Pupillary responses on the visual backward masking task reflect general cognitive ability

Verney, Steven P.; Granholm, Eric; Marshall, Sandra P.

doi:10.1016/j.ijpsycho.2003.12.003

Cited by 115 publications

(106 citation statements)

References 25 publications

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“…Klingner (2010) introduced the fixation-aligned pupillary response averaging, in which eye fixations-instead of experimental stimuli-are used to temporally align PD time windows. In addition, TEPRs have been analyzed using principal component analysis and independent component analysis in an attempt to reduce the large number of time points to a smaller set-usually two or three factors (see Jainta & Baccino, 2010;Kuchinke, Vo, Hofmann, & Jacobs, 2007;Verney, Granholm, & Marshall, 2004).…”

Section: Methods For Pd Data Analysismentioning

confidence: 99%

Automatic Stress Classification With Pupil Diameter Analysis

Pedrotti

Mirzaei

Tedesco³

et al. 2014

International Journal of Human-Computer Interaction

150

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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. This article proposes a method based on wavelet transform and neural networks for relating pupillary behavior to psychological stress. The proposed method was tested by recording pupil diameter and electrodermal activity during a simulated driving task. Self-report measures were also collected. Participants performed a baseline run with the driving task only, followed by three stress runs where they were required to perform the driving task along with sound alerts, the presence of two human evaluators, and both. Self-reports and pupil diameter successfully indexed stress manipulation, and significant correlations were found between these measures. However, electrodermal activity did not vary accordingly. After training, the four-way parallel neural network classifier could guess whether a given unknown pupil diameter signal came from one of the four experimental trials with 79.2% precision. The present study shows that pupil diameter signal has good discriminating power for stress detection.

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Section: Methods For Pd Data Analysismentioning

confidence: 99%

Automatic Stress Classification With Pupil Diameter Analysis

Pedrotti

Mirzaei

Tedesco³

et al. 2014

International Journal of Human-Computer Interaction

150

View full text Add to dashboard Cite

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“…The size of the pupil is not only affected by light conditions, but also by cognitive and physical efforts and emotional arousal (Beatty and Kahneman 1966;Granholm and Steinhauer 2004;Hess and Polt 1964;Kahneman and Beatty 1967;Loewenfeld 1993). It has been demonstrated throughout various cognitive domains and across different modalities of stimulus presentation that pupil dilation is greater the more difficult the task is (Hess and Polt 1964;Nuthmann and van der Meer 2005;Raisig, Welke, Hagendorf and van der Meer 2007;Verney, Granholm and Marshall 2004). For instance, Hess and Polt (1964) found that pupillary dilation is higher while solving difficult arithmetical problems, such as 11 9 6, than while solving easy problems, such as 4 9 7.…”

Section: Pupillometry As a Way To Assess Resource Allocationmentioning

confidence: 99%

Mathematical cognition: individual differences in resource allocation

Bornemann

Foth

Horn

et al. 2010

ZDM Mathematics Education

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Individuals scoring higher in tests of general cognitive abilities tend to perform better on novel and familiar mathematical tasks. It has been scarcely investigated how this superior mathematical performance relates to the amount of cognitive resources that is invested to solve a given task. In this study we propose that, on novel tasks, individuals with high cognitive abilities outperform less able individuals, because they allocate a higher amount of resources. On familiar tasks, however, individuals with higher abilities profit from more efficient processes compared to individuals of lower cognitive abilities. We tested this hypothesis by administering to 11th graders a geometric analogy task not practiced at school and an algebraic transformation task comprising operations that are routinely required during mathematical courses. General cognitive abilities were measured with Ravens Advanced Progressive matrices (fluid intelligence), the d2 (focused attention) and KAI-N (working memory capacity). Resource allocation was measured by assessing pupil diameter during the problem-solving process. Performance on both the analogy and the algebra task was correlated with general cognitive abilities, especially fluid intelligence. In line with our assumptions, a positive correlation between fluid intelligence and resource allocation was observed in the novel geometric analogy task, whereas the correlation was not significant in the more familiar algebra task.

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“…Assessment of pupil reXex has previously been used in patients with heart failure (Keivanidou et al 2010), diabetes (Hreidarsson and Gundersen 1985;Piha and Halonen 1994), Parkinson's disease (Stergiou et al 2009), Alzheimer's disease (Fotiou et al 2000a, b), Myasthenia gravis (Tsiptsios et al 2008), alcoholism (Tan et al 1980), anxiety (Nagai et al 1980), depression (Sokolski and Demet 1996), Down's syndrome (Sacks and Smith 1989) and mental retardation (Chaney et al 1989). Pupillometry is further used in the investigation of central autonomic pathways , lateralization (Bär et al 2005), alertness or anxiety (Bitsios et al 2004), in Pavlonian conditioning (Reinhard et al 2006), cognitive ability (Verney et al 2004), memory load (Van Gerven et al 2004), attention (Karatekin 2004) and in the study of temporal orientation (Nuthmann and Van Der Meer 2005).…”

Section: Introductionmentioning

confidence: 99%

The use of pupillometry in the assessment of cardiac autonomic function in elite different type trained athletes

et al. 2011

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The aim of the present study was to evaluate cardiac autonomic function by pupillometry in male athletes. Fifteen elite endurance- (END) and eleven power-trained (POWER) athletes and fifteen sedentary individuals (CONTROL) were studied. All subjects underwent three pupillometric measurements: at rest, peak exercise testing and recovery phase. The pupillometric indices studied were: baseline pupil radius (R1), minimum pupil radius (R2), maximum constriction velocity (VC(max)), maximum constriction acceleration (AC(max)), amplitude (AMP, R1-R2), constriction ratio (AMP%). During exercise, RR intervals were obtained for each subject with a Polar S810i for time and frequency domain heart rate variability (HRV) analysis. The following parameters of HRV were measured: standard deviation of all NN intervals (SDNN), the mean square successive differences (rMSSD), percent of NN intervals differing >50 ms from the preceding NN (pNN50), low (LF)- and high (HF)- frequency components of the autoregressive power spectrum of the NN intervals and their ratio (LF/HF). At rest and recovery, END showed significantly increased VC(max) and AC(max) compared to POWER and CONTROL. AMP% was significantly greater in END at rest, peak exercise and recovery compared to POWER and CONTROL. END and POWER had significantly greater AMP at rest and recovery compared to CONTROL. Moreover, all HRV indices were significantly increased in END compared to POWER and CONTROL. However, POWER showed significantly increased rMSSD and LF compared to CONTROL. HRV parameters were significantly correlated with pupillometric parameters during exercise. Our results indicated that any kind of exercise training and mainly endurance one affects autonomic regulation of pupillary light reflex.

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Pupillary responses on the visual backward masking task reflect general cognitive ability

Cited by 115 publications

References 25 publications

Automatic Stress Classification With Pupil Diameter Analysis

Automatic Stress Classification With Pupil Diameter Analysis

Mathematical cognition: individual differences in resource allocation

The use of pupillometry in the assessment of cardiac autonomic function in elite different type trained athletes

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