Food Decision-Making: Effects of Weight Status and Age

Floor van Meer, Lisette Charbonnier, Paul A M Smeets, Floor van Meer, Lisette Charbonnier, Paul A M Smeets

Abstract

Food decisions determine energy intake. Since overconsumption is the main driver of obesity, the effects of weight status on food decision-making are of increasing interest. An additional factor of interest is age, given the rise in childhood obesity, weight gain with aging, and the increased chance of type 2 diabetes in the elderly. The effects of weight status and age on food preference, food cue sensitivity, and self-control are discussed, as these are important components of food decision-making. Furthermore, the neural correlates of food anticipation and choice and how these are affected by weight status and age are discussed. Behavioral studies show that in particular, poor self-control may have an adverse effect on food choice in children and adults with overweight and obesity. Neuroimaging studies show that overweight and obese individuals have altered neural responses to food in brain areas related to reward, self-control, and interoception. Longitudinal studies across the lifespan will be invaluable to unravel the causal factors driving (changes in) food choice, overconsumption, and weight gain.

Keywords: Decision-making; Development; Food choice; Neural correlates; Obesity.

Figures

Fig. 1
Fig. 1
Schematic overview of the factors affecting food decision-making which are discussed in this review. Note that external factors are outside the scope. The effects of weight status and age on factors examined in behavioral studies (food preference, food cue sensitivity, and self-control capacity) and factors examined in neuroimaging studies (food anticipation and food choice) are shown in the order in which they are discussed in the text

References

    1. Wansink B, Sobal J. Mindless eating: the 200 daily food decisions we overlook. Environ Behav. 2007;39:106–23. doi: 10.1177/0013916506295573.
    1. Blundell JE, Cooling J. Routes to obesity: phenotypes, food choices and activity. Brit J Nutr. 2000;83:S33–S8. doi: 10.1017/S0007114500000933.
    1. Wang Y, Lobstein TIM. Worldwide trends in childhood overweight and obesity. Int J Pediatr Obes. 2006;1:11–25. doi: 10.1080/17477160600586747.
    1. Styne DM. Childhood and adolescent obesity: prevalence and significance. Pediatr Clin N Am. 2001;48:823–54. doi: 10.1016/S0031-3955(05)70344-8.
    1. Nijs IM, Muris P, Euser AS, et al. Differences in attention to food and food intake between overweight/obese and normal-weight females under conditions of hunger and satiety. Appetite. 2010;54:243–54. doi: 10.1016/j.appet.2009.11.004.
    1. Berridge KC. Food reward: brain substrates of wanting and liking. Neurosci Biobehav R. 1996;20:1–25. doi: 10.1016/0149-7634(95)00033-B.
    1. Mela DJ. Determinants of food choice: relationships with obesity and weight control. Obes Res. 2001;9:249S–55S. doi: 10.1038/oby.2001.127.
    1. Dressler H, Smith C. Food choice, eating behavior, and food liking differs between lean/normal and overweight/obese, low-income women. Appetite. 2013;65:145–52. doi: 10.1016/j.appet.2013.01.013.
    1. Cox DN, Hendrie GA, Carty D. Sensitivity, hedonics and preferences for basic tastes and fat amongst adults and children of differing weight status: a comprehensive review. Food Qual Prefer. 2016;48:359–67. doi: 10.1016/j.foodqual.2015.01.006.
    1. Skinner JD, Carruth BR, Bounds W, et al. Children’s food preferences: a longitudinal analysis. J Am Diet Assoc. 2002;102:1638–47. doi: 10.1016/S0002-8223(02)90349-4.
    1. de Wild VW, de Graaf C, Jager G. Effectiveness of flavour nutrient learning and mere exposure as mechanisms to increase toddler’s intake and preference for green vegetables. Appetite. 2013;64:89–96. doi: 10.1016/j.appet.2013.01.006.
    1. Boyland EJ, Halford JC. Television advertising and branding. Effects on eating behaviour and food preferences in children. Appetite. 2013;62:236–41. doi: 10.1016/j.appet.2012.01.032.
    1. Brown K, McIlveen H, Strugnell C. Nutritional awareness and food preferences of young consumers. Nutr Food Sci. 2000;30:230–5. doi: 10.1108/00346650010340963.
    1. Lumbers M, Raats M. Food choices in later life. In: Shepherd R, Raats M, editors. The psychology of food choice. Frontiers in nutritional science 3. Wallingford: CABI; 2006. p. 289.
    1. Schiffman S, Graham B. Taste and smell perception affect appetite and immunity in the elderly. Eur J Clin Nutr. 2000;54:S54. doi: 10.1038/sj.ejcn.1601026.
    1. MacLeod C, Mathews A, Tata P. Attentional bias in emotional disorders. J Abnorm Psychol. 1986;95:15. doi: 10.1037/0021-843X.95.1.15.
    1. Mogg K, Bradley BP, Hyare H, et al. Selective attention to food-related stimuli in hunger: are attentional biases specific to emotional and psychopathological states, or are they also found in normal drive states? Behav Res Ther. 1998;36:227–37. doi: 10.1016/S0005-7967(97)00062-4.
    1. Castellanos EH, Charboneau E, Dietrich MS, et al. Obese adults have visual attention bias for food cue images: evidence for altered reward system function. Int J Obes. 2009;33:1063–73. doi: 10.1038/ijo.2009.138.
    1. Werthmann J, Roefs A, Nederkoorn C, et al. Can(not) take my eyes off it: attention bias for food in overweight participants. Health Psychol. 2011;30:561–9. doi: 10.1037/a0024291.
    1. Stroop JR. Studies of interference in serial verbal reactions. J Exp Psychol. 1935;18:643. doi: 10.1037/h0054651.
    1. Braet C, Crombez G. Cognitive interference due to food cues in childhood obesity. J Clin Child Adolesc. 2003;32:32–9. doi: 10.1207/S15374424JCCP3201_04.
    1. Wenzlaff RM, Stultz CH. A new method of assessing attentional bias in depression. Washington, DC: In Meeting of the American Psychological Society; 1998.
    1. Soetens B, Braet C. Information processing of food cues in overweight and normal weight adolescents. Brit J Health Psych. 2007;12:285–304. doi: 10.1348/135910706X107604.
    1. Werthmann J, Jansen A, Vreugdenhil ACE, et al. Food through the child’s eye: an eye-tracking study on attentional bias for food in healthy-weight children and children with obesity. Health Psychol. 2015;34:1123–32. doi: 10.1037/hea0000225.
    1. Theeuwes J, Atchley P, Kramer AF. Attentional control within 3-D space. J Exp Psychol-Hum Percept Perform. 1998;24:1476. doi: 10.1037/0096-1523.24.5.1476.
    1. Junghans AF, Hooge IT, Maas J, et al. Unadulterated—children and adults’ visual attention to healthy and unhealthy food. Eat Behav. 2015;17:90–3. doi: 10.1016/j.eatbeh.2015.01.009.
    1. Weller RE, Cook EW, Avsar KB, et al. Obese women show greater delay discounting than healthy-weight women. Appetite. 2008;51:563–9. doi: 10.1016/j.appet.2008.04.010.
    1. Gunstad J, Paul RH, Cohen RA, et al. Elevated body mass index is associated with executive dysfunction in otherwise healthy adults. Compr Psychiat. 2007;48:57–61. doi: 10.1016/j.comppsych.2006.05.001.
    1. Cserjési R, Luminet O, Poncelet A-S, et al. Altered executive function in obesity. Exploration of the role of affective states on cognitive abilities. Appetite. 2009;52:535–9. doi: 10.1016/j.appet.2009.01.003.
    1. Nederkoorn C, Smulders FTY, Havermans RC, et al. Impulsivity in obese women. Appetite. 2006;47:253–6. doi: 10.1016/j.appet.2006.05.008.
    1. Mostofsky SH, Simmonds DJ. Response inhibition and response selection: two sides of the same coin. J Cognitive Neurosci. 2008;20:751–61. doi: 10.1162/jocn.2008.20500.
    1. Fillmore MT, Rush CR. Polydrug abusers display impaired discrimination-reversal learning in a model of behavioural control. J Psychopharmacol. 2006;20:24–32. doi: 10.1177/0269881105057000.
    1. Logan GD. On the ability to inhibit thought and action: a users’ guide to the stop signal paradigm. 1994.
    1. Mazur JE. An adjusting procedure for studying delayed reinforcement. Commons ML, Mazur JE, Nevin JA; 1987:55–73.
    1. Mischel W, Ebbesen EB, Raskoff ZA. Cognitive and attentional mechanisms in delay of gratification. J Pers Soc Psychol. 1972;21:204. doi: 10.1037/h0032198.
    1. Robbins TW, Weinberger D, Taylor JG, Morris RG. Dissociating executive functions of the prefrontal cortex [and discussion] Phil Trans R Soc B. 1996;351:1463–71. doi: 10.1098/rstb.1996.0131.
    1. Casey B. Beyond simple models of self-control to circuit-based accounts of adolescent behavior. Annu Rev Psychol. 2015;66:295–319. doi: 10.1146/annurev-psych-010814-015156.
    1. Barkin SL. The relationship between executive function and obesity in children and adolescents: a systematic literature review. J Obes. 2013;2013.
    1. Braet C, Claus L, Verbeken S, et al. Impulsivity in overweight children. Eur Chil Adoles Psy. 2007;16:473–83. doi: 10.1007/s00787-007-0623-2.
    1. Cserjési R, Molnár D, Luminet O, et al. Is there any relationship between obesity and mental flexibility in children? Appetite. 2007;49:675–8. doi: 10.1016/j.appet.2007.04.001.
    1. Schlam TR, Wilson NL, Shoda Y, et al. Preschoolers’ delay of gratification predicts their body mass 30 years later. J Pediatr. 2012
    1. Green L, Myerson J, Ostaszewski P. Discounting of delayed rewards across the life span: age differences in individual discounting functions. Behav Process. 1999;46:89–96. doi: 10.1016/S0376-6357(99)00021-2.
    1. Smeets PA, Charbonnier L, van Meer F, et al. Food-induced brain responses and eating behaviour. P Nutr Soc. 2012;71:511–20. doi: 10.1017/S0029665112000808.
    1. Huttenlocher PR, Dabholkar AS. Regional differences in synaptogenesis in human cerebral cortex. J Comp Neurol. 1997;387:167–78. doi: 10.1002/(SICI)1096-9861(19971020)387:2<167::AID-CNE1>;2-Z.
    1. Sowell ER, Thompson PM, Holmes CJ, et al. In vivo evidence for post-adolescent brain maturation in frontal and striatal regions. Nat Neurosci. 1999;2:859–61. doi: 10.1038/13154.
    1. Giedd JN, Blumenthal J, Jeffries NO, et al. Brain development during childhood and adolescence: a longitudinal MRI study. Nat Neurosci. 1999;2:861–3. doi: 10.1038/13158.
    1. Booth JR, Burman DD, Meyer JR, et al. Neural development of selective attention and response inhibition. Neuroimage. 2003;20:737–51. doi: 10.1016/S1053-8119(03)00404-X.
    1. Watanabe J, Sugiura M, Sato K, et al. The human prefrontal and parietal association cortices are involved in NO-GO performances: an event-related fMRI study. Neuroimage. 2002;17:1207–16. doi: 10.1006/nimg.2002.1198.
    1. Sowell ER, Peterson BS, Thompson PM, et al. Mapping cortical change across the human life span. Nat Neurosci. 2003;6:309–15. doi: 10.1038/nn1008.
    1. Van der Laan LN, De Ridder DTD, Viergever MA, et al. The first taste is always with the eyes: a meta-analysis on the neural correlates of processing visual food cues. Neuroimage. 2011;55:296–303. doi: 10.1016/j.neuroimage.2010.11.055.
    1. Yokum S, Ng J, Stice E. Attentional bias to food images associated with elevated weight and future weight gain: an fMRI study. Obesity. 2011;19:1775–83. doi: 10.1038/oby.2011.168.
    1. Demos KE, Heatherton TF, Kelley WM. Individual differences in nucleus accumbens activity to food and sexual images predict weight gain and sexual behavior. J Neurosci. 2012;32:5549–52. doi: 10.1523/JNEUROSCI.5958-11.2012.
    1. Van der Laan LN, De Ridder DTD, Viergever MA, et al. Appearance matters: neural correlates of food choice and packaging aesthetics. PLoS One. 2012;7 doi: 10.1371/journal.pone.0041738.
    1. Mehta S, Melhorn SJ, Smeraglio A, et al. Regional brain response to visual food cues is a marker of satiety that predicts food choice. Am J Clin Nutr. 2012
    1. Lawrence NS, Hinton EC, Parkinson JA, et al. Nucleus accumbens response to food cues predicts subsequent snack consumption in women and increased body mass index in those with reduced self-control. Neuroimage. 2012;63:415–22. doi: 10.1016/j.neuroimage.2012.06.070.
    1. Killgore W, Weber M, Schwab Z, et al. Cortico-limbic responsiveness to high-calorie food images predicts weight status among women. Int J Obes. 2013;37:1435–42. doi: 10.1038/ijo.2013.26.
    1. Murdaugh DL, Cox JE, Cook EW, III, et al. fMRI reactivity to high-calorie food pictures predicts short-and long-term outcome in a weight-loss program. Neuroimage. 2012;59:2709–21. doi: 10.1016/j.neuroimage.2011.10.071.
    1. Dagher A. Functional brain imaging of appetite. Trends Endocrin Met. 2012;23:250–60. doi: 10.1016/j.tem.2012.02.009.
    1. Schachter S. Obesity and Eating Science. 1968;161:751–6.
    1. Brooks SJ, Cedernaes J, Schiöth HB. Increased prefrontal and parahippocampal activation with reduced dorsolateral prefrontal and insular cortex activation to food images in obesity: a meta-analysis of fMRI studies. PLoS One. 2013;8 doi: 10.1371/journal.pone.0060393.
    1. Kennedy J, Dimitropoulos A. Influence of feeding state on neurofunctional differences between individuals who are obese and normal weight: a meta-analysis of neuroimaging studies. Appetite. 2014;75:103–9. doi: 10.1016/j.appet.2013.12.017.
    1. van Meer F, van der Laan LN, Adan RAH, et al. What you see is what you eat: an ALE meta-analysis of the neural correlates of food viewing in children and adolescents. Neuroimage. 2015;104:35–43. doi: 10.1016/j.neuroimage.2014.09.069.
    1. Davids S, Lauffer H, Thoms K, et al. Increased dorsolateral prefrontal cortex activation in obese children during observation of food stimuli. Int J Obes. 2010;34:94–104. doi: 10.1038/ijo.2009.193.
    1. Bruce AS, Holsen LM, Chambers RJ, et al. Obese children show hyperactivation to food pictures in brain networks linked to motivation, reward and cognitive control. Int J Obes. 2010;34:1494–500. doi: 10.1038/ijo.2010.84.
    1. Stice E, Spoor S, Bohon C, et al. Relation of reward from food intake and anticipated food intake to obesity: a functional magnetic resonance imaging study. J Abnorm Psychol. 2008;117:924–35. doi: 10.1037/a0013600.
    1. Grabenhorst F, Schulte FP, Maderwald S, et al. Food labels promote healthy choices by a decision bias in the amygdala. Neuroimage. 2013;74:152–63. doi: 10.1016/j.neuroimage.2013.02.012.
    1. van der Laan LN, De Ridder DT, Charbonnier L, et al. Sweet lies: neural, visual, and behavioral measures reveal a lack of self-control conflict during food choice in weight-concerned women. Front Behav Neurosci. 2014;8.
    1. van der Laan LN, de Ridder DT, Viergever MA, et al. Activation in inhibitory brain regions during food choice correlates with temptation strength and self-regulatory success in weight-concerned women. Front Neurosci. 2014;8.
    1. Charbonnier L, van der Laan LN, Viergever MA, et al. Functional MRI of challenging food choices: forced choice between equally liked high- and low-calorie foods in the absence of hunger. PLoS One. 2015;10 doi: 10.1371/journal.pone.0131727.
    1. Litt A, Plassmann H, Shiv B, et al. Dissociating valuation and saliency signals during decision-making. Cereb Cortex. 2011;21:95–102. doi: 10.1093/cercor/bhq065.
    1. Lim S-L, O’Doherty JP, Rangel A. The decision value computations in the vmPFC and striatum use a relative value code that is guided by visual attention. J Neurosci. 2011;31:13214–23. doi: 10.1523/JNEUROSCI.1246-11.2011.
    1. Hare TA, Camerer CF, Rangel A. Self-control in decision-making involves modulation of the vmPFC valuation system. Science. 2009;324:646–8. doi: 10.1126/science.1168450.
    1. Hare TA, Malmaud J, Rangel A. Focusing attention on the health aspects of foods changes value signals in vmPFC and improves dietary choice. J Neurosci. 2011;31:11077–87. doi: 10.1523/JNEUROSCI.6383-10.2011.
    1. Chib VS, Rangel A, Shimojo S, et al. Evidence for a common representation of decision values for dissimilar goods in human ventromedial prefrontal cortex. J Neurosci. 2009;29:12315–20. doi: 10.1523/JNEUROSCI.2575-09.2009.
    1. Plassmann H, O’Doherty JP, Rangel A. Appetitive and aversive goal values are encoded in the medial orbitofrontal cortex at the time of decision making. J Neurosci. 2010;30:10799–808. doi: 10.1523/JNEUROSCI.0788-10.2010.
    1. Plassmann H, O’Doherty J, Rangel A. Orbitofrontal cortex encodes willingness to pay in everyday economic transactions. J Neurosci. 2007;27:9984–8. doi: 10.1523/JNEUROSCI.2131-07.2007.
    1. Hare TA, O’Doherty J, Camerer CF, et al. Dissociating the role of the orbitofrontal cortex and the striatum in the computation of goal values and prediction errors. J Neurosci. 2008;28:5623–30. doi: 10.1523/JNEUROSCI.1309-08.2008.
    1. Linder NS, Uhl G, Fliessbach K, et al. Organic labeling influences food valuation and choice. Neuroimage. 2010;53:215–20. doi: 10.1016/j.neuroimage.2010.05.077.
    1. Bettman JR, Luce MF, Payne JW. Constructive consumer choice processes. J Consum Res. 1998;25:187–217. doi: 10.1086/209535.
    1. Rangel A. Regulation of dietary choice by the decision-making circuitry. Nat Neurosci. 2013;16:1717–24. doi: 10.1038/nn.3561.
    1. Kang MJ, Rangel A, Camus M, et al. Hypothetical and real choice differentially activate common valuation areas. J Neurosci. 2011;31:461–8. doi: 10.1523/JNEUROSCI.1583-10.2011.
    1. Thorndike AN, Sonnenberg L, Riis J, et al. A 2-phase labeling and choice architecture intervention to improve healthy food and beverage choices. Am J Public Health. 2012;102:527–33. doi: 10.2105/AJPH.2011.300391.
    1. Enax L, Hu Y, Trautner P, Weber B. Nutrition labels influence value computation of food products in the ventromedial prefrontal cortex. Obesity. 2015;23:786–92. doi: 10.1002/oby.21027.
    1. Barnard ND, Bush AI, Ceccarelli A, et al. Dietary and lifestyle guidelines for the prevention of Alzheimer’s disease. Neurobiol Aging. 2014;35(2):S74–S8. doi: 10.1016/j.neurobiolaging.2014.03.033.

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