Every day we mentally process new information that needs to be attended, encoded and retrieved. Processing demands depend on the amount of information and the mental attentional capacity of the individual. Research shows that eye movement indices such as peak saccade velocity and blink rate are related to processes of attentional control, however it is still unclear how eye movements are affected by graded changes in task demand. We examine for the first time relations of eye movements to mental attentional tasks with six levels of task demand and two interference conditions. We report data on 57 adults who completed two versions of the color matching task and provided subjective self rating for each mental attentional demand level. Results show that peak saccade velocity and blink rate decrease as a function of mental attentional demand and correlate negatively with self rating of mental effort. Theoretically, new findings related to mental attentional demand and eye movements inform models of visual processing and cognition. Practically, results point to directions for further research to better understand complex relations among eye movements and mental attentional demand in pediatric populations and individuals with cognitive deficits.
Recent studies have revealed that gamma-band oscillatory and transient evoked potentials may change with age during childhood. It is hypothesized that these changes can be associated with a maturation of GABAergic neurotransmission and, subsequently, the age-related changes of excitation–inhibition balance in the neural circuits. One of the reliable paradigms for investigating these effects in the auditory cortex is 40 Hz Auditory Steady-State Response (ASSR), where participants are presented with the periodic auditory stimuli. It is known that such stimuli evoke two types of responses in magnetoencephalography (MEG)—40 Hz steady-state gamma response (or 40 Hz ASSR) and auditory evoked response called sustained Event-Related Field (ERF). Although several studies have been conducted in children, focusing on the changes of 40 Hz ASSR with age, almost nothing is known about the age-related changes of the sustained ERF to the same periodic stimuli and their relationships with changes in the gamma strength. Using MEG, we investigated the association between 40 Hz steady-state gamma response and sustained ERF response to the same stimuli and also their age-related changes in the group of 30 typically developing 7-to-12-year-old children. The results revealed a tight relationship between 40 Hz ASSR and ERF, indicating that the age-related increase in strength of 40 Hz ASSR was associated with the age-related decrease of the amplitude of ERF. These effects were discussed in the light of the maturation of the GABAergic system and excitation–inhibition balance development, which may contribute to the changes in ASSR and ERF.
In this study we replicated the explanatory effect of a label which had been found by Giffin et al. (2017). In their experiments, they used vignettes describing an odd behavior of a person based on culturally specific disorders that were unfamiliar to respondents. It turned out that explanations which explain an odd behavior through a person’s tendency to behave that way (circulus vitiosus) seemed more persuasive if the disorder was given a label that was used in the explanation. We replicated these results in Experiment 1, and in a follow-up Experiment 2 we examined the familiarity with category information and the evaluation of that category over time (the delay lasted one week). We realized that the label effect persists even when people make judgments based on their recollections about a category. Furthermore, according to a content analysis of the recollections, participants in the label condition remembered more information from the vignettes but tended to forget an artificial label; however, they used other words from the disorder domain instead (like “disease” or “kleptomania”). This allowed us to suggest a new interpretation of this effect: we suppose that in the Giffin et al. (2017) experiments the label did not bring any new features to a category itself, but pointed to a relevant domain instead, so the effect appeared from the activation of areas of knowledge in semantic memory and the application of relevant schema for learning a new phenomenon.
In this study, we examined how metaphors used in the Russian media to describe the COVID-19 virus affect the audience’s judgment about the virus and their willingness to take a vaccine. We found that the two conventional metaphors used to describe the dynamics of the spread of the coronavirus (‘wave’ and ‘flash’) have a limited impact on the audience. In particular, by conducting an online experiment (N=737), we revealed that texts in which the virus and vaccination were described metaphorically (‘a new flash of coronavirus’ / ‘vaccination could extinguish the flames of a new flash of coronavirus’; ‘a new wave of coronavirus’ / ‘vaccination could curb the onslaught of a new wave of coronavirus’) reduced fear and anxiety at the thought of the coronavirus, but this effect appears only in vaccinated participants. Metaphorical framing, while impactful at the affective level, did not affect ‘rational’ reasoning, such as estimates of the likelihood of becoming vaccinated or estimates of the number of cases in the country. Also, subjects’ responses to most of the questions correlated positively with their confidence in official information about the coronavirus. The article interprets the results in the context of current work in the field of metaphorical framing and health communication.
Predicting accuracy in cognitively challenging tasks has potential applications in education and industry. Task demand has been linked with increases in response time and variations in reaction time and eye-tracking metrics, however, machine learning research has not been used to predict performance on tasks with multiple levels of difficulty. We report data on adult participants who performed tasks of mental attentional capacity with six levels of difficulty and use machine learning methods to predict accuracy scores considering metrics associated with task difficulty, reaction time and eye movements. Results show that machine learning models can robustly predict performance with reaction times and difficulty level being the strongest predictors. Eye-tracking indices can also predict accuracy independently, with the most important features of the model driven by the number of fixations, number of saccades, duration of the current fixation and pupil size. Practical and theoretical implications of the results are discussed.
Eye-tracking is widely used in research of attentional strategies in tasks with visual representations. Strategies improve with learning and many have examined differences in attention allocation between experts and novices. Research show that when math problems are presented on the screen with response options, novices fixated more on response options that included distractors, whereas experts fixated more on the math problem and the correct answer. If experts and novices apply their attention on different parts of the scree these strategy differences would also be observable when comparing high and low performers. Participants (N = 26; 20-30 years), were non-math university majors who completed the Parametric Math Task (PMT; Konopkina, 2019) while their eye movements were recorded in a remote head-free-to-move mode. The PMT contains mathematical problems of addition, subtraction, multiplication and division with three levels of difficulty. Individuals who scored above median were high performers and below were considered as low performers. Data were analyzed by evaluating dwell time (total duration of fixation) to the math problem area (top of screen) and response options areas (bottom of screen). Results showed that high performers and low performers were significantly different in their dwell times for two interest areas: problem area and distractor responses (problem area: p = 0.029, Cohen's d = 0.92; distractor responses: p = 0.018, Cohen's d = 0.99) ... Findings indicate that high performers spent significantly more time on the math problem area of the screen whereas low performers spent more time on distractor options. In educational practice,
White matter makes up about fifty percent of the human brain. Maturation of white matter accompanies biological development and undergoes the most dramatic changes during childhood and adolescence. Despite the advances in neuroimaging techniques, controversy concerning spatial, and temporal patterns of myelination, as well as the degree to which the microstructural characteristics of white matter can vary in a healthy brain as a function of age, gender and cognitive abilities still exists. In a selective review we describe methods of assessing myelination and evaluate effects of age and gender in nine major fiber tracts, highlighting their role in higher-order cognitive functions. Our findings suggests that myelination indices vary by age, fiber tract, and hemisphere. Effects of gender were also identified, although some attribute differences to methodological factors or social and learning opportunities. Findings point to further directions of research that will improve our understanding of the complex myelination- behavior relation across development that may have implications for educational and clinical practice.
It is widely accepted that higher order thinking, such as working memory and mathematical problem solving are associated with activation in the prefrontal cortex. Thinking about thinking, however, often referred to as meta-cognition is less well understood. Converging evidence suggests that the function of the prefrontal cortex is also key for meta-cognitive judgments, particularly the most anterior part of the prefrontal cortex, Brodmann Area (BA) 10. The current research examined functional magnetic resonance imaging (fMRI) signal associated with BA 10 during metacognition related to self-ratings of mental effort exerted during mathematical operations. We analyzed data from young adult participants who solved addition problems with three levels of difficulty. Our results showed fMRI signal in BA 10 is modulated during the metacognition task, with the left BA 10 showing decreasing fMRI signal with difficulty, whereas the right BA 10 is more stable. These preliminary findings point to further directions for research that should consider rostrolateral and medial aspects of BA 10, and individual differences in performance.
Major discoveries in technology and science often rely on mathematical skills. Mathematical knowledge is founded on basic math problem solving such as addition, subtraction, multiplication, and division. Research shows that problem solving is associated with eye movements that index allocation of attention. Machine learning has been used with eye-tracking metrics to predict performance on real-life user efficiency tasks and classic puzzle games. Critically, no study to date has evaluated eye-tracking metrics associated with mathematical operations using machine learning approaches to classify trial correctness and predict task difficulty level. Participants (n = 26, 20-30 years) viewed mathematical problems in three levels of difficulty indexed by 1-, 2-, and 3-digit problems along with four possible answers, while their eye movements were being recorded. Eye-tracking data were acquired with EyeLink Portable Duo SR Research eye-tracker with 1ms temporal resolution (at 1000 Hz frequency) in remote head-free-to-move mode. Results show that trial correctness can be classified with a 0.81 ROC AUC score based on 5 fold cross-validation. Predicting task difficulty level of each trial was attained with 72% accuracy, which is significantly better than the random prediction (ie, 50%). The most important features for both machine learning models include metrics associated with current pupil fixation, current saccade amplitudes, and current fixation duration. Theoretically, findings contribute to theories of mathematical cognition. Practically, algorithms can contribute to further research in mathematical problem solving and machine learning.
Cognitively challenging tasks require complex coordination of information beyond visual input. Predicting accuracy on such tasks has potential applications in education and industry. Task difficulty is associated with increases in reaction time and variation in eye tracking indices. Critically, machine learning has not yet been used to predict accuracy on cognitive tasks with multiple difficulty levels. We report data on 57 (34 females; 20-30 years) participants who completed visuospatial tasks of mental attentional capacity with six levels of difficulty while their eye movements were recorded using EyeLink Portable Duo SR Research eye-tracker with 1ms temporal resolution (at 1000 Hz frequency) in remote head-free-to-move mode. Results show that task accuracy scores can be robustly predicted when all variables (e.g., eye-tracking, difficulty level and reaction time) are considered together (R2 = .80). Reaction time, difficulty level and eye tracking metrics are also effective independent predictors with R2 equaling .73, .58, and .36, respectively. Analyses for feature importance suggest eye-tracking indices with the most importance for the models include the number of fixations, number of saccades, duration of the current fixation and pupil size. Notably, our machine learning algorithms target a prediction question, rather than a classification one, and the current algorithm can be useful for future research and applications in other contexts where visuospatial processing is required. Theoretically, findings show common and distinct metrics that can inform theories of cognition and vision science.
This study investigated teachers’ experience of distant teaching during the COVID-19 pandemic and lockdown in spring 2020, their perceived level of psychological stress and its associated factors and their coping strategies. The aim was to present a successful case of teachers organizing their work and maintaining their mental health under the pressure of the pandemic. To achieve this aim, a case study design was chosen, and interviews with five teachers from one of Berlin’s private bilingual Russian-German schools were conducted. The interviews included blocks of questions on a) the changing context of work and life, b) stress level self-assessment and coping strategies used, and c) teachers’ ideas about the future. Furthermore, the results of the interviews were generated through content analysis. The findings of the present case identified several supportive factors that decrease psychological stress: school administrative and technical support and the maintenance of workload at almost the same level as that before the transition to distant teaching. Other coping strategies that act as preventive factors supporting mental health were also identified: positive rethinking of the situation; seeking social support; and maintaining work-life balance through self-care, physical activities and hobbies. Implications of the present findings are provided.
The article provides an overview of modern theoretical concepts and empirical data on mental stability. It examines theoretical approaches to the study of this psychological construct, which allows you to overcome negative stressful circumstances. Various theoretical models of mental stability are presented, as well as various methods for measuring it. The relationship of mental stability with a number of other psychological constructs (personality, psychological well-being, cognitive abilities, relationships with others, etc.) is analyzed. The problems of the formation of mental stability are discussed.
Studying at school requires students to understand and memorize many new categories – inductive rules, which contain the commonalities of specific category examples. Studies indicate that verbal labels enhance learning and memorizing such inductive rules. However, it is not known whether the type of lexical labels plays a crucial role in these processes. The goal of this study was to compare different types of verbalization and their effect on retention and application by participants from different age groups. We conducted an experimental study where adult participants (N=50) and primary-school-age children (N=44) were asked to solve Bongard problems with different geometric inductive rules that they also needed to memorize. Each problem was presented with a rule either in geometric labels or in figurative labels depending on the experimental condition. Participants then transferred the rules on the problem solving. Adult participants in the group with figurative labels were found to transfer rules faster compared to the adult participants in the group with geometric labels. No such difference among experimental conditions was found for primary schoolers. Our results demonstrate the beneficial effect of figurative labels for tasks containing inductive rules. We also show that this effect is age dependent.
In traditional studies of categorical learning, its conditions are usually limited to only two categories for which a categorization rule needs to be found. However, in practice, when learning any new category, people often use examples from other, additional categories as well. How does this affect the learning of the main category? In the present experiment, participants performed a task to form new categories. They had to learn a probabilistic rule, a prototype. In addition to the main task (distinguishing between examples from two main categories), participants were presented with examples from a third category. The conditions of presentation of this category were varied: existence or absence for the examples included in it of their own categorization rule, as well as feedback. After the formation of categories, participants performed a transfer test. It was found that the accuracy of learning basic categories was influenced by the existence of a categorization rule for additional category and was not influenced by the presence of feedback for it. However, when there was feedback, participants categorized new examples more quickly. The findings are discussed in the context of the multiple categorization systems model (COVIS) and studies of learning with partial feedback.