Additional Funding Sources
This project was funded by an Idaho State University start up grant for new faculty.
Presentation Date
7-2022
Abstract
The exact nature of how executive control resources are deployed as the cognitive demands of a task change over time is poorly understood. One theory, the dual mechanisms of control model (Braver, 2012) suggests that executive control resources get deployed depending upon whether the need for cognitive resources remains stable during a task, or if an unexpected or unpredictable change in the environment triggers the need for an increase in executive control. For example, watching your piano teacher play a C-major scale with the intention of imitating her performance requires that you engage several difference cognitive processes simultaneously(e.g. remembering the sequence of the action, the goal, the individual action elements, etc.); however, if you are watching your piano teacher play a C-major scale with the intent to imitate, but she plays a note that you do not expect (such as a flatted 7th ), an increase in executive control resources are required in order for you to modulate your response/behavior. In this way, “proactive control relies upon the anticipation and prevention of interference before it occurs, whereas reactive control relies upon the detection and resolution of interference after its onset,” (Braver 2012). Our goal in this experiment was to see if changes in the EEG reflected proactive and reactive control processes, particularly during trials in which proactive control resources could not be deployed. To test this, we collected whole scalp EEG while participants observed and imitated goal directed behaviors in which the action goal was either discernable early or late in the sequence. EEG is well suited to investigate this question because EEG can measure rapid changes in power amplitude across frequencies while also providing decent cortical source localization. These changes in frequency amplitude have been associated with changes in executive function. For example, increases in the theta-band in the frontal cortex reflect increases in both proactive (Blais, Harris, Guerrero, & Bunge, 2012; Töllner et al., 2017) and reactive control (Amer, Campbell, & Hasher, 2016; Munakata, Snyder, & Chatham, 2012) depending on factors such as increased task demands, changing conditions, and during implicit/explicit learning of stimulus-response associations. During the experiment, participants watched videos of an actor centrally seated behind four objects, all within comfortable reaching distance: a wash tub to their far left, a small pedestal on their immediate left, an 8 oz ceramic coffee mug with a handle to their immediate right, and a small cup rack to their far right. Participants were also seated in front of the same objects in the identical configuration. Participants were instructed to watch the videos with the expectation that they would imitate the action they were observing. Trials consisted of three 2500 ms intervals separated by a brief fixation cross. Each video began with the actor passively seated behind the objects, followed by a grasp interval in which the actor gasped a coffee mug, and then a goal interval in which the actor completed a goal-directed behavior using the coffee mug. After the three intervals, the participants were prompted by a cue (“IMITATE”) to imitate the action as identically as possible to what the actor did in the video. During unambiguous goal trials, the actor grasped the coffee mug with their right hand using a precision grip (grasping the mug at the handle, using the thumb, index, and middle fingers) and hung the mug on a cup rack.
Importantly, every time the actor grasped the mug using a precision grip, they always hung the mug on the rack. In this case, the type of grip, and not the action of hanging the mug, conveyed the action goal 100% of the time. During the ambiguous goal trials, the actor grasped the mug at the rim with their right hand, using a whole hand prehension grip. During these trials, 50% of the time, the mug was placed in the wash bin, and 50% of the time, the mug was placed on the pedestal. During these trials, the action goal was only discernable when the actor executed the goal. The grasp conveyed no predictive relationship with the goal. There was also a control condition where the actor stayed still, and did not engage in any motor behavior. We performed an independent component analysis on the EEG data and localized neural activity to the frontal cortex using a dipole fitting procedure, extracting changes in amplitude in the theta-band for the control, ambiguous, and unambiguous conditions (trial type) across the setting, grasp, and goal intervals (intervals). Consistent with proactive control predictions, unambiguous trials generated an increase in frontal-theta that was greater than baseline and remained equal across all three intervals. Ambiguous trials also generated an increase in frontal-theta compared to baseline; however, frontal-theta during the goal interval was significantly greater that theta-power during the setting and grasp intervals which is consistent with reactive control predictions. These findings suggest that executive control resources are readily maintained when task conditions remain stable, but that additional resources can also be deployed in response to unexpected or unpredictable changes. Future work should look at theta-power changes in anterior cingulate cortex as a function of the need for proactive versus reactive control.
References
Amer, T., Campbell, K. L., & Hasher, L. (2016). Cognitive control as a double-edged sword. Trends in cognitive sciences, 20(12), 905-915.
Blais, C., Harris, M. B., Guerrero, J. V., & Bunge, S. A. (2012). Rethinking the role ofautomaticity in cognitive control. Quarterly Journal of Experimental Psychology, 65(2), 268-276.
Braver, T. S. (2012). The variable nature of cognitive control: a dual mechanisms framework. Trends in cognitive sciences, 16(2), 106-113.
Munakata, Y., Snyder, H. R., & Chatham, C. H. (2012). Developing cognitive control: Three keytransitions. Current directions in psychological science, 21(2), 71-77.
Töllner, T., Wang, Y., Makeig, S., Müller, H. J., Jung, T. P., & Gramann, K. (2017). Twoindependent frontal midline theta oscillations during conflict detection and adaptation in aSimon-type manual reaching task. Journal of Neuroscience, 37(9), 2504-2515.
Increases in Frontal Theta-Power in the Egg Reflect the Need for Greater Executive Control Resources in Response to Changes in the Environment
The exact nature of how executive control resources are deployed as the cognitive demands of a task change over time is poorly understood. One theory, the dual mechanisms of control model (Braver, 2012) suggests that executive control resources get deployed depending upon whether the need for cognitive resources remains stable during a task, or if an unexpected or unpredictable change in the environment triggers the need for an increase in executive control. For example, watching your piano teacher play a C-major scale with the intention of imitating her performance requires that you engage several difference cognitive processes simultaneously(e.g. remembering the sequence of the action, the goal, the individual action elements, etc.); however, if you are watching your piano teacher play a C-major scale with the intent to imitate, but she plays a note that you do not expect (such as a flatted 7th ), an increase in executive control resources are required in order for you to modulate your response/behavior. In this way, “proactive control relies upon the anticipation and prevention of interference before it occurs, whereas reactive control relies upon the detection and resolution of interference after its onset,” (Braver 2012). Our goal in this experiment was to see if changes in the EEG reflected proactive and reactive control processes, particularly during trials in which proactive control resources could not be deployed. To test this, we collected whole scalp EEG while participants observed and imitated goal directed behaviors in which the action goal was either discernable early or late in the sequence. EEG is well suited to investigate this question because EEG can measure rapid changes in power amplitude across frequencies while also providing decent cortical source localization. These changes in frequency amplitude have been associated with changes in executive function. For example, increases in the theta-band in the frontal cortex reflect increases in both proactive (Blais, Harris, Guerrero, & Bunge, 2012; Töllner et al., 2017) and reactive control (Amer, Campbell, & Hasher, 2016; Munakata, Snyder, & Chatham, 2012) depending on factors such as increased task demands, changing conditions, and during implicit/explicit learning of stimulus-response associations. During the experiment, participants watched videos of an actor centrally seated behind four objects, all within comfortable reaching distance: a wash tub to their far left, a small pedestal on their immediate left, an 8 oz ceramic coffee mug with a handle to their immediate right, and a small cup rack to their far right. Participants were also seated in front of the same objects in the identical configuration. Participants were instructed to watch the videos with the expectation that they would imitate the action they were observing. Trials consisted of three 2500 ms intervals separated by a brief fixation cross. Each video began with the actor passively seated behind the objects, followed by a grasp interval in which the actor gasped a coffee mug, and then a goal interval in which the actor completed a goal-directed behavior using the coffee mug. After the three intervals, the participants were prompted by a cue (“IMITATE”) to imitate the action as identically as possible to what the actor did in the video. During unambiguous goal trials, the actor grasped the coffee mug with their right hand using a precision grip (grasping the mug at the handle, using the thumb, index, and middle fingers) and hung the mug on a cup rack.
Importantly, every time the actor grasped the mug using a precision grip, they always hung the mug on the rack. In this case, the type of grip, and not the action of hanging the mug, conveyed the action goal 100% of the time. During the ambiguous goal trials, the actor grasped the mug at the rim with their right hand, using a whole hand prehension grip. During these trials, 50% of the time, the mug was placed in the wash bin, and 50% of the time, the mug was placed on the pedestal. During these trials, the action goal was only discernable when the actor executed the goal. The grasp conveyed no predictive relationship with the goal. There was also a control condition where the actor stayed still, and did not engage in any motor behavior. We performed an independent component analysis on the EEG data and localized neural activity to the frontal cortex using a dipole fitting procedure, extracting changes in amplitude in the theta-band for the control, ambiguous, and unambiguous conditions (trial type) across the setting, grasp, and goal intervals (intervals). Consistent with proactive control predictions, unambiguous trials generated an increase in frontal-theta that was greater than baseline and remained equal across all three intervals. Ambiguous trials also generated an increase in frontal-theta compared to baseline; however, frontal-theta during the goal interval was significantly greater that theta-power during the setting and grasp intervals which is consistent with reactive control predictions. These findings suggest that executive control resources are readily maintained when task conditions remain stable, but that additional resources can also be deployed in response to unexpected or unpredictable changes. Future work should look at theta-power changes in anterior cingulate cortex as a function of the need for proactive versus reactive control.
References
Amer, T., Campbell, K. L., & Hasher, L. (2016). Cognitive control as a double-edged sword. Trends in cognitive sciences, 20(12), 905-915.
Blais, C., Harris, M. B., Guerrero, J. V., & Bunge, S. A. (2012). Rethinking the role ofautomaticity in cognitive control. Quarterly Journal of Experimental Psychology, 65(2), 268-276.
Braver, T. S. (2012). The variable nature of cognitive control: a dual mechanisms framework. Trends in cognitive sciences, 16(2), 106-113.
Munakata, Y., Snyder, H. R., & Chatham, C. H. (2012). Developing cognitive control: Three keytransitions. Current directions in psychological science, 21(2), 71-77.
Töllner, T., Wang, Y., Makeig, S., Müller, H. J., Jung, T. P., & Gramann, K. (2017). Twoindependent frontal midline theta oscillations during conflict detection and adaptation in aSimon-type manual reaching task. Journal of Neuroscience, 37(9), 2504-2515.