Abstract
Food preference and reward processing are crucial determinants of dietary behavior and energy balance, and their dysregulation is closely linked to major public health problems such as obesity and eating disorders. This narrative review synthesizes current evidence on the cognitive and neural foundations of food preference and reward processing, examines multi-level determinants ranging from individual biology to broader social context, and discusses major intervention strategies. Research in cognitive neuroscience points to a distributed network involving the midbrain dopamine system, prefrontal cortex, amygdala, insula, and hypothalamus, which together regulate food “wanting” and “liking” as well as related processes of valuation, decision-making, and inhibitory control. These mechanisms are further shaped by genetic susceptibility, physiological state, developmental stage, stress, socioeconomic conditions, social learning, and cultural context. Building on this framework, current interventions target different components of the reward-control system, including appetite and gut-brain signaling (e.g., GLP-1-based approaches), cognitive control and behavioral regulation, and post-surgical changes in hormonal and neural responses. We also highlight emerging digital and real-world intervention models, including personalized and just-in-time approaches, while noting that their evidence base remains developing. Overall, this review emphasizes the need for integrative, mechanistically informed, and personalized strategies to improve dietary health.
1 Introduction
In recent decades, there has been a sharp increase in the global prevalence of obesity and related metabolic disorders, posing a significant public health challenge (1–5). The modern obesogenic environment is characterized by the widespread availability of high-energy, palatable foods, which greatly challenges the physiological systems evolved by humans to efficiently store energy (6–9). Individual dietary behaviors, particularly food preferences and responses to food reward, are central drivers of energy intake (10–13). Understanding the cognitive and neural mechanisms underlying these behaviors is crucial for explaining why many individuals struggle to maintain energy balance when faced with abundant food choices.
Food preference is not simply a matter of taste experience but a complex cognitive process involving learning, memory, emotion, and decision-making (14). It determines which food individuals choose among various options. Closely related to this is reward processing, a fundamental neurobiological process often divided into two core components: “liking” and “wanting” (15, 16). “Liking” refers to the hedonic pleasure experienced when consuming food (17–20), while “wanting” is the motivational drive generated upon encountering food or related cues (21, 22). Although these processes are interrelated, they are mediated by different neural circuits, with their imbalance considered a key factor leading to overeating and addictive behaviors (23–26).
The brain processes food rewards and guides food choices through a complex network that integrates internal physiological signals (such as hunger, satiety) and external environmental cues (27). This network is centered around the reward system but is also modulated by higher cognitive functions (such as executive function and inhibitory control) (28–31). When these cognitive control functions weaken or when the drive of the reward system is too strong, individuals may be more inclined to choose high-calorie foods for immediate gratification, disregarding their long-term health consequences (10).
However, food preferences and reward processing are not solely determined by intrinsic neurobiology. They are dynamically shaped by multiple levels of factors from individual to societal (32, 33). At the individual level, genetic background (34), age (35), stress levels (36), and health status (37) all influence the brain’s response to food. At the societal level, socioeconomic status (38), cultural norms (39), gender (40, 41), social interactions (42), and learning processes (43) play crucial roles. For instance, positive emotions can be transmitted between individuals through social interactions, even affecting food preferences (42).
Given this complexity, the present review has three aims. First, we synthesize current evidence on the cognitive and neural foundations of food preference and reward processing, with a focus on key brain regions, neurotransmitters, hormones, and cognitive processes. Second, we examine the multi-level factors that shape these mechanisms, spanning individual biology, developmental and psychological influences, and broader social and environmental contexts. Third, we discuss existing intervention strategies—including pharmacological, behavioral, surgical, and emerging digital approaches—in relation to the underlying reward-control framework. By integrating these perspectives, we aim to clarify the current state of knowledge, identify major unresolved questions, and outline priorities for future research and translational application.
This article is intended as a narrative review that integrates evidence across neurobiological, cognitive, behavioral, and socio-environmental domains. Our goal is conceptual synthesis rather than exhaustive study capture or quantitative aggregation. Accordingly, we focus on influential theoretical models, representative empirical findings, and translational implications that help explain how food preference and reward processing emerge from interactions between reward, homeostatic, and control systems across different levels of analysis.
2 Cognitive and neural foundations of food preferences and reward processing
The neural basis of food preference and reward processing is a complex network involving multiple brain regions and neurotransmitter systems. It integrates sensory, emotional, motivational, and cognitive information to ultimately guide behavioral decisions (Figure 1).
Figure 1
2.1 Core brain regions and neural circuits
The mesolimbic dopamine system, particularly the projection from the ventral tegmental area (VTA) to the nucleus accumbens (NAc), is considered central to reward processing and motivated behavior (44–47). Dopamine release primarily encodes reward prediction errors and the salience of motivation, driving individuals to seek and obtain rewards, closely linked to the concept of “wanting” (48–50). Dysfunction in the dopamine pathways in obese individuals may lead to reduced sensitivity to food rewards and compensatory overeating (51–53). The escalating availability of ultra-processed foods (UPFs) presents a significant challenge to the mesolimbic dopamine system. Chronic exposure to the synergistic combination of refined carbohydrates and fats in UPFs can trigger neuroplastic adaptations akin to substance use disorders, characterized by a progressive downregulation of striatal dopamine D2 receptors. This induction of a hypo-dopaminergic state leads to an upward shift in reward thresholds (54), driving compensatory overeating as individuals seek to overcome a diminished hedonic response to standard nutritional stimuli. The NAc, serving as an interface between cognition, emotion, and action, plays a crucial role in integrating information from different brain regions to optimize behavioral choices (55).
The prefrontal cortex (PFC) is central to reward regulation because it supports value computation, goal-directed choice, and inhibitory control (56–59). Within this system, the orbitofrontal cortex (OFC) and ventromedial prefrontal cortex (vmPFC) primarily encode the subjective value of food options by integrating sensory features, prior experience, and current physiological needs (60–63), whereas the dorsolateral prefrontal cortex (dlPFC) and ACC contribute more strongly to self-control, conflict monitoring, and the implementation of long-term goals (64–72). Thus, prefrontal regions do not merely evaluate food rewards; they also influence whether immediate reward signals are translated into action or restrained in favor of broader health goals.
The amygdala, traditionally associated with fear and negative emotions (73–77), is increasingly recognized for its crucial role in processing reward-related information (78–80), particularly in learning and memory of reward-associated cues (78, 79). It assigns emotional and motivational significance to stimuli (81–83), influencing attention (84, 85) and food choices (86, 87).
The hypothalamus, a traditional center for energy homeostasis, regulates hunger and satiety by integrating peripheral hormone signals such as leptin and insulin (88–91). However, hypothalamic neurons, especially orexin/hypocretin neurons in the lateral hypothalamus, project directly to reward centers like the VTA, linking homeostatic needs with hedonic feeding drive (92, 93). Recent research indicates that orexin neuron activity mediates mice’s choice between palatable food and voluntary exercise, prioritizing exercise, highlighting the critical role of this system in balancing different reward options (94).
Although traditionally viewed as a motor control center, the cerebellum is now implicated in reward processing (95–97), emotions (98, 99), and cognitive functions (100, 101). Through extensive connections with the cortex and basal ganglia, the cerebellum may modulate reward prediction and learning (102, 103) (Table 1).
Table 1
| Brain region | Primary neurobiological function | Specific role in dietary behavior |
|---|---|---|
| Nucleus Accumbens (NAc) | Interface between cognition, emotion, and action. | Integrates multi-regional information to optimize behavioral choices and drive “wanting.” |
| Ventral Tegmental Area (VTA) | Hub for the mesolimbic dopamine system. | Encodes reward prediction errors and motivational salience to drive food-seeking behavior. |
| Orbitofrontal Cortex (OFC)/vmPFC | Evaluation and representation of reward value. | Encodes relative preferences and dynamically updates food value based on internal states like satiety. |
| Dorsolateral Prefrontal Cortex (dlPFC) | Executive function and top-down inhibitory control. | Suppresses impulsive responses to tempting, unhealthy foods to facilitate long-term health goals. |
| Anterior Cingulate Cortex (ACC) | Performance monitoring and cost–benefit analysis. | Allocates cognitive resources and regulates the effort required to obtain specific food rewards. |
| Amygdala | Processing of emotional and motivational significance. | Facilitates the learning and memory of reward-associated cues and influences attentional bias. |
| Hypothalamus | Central regulator of energy homeostasis. | Integrates peripheral signals (leptin/insulin) and links homeostatic needs with hedonic drives via orexin neurons. |
| Cerebellum | Modulation of reward prediction and learning. | Contributes to cognitive and affective processing related to food rewards through extensive cortical connections. |
Core brain regions and their functional roles in food decision-making.
2.2 Key neurotransmitters and hormones
As noted above, dopamine is more strongly implicated in the motivational component of reward, or “wanting,” than in hedonic pleasure itself (104–106). Its major function is to encode incentive salience and reward prediction, thereby energizing food seeking and cue responsiveness. Experimental findings further suggest that even when dopamine synthesis is impaired, basic hedonic preference for sweet taste may remain relatively preserved (107), indicating that dopaminergic dysfunction is more likely to reduce motivation to obtain food than the capacity to experience pleasure from it (105, 106).
Ghrelin, a primary “hunger hormone” secreted by the stomach, acts on the hypothalamus to stimulate appetite and directly modulates dopamine neurons in the VTA, thereby enhancing the rewarding value of food (108–110). Recent research has unveiled the complexity of this system. For instance, the endogenous ghrelin receptor antagonist LEAP2 has been found to reduce the intake of highly palatable foods and attenuate food-related dopamine release in the nucleus accumbens (111). Moreover, des-acyl ghrelin (DAG) has been shown to diminish alcohol reward, suggesting that different members of the ghrelin family may play distinct regulatory roles in the reward system (112).
The endocannabinoid system, crucial in regulating hedonic eating, is influenced by the consumption of high-fat, high-sugar foods (113–115), which stimulate the synthesis of endogenous cannabinoids like anandamide and 2-AG in the brain. These substances, acting on CB1 receptors, enhance food palatability and dopamine release, establishing a “tasty loop” that may lead to overeating (116–120) (Figure 2).
Figure 2
2.3 Core cognitive processes
Valuation and decision-making: When faced with multiple food choices, the brain must assign a subjective value to each option and make decisions based on this assessment (121–123). This process is primarily governed by the OFC and vmPFC, which integrate the sensory characteristics of food (taste, texture), past experiences, current physiological state, and potential outcomes (124–126).
Inhibitory control: In the context of executive function, inhibitory control refers to the ability to suppress inappropriate or impulsive behaviors (127). Within the realm of dietary behavior, inhibitory control is crucial for resisting the temptation of high-calorie foods and adhering to long-term dietary goals. The PFC, particularly the dlPFC and ACC, serves as the neural basis for inhibitory control (72, 128). Variability in inhibitory control capacity among individuals may account for why some people are more susceptible to food cues.
Learning and memory: Food preferences are largely formed through associative learning (129). Pavlovian conditioning associates neutral environmental cues (such as restaurant signs, food packaging) with the rewarding value of food, independently triggering “cravings” and eating behavior (130–132). The hippocampus and amygdala play central roles in this learning and memory consolidation process (133, 134).
Attention: The attentional bias of the brain towards food-related cues is another important cognitive component of reward processing. Cues of highly palatable foods can automatically capture attention, making it harder for individuals to disengage attention from these temptations (135). This attentional bias is considered a key factor in driving food cravings and impulsive eating behaviors (136, 137). Eye-tracking studies have also revealed how cognitive processes guide the selection of visual information, which is particularly crucial in natural behavior (138).
3 Multifaceted influencing factors of food preferences and reward processing
Individual food choices are made within a complex biological, psychological, and social context. Various factors interact to shape the brain’s response to food rewards (Figure 3).
Figure 3
3.1 Individual factors
Genetic Factors: Genetics play a crucial role in determining food preferences and susceptibility to obesity (139–142). For instance, variations in the FTO gene linked to obesity risk (143, 144) have been associated with dysregulation of ghrelin levels and reduced responses in the brain’s reward regions to food cues, potentially leading individuals to require greater food intake to achieve the same level of satisfaction (145, 146).
Physiological States and Circadian Rhythms: Hunger, satiety, and related physiological states directly alter the reward value of food (147, 148). The circadian system also regulates metabolic and appetite-related hormones (149, 150), thereby shaping food preference (151) and food intake at different times of day (152).
Age and Development: During the lifespan, food preferences and reward processing undergo continuous changes (153, 154). Adolescence represents a period of significant neural remodeling, particularly in the PFC, leading to alterations in emotional regulation and cognitive control, potentially increasing impulsive choices for highly rewarding foods (155, 156). Conversely, in older adults, factors such as taste perception (157) and chewing functionality (158) may become more critical determinants of food preferences, although their direct association with cognitive function remains unclear (159).
Stress: Acute and chronic stress significantly impact dietary behavior by altering the sensitivity of the reward system and weakening the cognitive control function of the PFC, leading individuals to prefer high-fat, high-sugar “comfort foods” (10, 160, 161). Interestingly, the consumption of these foods can, in turn, buffer the stress response of the hypothalamic–pituitary–adrenal (HPA) axis, creating a potential vicious cycle (36). Social stress experienced during adolescence may even have lasting negative effects on cognitive functions in adulthood, possibly linked to impaired development of the mesocortical dopamine system (162–164).
Health Status (Obesity and Psychiatric Disorders): Obesity induces alterations in both the structure and function of the brain (165). Individuals with obesity typically exhibit heightened responses in reward-related brain regions to food cues, alongside decreased activity in cognitive control regions (166). Moreover, obesity-related systemic low-grade inflammation may lead to neuroinflammation, particularly in critical regions like the hypothalamus, further impairing cognitive function and energy homeostasis regulation (167). Beyond endocrine signaling, the gut microbiota serves as a critical transducer of dietary signals into neurobiological responses (168). Specifically, microbial metabolites such as short-chain fatty acids (SCFAs) exert anorexigenic effects by modulating hypothalamic pro-opiomelanocortin (POMC) neurons via vagal afferent signaling or direct systemic circulation. Furthermore, dysbiosis-induced intestinal permeability may exacerbate neuroinflammation in reward-related hubs, thereby altering the sensitivity of the homeostatic-hedonic crosstalk that governs long-term energy balance. Psychiatric disorders such as schizophrenia are often accompanied by abnormalities in reward processing and motivational deficits, potentially influencing patients’ eating behaviors and weight (169).
Importantly, these individual-level determinants do not operate in isolation. Their effects are continuously amplified, buffered, or reshaped by social and environmental context. In other words, food-related reward processing emerges from a dynamic interaction between internal susceptibility and external exposure: biological predispositions influence how individuals respond to food cues, while socioeconomic conditions, social learning, and cultural norms affect which cues are encountered, how often they are reinforced, and whether cognitive control can be effectively deployed. This interactional perspective helps link individual neurobiology with broader real-world eating environments.
3.2 Social and environmental factors
Social and environmental influences do not act merely as background conditions for eating behavior; they shape food-related decisions through identifiable neurocognitive pathways. By altering chronic stress exposure, learned cue associations, attentional salience, and the demands placed on self-regulation, contextual environments can modify how reward-related regions, valuation systems, and executive control networks respond to food. The following factors should therefore be understood as interacting with, rather than operating separately from, the neurobiological mechanisms outlined above.
Socioeconomic status (SES): Lower SES is often associated with poorer diet quality and with developmental conditions that can shape long-term health and cognitive outcomes (170–172). Research indicates that the SES gradient can forecast individual differences in children’s neurocognitive abilities, such as language and executive functions (173), which may impact their understanding of nutritional information, planning of healthy diets, and ability to resist unhealthy food temptations.
Social Learning and Emotional Contagion: Humans, as social animals, acquire their food preferences largely through observation and imitation of others, especially family members and peers (174). Studies on animals indicate that rodents can learn preferences for new foods from the odors of their peers through the “social transmission of food preferences” paradigm (42). This social learning mechanism may be more complex in humans, involving cultural and emotional factors.
Culture and Gender: In the context of diverse cultural backgrounds, significant disparities exist in the symbolic meanings of food, culinary practices, and dietary habits, all of which profoundly shape individual food preferences (175, 176). Moreover, within the same culture, notable gender differences are present. A large-scale study conducted on the Italian population revealed that men tend to prefer red meat and processed meats, while women show a greater inclination towards healthier food choices such as vegetables, whole grains, and dark chocolate (177). These variations may stem from a combination of physiological needs, societal expectations, and gender roles.
Significant Societal Events: Large-scale societal disruptions, such as the COVID-19 pandemic, can also reshape food preferences and food-related decision-making. A longitudinal study of Chilean students found greater sensitivity to food costs during the pandemic, together with a stronger preference for foods perceived as healthier and more environmentally sustainable, possibly reflecting heightened awareness of health risks (178).
4 Intervention strategies
Based on an understanding of food preferences and reward processing mechanisms, researchers and clinicians are developing various intervention strategies aimed at assisting individuals in establishing healthier eating habits.
4.1 Pharmacological intervention
In recent years, significant progress has been made in developing drugs targeting the gut-brain axis. Glucagon-like peptide-1 (GLP-1) analogs, such as semaglutide and liraglutide, initially used for type 2 diabetes treatment, have emerged as effective weight-loss medications. These drugs not only suppress appetite by acting on the hypothalamus but may also directly impact the brain’s reward pathways. Clinical studies indicate that GLP-1 analog therapy can reduce energy intake, decrease preference for high-fat foods, and enhance feelings of dietary control (179). However, further research is needed to understand the long-term effects of these drugs in weight maintenance and their specific neural mechanisms influencing eating behavior (180). Other drugs targeting neurotransmitter systems, such as the opioid system, are also under investigation, although their use is often limited by side effects (181).
4.2 Behavioral and cognitive interventions
Physical exercise not only increases energy expenditure but also positively impacts brain function. Research indicates that acute high-intensity interval exercise (HIIE) can enhance inhibitory control and working memory in young women, with the improvement in inhibitory control mediating the exercise’s positive effects on food choices (182). Long-term exercise can also improve executive function and reward processing in patients with depression through mechanisms such as increasing brain-derived neurotrophic factor (BDNF) (72). Furthermore, the hypothalamic orexin system, as mentioned earlier, plays a role in balancing exercise and food rewards, providing a new neurobiological perspective on how exercise may help individuals resist food temptations (94).
4.3 Surgical intervention
For severely obese individuals, bariatric surgeries such as Roux-en-Y gastric bypass and vertical sleeve gastrectomy are the most effective long-term weight loss methods. These surgeries work not only by restricting stomach capacity and reducing nutrient absorption but also by altering the secretion of gut hormones (e.g., GLP-1, PYY), profoundly impacting gut-brain axis signaling. A systematic review of functional magnetic resonance imaging (fMRI) studies indicates that following bariatric surgery, regions in the brain associated with reward processing (e.g., striatum, insula) show reduced reactivity to high-calorie food cues, while activity in regions linked to cognitive control (e.g., dlPFC) may increase (166). These neural changes are closely associated with postoperative decreases in appetite, shifts in food preferences towards healthier options, and significant weight loss (Figure 4).
Figure 4
4.4 Other intervention strategies
Sensory Intervention: The sensory characteristics of food, such as taste, aroma, and texture, play a crucial role in determining its hedonic value. Enhancing the sensory experience of food, for instance, by boosting umami taste in the diet of elderly individuals or those with swallowing difficulties, may help increase their acceptance and consumption of nutritious foods, thereby improving their health status. Furthermore, some studies have also highlighted the benefits of nutrients like docosahexaenoic acid (DHA) on brain function and cognitive development, which could indirectly influence long-term dietary choices and health outcomes (Table 2).
Table 2
| Intervention category | Representative modality | Core mechanism of action | Observed clinical/Behavioral outcomes |
|---|---|---|---|
| Pharmacological | GLP-1 Analogs (e.g., Semaglutide) | Modulates gut-brain axis signaling and impacts central reward pathways. | Reduced energy intake, decreased preference for high-fat foods, and enhanced dietary control. |
| Behavioral and Cognitive | Physical Exercise | Enhances inhibitory control, improves executive function, and increases BDNF levels. | Directly mediates healthier food choices by strengthening top-down cognitive regulation. |
| Surgical | Bariatric Surgery | Reshapes gut hormone secretion (GLP-1, PYY) and alters neurobiological reward sensitivity. | Reduced neural reactivity to high-calorie cues and increased activity in cognitive control regions. |
| Sensory and Nutritional | Umami Enhancement; DHA Supplementation | Optimizes the hedonic value of nutritious options and supports overall brain function. | Improved acceptance and consumption of nutritious foods, particularly in elderly or clinical populations. |
| Digital and Personalized | JITAIs via Digital Phenotyping | Utilizes machine learning and biosensors to identify idiosyncratic “vulnerability windows.” | Delivers real-time cognitive support precisely when inhibitory capacity is most compromised. |
Comparative analysis of intervention strategies for food preference and reward dysregulation.
Taken together, these interventions can be understood as targeting different nodes within the reward-control system described earlier in the manuscript. Pharmacological approaches primarily act on homeostatic and neurochemical signaling pathways, especially those linking the gut, hypothalamus, and reward circuitry. Behavioral and cognitive interventions mainly aim to strengthen executive control, reduce cue-driven responding, and reshape learned associations. Bariatric surgery produces broader physiological and neural changes, altering gut-hormone signaling as well as food-cue reactivity in reward-related regions. Sensory and nutrition-based approaches may modify the hedonic and perceptual dimensions of food experience, thereby influencing valuation and acceptance. Framing interventions in this mechanistic way helps clarify why combined or personalized approaches may be more effective than single-component strategies.
5 Discussion and future perspectives
This review brings together evidence indicating that eating behavior is shaped by the interaction of homeostatic regulation, hedonic reward processing, and executive control. Rather than being driven by a single neural substrate, food choice appears to emerge from coordinated activity across multiple systems, including mesolimbic motivational circuits, cortical valuation networks, hypothalamic metabolic signaling, and prefrontal control mechanisms. A central implication of this framework is that maladaptive eating is best understood as a systems-level imbalance: excessive cue-driven “wanting,” heightened valuation of palatable foods, impaired inhibitory control, or altered homeostatic signaling may each contribute, and in many individuals these mechanisms likely co-occur.
At the same time, these neurobiological processes are embedded within broader developmental, psychological, and social environments. Genetic variation, age-related neurodevelopment, stress exposure, and metabolic status may alter sensitivity to reward and control demands, while socioeconomic conditions, cultural norms, and social learning shape the availability, meaning, and reinforcement of food cues in daily life. This cross-level view is important because it helps explain why similar food environments do not affect all individuals in the same way, and why effective intervention is unlikely to rely on a purely biological or purely behavioral model alone.
Several important issues remain unresolved. First, although the distinction between “wanting” and “liking” is conceptually useful, the empirical literature is still uneven across methods, populations, and behavioral phenotypes. In some domains, especially obesity and stress-related eating, findings are not always fully consistent, partly because studies differ in task design, outcome measures, and the degree to which homeostatic, affective, and contextual variables are controlled. Second, the neural mechanisms through which social and environmental influences alter food reward remain incompletely understood. Social modeling, emotional contagion, and socioeconomic adversity likely affect valuation and control processes, but the relevant pathways have not yet been mapped with sufficient precision. Third, more integrative research designs are needed. Future work should more directly combine neuroimaging, ecological assessment, longitudinal follow-up, and computational or mechanistic modeling in order to capture how food-related decisions unfold across time and context (183). Finally, intervention research would benefit from stronger personalization frameworks. Different individuals may show different combinations of reward hyper-reactivity, impaired control, stress sensitivity, metabolic dysregulation, or social vulnerability, suggesting that stratified or multi-component approaches may be more effective than uniform interventions.
Emerging digital health approaches may help operationalize such personalization in real-world settings. Digital phenotyping based on wearable sensors, ecological momentary assessment, and mobile behavior tracking offers a practical way to detect fluctuations in stress, sleep, affect, and eating-related vulnerability. In principle, these data streams could support Just-In-Time Adaptive Interventions (JITAIs), in which support is delivered when self-regulatory capacity is low or cue reactivity is elevated. However, the current evidence base in nutritional neuroscience remains preliminary. Although feasibility is increasingly plausible, stronger prospective and intervention studies are still needed to determine whether these approaches produce durable improvements in eating behavior, adherence, and metabolic outcomes across diverse populations (Figure 5).
Figure 5
6 Conclusion
Food preference and reward processing are best understood as the products of interacting neural, physiological, cognitive, and social processes rather than as isolated functions of a single brain region or pathway. The literature reviewed here suggests that maladaptive eating may arise from multiple forms of imbalance within the broader reward-control system, including altered motivational salience, disrupted valuation, impaired inhibitory control, and dysregulated homeostatic signaling. This perspective helps explain both the heterogeneity of eating-related problems and the need for interventions that are mechanistically informed and context sensitive. Looking forward, the most important challenge is to move from descriptive models toward integrative and personalized frameworks that can better connect neurobiological insight with real-world prevention and treatment.
Statements
Author contributions
XG: Investigation, Writing – review & editing. YW: Writing – original draft, Conceptualization, Investigation. RZ: Writing – original draft, Investigation, Conceptualization. YK: Writing – original draft, Investigation, Conceptualization. HX: Conceptualization, Investigation, Writing – original draft. KL: Supervision, Conceptualization, Project administration, Writing – review & editing, Visualization. MZ: Project administration, Validation, Conceptualization, Supervision, Writing – review & editing.
Funding
The author(s) declared that financial support was received for this work and/or its publication. MZ received support from the National Natural Science Foundation of China (31600927 and 81830040) and the Youth Foundation of Social Science and Humanity, China Ministry of Education (21YJC190023).
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that Generative AI was not used in the creation of this manuscript.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
1.
AvagimyanAPogosovaNFogacciFAghajanovaEDjndoyanZPatouliasDet al. Triglyceride-glucose index (TyG) as a novel biomarker in the era of cardiometabolic medicine. Int J Cardiol. (2025) 418:132663. doi: 10.1016/j.ijcard.2024.132663
2.
EckelRHGrundySMZimmetPZ. The metabolic syndrome. Lancet. (2005) 365:1415–28. doi: 10.1016/S0140-6736(05)66378-7,
3.
KlopBElteJWFCabezasMC. Dyslipidemia in obesity: mechanisms and potential targets. Nutrients. (2013) 5:1218–40. doi: 10.3390/nu5041218,
4.
OdegaardJIChawlaA. Alternative macrophage activation and metabolism. Annu Rev Pathol. (2011) 6:275–97. doi: 10.1146/annurev-pathol-011110-130138
5.
Powell-WileyTMPoirierPBurkeLEDesprésJ-PGordon-LarsenPLavieCJet al. American Heart Association Council on lifestyle and Cardiometabolic health; council on cardiovascular and stroke nursing; council on clinical cardiology; council on epidemiology and prevention; stroke council. Obesity and cardiovascular disease: a scientific statement from the American Heart Association. Circulation. (2021) 143:e984–e1010. doi: 10.1161/CIR.0000000000000973
6.
AnastasiouIAKounatidisDVallianouNGSkourtisADimitriouKTzivakiIet al. Beneath the surface: the emerging role of ultra-processed foods in obesity-related cancer. Curr Oncol Rep. (2025) 27:390–414. doi: 10.1007/s11912-025-01654-6,
7.
GreenwayFL. Physiological adaptations to weight loss and factors favouring weight regain. Int J Obes. (2015) 39:1188–96. doi: 10.1038/ijo.2015.59,
8.
HeberD. An integrative view of obesity. Am J Clin Nutr. (2010) 91:280S–3S. doi: 10.3945/ajcn.2009.28473B,
9.
LobsteinTBaurLUauyRIASO International Obesity TaskForce. Obesity in children and young people: a crisis in public health. Obes Rev. (2004) 5:4–104. doi: 10.1111/j.1467-789X.2004.00133.x,
10.
AppelhansBM. Neurobehavioral inhibition of reward-driven feeding: implications for dieting and obesity. Obesity. (2009) 17:640–7. doi: 10.1038/oby.2008.638,
11.
CoccurelloRMaccarroneM. Hedonic eating and the “delicious circle”: from lipid-derived mediators to brain dopamine and back. Front Neurosci. (2018) 12:271. doi: 10.3389/fnins.2018.00271,
12.
KayeW. Neurobiology of anorexia and bulimia nervosa. Physiol Behav. (2008) 94:121–35. doi: 10.1016/j.physbeh.2007.11.037,
13.
SinaEBoakyeDChristiansonLAhrensWHebestreitA. Social media and children’s and adolescents’ diets: a systematic review of the underlying social and physiological mechanisms. Adv Nutr. (2022) 13:913–37. doi: 10.1093/advances/nmac018,
14.
ZhangRZhangBShenCSahakianBLiZZhangWet al. Associations of dietary patterns with brain health from behavioral, neuroimaging, biochemical and genetic analyses. Nat Ment Health. (2024) 2:535–52. doi: 10.1038/s44220-024-00226-0
15.
BerridgeKCRobinsonTE. Parsing reward. Trends Neurosci. (2003) 26:507–13. doi: 10.1016/S0166-2236(03)00233-9,
16.
FinlaysonGKingNBlundellJE. Liking vs. wanting food: importance for human appetite control and weight regulation. Neurosci Biobehav Rev. (2007) 31:987–1002. doi: 10.1016/j.neubiorev.2007.03.004,
17.
KelleyAEBakshiVPHaberSNSteiningerTLWillMJZhangM. Opioid modulation of taste hedonics within the ventral striatum. Physiol Behav. (2002) 76:365–77. doi: 10.1016/S0031-9384(02)00751-5,
18.
MelaDJ. Eating for pleasure or just wanting to eat? Reconsidering sensory hedonic responses as a driver of obesity. Appetite. (2006) 47:10–7. doi: 10.1016/j.appet.2006.02.006
19.
PeciñaSSmithKSBerridgeKC. Hedonic hot spots in the brain. Neuroscientist. (2006) 12:500–11. doi: 10.1177/1073858406293154
20.
XieXChenH. Dissociation between wanting and liking for high-calorie foods across physiological states in restrained eating subgroups. Eat Behav. (2025) 59:102048. doi: 10.1016/j.eatbeh.2025.102048
21.
JoynerMKimSGearhardtA. Investigating an incentive-sensitization model of eating behavior: impact of a simulated fast-food laboratory. Clin Psychol Sci. (2017) 5:1014–26. doi: 10.1177/2167702617718828
22.
SchamarekIRichterFCFinlaysonGTönjesAStumvollMBlüherMet al. Association of salty and sweet taste recognition with food reward and subjective control of eating behavior. Nutrients. (2024) 16:2661. doi: 10.3390/nu16162661,
23.
BerridgeKCRobinsonTE. Liking, wanting, and the incentive-sensitization theory of addiction. Am Psychol. (2016) 71:670–9. doi: 10.1037/amp0000059,
24.
BerridgeKCRobinsonTEAldridgeJW. Dissecting components of reward: “liking”, “wanting”, and learning. Curr Opin Pharmacol. (2009) 9:65–73. doi: 10.1016/j.coph.2008.12.014,
25.
RobinsonMJFFischerAMAhujaALesserENManiatesH. Roles of “wanting” and “liking” in motivating behavior: gambling, food, and drug addictions. Curr Top Behav Neurosci. (2016) 27:105–36. doi: 10.1007/7854_2015_387,
26.
SmithKSBerridgeKCAldridgeJW. Disentangling pleasure from incentive salience and learning signals in brain reward circuitry. Proc Natl Acad Sci USA. (2011) 108:E255–64. doi: 10.1073/pnas.1101920108,
27.
RakhaAMehakFShabbirMAArslanMRanjhaMMANAhmedWet al. Insights into the constellating drivers of satiety impacting dietary patterns and lifestyle. Front Nutr. (2022) 9:1002619. doi: 10.3389/fnut.2022.1002619,
28.
RichsonBNAbberSRWierengaCE. Conceptualizing avoidant/restrictive food intake disorder via an executive functioning lens. Int J Eat Disord. (2024) 57:1991–8. doi: 10.1002/eat.24233,
29.
SchultzW. Predictive reward signal of dopamine neurons. J Neurophysiol. (1998) 80:1–27. doi: 10.1152/jn.1998.80.1.1,
30.
StuberGDSchwitzgebelVMLüscherC. The neurobiology of overeating. Neuron. (2025) 113:1680–93. doi: 10.1016/j.neuron.2025.03.010,
31.
van der VeerAMadernTvan LentheFJ. Tunneling, cognitive load and time orientation and their relations with dietary behavior of people experiencing financial scarcity—an AI-assisted scoping review elaborating on scarcity theory. Int J Behav Nutr Phys Act. (2024) 21:26. doi: 10.1186/s12966-024-01576-9,
32.
KoobGFSannaPPBloomFE. Neuroscience of addiction. Neuron. (1998) 21:467–76. doi: 10.1016/S0896-6273(00)80557-7,
33.
RushworthMFSWaltonME. Neuroeconomics: decision making and the brain. Neuron. (2009) 63:150–3. doi: 10.1016/j.neuron.2009.07.005
34.
FlorsheimEBBachtelNDCullenJLLimaBGCGodazgarMCarvalhoFet al. Immune sensing of food allergens promotes avoidance behaviour. Nature. (2023) 620:643–50. doi: 10.1038/s41586-023-06362-4,
35.
KowalskiKMulakA. Brain–gut–microbiota axis in Alzheimer’s disease. J Neurogastroenterol Motil. (2019) 25:48–60. doi: 10.5056/jnm18087,
36.
SnyderJSSoumierABrewerMPickelJCameronHA. Adult hippocampal neurogenesis buffers stress responses and depressive behaviour. Nature. (2011) 476:458–61. doi: 10.1038/nature10287,
37.
TallimaHEl RidiR. Arachidonic acid: physiological roles and potential health benefits—a review. J Adv Res. (2018) 11:33–41. doi: 10.1016/j.jare.2017.11.004,
38.
AndrettiBVieitesYElmorLAndradeE. How socioeconomic status shapes food preferences and perceptions. J Mark. (2024) 89:33–56. doi: 10.1177/00222429241296048
39.
JayasingheSByrneNMHillsAP. Cultural influences on dietary choices. Prog Cardiovasc Dis. (2025) 90:22–6. doi: 10.1016/j.pcad.2025.02.003
40.
HossainMSWazedMAAshaSHossenMAFimeSNMTeeyaSTet al. Flavor and well-being: a comprehensive review of food choices, nutrition, and health interactions. Food Sci Nutr. (2025) 13:e70276. doi: 10.1002/fsn3.70276,
41.
JéquierE. Leptin signaling, adiposity, and energy balance. Ann N Y Acad Sci. (2002) 967:379–88. doi: 10.1111/j.1749-6632.2002.tb04293.x
42.
BrosnanAKnapskaE. Cheerful tails: delving into positive emotional contagion. Neurosci Biobehav Rev. (2024) 161:105674. doi: 10.1016/j.neubiorev.2024.105674
43.
NavratilovaHFWhettonADGeifmanN. Integrating food preference profiling, behavior change strategies, and machine learning for cardiovascular disease prevention in a personalized nutrition digital health intervention: conceptual pipeline development and proof-of-principle study. J Med Internet Res. (2025) 27:e75106. doi: 10.2196/75106
44.
BorlandJMKohut-JacksonALPeylaACHallMAMermelsteinPGMeiselRL. Female Syrian hamster analyses of bremelanotide, a US FDA approved drug for the treatment of female hypoactive sexual desire disorder. Neuropharmacology. (2025) 267:110299. doi: 10.1016/j.neuropharm.2025.110299,
45.
MaoQWangJYangZDingRLvSJiX. The pathologic roles and therapeutic implications of ghrelin/GHSR system in mental disorders. Depress Anxiety. (2024) 2024:5537319. doi: 10.1155/2024/5537319,
46.
SongNLiuZGaoYLuSYangSYuanC. NAc-DBS corrects depression-like behaviors in CUMS mouse model via disinhibition of DA neurons in the VTA. Mol Psychiatry. (2024) 29:1550–66. doi: 10.1038/s41380-024-02476-x,
47.
YangHde JongJWTakYPeckJBateupHSLammelS. Nucleus accumbens subnuclei regulate motivated behavior via direct inhibition and disinhibition of VTA dopamine subpopulations. Neuron. (2018) 97:e4:434–49. doi: 10.1016/j.neuron.2017.12.022,
48.
DayJJRoitmanMFWightmanRMCarelliRM. Associative learning mediates dynamic shifts in dopamine signaling in the nucleus accumbens. Nat Neurosci. (2007) 10:1020–8. doi: 10.1038/nn1923,
49.
García-LeónMÁFuentes-ClaramontePGeeARamiro-SousaNSoler-VidalJSalgado-PinedaPet al. Are delusions and/or referentiality associated with aberrant reward prediction error (RPE) signaling? Evidence from fMRI using a probabilistic monetary reward task. Psychol Med. (2025) 55:e55. doi: 10.1017/S0033291724003258,
50.
McClureSMDawNDMontaguePR. A computational substrate for incentive salience. Trends Neurosci. (2003) 26:423–8. doi: 10.1016/S0166-2236(03)00177-2
51.
QinDQiJShiFGuoZLiH. Sugar addiction: neural mechanisms and health implications. Brain Behav. (2025) 15:e70338. doi: 10.1002/brb3.70338,
52.
VolkowNDWangG-JFowlerJSTomasiDBalerR. Food and drug reward: overlapping circuits in human obesity and addiction. Curr Top Behav Neurosci. (2012) 11:1–24. doi: 10.1007/7854_2011_169,
53.
VolkowNDWangG-JTomasiDBalerRD. Obesity and addiction: neurobiological overlaps. Obes Rev. (2013) 14:2–18. doi: 10.1111/j.1467-789X.2012.01031.x,
54.
StupacherJMatarrelliBCozzoliDVenturaMMontinaroFde GennaroLet al. Individuals with substance use disorders experience an increased urge to move to complex music. Proc Natl Acad Sci USA. (2025) 122:e2502656122. doi: 10.1073/pnas.2502656122,
55.
IyerESVitaroPWuSMuirJTseYCCvetkovskaVet al. Reward integration in prefrontal-cortical and ventral-hippocampal nucleus accumbens inputs cooperatively modulates engagement. Nat Commun. (2025) 16:3573. doi: 10.1038/s41467-025-58858-4,
56.
HoyCWde HemptinneCWangSSHarmerCJAppsMAJHusainMet al. Beta and theta oscillations track effort and previous reward in the human basal ganglia and prefrontal cortex during decision making. Proc Natl Acad Sci USA. (2024) 121:e2322869121. doi: 10.1073/pnas.2322869121,
57.
IsaacJKarkareSCBalasubramanianHSchappaughNJavierJLRashidMet al. Sex differences in neural representations of social and nonsocial reward in the medial prefrontal cortex. Nat Commun. (2024) 15:8018. doi: 10.1038/s41467-024-52294-6,
58.
RidderinkhofKRvan den WildenbergWPMSegalowitzSJCarterCS. Neurocognitive mechanisms of cognitive control: the role of prefrontal cortex in action selection, response inhibition, performance monitoring, and reward-based learning. Brain Cogn. (2004) 56:129–40. doi: 10.1016/j.bandc.2004.09.016,
59.
SmithACWGhoshalSCentanniSWHeyerMPCoronaAWillsLet al. A master regulator of opioid reward in the ventral prefrontal cortex. Science. (2024) 384:eadn0886. doi: 10.1126/science.adn0886,
60.
KnutsonBCooperJC. Functional magnetic resonance imaging of reward prediction. Curr Opin Neurol. (2005) 18:411–7. doi: 10.1097/01.wco.0000173463.24758.f6,
61.
RollsET. The orbitofrontal cortex and emotion in health and disease, including depression. Neuropsychologia. (2019) 128:14–43. doi: 10.1016/j.neuropsychologia.2017.09.021
62.
RollsETGrabenhorstF. The orbitofrontal cortex and beyond: from affect to decision-making. Prog Neurobiol. (2008) 86:216–44. doi: 10.1016/j.pneurobio.2008.09.001,
63.
SmallDMZatorreRJDagherAEvansACJones-GotmanM. Changes in brain activity related to eating chocolate: from pleasure to aversion. Brain. (2001) 124:1720–33. doi: 10.1093/brain/124.9.1720,
64.
BigliassiMCabralDFEvansAC. Improving brain health via the central executive network. J Physiol. (2025) 604:99. doi: 10.1113/JP287099,
65.
CahnBRPolichJ. Meditation states and traits: EEG, ERP, and neuroimaging studies. Psychol Bull. (2006) 132:180–211. doi: 10.1037/0033-2909.132.2.180,
66.
CarterCSBraverTSBarchDMBotvinickMMNollDCohenJD. Anterior cingulate cortex, error detection, and the online monitoring of performance. Science. (1998) 280:747–9. doi: 10.1126/science.280.5364.747,
67.
HillmanKLBilkeyDK. Neurons in the rat anterior cingulate cortex dynamically encode cost–benefit in a spatial decision-making task. J Neurosci. (2010) 30:7705–13. doi: 10.1523/JNEUROSCI.1273-10.2010,
68.
KaneMJEngleRW. The role of prefrontal cortex in working-memory capacity, executive attention, and general fluid intelligence: an individual-differences perspective. Psychon Bull Rev. (2002) 9:637–71. doi: 10.3758/bf03196323,
69.
KernsJGCohenJDMacDonaldAWChoRYStengerVACarterCS. Anterior cingulate conflict monitoring and adjustments in control. Science. (2004) 303:1023–6. doi: 10.1126/science.1089910,
70.
PanahiFDaliriMR. Neural mechanisms of cost-benefit decision-making in the rodent anterior cingulate cortex: a systematic review of extracellular recordings. Physiol Behav. (2026) 304:115184. doi: 10.1016/j.physbeh.2025.115184,
71.
ShenhavABotvinickMMCohenJD. The expected value of control: an integrative theory of anterior cingulate cortex function. Neuron. (2013) 79:217–40. doi: 10.1016/j.neuron.2013.07.007,
72.
TianHWangZMengYGengLLianHShiZet al. Neural mechanisms underlying cognitive impairment in depression and cognitive benefits of exercise intervention. Behav Brain Res. (2025) 476:115218. doi: 10.1016/j.bbr.2024.115218
73.
AdolphsRTranelDDamasioHDamasioA. Impaired recognition of emotion in facial expressions following bilateral damage to the human amygdala. Nature. (1994) 372:669–72. doi: 10.1038/372669a0
74.
DavisMWhalenPJ. The amygdala: vigilance and emotion. Mol Psychiatry. (2001) 6:13–34. doi: 10.1038/sj.mp.4000812,
75.
KlugMEnnekingVBorgersTJacobsCMDohmKKrausAet al. Persistence of amygdala hyperactivity to subliminal negative emotion processing in the long-term course of depression. Mol Psychiatry. (2024) 29:1501–9. doi: 10.1038/s41380-024-02429-4,
76.
LabuschagneIDominguezJFGraceSMizziSHenryJDPetersCet al. Specialization of amygdala subregions in emotion processing. Hum Brain Mapp. (2024) 45:e26673. doi: 10.1002/hbm.26673,
77.
PhelpsEALeDouxJE. Contributions of the amygdala to emotion processing: from animal models to human behavior. Neuron. (2005) 48:175–87. doi: 10.1016/j.neuron.2005.09.025
78.
SiasACJafarYGoodpasterCMRamírez-ArmentaKWrennTMGriffinNKet al. Dopamine projections to the basolateral amygdala drive the encoding of identity-specific reward memories. Nat Neurosci. (2024) 27:728–36. doi: 10.1038/s41593-024-01586-7,
79.
ZhangXFlickKRizzoMPignatelliMTonegawaS. Dopamine induces fear extinction by activating the reward-responding amygdala neurons. Proc Natl Acad Sci USA. (2025) 122:e2501331122. doi: 10.1073/pnas.2501331122,
80.
ZhangXKimJTonegawaS. Amygdala reward neurons form and store fear extinction memory. Neuron. (2020) 105:1077–93. doi: 10.1016/j.neuron.2019.12.025,
81.
CardinalRNParkinsonJAHallJEverittBJ. Emotion and motivation: the role of the amygdala, ventral striatum, and prefrontal cortex. Neurosci Biobehav Rev. (2002) 26:321–52. doi: 10.1016/S0149-7634(02)00007-6,
82.
MorrisJSFrithCDPerrettDIRowlandDYoungAWCalderAJet al. A differential neural response in the human amygdala to fearful and happy facial expressions. Nature. (1996) 383:812–5. doi: 10.1038/383812a0,
83.
SunWLiuZJiangXChenMBDongHLiuJet al. Spatial transcriptomics reveal neuron–astrocyte synergy in long-term memory. Nature. (2024) 627:374–81. doi: 10.1038/s41586-023-07011-6,
84.
GamerMZurowskiBBüchelC. Different amygdala subregions mediate valence-related and attentional effects of oxytocin in humans. Proc Natl Acad Sci USA. (2010) 107:9400–5. doi: 10.1073/pnas.1000985107,
85.
HollandPCGallagherM. Amygdala circuitry in attentional and representational processes. Trends Cogn Sci. (1999) 3:65–73. doi: 10.1016/S1364-6613(98)01271-6,
86.
DodtSWiddershoovenNVDreisowMLWeiherLSteuernagelLWunderlichFTet al. NPY-mediated synaptic plasticity in the extended amygdala prioritizes feeding during starvation. Nat Commun. (2024) 15:5439. doi: 10.1038/s41467-024-49766-0,
87.
ShrivastavaKAthreyaVLuYLuis-IslasJHanAKowalskiTFet al. Energy state guides reward seeking via an extended amygdala to lateral hypothalamus pathway. Nat Commun. (2025) 16:4474. doi: 10.1038/s41467-025-59686-2,
88.
BaskinDGFiglewicz LattemannDSeeleyRJWoodsSCPorteDSchwartzMW. Insulin and leptin: dual adiposity signals to the brain for the regulation of food intake and body weight. Brain Res. (1999) 848:114–23. doi: 10.1016/S0006-8993(99)01974-5,
89.
ConeRD. Anatomy and regulation of the central melanocortin system. Nat Neurosci. (2005) 8:571–8. doi: 10.1038/nn1455
90.
JinHYaoLWangSXiaPHouTLiBet al. Effects of KGM and degradation products on appetite regulation and energy expenditure in high-fat-diet mice via the adipocyte–hypothalamus axis. J Agric Food Chem. (2024) 72:15765–77. doi: 10.1021/acs.jafc.4c03819,
91.
KayaEWegienkaEAkhtarzandi-DasADoHEban-RothschildARothschildG. Food intake enhances hippocampal sharp wave-ripples. eLife. (2025) 14:e105059. doi: 10.7554/eLife.105059.3
92.
OswalAYeoG. Leptin and the control of body weight: a review of its diverse central targets, signaling mechanisms, and role in the pathogenesis of obesity. Obesity. (2010) 18:221–9. doi: 10.1038/oby.2009.228
93.
TrueCArikALindsleySKirigitiMSullivanEKievitP. Early high-fat diet exposure causes dysregulation of the orexin and dopamine neuronal populations in nonhuman primates. Front Endocrinol. (2018) 9:508. doi: 10.3389/fendo.2018.00508,
94.
TesmerALLiXBraceyESchmandtCPolaniaRPeleg-RaibsteinDet al. Orexin neurons mediate temptation-resistant voluntary exercise. Nat Neurosci. (2024) 27:1774–82. doi: 10.1038/s41593-024-01696-2,
95.
KillgoreWDSYoungADFemiaLABogorodzkiPRogowskaJYurgelun-ToddDA. Cortical and limbic activation during viewing of high- versus low-calorie foods. NeuroImage. (2003) 19:1381–94. doi: 10.1016/S1053-8119(03)00191-5,
96.
LarryNZurGJoshuaM. Organization of reward and movement signals in the basal ganglia and cerebellum. Nat Commun. (2024) 15:2119. doi: 10.1038/s41467-024-45921-9,
97.
MoultonEAElmanIBecerraLRGoldsteinRZBorsookD. The cerebellum and addiction: insights gained from neuroimaging research. Addict Biol. (2014) 19:317–31. doi: 10.1111/adb.12101
98.
BaumannOMattingleyJB. Functional topography of primary emotion processing in the human cerebellum. NeuroImage. (2012) 61:805–11. doi: 10.1016/j.neuroimage.2012.03.044,
99.
StoodleyCJSchmahmannJD. Evidence for topographic organization in the cerebellum of motor control versus cognitive and affective processing. Cortex. (2010) 46:831–44. doi: 10.1016/j.cortex.2009.11.008,
100.
BucknerRL. The cerebellum and cognitive function: 25 years of insight from anatomy and neuroimaging. Neuron. (2013) 80:807–15. doi: 10.1016/j.neuron.2013.10.044,
101.
StrickPLDumRPFiezJA. Cerebellum and nonmotor function. Annu Rev Neurosci. (2009) 32:413–34. doi: 10.1146/annurev.neuro.31.060407.125606
102.
JoyceAW. Implicit working memory: implications for assessment and treatment. Appl Neuropsychol Child. (2016) 5:223–34. doi: 10.1080/21622965.2016.1167497,
103.
PidouxLLe BlancPLevenesCLebloisA. A subcortical circuit linking the cerebellum to the basal ganglia engaged in vocal learning. eLife. (2018) 7:e32167. doi: 10.7554/eLife.32167,
104.
BalerRDVolkowND. Drug addiction: the neurobiology of disrupted self-control. Trends Mol Med. (2006) 12:559–66. doi: 10.1016/j.molmed.2006.10.005
105.
RobinsonTEBerridgeKC. The incentive-sensitization theory of addiction 30 years on. Annu Rev Psychol. (2025) 76:29–58. doi: 10.1146/annurev-psych-011624-024031,
106.
TreadwayMTZaldDH. Reconsidering anhedonia in depression: lessons from translational neuroscience. Neurosci Biobehav Rev. (2011) 35:537–55. doi: 10.1016/j.neubiorev.2010.06.006,
107.
CannonCMPalmiterRD. Reward without dopamine. J Neurosci. (2003) 23:10827–31. doi: 10.1523/JNEUROSCI.23-34-10827.2003,
108.
MalikSMcGloneFBedrossianDDagherA. Ghrelin modulates brain activity in areas that control appetitive behavior. Cell Metab. (2008) 7:400–9. doi: 10.1016/j.cmet.2008.03.007,
109.
PalmiterRD. Is dopamine a physiologically relevant mediator of feeding behavior?Trends Neurosci. (2007) 30:375–81. doi: 10.1016/j.tins.2007.06.004,
110.
van ZessenRPlasseGAdanRAH. Contribution of the mesolimbic dopamine system in mediating the effects of leptin and ghrelin on feeding. Proc Nutr Soc. (2012) 71:435–45. doi: 10.1017/S0029665112000614,
111.
Tufvesson-AlmMZhangQAranäsCBlid SköldhedenSEdvardssonCEJerlhagE. Decoding the influence of central LEAP2 on food intake and its effect on accumbal dopamine release. Prog Neurobiol. (2024) 236:102615. doi: 10.1016/j.pneurobio.2024.102615,
112.
WitleySEdvardssonCEAranäsCTufvesson-AlmMStalbergaDGreenHet al. Des-acyl ghrelin reduces alcohol intake and alcohol-induced reward in rodents. Transl Psychiatry. (2024) 14:277. doi: 10.1038/s41398-024-02996-8,
113.
HansenHS. Role of anorectic N-acylethanolamines in intestinal physiology and satiety control with respect to dietary fat. Pharmacol Res. (2014) 86:18–25. doi: 10.1016/j.phrs.2014.03.006,
114.
SchwartzMW. Central nervous system regulation of food intake. Obesity. (2006) 14:1S–8S. doi: 10.1038/oby.2006.275,
115.
WoodsSC. Role of the endocannabinoid system in regulating cardiovascular and metabolic risk factors. Am J Med. (2007) 120:S19–25. doi: 10.1016/j.amjmed.2007.01.004
116.
GómezRNavarroMFerrerBTrigoJMBilbaoADel ArcoIet al. A peripheral mechanism for CB1 cannabinoid receptor-dependent modulation of feeding. J Neurosci. (2002) 22:9612–7. doi: 10.1523/JNEUROSCI.22-21-09612.2002,
117.
HungundBSzakallIÁdámÁBasavarajappaBVadászC. Cannabinoid CB1 receptor knockout mice exhibit markedly reduced voluntary alcohol consumption and lack alcohol-induced dopamine release in the nucleus accumbens. J Neurochem. (2003) 84:698–704. doi: 10.1046/j.1471-4159.2003.01576.x,
118.
JamshidiNTaylorDA. Anandamide administration into the ventromedial hypothalamus stimulates appetite in rats. Br J Pharmacol. (2001) 134:1151–4. doi: 10.1038/sj.bjp.0704379,
119.
SalamoneJDMcLaughlinPJSinkKMakriyannisAParkerLA. Cannabinoid CB1 receptor inverse agonists and neutral antagonists: effects on food intake, food-reinforced behavior and food aversions. Physiol Behav. (2007) 91:383–8. doi: 10.1016/j.physbeh.2007.04.013,
120.
SinkKSMcLaughlinPJWoodJABrownCFanPVemuriVKet al. The novel cannabinoid CB1 receptor neutral antagonist AM4113 suppresses food intake and food-reinforced behavior but does not induce signs of nausea in rats. Neuropsychopharmacology. (2008) 33:946–55. doi: 10.1038/sj.npp.1301476,
121.
DawNDO’DohertyJPDayanPSeymourBDolanRJ. Cortical substrates for exploratory decisions in humans. Nature. (2006) 441:876–9. doi: 10.1038/nature04766,
122.
KingJABernardoniFWestbrookAKorbFMBoehmIDooseAet al. Exaggerated frontoparietal control over cognitive effort-based decision-making in young women with anorexia nervosa. Mol Psychiatry. (2025) 30:861–9. doi: 10.1038/s41380-024-02712-4,
123.
KringelbachML. The human orbitofrontal cortex: linking reward to hedonic experience. Nat Rev Neurosci. (2005) 6:691–702. doi: 10.1038/nrn1747,
124.
FoldiCJMorrisMJOldfieldBJ. Executive function in obesity and anorexia nervosa: opposite ends of a spectrum of disordered feeding behaviour?Prog Neuro-Psychopharmacol Biol Psychiatry. (2021) 111:110395. doi: 10.1016/j.pnpbp.2021.110395,
125.
GourleySLZimmermannKSAllenAGTaylorJR. The medial orbitofrontal cortex regulates sensitivity to outcome value. J Neurosci. (2016) 36:4600–13. doi: 10.1523/JNEUROSCI.4253-15.2016,
126.
HowardJDKahntT. Identity-specific reward representations in orbitofrontal cortex are modulated by selective devaluation. J Neurosci. (2017) 37:2627–38. doi: 10.1523/JNEUROSCI.3473-16.2017,
127.
BariARobbinsTW. Inhibition and impulsivity: behavioral and neural basis of response control. Prog Neurobiol. (2013) 108:44–79. doi: 10.1016/j.pneurobio.2013.06.005,
128.
KoobGFVolkowND. Neurocircuitry of addiction. Neuropsychopharmacology. (2010) 35:217–38. doi: 10.1038/npp.2009.110,
129.
SahleyCGelperinARudyJW. One-trial associative learning modifies food odor preferences of a terrestrial mollusc. Proc Natl Acad Sci USA. (1981) 78:640–2. doi: 10.1073/pnas.78.1.640,
130.
GallagherMGrahamPWHollandPC. The amygdala central nucleus and appetitive Pavlovian conditioning: lesions impair one class of conditioned behavior. J Neurosci. (1990) 10:1906–11. doi: 10.1523/JNEUROSCI.10-06-01906.1990,
131.
JohnstonMPGarcia-CastañedaBICedilloLGPatelSKVargasVSWanatMJ. Estrous cycle stage gates the effect of stress on reward learning. Neuropsychopharmacology. (2025) 51:2170. doi: 10.1038/s41386-025-02170-8,
132.
RescorlaRA. Behavioral studies of Pavlovian conditioning. Annu Rev Neurosci. (1988) 11:329–52. doi: 10.1146/annurev.ne.11.030188.001553,
133.
LeiBKangBHaoYYangHZhongZZhaiZet al. Reconstructing a new hippocampal engram for systems reconsolidation and remote memory updating. Neuron. (2025) 113:471–485.e6. doi: 10.1016/j.neuron.2024.11.010
134.
SacchettiBLorenziniCABaldiETassoniGBucherelliC. Auditory thalamus, dorsal hippocampus, basolateral amygdala, and perirhinal cortex role in the consolidation of conditioned freezing to context and to acoustic conditioned stimulus in the rat. J Neurosci. (1999) 19:9570–8. doi: 10.1523/JNEUROSCI.19-21-09570.1999,
135.
SchmitzFNaumannETrentowskaMSvaldiJ. Attentional bias for food cues in binge eating disorder. Appetite. (2014) 80:70–80. doi: 10.1016/j.appet.2014.04.023,
136.
HouRMoggKBradleyBPMoss-MorrisRPevelerRRoefsA. External eating, impulsivity and attentional bias to food cues. Appetite. (2011) 56:424–7. doi: 10.1016/j.appet.2011.01.019,
137.
WerthmannJJansenARoefsA. Worry or craving? A selective review of evidence for food-related attention biases in obese individuals, eating-disorder patients, restrained eaters and healthy samples. Proc Nutr Soc. (2015) 74:99–114. doi: 10.1017/S0029665114001451,
138.
HayhoeMBallardD. Eye movements in natural behavior. Trends Cogn Sci. (2005) 9:188–94. doi: 10.1016/j.tics.2005.02.009
139.
BlüherM. An overview of obesity-related complications: the epidemiological evidence linking body weight and other markers of obesity to adverse health outcomes. Diabetes Obes Metab. (2025) 27:3–19. doi: 10.1111/dom.16263,
140.
MansourSAlkhaaldiSMISammanasunathanAFIbrahimSFarhatJAl-OmariB. Precision nutrition unveiled: gene–nutrient interactions, microbiota dynamics, and lifestyle factors in obesity management. Nutrients. (2024) 16:581. doi: 10.3390/nu16050581,
141.
ReedDRBachmanovAABeauchampGKTordoffMGPriceRA. Heritable variation in food preferences and their contribution to obesity. Behav Genet. (1997) 27:373–87. doi: 10.1023/A:1025692031673
142.
SantanaCVNMagnoLAVRamosAVRiosMASandrimVCDe MarcoLAet al. Genetic variations in AMPK, FOXO3A, and POMC increase the risk of extreme obesity. J Obes. (2024) 2024:3813621. doi: 10.1155/2024/3813621,
143.
ChangYCLiuPHLeeWJChangTJJiangYDLiHYet al. Common variation in the fat mass and obesity-associated (FTO) gene confers risk of obesity and modulates BMI in the Chinese population. Diabetes. (2008) 57:2245–52. doi: 10.2337/db08-0377,
144.
LiHKilpeläinenTOLiuCZhuJLiuYHuCet al. Association of genetic variation in FTO with risk of obesity and type 2 diabetes with data from 96,551 east and south Asians. Diabetologia. (2012) 55:981–95. doi: 10.1007/s00125-011-2370-7,
145.
FarooqiISBullmoreEKeoghJGillardJO’RahillySFletcherPC. Leptin regulates striatal regions and human eating behavior. Science. (2007) 317:1355. doi: 10.1126/science.1144599,
146.
KarraEO’DalyOGChoudhuryAIYousseifAMillershipSNearyMTet al. A link between FTO, ghrelin, and impaired brain food-cue responsivity. J Clin Invest. (2013) 123:3539–51. doi: 10.1172/JCI44403,
147.
FeighanSMMcArdleKLisantiLRocheEOrmondeCHeronEet al. Measuring shifts in attentional bias following satiety: a within-subject eye-tracking study in healthy-weight adults. Physiol Behav. (2025) 299:114993. doi: 10.1016/j.physbeh.2025.114993,
148.
RothemundYPreuschhofCBohnerGBauknechtHCKlingebielRFlorHet al. Differential activation of the dorsal striatum by high-calorie visual food stimuli in obese individuals. NeuroImage. (2007) 37:410–21. doi: 10.1016/j.neuroimage.2007.05.008,
149.
KulkarniSSSinghOZigmanJM. The intersection between ghrelin, metabolism and circadian rhythms. Nat Rev Endocrinol. (2024) 20:228–38. doi: 10.1038/s41574-023-00927-z,
150.
RibeiroFMArnaldoLMilhomemLPAguiarSSFrancoOL. The intricate relationship between circadian rhythms and gastrointestinal peptides in obesity. Peptides. (2025) 185:171356. doi: 10.1016/j.peptides.2025.171356
151.
de AssisMAKupekENahasMVBellisleF. Food intake and circadian rhythms in shift workers with a high workload. Appetite. (2003) 40:175–83. doi: 10.1016/S0195-6663(02)00133-2,
152.
BegemannKRawashdehOOlejniczakIPilorzVAssisLOsorio-MendozaJet al. Endocrine regulation of circadian rhythms. NPJ Biol. Timing Sleep. (2025) 2:246. doi: 10.1038/s44323-025-00024-6
153.
BreslinPASpectorAC. Mammalian taste perception. Curr Biol. (2008) 18:R148–55. doi: 10.1016/j.cub.2007.12.017,
154.
RoJPakGMalecPALyuYAllisonDBKennedyRTet al. Serotonin signaling mediates protein valuation and aging. eLife. (2016) 5:e16843. doi: 10.7554/eLife.16843,
155.
ReicheltACRankMM. The impact of junk foods on the adolescent brain. Birth Defects Res. (2017) 109:1649–58. doi: 10.1002/bdr2.1173
156.
Yurgelun-ToddD. Emotional and cognitive changes during adolescence. Curr Opin Neurobiol. (2007) 17:251–7. doi: 10.1016/j.conb.2007.03.009
157.
RollsBJ. Do chemosensory changes influence food intake in the elderly?Physiol Behav. (1999) 66:193–7. doi: 10.1016/S0031-9384(98)00264-9,
158.
AillsLBlankenshipJBuffingtonCFurtadoMParrottJ. ASMBS allied health nutritional guidelines for the surgical weight loss patient. Surg Obes Relat Dis. (2008) 4:S73–S108. doi: 10.1016/j.soard.2008.03.002,
159.
LiYWangSZhangLDongQHuXYangYet al. Sensory insights in aging: exploring the impact on improving dietary through sensory enhancement. Food Sci Nutr. (2025) 13:e70074. doi: 10.1002/fsn3.70074,
160.
MorrisMJBeilharzJEManiamJReicheltACWestbrookRF. Why is obesity such a problem in the 21st century? The intersection of palatable food, cues and reward pathways, stress, and cognition. Neurosci Biobehav Rev. (2015) 58:36–45. doi: 10.1016/j.neubiorev.2014.12.002,
161.
YangDFHuangWCWuCWHuangCYYangYSHTungYT. Acute sleep deprivation exacerbates systemic inflammation and psychiatric disorders through gut microbiota dysbiosis and disruption of circadian rhythms. Microbiol Res. (2023) 268:127292. doi: 10.1016/j.micres.2022.127292
162.
ChenHYChiangHYLeeTHChanPSYangCYLeeHMet al. Effects of chronic social defeat stress on social behavior and cognitive flexibility for early and late adolescent. Behav Brain Res. (2025) 476:115251. doi: 10.1016/j.bbr.2024.115251,
163.
NaneixFMarchandARPichonAPapeJRCoutureauE. Adolescent stimulation of D2 receptors alters the maturation of dopamine-dependent goal-directed behavior. Neuropsychopharmacology. (2013) 38:1566–74. doi: 10.1038/npp.2013.55
164.
OgleCMRubinDCSieglerIC. The impact of the developmental timing of trauma exposure on PTSD symptoms and psychosocial functioning among older adults. Dev Psychol. (2013) 49:2191–200. doi: 10.1037/a0031985,
165.
TanakaHGourleyDDDekhtyarMHaleyAP. Cognition, brain structure, and brain function in individuals with obesity and related disorders. Curr Obes Rep. (2020) 9:544–9. doi: 10.1007/s13679-020-00412-y,
166.
AlabdulkaderSAl-AlsheikhASMirasADGoldstoneAP. Obesity surgery and neural correlates of human eating behaviour: a systematic review of functional MRI studies. Neuroimage Clin. (2024) 41:103563. doi: 10.1016/j.nicl.2024.103563,
167.
MillerAASpencerSJ. Obesity and neuroinflammation: a pathway to cognitive impairment. Brain Behav Immun. (2014) 42:10–21. doi: 10.1016/j.bbi.2014.04.001
168.
RuYJiaJYangXBaXYanJZhangHet al. From bugs to behavior: targeting the gut-brain interface for addiction therapeutics. Gut Microbes. (2025) 17:2569740. doi: 10.1080/19490976.2025.2569740,
169.
StraussGPWaltzJAGoldJM. A review of reward processing and motivational impairment in schizophrenia. Schizophr Bull. (2014) 40:S107–16. doi: 10.1093/schbul/sbt197,
170.
AdlerNENewmanK. Socioeconomic disparities in health: pathways and policies. Health Aff. (2002) 21:60–76. doi: 10.1377/hlthaff.21.2.60,
171.
Ergović RavančićMObradovićVVranekovićJ. The connection between socioeconomic factors and dietary habits of children with down syndrome in Croatia. Foods. (2025) 14:1910. doi: 10.3390/foods14111910,
172.
NajmanJMAirdRBorWO’CallaghanMWilliamsGMShuttlewoodGJ. The generational transmission of socioeconomic inequalities in child cognitive development and emotional health. Soc Sci Med. (2004) 58:1147–58. doi: 10.1016/S0277-9536(03)00286-7,
173.
NobleKGMcCandlissBDFarahMJ. Socioeconomic gradients predict individual differences in neurocognitive abilities. Dev Sci. (2007) 10:464–80. doi: 10.1111/j.1467-7687.2007.00600.x
174.
ChenPJAntonelliM. Conceptual models of food choice: influential factors related to foods, individual differences, and society. Foods. (2020) 9:1898. doi: 10.3390/foods9121898,
175.
MonterrosaECFrongilloEADrewnowskiAde PeeSVandevijvereS. Sociocultural influences on food choices and implications for sustainable healthy diets. Food Nutr Bull. (2020) 41:59S–73S. doi: 10.1177/0379572120975874
176.
RozinP. The selection of foods by rats, humans, and other animals. Adv Study Behav. (1976) 6:21–76. doi: 10.1016/S0065-3454(08)60081-9
177.
FeracoAArmaniAAmoahIGusevaECamajaniEGoriniSet al. Assessing gender differences in food preferences and physical activity: a population-based survey. Front Nutr. (2024) 11:1348456. doi: 10.3389/fnut.2024.1348456,
178.
KanbergerEDLeon KöbrichASchobinJ. COVID-19 and shifting food preferences: a panel study among Chilean students. Appetite. (2025) 204:107721. doi: 10.1016/j.appet.2024.107721
179.
BlundellJFinlaysonGAxelsenMFlintAGibbonsCKvistTet al. Effects of once-weekly semaglutide on appetite, energy intake, control of eating, food preference and body weight in subjects with obesity. Diabetes Obes Metab. (2017) 19:1242–51. doi: 10.1111/dom.12932,
180.
BettadapuraSDowlingKJablonKAl-HumadiAWle RouxCW. Changes in food preferences and ingestive behaviors after glucagon-like peptide-1 analog treatment: techniques and opportunities. Int J Obes. (2025) 49:418–26. doi: 10.1038/s41366-024-01500-y,
181.
WillnerPBergmanJVanderschurenL. The behavioural pharmacology of opioids. Behav Pharmacol. (2020) 31:109–11. doi: 10.1097/FBP.0000000000000560,
182.
SardjoeMAldredSAdamTPlasquiGBrunstromJMDourishCTet al. Inhibitory control mediates the effect of high-intensity interval exercise on food choice. Appetite. (2024) 200:107499. doi: 10.1016/j.appet.2024.107499
183.
UrbaniakRXieMMackeviciusE. Linking cognitive strategy, neural mechanism, and movement statistics in group foraging behaviors. Sci Rep. (2024) 14:21770. doi: 10.1038/s41598-024-71931-0,
Summary
Keywords
cognitive neuroscience, food preference, influencing factors, intervention strategies, reward processing
Citation
Gao X, Wu Y, Zheng R, Kou Y, Xing H, Li K and Zhang M (2026) Neural mechanisms of food preference and reward processing: a review of multifaceted influencing factors and intervention strategies. Front. Nutr. 13:1823431. doi: 10.3389/fnut.2026.1823431
Received
05 March 2026
Revised
12 March 2026
Accepted
25 March 2026
Published
10 April 2026
Volume
13 - 2026
Edited by
Francesco Monaco, Azienda Sanitaria Locale Salerno, Italy
Reviewed by
Ilaria Pullano, University of Catanzaro Magna Graecia, Italy
Anna Maria Iazzolino, Azienda Ospedaliera Pugliese Ciaccio, Italy
Updates
Copyright
© 2026 Gao, Wu, Zheng, Kou, Xing, Li and Zhang.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Kun Li, likunyjs@163.com; Meng Zhang, mengzhang.1985@163.com
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.