Metacognitive monitoring and metacognitive strategies of gifted and average children on dealing with deductive reasoning task

In this paper, we inquire into possible differences between children with exceptionally high intellectual abilities and their average peers as regards metacognitive monitoring and related metacognitive strategies. The question whether gifted children surpass their typically developing peers not only in the intellectual abilities, but also in their level of metacognitive skills, has not been convincingly answered so far. We sought to examine the indicators of metacognitive behavior by means of eye-tracking technology and to compare these findings with the participants’ subjective confidence ratings. Eye-movement data of gifted and average students attending final grades of primary school (4th and 5th grades) were recorded while they dealt with a deductive reasoning task, and four metrics supposed to bear on metacognitive skills, namely the overall trial duration, mean fixation duration, number of regressions and normalized gaze transition entropy, were analyzed. No significant differences between gifted and average children were found in the normalized gaze transition entropy, in mean fixation duration, nor - after controlling for the trial duration – in number of regressions. Both groups of children differed in the time devoted to solving the task. Both groups significantly differed in the association between time devoted to the task and the participants’ subjective confidence rating, where only the gifted children tended to devote more time when they felt less confident. Several implications of these findings are discussed.

Gaze and pupil size variability predict difficulty-level and safe intersection crosses in a driving simulator

Western populations are ageing. With age comes an increased risk of mild cognitive impairment (MCI) and fragility that leads to higher fatal car crashes. This study develops a driving simulation paradigm that seeks to detect unsafe drivers, particularly among older drivers with MCI. The paradigm includes repeated urban intersection crossings at three difficulty levels while eye movements are tracked. The internal validity of this part of the paradigm was tested with young adults ( N = 7). Results indicated that the simulator tests elicited unsafe driving behaviors that varied across difficulty and avoided ceiling and floor effects. Eye movement metrics associated with cognitive load also varied with difficulty and predicted safe crosses. The strongest predictors were gaze transition entropy, gaze variability, and pupil size entropy. These findings indicate internal validity of the tests. Future research should test the external validity of this paradigm with a larger, more diverse sample.

Quantifying the Predictability of Visual Scanpaths Using Active Information Storage

Entropy-based measures are an important tool for studying human gaze behavior under various conditions. In particular, gaze transition entropy (GTE) is a popular method to quantify the predictability of a visual scanpath as the entropy of transitions between fixations and has been shown to correlate with changes in task demand or changes in observer state. Measuring scanpath predictability is thus a promising approach to identifying viewers’ cognitive states in behavioral experiments or gaze-based applications. However, GTE does not account for temporal dependencies beyond two consecutive fixations and may thus underestimate the actual predictability of the current fixation given past gaze behavior. Instead, we propose to quantify scanpath predictability by estimating the active information storage (AIS), which can account for dependencies spanning multiple fixations. AIS is calculated as the mutual information between a processes’ multivariate past state and its next value. It is thus able to measure how much information a sequence of past fixations provides about the next fixation, hence covering a longer temporal horizon. Applying the proposed approach, we were able to distinguish between induced observer states based on estimated AIS, providing first evidence that AIS may be used in the inference of user states to improve human–machine interaction.

Node and Network Entropy—A Novel Mathematical Model for Pattern Analysis of Team Sports Behavior

Pattern analysis is a well-established topic in team sports performance analysis, and is usually centered on the analysis of passing sequences. Taking a Bayesian approach to the study of these interactions, this work presents novel entropy mathematical models for Markov chain-based pattern analysis in team sports networks, with Relative Transition Entropy and Network Transition Entropy applied to both passing and reception patterns. To demonstrate their applicability, these mathematical models were used in a case study in football—the 2016/2017 Champions League Final, where both teams were analyzed. The results show that the winning team, Real Madrid, presented greater values for both individual and team transition entropies, which indicate that greater levels of unpredictability may bring teams closer to victory. In conclusion, these metrics may provide information to game analysts, allowing them to provide coaches with accurate and timely information about the key players of the game.

Viewing garden scenes: Interaction between gaze behavior and physiological responses

Previous research has shown that exposure to Japanese gardens reduces physiological measures of stress, e.g. heart rate, in both healthy subjects and dementia patients. However, the correlation between subjects’ physiological responses and their visual behavior while viewing the garden has not yet been investigated. To address this, we developed a system to collect simultaneous measurements of eye gaze and three physiological indicators of autonomic nervous system activity: electrocardiogram, blood volume pulse, and galvanic skin response. We recorded healthy subjects’ physiological/behavioral responses when they viewed two environments (an empty courtyard and a Japanese garden) in two ways (directly or as a projected 2D photograph). Similar to past work, we found that differences in subject’s physiological responses to the two environments when viewed directly, but not as a photograph. We also found differences in their behavioral responses. We quantified subject’s behavioral responses using several gaze metrics commonly considered to be measures of engagement of focus: average fixation duration, saccade amplitude, spatial entropy and gaze transition entropy. We found decrease in gaze transition entropy, the only metric that accounts for both the spatial and temporal properties of gaze, to have a weak positive correlation with decrease in heart rate. This suggests a relationship between engagement/focus and relaxation. Finally, we found gender differences: females’ gaze patterns were more spatially distributed and had higher transition entropy than males.

Entropy-Based Effect Evaluation of Delineators in Tunnels on Drivers’ Gaze Behavior

Driving safety in tunnels has always been an issue of great concern. Establishing delineators to improve drivers’ instantaneous cognition of the surrounding environment in tunnels can effectively enhance driver safety. Through a simulation study, this paper explored how delineators affect drivers’ gaze behavior (including fixation and scanpath) in tunnels. In addition to analyzing typical parameters, such as fixation position and fixation duration in areas of interest (AOIs), by modeling drivers’ switching process as Markov chains and calculating Shannon’s entropy of the fit Markov model, this paper quantified the complexity of individual switching patterns between AOIs under different delineator configurations and with different road alignments. A total of 25 subjects participated in this research. The results show that setting delineators in tunnels can attract drivers’ attention and make them focus on the pavement. When driving in tunnels equipped with delineators, especially tunnels with both wall delineators and pavement delineators, the participants exhibited a smaller transition entropy H t and stationary entropy H s , which can greatly reduce drivers’ visual fatigue. Compared with left curve and right curve, participants obtained higher H t and H s values in the straight section.