Annotated Translation of Udayana's Aatmatattvaviveka

2021 ◽  
Vol 26 ◽  
pp. 155-164
Author(s):  
Kisor Kumar Chakrabarti ◽  
Keyword(s):  
Cognitive State ◽  
Perceptual Awareness ◽  
Cognitive States ◽  
Fig Tree ◽  
Annotated Translation

One approaching a thing from a distance may perceive it as existent, then as a substance, then as a tree and, finally, as a fig tree. Thus, the same fig tree can be the object of all these different perceptions. This shows, Udayana argues, that difference in cognitive states does not necessarily prove that their objects are different. This argument is in response to the Buddhist claim that since perceptual cognitive states and non-perceptual cognitive states are different, their respective objects are also different; unique particulars (svalakSaNa) that alone are real, are grasped in perception; general features (saamaanyalakSaNa) that are not real are grasped in non-perceptual cognitive states. The Buddhist objects: when the same thing appears to be the object of different cognitive states, only that cognitive state which leads to useful result is reliable. Udayana replies: More than one cognitive state in the above situation may lead to useful result; it is not justified to accept only one of them as reliable and reject the others. The Buddhist objects again: perceptual awareness is direct but non-perceptual awareness is indirect: hence their objects are different. Udayana replies: The same thing may be perceived when there is sensory connection with it and then inferred from an invariably connected sign when there is no sensory connection. Thus, the same thing may be the object of both direct and indirect cognitive states depending on different causal conditions.

Context-specific abnormalities of the central executive network in first-episode psychosis: relationship with cognition

2020 ◽  
pp. 1-10
Author(s):  
Deepak K. Sarpal ◽  
Goda Tarcijonas ◽  
Finnegan J. Calabro ◽  
William Foran ◽  
Gretchen L. Haas ◽  
...  
Keyword(s):  
Cognitive Control ◽  
Psychotic Disorders ◽  
First Episode Psychosis ◽  
First Episode ◽  
Cognitive State ◽  
Cognitive States ◽  
Episode Psychosis ◽  
Dorsolateral Prefrontal ◽  
Parietal Lobule ◽  
Context Specific

Abstract Background Cognitive impairments, which contribute to the profound functional deficits observed in psychotic disorders, have found to be associated with abnormalities in trial-level cognitive control. However, neural tasks operate within the context of sustained cognitive states, which can be assessed with ‘background connectivity’ following the removal of task effects. To date, little is known about the integrity of brain processes supporting the maintenance of a cognitive state in individuals with psychotic disorders. Thus, here we examine background connectivity during executive processing in a cohort of participants with first-episode psychosis (FEP). Methods The following fMRI study examined background connectivity of the dorsolateral prefrontal cortex (DLPFC), during working memory engagement in a group of 43 patients with FEP, relative to 35 healthy controls (HC). Findings were also examined in relation to measures of executive function. Results The FEP group relative to HC showed significantly lower background DLPFC connectivity with bilateral superior parietal lobule (SPL) and left inferior parietal lobule. Background connectivity between DLPFC and SPL was also positively associated with overall cognition across all subjects and in our FEP group. In comparison, resting-state frontoparietal connectivity did not differ between groups and was not significantly associated with overall cognition, suggesting that psychosis-related alterations in executive networks only emerged during states of goal-oriented behavior. Conclusions These results provide novel evidence indicating while frontoparietal connectivity at rest appears intact in psychosis, when engaged during a cognitive state, it is impaired possibly undermining cognitive control capacities in FEP.

scholarly journals Can EEG Be Adopted as a Neuroscience Reference for Assessing Software Programmers’ Cognitive Load?

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2338
Author(s):  
Júlio Medeiros ◽  
Ricardo Couceiro ◽  
Gonçalo Duarte ◽  
João Durães ◽  
João Castelhano ◽  
...  
Keyword(s):  
Software Development ◽  
Cognitive Load ◽  
Mental Effort ◽  
Research Area ◽  
Cognitive State ◽  
Accurate Identification ◽  
Cognitive States ◽  
Code Complexity ◽  
Time Accuracy ◽  
Future Evaluation

An emergent research area in software engineering and software reliability is the use of wearable biosensors to monitor the cognitive state of software developers during software development tasks. The goal is to gather physiologic manifestations that can be linked to error-prone scenarios related to programmers’ cognitive states. In this paper we investigate whether electroencephalography (EEG) can be applied to accurately identify programmers’ cognitive load associated with the comprehension of code with different complexity levels. Therefore, a controlled experiment involving 26 programmers was carried. We found that features related to Theta, Alpha, and Beta brain waves have the highest discriminative power, allowing the identification of code lines and demanding higher mental effort. The EEG results reveal evidence of mental effort saturation as code complexity increases. Conversely, the classic software complexity metrics do not accurately represent the mental effort involved in code comprehension. Finally, EEG is proposed as a reference, in particular, the combination of EEG with eye tracking information allows for an accurate identification of code lines that correspond to peaks of cognitive load, providing a reference to help in the future evaluation of the space and time accuracy of programmers’ cognitive state monitored using wearable devices compatible with software development activities.

scholarly journals Changing what you see by changing what you know: the role of attention

2017 ◽  
Author(s):  
Gary Lupyan
Keyword(s):  
Perceptual Content ◽  
Current Evidence ◽  
Cognitive State ◽  
Perceptual Process ◽  
Cognitive Influences ◽  
Cognitive States ◽  
Visual Hierarchy ◽  
Visual Inputs ◽  
Perceptual Representations ◽  
Perceptual Systems

Attending is a cognitive process that incorporates a person’s knowledge, goals, and expectations. What we perceive when we attend to one thing is different from what we perceive when we attend to something else. Yet, it is often argued that attentional effects do not count as evidence that perception is influenced by cognition. I investigate two arguments often given to justify excluding attention. The first is arguing that attention is a post-perceptual process reflecting selection between fully constructed perceptual representations. The second is arguing that attention as a pre-perceptual process that simply changes the input to encapsulated perceptual systems. Both of these arguments are highly problematic. Although some attentional effects can indeed be construed as post-perceptual, others operate by changing perceptual content across the entire visual hierarchy. Although there is a natural analogy between spatial attention and a change of input, the analogy falls apart when we consider other forms of attention. After dispelling these arguments, I make a case for thinking of attention not as a confound, but as one of the mechanisms by which cognitive states affect perception by going through cases in which the same or similar visual inputs are perceived differently depending on the observer’s cognitive state, and instances where cuing an observer using language affects what one sees. Lastly, I provide two compelling counter-examples to the critique that although cognitive influences on perception can be demonstrated in the laboratory, it is impossible to really experience them for oneself in a phenomenologically compelling way. Taken together, the current evidence strongly supports the thesis that what we know routinely influences what we see, that the same sensory input can be perceived differently depending on the current cognitive state of the viewer, and that phenomenologically salient demonstrations are possible if certain conditions are met.

This Is Your Brain on Autopilot: Neural Indices of Driver Workload and Engagement During Partial Vehicle Automation

2021 ◽  
pp. 001872082110390
Author(s):  
Amy S. McDonnell ◽  
Trent G. Simmons ◽  
Gus G. Erickson ◽  
Monika Lohani ◽  
Joel M. Cooper ◽  
...  
Keyword(s):  
Mental Workload ◽  
Cognitive State ◽  
Alpha Power ◽  
Age Cohorts ◽  
Cognitive States ◽  
Driver Workload ◽  
Automated Technology ◽  
Vehicle Automation ◽  
Visual Engagement ◽  
Insight Into

Objective This research explores the effect of partial vehicle automation on neural indices of mental workload and visual engagement during on-road driving. Background There is concern that the introduction of automated technology in vehicles may lead to low driver stimulation and subsequent disengagement from the driving environment. Simulator-based studies have examined the effect of automation on a driver’s cognitive state, but it is unknown how the conclusions translate to on-road driving. Electroencephalographic (EEG) measures of frontal theta and parietal alpha can provide insight into a driver’s mental workload and visual engagement while driving under various conditions. Method EEG was recorded from 71 participants while driving on the roadway. We examined two age cohorts, on two different highway configurations, in four different vehicles, with partial vehicle automation both engaged and disengaged. Results Analysis of frontal theta and parietal alpha power revealed that there was no change in mental workload or visual engagement when driving manually compared with driving under partial vehicle automation. Conclusion Drivers new to the technology remained engaged with the driving environment when operating under partial vehicle automation. These findings suggest that the concern surrounding driver disengagement under vehicle automation may need to be tempered, at least for drivers new to the experience. Application These findings expand our understanding of the effects of partial vehicle automation on drivers’ cognitive states.

scholarly journals Channel Selection and Feature Projection for Cognitive Load Estimation Using Ambulatory EEG

2007 ◽  
Vol 2007 ◽  
pp. 1-12 ◽  
Author(s):  
Tian Lan ◽  
Deniz Erdogmus ◽  
Andre Adami ◽  
Santosh Mathan ◽  
Misha Pavel
Keyword(s):  
Mutual Information ◽  
Cognitive Load ◽  
Channel Selection ◽  
Estimation Accuracy ◽  
Cognitive State ◽  
Load Estimation ◽  
Mental Load ◽  
Cognitive States ◽  
State Classification ◽  
Feature Projection

We present an ambulatory cognitive state classification system to assess the subject's mental load based on EEG measurements. The ambulatory cognitive state estimator is utilized in the context of a real-time augmented cognition (AugCog) system that aims to enhance the cognitive performance of a human user through computer-mediated assistance based on assessments of cognitive states using physiological signals including, but not limited to, EEG. This paper focuses particularly on the offline channel selection and feature projection phases of the design and aims to present mutual-information-based techniques that use a simple sample estimator for this quantity. Analyses conducted on data collected from 3 subjects performing 2 tasks (n-back/Larson) at 2 difficulty levels (low/high) demonstrate that the proposed mutual-information-based dimensionality reduction scheme can achieve up to 94% cognitive load estimation accuracy.

L2 cognitive states and the Full Transfer/Full Access model

1996 ◽  
Vol 12 (1) ◽  
pp. 40-72 ◽  
Author(s):  
Bonnie D. Schwartz ◽  
Rex A. Sprouse
Keyword(s):  
Input Data ◽  
Cognitive State ◽  
Functional Categories ◽  
L2 Acquisition ◽  
Initial State ◽  
Final State ◽  
Cognitive States ◽  
Will Force ◽  
L1 Acquisition ◽  
Coherent Picture

This article is a defence of the Full Transfer/Full Access (FT/FA) model. FT/FA hypothesizes that the initial state of L2 acquisition is the final state of L1 acquisition (Full Transfer) and that failure to assign a representation to input data will force subsequent restructurings, drawing from options of UG (Full Access). We illustrate the FT/FA model by reviewing our analysis of the developmental Turkish-German Interlanguage data of Schwartz and Sprouse (1994) and then turn to other data that similarly receive straightforward accounts under FT/FA. We also consider two other competing hypotheses, both of which accept Full Access but not Full Transfer: the Minimal Trees hypothesis (no transfer of functional categories) of Vainikka and Young-Scholten (1994; 1996) and the Weak Transfer hypothesis (no transfer of the values associated with functional categories) of Eubank (1993/94). We provide an example of (extremely robust) L2 acquisition data that highlight the inadequacy of the Minimal Trees hypothesis in regard to stages of Interlanguage subsequent to the L2 initial state. As for Weak Transfer, we show that the morphosyntactic empirical foundations which drive the entire approach are flawed; hence the Weak Transfer hypothesis remains without motivation. Finally, we consider several conceptual issues relating to transfer. These all argue that the FT/FA model provides the most coherent picture of the L2 initial cognitive state. In short, FT/FA embodies the most suitable programme for understanding comparative Interlanguage development.

PHYSIOLOGICAL SIGNAL ANALYSIS FOR COGNITIVE STATE ESTIMATION

2012 ◽  
Vol 24 (01) ◽  
pp. 57-69 ◽  
Author(s):  
Mohiuddin Ahmad ◽  
Atiqul Islam ◽  
T. T. Khan Munia ◽  
M. A. Rashid ◽  
T. M. N. Tunku Mansur
Keyword(s):  
Signal Analysis ◽  
Occipital Lobe ◽  
Physiological Signals ◽  
Cognitive State ◽  
Specific Sequence ◽  
Motor Action ◽  
Physiological Signal ◽  
Eyes Closed ◽  
Cognitive States ◽  
Emg Signal

The purpose of this paper is to identify inconsistency in human physiological signals based on cognitive states by measuring and analyzing bio-signals. In this paper, the cognitive states are estimated using physiological signal analysis. The parameters are electrocardiogram (ECG), electromyogram (EMG), electroencephalogram (EEG) and blood pressure (BP). The signals have been collected using BIOPAC system in which the subjects were induced to undergo the specific sequence of the cognitive state. For getting physiological signals during different conditions, we utilized power point slide show, video clips and question answer method which elicits mental reactions from the subjects. Data is taken before and after four tasks that encompassed the motor action (MA), thought (TH), memory related (MR) and emotion (EM). These measured values are analyzed using BIOPAC Acknowledge software. It was found that the motor action and thought states have effects on BP while MR and EM state mainly affect the ECG measurement. The decibel value and frequency found for EM state in ECG are minimum compared to relaxed state (RS) condition. Similarly, the maximum frequency and dB value is found for MR state. No significant variation was seen for MA and TH states. Thus it was decided that the MR and EM states mainly affect the ECG measurement. For BP the value increases in MA state and decreases in TH state. The MA state mainly affects the EMG signal while other states have no significant changes. The EEG mainly detects the signal of task performed by the specific brain region where the electrodes are placed. In EEG analysis, the electrodes are placed in occipital lobe region which gives mainly the variation in alpha amplitude of EEG with eyes closed and eyes opened. Alpha wave amplitudes vary with the subjects attention to mental tasks performed with eyes closed.

scholarly journals Decode Brain System: A Dynamic Adaptive Convolutional Quorum Voting Approach for Variable-Length EEG Data

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Tao Xu ◽  
Yun Zhou ◽  
Zekai Hou ◽  
Wenlan Zhang
Keyword(s):  
Time Window ◽  
Brain Activity ◽  
Sliding Window ◽  
Variable Length ◽  
Cognitive State ◽  
Human Beings ◽  
Processing Variable ◽  
Cognitive States ◽  
Eeg Data ◽  
Eeg Recordings

The brain is a complex and dynamic system, consisting of interacting sets and the temporal evolution of these sets. Electroencephalogram (EEG) recordings of brain activity play a vital role to decode the cognitive process of human beings in learning research and application areas. In the real world, people react to stimuli differently, and the duration of brain activities varies between individuals. Therefore, the length of EEG recordings in trials gathered in the experiment is variable. However, current approaches either fix the length of EEG recordings in each trial which would lose information hidden in the data or use the sliding window which would consume large computation on overlapped parts of slices. In this paper, we propose TOO (Traverse Only Once), a new approach for processing variable-length EEG trial data. TOO is a convolutional quorum voting approach that breaks the fixed structure of the model through convolutional implementation of sliding windows and the replacement of the fully connected layer by the 1 × 1 convolutional layer. Each output cell generated from 1 × 1 convolutional layer corresponds to each slice created by a sliding time window, which reflects changes in cognitive states. Then, TOO employs quorum voting on output cells and determines the cognitive state representing the entire single trial. Our approach provides an adaptive model for trials of different lengths with traversing EEG data of each trial only once to recognize cognitive states. We design and implement a cognitive experiment and obtain EEG data. Using the data collecting from this experiment, we conducted an evaluation to compare TOO with a state-of-art sliding window end-to-end approach. The results show that TOO yields a good accuracy (83.58%) at the trial level with a much lower computation (11.16%). It also has the potential to be used in variable signal processing in other application areas.

Using Real-Time Physiological Monitoring for Assessing Cognitive States

2006 ◽  
pp. 170-186 ◽  
Author(s):  
Martha E. Crosby ◽  
Curtis S. Ikehara
Keyword(s):  
Real Time ◽  
Information Filtering ◽  
Cognitive State ◽  
Performance Study ◽  
Computer Performance ◽  
Advantages And Disadvantages ◽  
Cognitive States ◽  
Computer Mouse ◽  
Study Results ◽  
Physiological Sensors

This chapter describes our research focused on deriving changing cognitive state information from the patterns of data acquired from the user, with the goal of using this information to improve the presentation of multimedia computer information. Detecting individual differences via performance and psychometric tools can be supplemented by using real-time physiological sensors. Described is an example computer task that demonstrates how cognitive load is manipulated. The different types of physiological and cognitive state measures are discussed along with their advantages and disadvantages. Experimental results from eye tracking and the pressures applied to a computer mouse are described in greater detail. Finally, adaptive information filtering is discussed as a model for using the physiological information to improve computer performance. Study results provide support that we can create effective ways to adapt to a person’s cognition in real time and thus facilitate real-world tasks.

Electroencephalography based detection of cognitive state during learning tasks: An extensive approach

2021 ◽  
Vol 25 (2) ◽  
pp. 157-178
Author(s):  
Theparambil Asharaf Suhail ◽  
◽  
Kottanayil Pally Indiradevi ◽  
Ekkarakkudy Makkar Suhara ◽  
Poovathinal Azhakan Suresh ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Cognitive Performance ◽  
Mental State ◽  
Assessment Model ◽  
Support Vector ◽  
Cognitive State ◽  
Linear Discriminant ◽  
Cognitive States ◽  
Learning Tasks ◽  
The Subject

Detecting cognitive states during learning tasks is an essential component in neurocognitive experiments for assessing and enhancing the cognitive performance of individuals. Studies have demonstrated that mental state recognition systems utilizing brain signals are proficient in the automated monitoring of learners’ cognitive states. The current study focuses on developing an efficient individualized and cross-subject cognitive state assessment model based on Electroencephalography (EEG) patterns during learning tasks. For this study, EEGs of 20 healthy subjects were recorded during a resting state followed by a learning task and examined EEG activations patterns in a wide perspective of feature types and rhythms. The extracted features included time-domain features such as Hjorth parameters, Wavelet-based features, and Spectral entropy. Three classifiers, Support Vector Machine, k-Nearest Neighbor, and Linear Discriminant Analysis were employed to recognize the mental state. A new EEG-based attention index using band ratios is proposed and is demonstrated as an effective predictor for recognizing attentive reading. The proposed model can yield recognition performance with an accuracy of 92.9% in the subject-dependent approach and 77.2% in the subject-independent approach with the Support Vector Machine Classifier. The findings are useful for the design and development of neurofeedback systems that monitor and enhance the cognitive performance in healthy individuals, as well as in individuals with cognitive deficits.

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