Children from rural communities are at greater risk for obesity than children from more urban communities. However, some children are resilient to obesity despite greater exposure to obesogenic influences in rural communities (e.g., fewer community-level physical activity or healthy eating resources). Identifying factors that promote this resiliency could inform obesity prevention. Eating habits are learned through reinforcement (e.g., hedonic, familial environment), the process through which environmental food cues become valued and influence behavior. Therefore, understanding individual differences in reinforcement learning is essential to uncovering the causes of obesity. Preclinical models have identified two reinforcement learning phenotypes that may have translational importance for understanding excess consumption in humans: 1) goal-tracking-environmental cues have predictive value; and 2) sign-tracking-environmental cues have predictive and hedonic value (i.e., incentive salience). Sign-tracking is associated with poorer attentional control, greater impulsivity, and lower prefrontal cortex (PFC) engagement in response to reward cues. This parallels neurocognitive deficits observed in pediatric obesity (i.e., worse impulsivity, lower PFC food cue reactivity). The proposed research aims to determine if reinforcement learning phenotype (i.e., sign- and goal-tracking) is 1) associated with adiposity due to its influence on neural food cue reactivity and 2) associated with reward-driven overconsumption and meal intake due to its influence on eating behaviors. The investigators hypothesize that goal-tracking will promote resiliency to obesity due to: 1) reduced attribution of incentive salience and greater PFC engagement to food cues; and 2) reduced reward-driven overconsumption. Finally, the investigators hypothesize reinforcement learning phenotype will be associated due to its influence on eating behaviors associated with overconsumption (e.g., larger bites, faster bite rat and eating sped). To test this hypothesis, the investigators will enroll 76, 8-9-year-old children, half with healthy weight and half with obesity based on Centers for Disease Control definitions. Methods will include computer tasks to assess reinforcement learning, dual x-ray absorptiometry to assess adiposity, and neural food cue reactivity from functional near-infrared spectroscopy (fNIRS).