Tyshae Davis and Michael J. Larson, Psychology and Neuroscience
Since the Yerkes-Dodson theory was initially put forward (as early as 1906), multiple theorists have suggested an inverted-U relationship between physiological arousal and cognitive performance. Literature on this topic, however, is mixed, with some studies showing evidence supporting the inverted-U relationship and others showing a more linear relationship. Exercise and food-related cognition is one area where the inverted-U hypothesis could be applied. Some studies have shown that high-intensity exercise induces an acute, but temporary suppression to hunger and energy intake compared to low-intensity exercise (King, Tremblay, & Blundell, 1997) and that high-intensity exercise favors negative energy balance to a greater extent than low-intensity exercise (Imbeault, Saint-Pierre, Almeras, Tremblay, 1997). However, others suggest that a suppression of hunger occurs following intense exercise, but the effect is brief and has no influence on energy intake (Klausen, Toubro, Ranneries, Rehfeld, Holst, Christensen, & Astrup 1999). Little research to date has parametrically manipulated levels of exercise intensity and the brain’s response to food cues. Thus, we aimed to parametrically manipulate the level of exercise intensity then study the food-related inhibition using electroencephalogram (EEG) and event-related potentials (ERPs). Specifically, we hypothesized that lower and vigorous levels of exercise were associated with poorer response inhibition towards food cues that are high in calories, sugar, and fat compared to individuals immediately following moderate intensity exercise. To test our hypothesis, we measured changes in brain activity that reflect inhibitory control using EEG and ERPs, response times (RTs), and error rates during a food-related go/no-go task. Due to lack of sufficient time during the course of the award to collect data on all three points, we were only able to collect data on exercise and non-exercise; thus only data from moderate exercise and non-exercise conditions will be presented here.
The effects of exercise on appetite and eating habits are highly applicable in today’s world and in our advancements in promoting health and achieving physical fitness. Physically-healthy individuals are less susceptible to disease, illness, (Thompson, Buchner, Pina, Balady, Williams, Marcus, Berra, Blair, Costa, Franklin, Fletcher, Gordon, Pate, Rodriguez, Yancey, Wenger, 2003) and fatigue (Herring, Puetz, O’Connor, & Dishman, 2012). Several studies have shown that a low response inhibition to food-related cues is associated with overeating (Meule, Lutz, Krawietz, Stutzer, Vogele, & Kubler, 2014). Response inhibition can be defined as the suppression of a typical response tendency in order to correctly respond to environmental or task-relevant information (Ko & Miller, 2013). In some studies following exercise, cognitive function and arousal have shown to follow the Yerkes-Dodson Inverted-U relationship, with the effects of a stimulus appearing to increase up to a maximum (Arent & Landers, 2003). However, the response to food cues is currently unclear and food related response inhibition patterns following high versus low intensity exercise are unknown. Furthermore, no studies have sought out an optimal intensity exercise to increase inhibitory control to food stimuli, which we would have liked to have more deeply pursued with more time. Measuring food-related response inhibition following two exercise levels of intensity is a step in the process of better identifying factors in appetite regulation and understanding the steps leading to overeating, unhealthy intake, and even obesity.
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Initially, we hypothesized that there was a quadratic relationship between response inhibition to food cues and exercise. More specifically, we hypothesized that lower and vigorous levels of exercise were associated with poorer response inhibition towards food cues that are high in calories, sugar, and fat compared to individuals immediately following moderate intensity exercise.
We measured the neural indices of response inhibition towards food-related stimuli in healthy individuals following non-exercise and exercise sessions. Participants wore a 128-electrode sensor EEG net in order to measure event-related potentials (ERPs). ERPs are changes in the brain’s electrical waveforms due to responses toward stimuli. The amplitudes of the ERPs differ depending on the strength of the stimulus or the reaction. We looked at a specific ERP called the N2, which is known as a neural indicator of response inhibition. The amplitude of the N2 hits a negative peak between 200 and 350 ms after the onset of a stimulus (Folstein & Van Petten, 2008). The N2 is elicited when an individual must withhold an overt response to a stimulus. (Smaller N2 amplitudes indicate that individuals have decreased neural inhibition to the food stimuli, whereas larger N2 amplitudes indicate increased neural resources are needed to accomplish the response inhibition.)
We recruited forty-four total participants (28 males and 26 females), and the average age was 25 (standard deviation=9.27). Each completed a rest and a moderate intensity walking session (3.8mph, 0% grade) of forty-five minutes before proceeding to the EEG task. Condition order was randomly assigned. Each participant completed High and Low calorie go/no-go tasks during both the exercise and rest sessions.
To elicit a N2 response, participants completed a go/no-go (GNG) task with food pictures as stimuli. Pictures were presented for 100 ms with 300 to 800 ms varied randomly between trials. In the GNG task, participants were told they must respond by pushing a button to a certain stimulus (go trial) but withhold that same response when a different stimulus is presented (no-go trial). For our study, participants completed the GNG tasks, immediately following sedentary tasks or moderate intensity exercise. For both conditions, there were five blocks of 50 trials, 40 trials in each block being go trials and 10 being no-go trials. Statistical analysis consisted of a 2-Response (go, no-go) x 2-Exercise (exercise, non-exercise) x 2-Food Type (high calorie, low calorie) repeated measures ANOVA.
We found that there was a main effect of the go vs no-go trials, (the N2 was more negative to no-go than go trials with a p<0.5), which verified that the N2 is bigger when a participant is inhibiting versus non-inhibiting. The main effect of calorie was not significant; however, there was a significant interaction of Go no-go stimulus by calorie of pictures (high versus low), we found that participants showed a disproportionately larger N2 to high calorie no-go trials than to the low calorie foods (p<0.5), but groups did not differ to the low calorie foods. Thus, it appears that greater inhibition occurred with high calorie versus low calorie foods regardless of exercise condition as there was no interaction with exercise (p>.05). Therefore the food inhibition was driven by the no-go to high calorie foods. None of the exercise variables were significant.
Our findings were similar to previous studies in our own lab using a separate sample (LeCheminant et al., in preparation). While Lecheminant et al. found a main effect in exercise vs non-exercise, we however, did not, thus our hypothesis was not confirmed. The larger difference in N2 amplitude to high calorie than low calorie foods implies that high calorie foods increased the inhibitory response during the go/no-go task as we hypothesized. While moderate intensity exercise did not play a role in the food inhibition responses of our subjects in this study, the exercise vs non-exercise effect found in the previous study may have disappeared with the greater sampling size, and higher intensities of exercise may be needed to produce measurable inhibition when presented with food stimuli.
It appears that greater inhibition occurs with high calorie versus low calorie foods regardless of exercise condition. Our hypothesis that exercise would increase inhibition to food cues was not confirmed as exercise did not seem to matter in our current study; however, we would like to conduct future studies to explore different levels of exercise intensity on food inhibition as a possible model of the Yerkes-Dodson theory and the possible role of higher-level of exercise intensity in food inhibition.
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