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Rapid gist perception of meaningful real-life scenes: Exploring individual and gender differences in multiple categorization tasks.

Vanmarcke S, Wagemans J - Iperception (2015)

Bottom Line: Since this pioneering work, follow-up studies consistently reported population-level reaction time differences on different categorization tasks, indicating a superordinate advantage (animal versus dog) and effects of perceptual similarity (animals versus vehicles) and object category size (natural versus animal versus dog).In this study, we replicated and extended these separate findings by using a systematic collection of different categorization tasks (varying in presentation time, task demands, and stimuli) and focusing on individual differences in terms of e.g., gender and intelligence.In addition to replicating the main findings from the literature, we find subtle, yet consistent gender differences (women faster than men).

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Experimental Psychology, University of Leuven (KU Leuven), Leuven, Belgium, e-mail: steven.vanmarcke@ppw.kuleuven.be.

ABSTRACT
In everyday life, we are generally able to dynamically understand and adapt to socially (ir)elevant encounters, and to make appropriate decisions about these. All of this requires an impressive ability to directly filter and obtain the most informative aspects of a complex visual scene. Such rapid gist perception can be assessed in multiple ways. In the ultrafast categorization paradigm developed by Simon Thorpe et al. (1996), participants get a clear categorization task in advance and succeed at detecting the target object of interest (animal) almost perfectly (even with 20 ms exposures). Since this pioneering work, follow-up studies consistently reported population-level reaction time differences on different categorization tasks, indicating a superordinate advantage (animal versus dog) and effects of perceptual similarity (animals versus vehicles) and object category size (natural versus animal versus dog). In this study, we replicated and extended these separate findings by using a systematic collection of different categorization tasks (varying in presentation time, task demands, and stimuli) and focusing on individual differences in terms of e.g., gender and intelligence. In addition to replicating the main findings from the literature, we find subtle, yet consistent gender differences (women faster than men).

No MeSH data available.


Related in: MedlinePlus

Visualization of the data by means of scatterplots. In (A), we placed the individual subjects' median RT difference scores of the Artificial versus the Vehicle condition on the abscissa and the difference scores of the Natural versus the Animal RT scores on the ordinate axis. Men are always depicted in blue, women in red. For both axes, the largest part of the RT distribution lies above the origin (positive difference scores). This would indicate that people are generally faster at detecting an object (e.g., Vehicle or Animal) than detecting the scene gist (e.g., Artificial or Natural) in ultrarapid categorization. In (B), we placed the individual subjects' median RT difference scores of the Vehicle versus the Car condition on the abscissa and the difference scores of the Animal versus the Dog RT scores on the ordinate axis. For both axes, the largest part of the RT distribution lies below the origin (negative difference scores). This would indicate that people are generally faster at detecting a superordinate object (e.g., Vehicle or Animal) than detecting a basic-level object (e.g., Car or Dog). Furthermore, both scatterplots indicate differences in the specific RT distribution for men or women. The latter finding is also exemplified in (C), in which all RT data in the Animal/Vehicle task are binned per 10 ms and plotted with respect to frequency. The female RT distribution (red) peeks slightly earlier than the male RT distribution (blue) and has a lighter right tale.
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Figure 4: Visualization of the data by means of scatterplots. In (A), we placed the individual subjects' median RT difference scores of the Artificial versus the Vehicle condition on the abscissa and the difference scores of the Natural versus the Animal RT scores on the ordinate axis. Men are always depicted in blue, women in red. For both axes, the largest part of the RT distribution lies above the origin (positive difference scores). This would indicate that people are generally faster at detecting an object (e.g., Vehicle or Animal) than detecting the scene gist (e.g., Artificial or Natural) in ultrarapid categorization. In (B), we placed the individual subjects' median RT difference scores of the Vehicle versus the Car condition on the abscissa and the difference scores of the Animal versus the Dog RT scores on the ordinate axis. For both axes, the largest part of the RT distribution lies below the origin (negative difference scores). This would indicate that people are generally faster at detecting a superordinate object (e.g., Vehicle or Animal) than detecting a basic-level object (e.g., Car or Dog). Furthermore, both scatterplots indicate differences in the specific RT distribution for men or women. The latter finding is also exemplified in (C), in which all RT data in the Animal/Vehicle task are binned per 10 ms and plotted with respect to frequency. The female RT distribution (red) peeks slightly earlier than the male RT distribution (blue) and has a lighter right tale.

Mentions: Other descriptive variables (e.g., Total IQ, BRIEF-A, SRS-A, and Age) taken into account as covariates did not provide a significant improvement in predicting RT or accuracy. But further exploratory analysis of the observed fixed effects indicated the possible presence of Gender differences (Figure 4). Specifically checking for these group-level Gender differences [Aim 2], we did find significantly faster RT for women in comparison to men. This could be modeled by adding the fixed (between-subjects) factor Gender (RT: t46.01 = −2.29; p = .02) as a predictor of RT outcomes. With regard to the accuracy values, adding Gender (Accuracy: Z = .86; p = .39) to the model did not lead to any significant improvements in prediction.


Rapid gist perception of meaningful real-life scenes: Exploring individual and gender differences in multiple categorization tasks.

Vanmarcke S, Wagemans J - Iperception (2015)

Visualization of the data by means of scatterplots. In (A), we placed the individual subjects' median RT difference scores of the Artificial versus the Vehicle condition on the abscissa and the difference scores of the Natural versus the Animal RT scores on the ordinate axis. Men are always depicted in blue, women in red. For both axes, the largest part of the RT distribution lies above the origin (positive difference scores). This would indicate that people are generally faster at detecting an object (e.g., Vehicle or Animal) than detecting the scene gist (e.g., Artificial or Natural) in ultrarapid categorization. In (B), we placed the individual subjects' median RT difference scores of the Vehicle versus the Car condition on the abscissa and the difference scores of the Animal versus the Dog RT scores on the ordinate axis. For both axes, the largest part of the RT distribution lies below the origin (negative difference scores). This would indicate that people are generally faster at detecting a superordinate object (e.g., Vehicle or Animal) than detecting a basic-level object (e.g., Car or Dog). Furthermore, both scatterplots indicate differences in the specific RT distribution for men or women. The latter finding is also exemplified in (C), in which all RT data in the Animal/Vehicle task are binned per 10 ms and plotted with respect to frequency. The female RT distribution (red) peeks slightly earlier than the male RT distribution (blue) and has a lighter right tale.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4441019&req=5

Figure 4: Visualization of the data by means of scatterplots. In (A), we placed the individual subjects' median RT difference scores of the Artificial versus the Vehicle condition on the abscissa and the difference scores of the Natural versus the Animal RT scores on the ordinate axis. Men are always depicted in blue, women in red. For both axes, the largest part of the RT distribution lies above the origin (positive difference scores). This would indicate that people are generally faster at detecting an object (e.g., Vehicle or Animal) than detecting the scene gist (e.g., Artificial or Natural) in ultrarapid categorization. In (B), we placed the individual subjects' median RT difference scores of the Vehicle versus the Car condition on the abscissa and the difference scores of the Animal versus the Dog RT scores on the ordinate axis. For both axes, the largest part of the RT distribution lies below the origin (negative difference scores). This would indicate that people are generally faster at detecting a superordinate object (e.g., Vehicle or Animal) than detecting a basic-level object (e.g., Car or Dog). Furthermore, both scatterplots indicate differences in the specific RT distribution for men or women. The latter finding is also exemplified in (C), in which all RT data in the Animal/Vehicle task are binned per 10 ms and plotted with respect to frequency. The female RT distribution (red) peeks slightly earlier than the male RT distribution (blue) and has a lighter right tale.
Mentions: Other descriptive variables (e.g., Total IQ, BRIEF-A, SRS-A, and Age) taken into account as covariates did not provide a significant improvement in predicting RT or accuracy. But further exploratory analysis of the observed fixed effects indicated the possible presence of Gender differences (Figure 4). Specifically checking for these group-level Gender differences [Aim 2], we did find significantly faster RT for women in comparison to men. This could be modeled by adding the fixed (between-subjects) factor Gender (RT: t46.01 = −2.29; p = .02) as a predictor of RT outcomes. With regard to the accuracy values, adding Gender (Accuracy: Z = .86; p = .39) to the model did not lead to any significant improvements in prediction.

Bottom Line: Since this pioneering work, follow-up studies consistently reported population-level reaction time differences on different categorization tasks, indicating a superordinate advantage (animal versus dog) and effects of perceptual similarity (animals versus vehicles) and object category size (natural versus animal versus dog).In this study, we replicated and extended these separate findings by using a systematic collection of different categorization tasks (varying in presentation time, task demands, and stimuli) and focusing on individual differences in terms of e.g., gender and intelligence.In addition to replicating the main findings from the literature, we find subtle, yet consistent gender differences (women faster than men).

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Experimental Psychology, University of Leuven (KU Leuven), Leuven, Belgium, e-mail: steven.vanmarcke@ppw.kuleuven.be.

ABSTRACT
In everyday life, we are generally able to dynamically understand and adapt to socially (ir)elevant encounters, and to make appropriate decisions about these. All of this requires an impressive ability to directly filter and obtain the most informative aspects of a complex visual scene. Such rapid gist perception can be assessed in multiple ways. In the ultrafast categorization paradigm developed by Simon Thorpe et al. (1996), participants get a clear categorization task in advance and succeed at detecting the target object of interest (animal) almost perfectly (even with 20 ms exposures). Since this pioneering work, follow-up studies consistently reported population-level reaction time differences on different categorization tasks, indicating a superordinate advantage (animal versus dog) and effects of perceptual similarity (animals versus vehicles) and object category size (natural versus animal versus dog). In this study, we replicated and extended these separate findings by using a systematic collection of different categorization tasks (varying in presentation time, task demands, and stimuli) and focusing on individual differences in terms of e.g., gender and intelligence. In addition to replicating the main findings from the literature, we find subtle, yet consistent gender differences (women faster than men).

No MeSH data available.


Related in: MedlinePlus