to perform music. Our protocol excludes cases where performance can be disrupted by motor deficits. The positive cortical sites (the posterior part of the superior temporal gyrus and supramarginal gyrus) related purely to music performance are also reported. We present the case of a patient, an amateur piano player who underwent surgery for a symptomatic supratentorial cavernoma while awake with intraoperative brain mapping. This case report shows that amateur and possibly professional musicians may benefit from awake procedures. This report confirms that stimulation of the specific area of the brain can disturb the function of a large network responsible for high-level cognitive task, like music performance.

Early deafness leads to re-shaping of functional connectivity beyond the auditory cortex

Early sensory deprivation, such as deafness, shapes brain development in multiple ways. Deprived auditory areas become engaged in the processing of stimuli from the remaining modalities and in high-level cognitive tasks. Yet, structural and functional changes were also observed in non-deprived brain areas, which may suggest the whole-brain network changes in deaf individuals. To explore this possibility, we compared the resting-state functional network organization of the brain in early deaf adults and hearing controls and examined global network segregation and integration. Relative to hearing controls, deaf adults exhibited decreased network segregation and an altered modular structure. In the deaf, regions of the salience network were coupled with the fronto-parietal network, while in the hearing controls, they were coupled with other large-scale networks. Deaf adults showed weaker connections between auditory and somatomotor regions, stronger coupling between the fronto-parietal network and several other large-scale networks (visual, memory, cingulo-opercular and somatomotor), and an enlargement of the default mode network. Our findings suggest that brain plasticity in deaf adults is not limited to changes in the auditory cortex but additionally alters the coupling between other large-scale networks and the development of functional brain modules. These widespread functional connectivity changes may provide a mechanism for the superior behavioral performance of the deaf in visual and attentional tasks.

A Virtual Reality Memory Palace Variant Aids Knowledge Retrieval from Scholarly Articles.

We present exploratory research of virtual reality techniques and mnemonic devices to assist in retrieving knowledge from scholarly articles. We used abstracts of scientific publications to represent knowledge in scholarly articles; participants were asked to read, remember, and retrieve knowledge from a set of abstracts. We conducted an experiment to compare participants' recall and recognition performance in three different conditions: a control condition without a pre-specified strategy to test baseline individual memory ability, a condition using an image-based variant of a mnemonic called a "memory palace," and a condition using a virtual reality-based variant of a memory palace. Our analyses show that using a virtual reality-based memory palace variant greatly increased the amount of knowledge retrieved and retained over the baseline, and it shows a moderate improvement over the other image-based memory palace variant. Anecdotal feedback from participants suggested that personalizing a memory palace variant would be appreciated. Our results support the value of virtual reality for some high-level cognitive tasks and help improve future applications of virtual reality and visualization.

Visual Analytics for Dimension Reduction and Cluster Analysis of High Dimensional Electronic Health Records

Recent advancement in EHR-based (Electronic Health Record) systems has resulted in producing data at an unprecedented rate. The complex, growing, and high-dimensional data available in EHRs creates great opportunities for machine learning techniques such as clustering. Cluster analysis often requires dimension reduction to achieve efficient processing time and mitigate the curse of dimensionality. Given a wide range of techniques for dimension reduction and cluster analysis, it is not straightforward to identify which combination of techniques from both families leads to the desired result. The ability to derive useful and precise insights from EHRs requires a deeper understanding of the data, intermediary results, configuration parameters, and analysis processes. Although these tasks are often tackled separately in existing studies, we present a visual analytics (VA) system, called Visual Analytics for Cluster Analysis and Dimension Reduction of High Dimensional Electronic Health Records (VALENCIA), to address the challenges of high-dimensional EHRs in a single system. VALENCIA brings a wide range of cluster analysis and dimension reduction techniques, integrate them seamlessly, and make them accessible to users through interactive visualizations. It offers a balanced distribution of processing load between users and the system to facilitate the performance of high-level cognitive tasks in such a way that would be difficult without the aid of a VA system. Through a real case study, we have demonstrated how VALENCIA can be used to analyze the healthcare administrative dataset stored at ICES. This research also highlights what needs to be considered in the future when developing VA systems that are designed to derive deep and novel insights into EHRs.

Multi-domain cognitive assessment of male mice shows space radiation is not harmful to high-level cognition and actually improves pattern separation

Astronauts on interplanetary missions - such as to Mars - will be exposed to space radiation, a spectrum of highly-charged, fast-moving particles that includes 56Fe and 28Si. Earth-based preclinical studies show space radiation decreases rodent performance in low- and some high-level cognitive tasks. Given astronaut use of touchscreen platforms during training and space flight and given the ability of rodent touchscreen tasks to assess functional integrity of brain circuits and multiple cognitive domains in a non-aversive way, here we exposed 6-month-old C57BL/6J male mice to whole-body space radiation and subsequently assessed them on a touchscreen battery. Relative to Sham treatment, 56Fe irradiation did not overtly change performance on tasks of visual discrimination, reversal learning, rule-based, or object-spatial paired associates learning, suggesting preserved functional integrity of supporting brain circuits. Surprisingly, 56Fe irradiation improved performance on a dentate gyrus-reliant pattern separation task; irradiated mice learned faster and were more accurate than controls. Improved pattern separation performance did not appear to be touchscreen-, radiation particle-, or neurogenesis-dependent, as 56Fe and 28Si irradiation led to faster context discrimination in a non-touchscreen task and 56Fe decreased new dentate gyrus neurons relative to Sham. These data urge revisitation of the broadly-held view that space radiation is detrimental to cognition.