Hunger shifts attention and attribute weighting in dietary choice

Many of us will recognise the impact that hunger can have on our food choices, such as making less healthy decisions when grocery shopping. From a scientific point of view, it is an evolutionary advantage to select calorie-rich food when resources are scarce. However, this natural behaviour can lead to unhealthy habits in societies where such foods are easily available.

Dietary choices are based on signals from the body as well as complex cognitive processes. Previous studies have identified that hunger drives increased attention towards foods high in calories. However, it is not clear how this process happens and how it influences decisions.

To address this, March and Gluth studied 70 people who completed a food choice task while either hungry or full. In each trial, participants chose between two foods based on an image (to convey taste) and nutritional scores (to convey health). As expected, hunger shifted choices towards the more appetising and less healthy foods.

Eye-tracking data measured during the experiment revealed that hungry participants spent more time looking at the food images than the nutritional information. March and Gluth then used computational modelling to confirm these findings, showing that hunger increases how strongly people value taste over health and decreases how much they consider nutritional information.

The findings provide new insights into the significant role of attention and valuation in food choices. This could help to advance research on improving dietary habits, which is increasingly important given rising global rates of obesity. The methods used by March and Gluth may also contribute to wider research into how attention to different factors shapes decisions.

Throughout a single day, we make numerous food choices. These choices are largely influenced by the food and its environment, as well as by the decision maker’s trait and state factors (Chen and Antonelli, 2020). For example, it has been shown that health information such as nutritional scores on food options (Rramani et al., 2020) or health primes (Hare et al., 2011; Sullivan and Huettel, 2021) can increase the number of healthy choices. On the other hand, a hungry decision maker is more likely to make unhealthy decisions (Cheung et al., 2017; Hoefling and Strack, 2010). Evolutionarily, a preference for energy dense foods was adaptive and ensured survival under conditions of scarcity (Hanßen et al., 2022; Mattson, 2019). While the food environment in Western societies has become increasingly obesogenic, with high caloric food options being affordable and easily available, the neurobiological mechanism continues to reward the consumption of energy dense foods contributing to a global surge in obesity rates (Mitchell et al., 2011; World Health Organization, 2021; Lobstein et al., 2023). The critical involvement of reward circuitries in the brain in determining food choice highlights the importance of cognitive affective drivers, alongside homeostatic ones, in shaping food-related behavior (Plassmann et al., 2022; Rangel, 2013). Here, we set out to shed light on these cognitive mechanisms underlying food choice which drive energy intake and weight, by investigating the effect of hunger on attention and valuation processes in multi-attribute dietary choice.

Consistent with the evolutionary mechanism that reinforces high-energy dense food options, behavioral (Cameron et al., 2014; Cheung et al., 2017; Epstein et al., 2003), and neuroimaging studies (e.g. Banica et al., 2023; Dagher, 2012; Malik et al., 2008) indicate that under hunger (high-caloric) food options are viewed more rewarding, are more frequently chosen over healthy alternatives, and draw more attention. Meta-analyses have revealed an attentional bias towards food versus neutral stimuli, which was further amplified by hunger state (Hardman et al., 2021; Pool et al., 2016). Given these findings, it appears critical to thoroughly understand the interplay between attention and decision-making processes in shaping maladaptive food choices under hunger. To better explain the mechanisms by which hunger affects attention and valuation processes in dietary choice, we leverage recent advances in modeling attentional dynamics in the accumulation of evidence in decision-making (Gluth et al., 2020; Krajbich et al., 2010; Shimojo et al., 2003). This work has provided evidence for a strong positive association between the time people spend looking at a (food) option and the probability with which they choose it (Krajbich, 2019). Recently, these models have also incorporated the distinct attentional influence of the options’ underlying attributes such as taste and health (Fisher, 2021; Yang and Krajbich, 2023). To the best of our knowledge, there is no study modeling attention and choice dynamics under different hunger states leaving the cognitive and attentional mechanisms underlying hunger-driven food choice unknown.

To fill this gap, we conducted a within-subject experiment, in which 70 participants completed a binary food choice task in hungry and sated states while their eye movements were being recorded (Figure 1). The considered attributes of the binary options were taste and health as represented by food images and their nutritional scores, respectively. Confirming our preregistered hypotheses, participants were more likely to choose tasty over healthy food items, and this difference was amplified under hunger. Notably, attention mediated the influence of hunger on dietary decisions, as participants focused more on taste information under hunger, leading them to make less healthy decisions. To better understand the cognitive mechanisms underlying hunger-driven dietary choice, we implemented different variants of the diffusion decision model (DDM, Ratcliff, 1978), which included the consideration of both attributes (Maier et al., 2020; Sullivan and Huettel, 2021) and the incorporation and extension of attentional mechanisms (Fisher, 2021; Krajbich et al., 2010; Yang and Krajbich, 2023). Critically, we extended the recently proposed multi-attribute attentional DDM (Yang and Krajbich, 2023) to allow the discounting of unattended information to differ across different attributes (here: taste vs. health). This model not only provided the best account of our behavioral data, but also revealed a twofold mechanism, wherein hunger affects valuation of choice options by shifting the relative weighting of taste information and by exacerbating the attentional discounting of health (but not taste) information.

Figure 1

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We used a within-subject experiment, in which 70 participants were tested in hungry and sated conditions in counterbalanced order. In the sated condition, participants received a Protein Shake, with its size being determined by their metabolic rate (see Methods). The experiment consisted of a food rating and a multi-attribute binary food choice task, as well as control measures including hunger state, mood, and eating behavior (Figure 1; see Methods for details).

Hunger state manipulation

First, we tested whether the manipulation of hunger state was successful (Figure 2a). Upon arrival at the lab, participants’ hunger ratings did not differ between the stated condition (${\displaystyle M_{satedt1}=51.98}$, ${\displaystyle S{D}_{satedt1}=27.54}$) and the hungry condition (${\displaystyle M_{hungryt1}=57.99}$, ${\displaystyle S{D}_{hungryt1}=23.54}$, t(63)=-1.265, p=0.211, d=0.159). The RM-ANOVA indicated that the change in hunger ratings between the last and first time point differed across conditions (F(1)=26.31, p<.001, d=0.708). Specifically, in the hungry condition the change was positive, meaning participants got hungrier throughout the experiment (${\displaystyle M_{hungrydiff}=22.4}$, ${\displaystyle S{D}_{hungrydiff}=20}$), whereas in the sated condition, this difference was negative (${\displaystyle M_{sateddiff}=-36.3}$, ${\displaystyle S{D}_{sateddiff}=31.3}$). Thus, our hunger state manipulation had the desired effect on the subjective feeling of hunger. Notably, there were no effects of hunger state on positive and negative affect across timepoints (Appendix 1—figure 1).

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Effect of hunger state on choice and RT

In both conditions, a larger value difference (VD) with respect to taste was predictive of tasty choice (Figure 2c), while a larger VD with respect to health was predictive of healthy choice, particularly in the sated condition (Figure 2d). The GLMM of choice (tasty vs. healthy) indicated that overall participants preferred tasty over healthy options (${\displaystyle {\beta }_{intercept}=0.73}$, SE = 0.098, p<0.001). In line with our preregistered hypotheses, we found that participants were less likely to choose the tasty option when being sated as compared to hungry (${\displaystyle {\beta }_{sated}=-0.211}$, SE = 0.103, p=0.04). Moreover, longer relative dwell time on the tasty option increased the likelihood of tasty choice (${\displaystyle {\beta }_{dwelltime}=0.998}$, SE = 0.027, p<0.001) (Figure 3a) (see Appendix 4—table 1 for model specifications, random effects, and an alternative model with additional predictors).

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Importantly, we assessed the robustness of our findings by testing alternative GLMMs to predict choices, in which we replaced the taste/health ratings as predictors by wanting/health ratings (Appendix 5—table 1) and by high/low caloric information (Appendix 6—table 1). Of note, the effects of hunger on higher wanted vs healthy and high vs low caloric choice were markedly stronger (Appendix 5—table 1, Appendix 6—table 1, Figure 2—figure supplement 3).

With respect to response time (RT), we found that RT was highest for choices in which taste ratings were similar for both options (Figure 2e), while health value did not affect RT (Figure 2f). The GLMM of RT indicated an average RT of 2.748 s (SE = 0.096). Tasty choices were associated with faster decisions, decreasing RT by 0.15 s (SE = 0.018, p<0f.001). Longer relative dwell time on the tasty option predicted slower choice in general (${\displaystyle {\beta }_{dwelltime}=0.065}$, SE = 0.014, p<0.001), but was sped-up for tasty choices (${\displaystyle {\beta }_{dwelltime\ast tastychoice}=-0.13}$, SE = 0.017, p<0.001) (see Appendix 4—table 2 for model specifications, random effects, and an alternative model with additional predictors).

Altogether, we found that participants preferred tasty over healthy options, and that this preference was amplified under hunger. While tasty choices were faster in general, we did not find an effect of hunger state on RT. Finally, our GLMMs indicate that dwell time is an important predictor of choice and RT.

Mechanisms underlying the effect of hunger on attention and dietary choice

In line with our hypotheses, we found that participants were more likely to choose tasty over healthy food items, and this difference was amplified by hunger (Figure 2b). Moreover, we demonstrated that attention mediated the effect of hunger on choice (Figure 3c). To further elucidate the cognitive processes underlying these effects, we estimated and compared different versions of DDMs against one another using hierarchical Bayesian cognitive modeling. Models varied in terms of whether and how they accounted for attention and whether a starting point bias (towards tasty vs. healthy options) was included (see Methods) Figure 4. We report parameter estimates for all models (Figure 5, Figure 5—figure supplement 1 and Appendix 8—figures 1–6), as well as posterior predictive checks (Figure 4, Figure 4—figure supplement 1 and Appendix 9—figure 1) and recoveries (Appendix 10—figures 1–4) for the best fitting models.

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Initial model comparison revealed that there was no evidence for a starting point effect, as models without starting point consistently outperformed models with starting point. In addition, the multi-attribute attentional DDMs (maaDDM and maaDDM2 $\varphi$), which allow modeling discounting of unattended options as well as unattended attributes, outperformed simpler variants (i.e. DDM, aDDM) (Table 1).

Table 1

Model	$\alpha$	nDT	$d$	$\omega$	$\beta$	$\theta$	${\varphi }_1$	${\varphi }_2$	DIC	Rhat
DDM	YES	YES	YES	YES	NO	NO	NO	NO	69646	1.002
DDMsp	YES	YES	YES	YES	YES	NO	NO	NO	69668	1.004
aDDM	YES	YES	YES	YES	NO	YES	NO	NO	65561	1.004
aDDMsp	YES	YES	YES	YES	YES	YES	NO	NO	65587	1.003
maaDDM	YES	YES	YES	YES	NO	YES	YES	NO	65155	1.005
maaDDMsp	YES	YES	YES	YES	YES	YES	YES	NO	65214	1.011
maaDDM2 ɸ	YES	YES	YES	YES	NO	YES	YES	YES	64002	1.017
maaDDM2 ${\displaystyle \varphi }$ sp	YES	YES	YES	YES	YES	YES	YES	YES	65070	1.027

1. The first column states the name of the model; the following nine columns indicate whether the drift diffusion model (DDM) variants included a given parameter or not. $\alpha$ refers to the boundary separation; nDT refers to non-decision time; $d$ refers to the drift scaling parameter; $\omega$ refers to the relative taste compared to health weight; $\beta$ refers to the starting point bias; $\theta$ refers to the discounting of the non-looked upon option; ${\varphi }_1$ refers to the discounting of the non-looked upon attribute, in case the model includes ${\varphi }_1$ and ${\varphi }_2$ they refer to the discounting of taste and heath information, respectively; The deviance information criterion (DIC) was used as goodness-of-fit measure. Rhat is the scale reduction factor, to accurately predict posterior distributions, it should be 1.00, according to Vuorre and Bolger, 2018 values within 0.05 are acceptable. The best model (i.e. maaDDM2 $\varphi$) is highlighted in bold.

Further inspection of differences in the maaDDM’s parameter estimates between the hungry and sated conditions suggested that hunger increased the weight of taste information relative to health information and exacerbated attribute-wise attentional discounting i.e., lower estimates of parameter ϕ (Figure 5—figure supplement 1). To shed more light on this attentional effect of hunger, we tested an extension of the maaDDM that assumed two separate attribute-wise discounting parameters for taste and health information (i.e. maaDDM2ϕ, see also Figure 6). Remarkably, this model provided a substantially improved model fit compared to the maaDDM and all other models (Table 1). Moreover, the posterior predictive checks of this model indicated that it provides an exquisite account of the choice and RT data (Figure 4). Again, we assessed the robustness of our results by also testing additional models in which health and taste attributes were replaced by Nutri-Scores (Appendix 11—table 1, Appendix 11—figures 1–8) and wanting (Appendix 12—table 1, Appendix 12—figures 1–4), respectively. Importantly, these complementary modeling analyses yielded comparable quantitative and qualitative results.

Figure 6

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Taking a closer look at the group parameter distributions of the winning model (i.e. maaDDM2 $\varphi$), we examined the highest density intervals (HDI), which reflects the part of the posterior distributions that contain 95% of all values. We found that participants relative taste weight was larger than 0.5 in both conditions, indicating a higher taste compared to health preference (${HDI}_{sated}$=[0.698,0.831]; ${HDI}_{hungry}$=[0.788,0.922]), as the HDIs did not include 0.5. Critically, this preference was credibly higher under hunger (HDI=[0.122, 0.642]; Figure 5a), as the HDI of the effect of hunger did not include 0. We did not find differences between conditions with respect to the drift scaling parameter $d$ (HDI=[–0.165, 0.025]; Figure 5b), the non-decision time nDT (HDI=[–0.02, 0.087]; Figure 5c), or the boundary separation $\alpha$ (HDI = [–0.12, 0.18]; Figure 5d). Similarly, there were no credible hunger effects with respect to the attentional discounting of the options $\theta$ (HDI = [–0.092, 0.017]; Figure 5e). Looking at the two attribute-wise attentional discounting parameters revealed that there was no condition effect on discounting of the taste attribute (i.e. ${\varphi }_T$, HDI=[–0.291, 0.16]; Figure 5f), but instead, hunger exclusively increased the discounting of health information (i.e. ${\varphi }_H$, HDI=[-1.088,–0.188]; Figure 5g).

Taken together, our extension of the multi-attribute attentional DDM with separate attention parameter for taste and health attributes (i.e. maaDDM2 $\varphi$) provided the best quantitative and an excellent qualitative account of the data, and it suggests that hunger affects the relative weighting of taste compared to health information and further increases the discounting of unattended health information during the evidence-accumulation process.

The goal of this study was to elucidate the cognitive mechanisms driving dietary choice under hunger. We found that individuals prefer tasty over healthy food options, and that this preference is amplified by hunger state. This pattern was also reflected in our modeling analyses, revealing that taste was weighted more than health in both, but especially in the hungry condition. Our mediation analysis suggests that the cognitive mechanism underlying the influence of hunger state on food choice is driven by a shift in attention. Specifically, hungry individuals pay more attention to tasty food options in general and the taste attribute in particular, which in turn increases the probability of tasty choices. Again, our cognitive modeling analyses integrated these findings, demonstrating increased attentional discounting of the health attribute under hunger. Together, our findings suggest a nuanced interplay between attention and the significance assigned to the options’ underlying attributes in dietary decision-making.

First, in line with previous research (Cheung et al., 2017; Otterbring, 2019; Read and Leeuwen, 1998) we demonstrate that hunger affects dietary choice. Participants were more likely to choose options that were rated more tasty than healthy, for which reported higher wanting, and that contained higher caloric content. Moreover, our findings indicate that higher taste ratings were strongly predictive of choice across conditions, whereas higher health ratings only predicted choice in the sated condition, albeit less influential than taste ratings (Figure 2c and d). Crucially, our modeling analyses endorsed this account: across models, we demonstrate that the relative taste weight was larger in both conditions and particularly in the hungry condition. This finding adds to previous work demonstrating the distinct influence of taste and health attributes in guiding choice (Barakchian et al., 2021; Enax et al., 2016; Hutcherson and Tusche, 2022; Maier et al., 2020; Rramani et al., 2020), by illustrating that these effects can differ across states. This also aligns with results from a food bidding task (Fisher and Rangel, 2014), wherein the authors find that hunger elevated the bids and speculate that this effect was driven by an increased taste but not health valuation of food items.

Second, we show that the valuation is not only influenced by the attributes underlying decision weights, but moreover by attention. Behaviorally, we show that attention mediates the impact of hunger state on food choice, such that hunger state predicts overall less dwell time on the healthy option, thereby increasing the probability of tasty choice. Upon closer examination of attention allocation to the respective attributes, we show that participants, especially when hungry, spent much more time on food images compared to Nutri-Scores. We see two explanations for the excessively high proportion of dwell time on food images compared to Nutri-Scores: First, food images were more important for deciding, as indicated by the relative weight parameter being larger than 0.5. Consequently and in line with previous work (Orquin et al., 2021), people pay more attention to more important attributes. Second, food images contain more complex information. Whereas extracting taste information from a food image can be seen as a complex inference process, a (color-coded) nutritional score provides salient and easily discernible evidence about a product’s healthiness and consequently requires less dwell time. This account is supported by studies showing that nutritional scores can promote healthy choice despite proportionally little dwell time (e.g. Bialkova et al., 2014; Gabor et al., 2020; Rramani et al., 2020).

To take these putatively different attentional demands of different attributes into account, we extended the maaDDM of Yang and Krajbich, 2023 and developed the maaDDM2 $\varphi$, which assumes separate discounting parameters for separate (unattended) attributes. Consistent with the above-mentioned attribute-specific effect of hunger on choices, we found that the attentional discounting of the health attribute but not the taste attribute was amplified under hunger, such that unattended nutritional information had a blunted influence on the evidence accumulation process. Notably, we also found a hunger effect on the attribute discounting parameter ϕ in the regular maaDDM (Figure 5—figure supplement 1), but our extension allowed us to pinpoint this effect specifically to health information. Given related work on the malleability of attentional discounting on mnemonic demands (Eum et al., 2023; Weilbächer et al., 2021), we speculate that hunger could impede a person’s ability or willingness to maintain health considerations in working memory when attention is currently drawn to the tasty food stimulus.

Several supplementary analyses demonstrate the robustness of our findings. In essence, and in line with a large body of literature (e.g. Garlasco et al., 2019; Otterbring, 2019; Read and Leeuwen, 1998), hungry participants were more likely to choose items which they rated higher in terms of tastiness, wanting and caloric content. Importantly, the pivotal role of attention was also established in the exploratory wanting and calorie analyses. Moreover, we performed a PCA to identify the major components that drive food choices, finding that two factors representing tastiness and healthiness aspects explain 81% of the variance in the data. We see this as a justification to describe and study dietary choices by means of these two attributes, essentially following a series of previous studies in our field (e.g. Hutcherson and Tusche, 2022; Maier et al., 2020; Rramani et al., 2020; Sullivan and Huettel, 2021).

The present results and supplementary analyses clearly support the twofold effect of hunger state on the cognitive mechanisms underlying choice. However, we acknowledge potential demand effects arising from the within-subject Protein-shake manipulation. A recent study (Khalid et al., 2024) showed that labeling water to decrease or increase hunger affected participants subsequent hunger ratings and food valuations. For instance, participants expecting the water to decrease hunger showed less wanting for food items. DDM modeling suggested that this placebo manipulation affected both drift rate and starting point. The absence of a starting point effect in our data speaks against any prior bias in participants due to any demand effects. Yet, we cannot rule out that such effects affected the decision-making process, for example by increasing the taste weight (and thus the drift rate) in the hungry condition.

From a neurobiological perspective, both homeostatic and hedonic mechanisms drive eating behavior. While homeostatic mechanisms regulate eating behavior based on energy needs, hedonic mechanisms operate independent of caloric deficit (Alonso-Alonso et al., 2015; Lowe and Butryn, 2007; Saper et al., 2002). Participants’ preference for tasty high-caloric food options in the hungry condition aligns with a drive for energy restoration and could thus be taken as an adaptive response to signals from the body. On the other hand, our data shows that participants preferred less healthy options also in the sated condition. Here, hedonic drivers could predominate indicating potentially maladaptive decision-making that could lead to adverse health outcomes if sustained. Notably, our modeling analyses indicated that participants in the sated condition showed reduced attentional discounting of health information, which poses potential for attention-based intervention strategies to counter hedonic hunger. This has been investigated, for example, in behavioral (Barakchian et al., 2021; Bucher et al., 2016; Cheung et al., 2017; Sullivan and Huettel, 2021), eye-tracking (Schomaker et al., 2022; Vriens et al., 2020) and neuroimaging studies (Hare et al., 2011; Hutcherson and Tusche, 2022) showing that focusing attention on health aspects increased healthy choice. For example, Hutcherson and Tusche, 2022 compellingly demonstrated that the mechanism through which health cues enhance healthy choice is shaped by increased value computations in the dorsolateral prefrontal cortex (dlPFC) when cue and choice are conflicting (i.e. health cue, tasty choice). In the context of hunger, these findings together with our analyses suggest that drawing people’s attention towards health information will promote healthy choice by mitigating the increased attentional discounting of such information in the presence of tempting food stimuli.

In conclusion, our study provides substantial insights into the mechanism underlying dietary choice across metabolic states. Our extension of the multi-attribute attentional DDM revealed that the valuation of food options under hunger is compromised by a relatively lower weighting and a stronger attentional discounting of health information. This modeling extension represents a general contribution to advance research on multi-attribute decision-making, as it allows modeling attribute-specific attentional discounting, which is likely to occur if attributes are described in markedly different formats.

Scripts and data for all behavioral and modeling analyses are available on https://github.com/JenniferMarch/HungryLens/ (copy archived at March, 2025).

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Author details

1. Jennifer March

   Department of Psychology and Hamburg Center of Neuroscience, University of Hamburg, Hamburg, Germany

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing – review and editing

   For correspondence

   jennifer.march@uni-hamburg.de

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0009-0002-3133-8170

2. Sebastian Gluth

   Department of Psychology and Hamburg Center of Neuroscience, University of Hamburg, Hamburg, Germany

   Contribution

   Conceptualization, Resources, Software, Formal analysis, Supervision, Funding acquisition, Validation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2241-5103

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