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Neuronal Correlates of Cognitive Control during Gaming Revealed by Near-Infrared Spectroscopy.

Witte M, Ninaus M, Kober SE, Neuper C, Wood G - PLoS ONE (2015)

Bottom Line: We found an increased change of oxygenated and deoxygenated hemoglobin during LEARN covering broad areas over right frontal, central and parietal cortex.Opposed to this, hemoglobin changes were less pronounced for RANDOM and APPLY.Along with the findings that fewer objects were caught during LEARN but stimulus-response mappings were successfully identified, we attribute the higher activations to an increased cognitive load when extracting an unknown mapping.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology, University of Graz, Universitaetsplatz 2, 8010 Graz, Austria; BioTechMed Graz, Graz, Austria.

ABSTRACT
In everyday life we quickly build and maintain associations between stimuli and behavioral responses. This is governed by rules of varying complexity and past studies have identified an underlying fronto-parietal network involved in cognitive control processes. However, there is only limited knowledge about the neuronal activations during more natural settings like game playing. We thus assessed whether near-infrared spectroscopy recordings can reflect different demands on cognitive control during a simple game playing task. Sixteen healthy participants had to catch falling objects by pressing computer keys. These objects either fell randomly (RANDOM task), according to a known stimulus-response mapping applied by players (APPLY task) or according to a stimulus-response mapping that had to be learned (LEARN task). We found an increased change of oxygenated and deoxygenated hemoglobin during LEARN covering broad areas over right frontal, central and parietal cortex. Opposed to this, hemoglobin changes were less pronounced for RANDOM and APPLY. Along with the findings that fewer objects were caught during LEARN but stimulus-response mappings were successfully identified, we attribute the higher activations to an increased cognitive load when extracting an unknown mapping. This study therefore demonstrates a neuronal marker of cognitive control during gaming revealed by near-infrared spectroscopy recordings.

No MeSH data available.


Related in: MedlinePlus

Experimental setup.(A) The 2D game displayed objects differing in color and shape on a computer screen. After an instruction, objects had to be caught by pressing the arrow keys associated with one of the three possible slots. A high-score in the upper left corner indicated the current performance. (B) Projections of the 24 channel positions (white points) on a MNI-152 compatible canonical brain. Two connected optode probe sets were positioned over the right hemisphe Similar to Fig 2 of the manuscript, these curves represent the amount of hemoglobin difference oxy-deoxy (delta Hb) in relation to the cumulative sum of objects successfully caught. This time each plot illustrates the grand average for our predefined ROIs (prefrontal: channels 1–5; sensorimotor: channels 11–14; parietal: channels 20–24). The rationale was to verify that the higher reactivity during the LEARN task can be found in each of these regions, as can be clearly corroborated here. In addition, one can observe an intermediate increase for activations of sensorimotor areas during the RANDOM task possibly reflecting the correlate of motor responses per se. Cost-benefit curves for the different ROIs.re according to the international 10/20 placement system. Red and blue circles indicate sensors and detectors, respectively. Numbers refer to the recorded NIRS channels.
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pone.0134816.g001: Experimental setup.(A) The 2D game displayed objects differing in color and shape on a computer screen. After an instruction, objects had to be caught by pressing the arrow keys associated with one of the three possible slots. A high-score in the upper left corner indicated the current performance. (B) Projections of the 24 channel positions (white points) on a MNI-152 compatible canonical brain. Two connected optode probe sets were positioned over the right hemisphe Similar to Fig 2 of the manuscript, these curves represent the amount of hemoglobin difference oxy-deoxy (delta Hb) in relation to the cumulative sum of objects successfully caught. This time each plot illustrates the grand average for our predefined ROIs (prefrontal: channels 1–5; sensorimotor: channels 11–14; parietal: channels 20–24). The rationale was to verify that the higher reactivity during the LEARN task can be found in each of these regions, as can be clearly corroborated here. In addition, one can observe an intermediate increase for activations of sensorimotor areas during the RANDOM task possibly reflecting the correlate of motor responses per se. Cost-benefit curves for the different ROIs.re according to the international 10/20 placement system. Red and blue circles indicate sensors and detectors, respectively. Numbers refer to the recorded NIRS channels.

Mentions: We designed a simple 2-D game ‘U get it U catch it’ (Fig 1A; online information at http://studies.seriousgamessociety.org) in Matlab (The MathWorks, Natick, USA), where participants had to catch falling objects with a moveable paddle by pressing three predefined keys (arrow left, middle, right) of a conventional computer keyboard. Objects fell, one after each other including a 500 ms pause between objects, from top to bottom in 20 steps with a high speed of 50 ms/step. This fast pace was chosen based on pilot data to ensure that objects could only be caught with knowledge of the stimulus-response mappings described below. The path was created by interpolating a route of six basic steps composed of left, middle and right positions. For the last three steps the object did not change position to enable appropriate responses of the player. Objects differed in shape (circle, rectangle or triangle) and color (red, green or black).


Neuronal Correlates of Cognitive Control during Gaming Revealed by Near-Infrared Spectroscopy.

Witte M, Ninaus M, Kober SE, Neuper C, Wood G - PLoS ONE (2015)

Experimental setup.(A) The 2D game displayed objects differing in color and shape on a computer screen. After an instruction, objects had to be caught by pressing the arrow keys associated with one of the three possible slots. A high-score in the upper left corner indicated the current performance. (B) Projections of the 24 channel positions (white points) on a MNI-152 compatible canonical brain. Two connected optode probe sets were positioned over the right hemisphe Similar to Fig 2 of the manuscript, these curves represent the amount of hemoglobin difference oxy-deoxy (delta Hb) in relation to the cumulative sum of objects successfully caught. This time each plot illustrates the grand average for our predefined ROIs (prefrontal: channels 1–5; sensorimotor: channels 11–14; parietal: channels 20–24). The rationale was to verify that the higher reactivity during the LEARN task can be found in each of these regions, as can be clearly corroborated here. In addition, one can observe an intermediate increase for activations of sensorimotor areas during the RANDOM task possibly reflecting the correlate of motor responses per se. Cost-benefit curves for the different ROIs.re according to the international 10/20 placement system. Red and blue circles indicate sensors and detectors, respectively. Numbers refer to the recorded NIRS channels.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4526694&req=5

pone.0134816.g001: Experimental setup.(A) The 2D game displayed objects differing in color and shape on a computer screen. After an instruction, objects had to be caught by pressing the arrow keys associated with one of the three possible slots. A high-score in the upper left corner indicated the current performance. (B) Projections of the 24 channel positions (white points) on a MNI-152 compatible canonical brain. Two connected optode probe sets were positioned over the right hemisphe Similar to Fig 2 of the manuscript, these curves represent the amount of hemoglobin difference oxy-deoxy (delta Hb) in relation to the cumulative sum of objects successfully caught. This time each plot illustrates the grand average for our predefined ROIs (prefrontal: channels 1–5; sensorimotor: channels 11–14; parietal: channels 20–24). The rationale was to verify that the higher reactivity during the LEARN task can be found in each of these regions, as can be clearly corroborated here. In addition, one can observe an intermediate increase for activations of sensorimotor areas during the RANDOM task possibly reflecting the correlate of motor responses per se. Cost-benefit curves for the different ROIs.re according to the international 10/20 placement system. Red and blue circles indicate sensors and detectors, respectively. Numbers refer to the recorded NIRS channels.
Mentions: We designed a simple 2-D game ‘U get it U catch it’ (Fig 1A; online information at http://studies.seriousgamessociety.org) in Matlab (The MathWorks, Natick, USA), where participants had to catch falling objects with a moveable paddle by pressing three predefined keys (arrow left, middle, right) of a conventional computer keyboard. Objects fell, one after each other including a 500 ms pause between objects, from top to bottom in 20 steps with a high speed of 50 ms/step. This fast pace was chosen based on pilot data to ensure that objects could only be caught with knowledge of the stimulus-response mappings described below. The path was created by interpolating a route of six basic steps composed of left, middle and right positions. For the last three steps the object did not change position to enable appropriate responses of the player. Objects differed in shape (circle, rectangle or triangle) and color (red, green or black).

Bottom Line: We found an increased change of oxygenated and deoxygenated hemoglobin during LEARN covering broad areas over right frontal, central and parietal cortex.Opposed to this, hemoglobin changes were less pronounced for RANDOM and APPLY.Along with the findings that fewer objects were caught during LEARN but stimulus-response mappings were successfully identified, we attribute the higher activations to an increased cognitive load when extracting an unknown mapping.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology, University of Graz, Universitaetsplatz 2, 8010 Graz, Austria; BioTechMed Graz, Graz, Austria.

ABSTRACT
In everyday life we quickly build and maintain associations between stimuli and behavioral responses. This is governed by rules of varying complexity and past studies have identified an underlying fronto-parietal network involved in cognitive control processes. However, there is only limited knowledge about the neuronal activations during more natural settings like game playing. We thus assessed whether near-infrared spectroscopy recordings can reflect different demands on cognitive control during a simple game playing task. Sixteen healthy participants had to catch falling objects by pressing computer keys. These objects either fell randomly (RANDOM task), according to a known stimulus-response mapping applied by players (APPLY task) or according to a stimulus-response mapping that had to be learned (LEARN task). We found an increased change of oxygenated and deoxygenated hemoglobin during LEARN covering broad areas over right frontal, central and parietal cortex. Opposed to this, hemoglobin changes were less pronounced for RANDOM and APPLY. Along with the findings that fewer objects were caught during LEARN but stimulus-response mappings were successfully identified, we attribute the higher activations to an increased cognitive load when extracting an unknown mapping. This study therefore demonstrates a neuronal marker of cognitive control during gaming revealed by near-infrared spectroscopy recordings.

No MeSH data available.


Related in: MedlinePlus