Decision-Making Processes Related to Perseveration Are Indirectly Associated With Weight Status in Children Through Laboratory-Assessed Energy Intake

Bari A Fuchs, Nicole J Roberts, Shana Adise, Alaina L Pearce, Charles F Geier, Corey White, Zita Oravecz, Kathleen L Keller, Bari A Fuchs, Nicole J Roberts, Shana Adise, Alaina L Pearce, Charles F Geier, Corey White, Zita Oravecz, Kathleen L Keller

Abstract

Decision-making contributes to what and how much we consume, and deficits in decision-making have been associated with increased weight status in children. Nevertheless, the relationships between cognitive and affective processes underlying decision-making (i.e., decision-making processes) and laboratory food intake are unclear. We used data from a four-session, within-subjects laboratory study to investigate the relationships between decision-making processes, food intake, and weight status in 70 children 7-to-11-years-old. Decision-making was assessed with the Hungry Donkey Task (HDT), a child-friendly task where children make selections with unknown reward outcomes. Food intake was measured with three paradigms: (1) a standard ad libitum meal, (2) an eating in the absence of hunger (EAH) protocol, and (3) a palatable buffet meal. Individual differences related to decision-making processes during the HDT were quantified with a reinforcement learning model. Path analyses were used to test whether decision-making processes that contribute to children's (a) expected value of a choice and (b) tendency to perseverate (i.e., repeatedly make the same choice) were indirectly associated with weight status through their effects on intake (kcal). Results revealed that increases in the tendency to perseverate after a gain outcome were positively associated with intake at all three paradigms and indirectly associated with higher weight status through intake at both the standard and buffet meals. Increases in the tendency to perseverate after a loss outcome were positively associated with EAH, but only in children whose tendency to perseverate persistedacross trials. Results suggest that decision-making processes that shape children's tendencies to repeat a behavior (i.e., perseverate) are related to laboratory energy intake across multiple eating paradigms. Children who are more likely to repeat a choice after a positive outcome have a tendency to eat more at laboratory meals. If this generalizes to contexts outside the laboratory, these children may be susceptible to obesity. By using a reinforcement learning model not previously applied to the study of eating behaviors, this study elucidated potential determinants of excess energy intake in children, which may be useful for the development of childhood obesity interventions.

Keywords: Hungry Donkey Task; childhood obesity; children; decision-making; eating behavior.

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Fuchs, Roberts, Adise, Pearce, Geier, White, Oravecz and Keller.

Figures

FIGURE 1
FIGURE 1
Trays of food and drinks presented during the three eating paradigms. (A) Standard Meal: (left tray) water, angel food cake, grapes, (right tray) broccoli, garlic bread, cherry tomatoes, macaroni and cheese; (B) Eating in the Absence of Hunger protocol: (left tray) chocolate kisses, buttered popcorn, nacho-flavored tortilla chips, fruit-flavored candies, chocolate chip cookies, (right tray) pretzels, fudge brownies, potato chips, chocolate candies, cheese crackers; (C) Buffet meal: (left tray) fruit-flavored candies, potato chips, donut holes, chocolate chip cookies, cheese bagel bites, strawberry licorice twists, (middle tray) fruit punch, chocolate cupcake, chocolate milk, cheese pizza rolls, strawberry fruit leather, (right tray) mozzarella sticks, gummy candy, fudge brownies, chicken nuggets.
FIGURE 2
FIGURE 2
Hungry Donkey Task. During each trial of the task, children were presented with a selection screen (A). During the selection screen, children selected one door by using one of four keyboard keys (C, V, B, N) that corresponded to each door from left to right. Following a selection, children were presented with an outcome screen (B). The number of apples won and lost during that trial were displayed in the frame of the selected door as green and red apples, respectively, and numerically as “profit” and “loss” values under the vertical bar. The vertical bar provided global feedback about the ratio of apples won (green) and lost (red) in the game so far, and the net total amount of apples won in the game so far was indicated under the doors.
FIGURE 3
FIGURE 3
Final expected value model for the standard meal with pre-standard meal fullness covariate. Expected value models include VPP model parameters involved in computing expected value. For path analyses, VPP model parameters were normalized and intake (kcal) was scaled by a factor of 100. Pre-standard meal fullness was rated on a 150 mm visual analog scale prior to the eating paradigm. Arrows indicate paths tested in the final model and are labeled with the unstandardized coefficient (B) and standard error for that path. Dotted lines indicate paths did not reach statistical significance (p > 0.05). Solid lines indicate statistically significant paths (*p < 0.05; **p < 0.01; ***p < 0.001). Explained variance (R2) is reported for endogenous variables.
FIGURE 4
FIGURE 4
Final perseveration model for the (A) Standard Meal, (B) Eating in the Absence of Hunger (EAH) protocol, and (C) Buffet meal. Perseveration models contain VPP model parameters involved in computing perseveration strength. For path analyses, VPP model parameters were normalized and intake (kcal) was scaled by a factor of 100. Arrows indicate paths tested in the final model and are labeled with the unstandardized parameter estimate (B) and standard error for that path. Dotted lines indicate paths did not reach statistical significance (p > 0.05). Solid lines indicate statistically significant paths (*p < 0.05; **p < 0.01; ***p < 0.001). Explained variance (R2) is reported for endogenous variables.
FIGURE 5
FIGURE 5
Relationship between the impact of gain on perseveration strength (i.e., εpos; x-axis) and intake (kcal; y-axis) during the (A) Standard meal, (B) Eating in the Absence of Hunger (EAH) protocol, and (C) Buffet meal. Blue lines reflect the best fit for the linear model between εpos and intake. Shaded gray regions reflects 95% confidence interval for the line of best fit.
FIGURE 6
FIGURE 6
Relationship between the impact of loss on perseveration strength (i.e., εneg) and intake (kcal) during the Eating in the Absence of Hunger (EAH) protocol at three levels of perseveration decay (i.e., k). (A) Three overlapping intervals of k that correspond to the three scatterplots in panel (B). (B) Scatterplots between εneg (x-axis) and EAH (y-axis). Normalized and raw values of εneg and k are presented. Left scatter plot: at the lower interval of k (normalized values: −2.15 to 0.03), the association between εneg and intake is negative. Middle scatter plot: at the middle interval of k (normalized values: −0.72 to 0.74), the association between εneg and intake is negative, although less negative than the lower interval. Right scatter plot: at the higher interval of k (normalized values: 0.08 to 2.39), the association between εneg and intake is positive.

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