The research, conducted by Marc Ballestero-Arnau and Toni Cunillera of the University of Barcelona’s Institute of Neurosciences, together with Borja Rodríguez-Herreros of the Centre Hospitalier Universitaire Vaudois in Lausanne, addressed a long-standing inconsistency in the literature on attentional bias. Attentional bias refers to the tendency of certain stimuli—threats, emotional faces, addictive substances, and especially palatable foods—to capture attention automatically, before conscious deliberation begins. Decades of work have linked such bias to overeating, yet studies examining how the bias changes with hunger, fullness, and body weight have produced conflicting results. Some experiments found that hungry participants attend more to food cues; others found no effect of hunger at all. The new study set out to resolve this confusion by manipulating metabolic state directly and measuring its interaction with BMI.

The methodological centerpiece of the work is the emotional attentional blink, a variant of a classic attention paradigm first described in the 1990s. In a rapid serial visual presentation, participants watch a fast stream of images and must detect a neutral target embedded within it. When an emotionally salient distractor appears shortly before the target, detection of the target drops sharply—the distractor effectively hijacks the attentional system for a few hundred milliseconds. By inserting photographs of real high-calorie foods as distractors at different points in the stream, the researchers could quantify how strongly food captured attention under different physiological conditions. Distractors appearing 300 milliseconds before the target, at so-called lag-3, probe the earliest and most automatic phase of attentional capture; distractors appearing 900 milliseconds before, at lag-9, serve as a comparison point when the blink has largely resolved.

The first experiment recruited 183 participants spanning the whole BMI range and randomly assigned them to one of three conditions. One group tasted the snacks without eating them to satiety, one group ate until satiated, and a third ate nothing at all. Crucially, the very snacks that participants had eaten—or had merely tasted or not encountered—later reappeared as distractors in the attentional task, allowing the researchers to test whether recent consumption of a specific food dampens its grip on attention. The results were unambiguous at the group level: food images presented at lag-3 significantly reduced target detection compared with lag-9, with an odds ratio of 0.61, confirming that real-food cues automatically captured attention regardless of metabolic state.

The most striking finding, however, emerged when BMI entered the statistical model. Higher body-mass index predicted a larger attentional bias when participants were hungry, but a smaller bias when they were satiated—an interaction reported with an odds ratio of 0.65 and a p-value below 0.01. In other words, hunger amplified the attentional pull of food most strongly in individuals with higher BMI, while satiety suppressed it more effectively in leaner individuals. This crossover pattern suggests that the metabolic signals of hunger and fullness do not act uniformly across the population; their influence on attention is calibrated, or miscalibrated, in ways that track body weight.

Because the first experiment could not fully separate hunger state from the type of food encountered, the team ran a second, more surgical experiment with 61 participants—31 with overweight or obesity and 30 of normal weight—across a double-session design. This time, hunger and satiety were manipulated orthogonally to snack type: participants encountered foods they had just consumed and foods that were entirely novel to the session. The results split cleanly along both dimensions. For novel foods, an interaction revealed that participants with overweight or obesity retained a robust attentional bias even after eating to satiation, whereas the bias declined for normal-weight participants once they were full, with an odds ratio of 2.45 and a p-value of 0.03.

For snacks that participants had just eaten, the picture changed in an unexpected way: the attentional bias remained significant in both weight groups, with lag-3 distractors producing far worse target detection than lag-9, at an odds ratio of 0.37 and a p-value below 0.001. This finding speaks to the phenomenon of sensory-specific satiety, the well-documented decline in the palatability of a food that has just been consumed. The behavioral expression of that satiety—the reduced desire to keep eating the same food—evidently does not translate into reduced automatic attention. A food can become less appealing to the palate while remaining fully capable of hijacking the earliest stages of visual attention, at least in the immediate aftermath of consumption.

Taken together, the two experiments sketch a model in which satiety signals alone are insufficient to curb attentional capture by food in obesity. In individuals with normal weight, the metabolic state of the body appears to gate the attentional system’s responsiveness to food cues: hunger opens the gate, fullness closes it. In individuals with overweight or obesity, that gating seems to fail for novel high-calorie foods, which continue to command attention even when the body has no need for energy. This dissociation between homeostatic signals and attentional orienting aligns with a broader theoretical framework in which the incentive salience of reward cues—their power to attract wanting, independent of pleasure or need—becomes exaggerated in obesity, potentially through altered function of dopaminergic reward circuitry.

The study also helps explain why previous findings in this field have been so inconsistent. Many earlier experiments relied on static food images viewed by participants in uncontrolled hunger states, or compared groups without manipulating satiety within the same individuals. By using real foods, experimentally induced hunger and satiety, and a temporally precise attention paradigm, the Barcelona team was able to detect interactions that simpler designs would average away. The authors’ own prior work had shown that food images capture attention by virtue of being food, independent of valence, arousal, appeal, or caloric content; the present study adds that this capture is not fixed but is modulated by the interplay of metabolic state and body weight.

The practical implications extend beyond the laboratory. If satiety cannot be relied upon to quiet attentional capture by food in individuals with higher BMI, then environments saturated with high-calorie cues—supermarket aisles, advertising screens, delivery apps—pose a persistent challenge that willpower deployed after a meal may be poorly equipped to meet. The authors suggest that interventions should target attentional control directly, through attention-bias modification training or similar approaches, and should limit food availability rather than trusting internal satiety signals to do the protective work. As obesity rates continue to climb worldwide, the study offers a sobering reframing: the battle against overeating may be lost or won not at the level of conscious craving, but in the first few hundred milliseconds after a pizza advertisement flickers across the visual field.

Subject of Research: Modulation of food-related attentional bias by hunger, satiety, and body-mass index

Article Title: Satiety does not always silence attention to high-calorie foods: BMI modulates food-related attentional bias

Article References: Satiety does not always silence attention to high-calorie foods: BMI modulates food-related attentional bias. (n.d.). https://doi.org/10.1038/s41366-026-02240-x

Image Credits: AI Generated

DOI: 10.1038/s41366-026-02240-x

Keywords: attentional bias, obesity, body-mass index, satiety, hunger, high-calorie foods, emotional attentional blink, sensory-specific satiety, attentional capture, eating behavior, reward circuitry, cognitive control

The task the researchers chose is the additional singleton paradigm, one of the most widely used tools in attention research. Participants search for a uniquely shaped target, such as a pear among apples, while a salient distractor, here a red object among green ones, appears on half of the trials. In traditional versions, people respond more slowly and make more errors when the distractor is present, a phenomenon known as attentional capture. The salient stimulus grabs attention automatically, even though it is irrelevant to the task. What has been missing is a way to watch that capture unfold in three dimensions, in movements that resemble the reaching and looking we do every day.

Fifty-four participants aged 18 to 26 wore an HTC Vive Pro Eye headset fitted with integrated eye tracking and held a motion-tracked controller in their dominant hand. The system recorded hand positions at roughly 90 hertz and gaze direction from the headset’s infrared eye tracker, allowing the team to capture reaching and looking behavior at the same time in the same trial. Participants completed the task in two contrasting virtual environments: a sparsely detailed, untextured room designed to mimic a bare laboratory, and a richly detailed virtual greenhouse filled with visual clutter. The order of the environments was counterbalanced across participants.

On each trial, four fruit-shaped stimuli appeared on a virtual tree at the top, left, right, and bottom locations. The target was always the unique shape, and its color and identity were randomized from trial to trial, encouraging participants to search for any singleton rather than a specific feature. When a distractor appeared, it was always the uniquely colored object. Participants initiated each reach by holding the controller inside a central starting sphere for two seconds, then had three seconds to move to the target. Correct responses earned a pleasant 600-hertz tone; errors or timeouts produced a low 300-hertz buzz.

The results were striking in their consistency. When a distractor was present, participants were slower to initiate their movements, slower to complete them, and slower overall. Their reach trajectories curved more, their hands spent a greater proportion of each movement being pulled toward the distractor location, their eyes traveled greater angular distances, and they took longer to fixate the target for the first time. Effect sizes were substantial, with distractor presence explaining between roughly a quarter and nearly two-thirds of the variance in individual measures. In short, the attentional capture effects documented over thirty years of button-press experiments generalize robustly to three-dimensional immersive environments.

But the study’s real methodological contribution lies in how the movements were analyzed. Traditional reach-tracking studies collapse three-dimensional trajectories into two-dimensional summary statistics such as maximum perpendicular deviation from a straight path. The problem is that a large deviation does not necessarily mean the hand was drawn toward the distractor; it could also reflect a delayed decision, with the hand drifting toward the center of the display before committing. Gurr and colleagues instead computed continuous target attraction and distractor attraction scores: for every time point in a movement, they determined which object the hand was moving closest toward, and calculated the proportion of the trajectory devoted to each. These measures revealed capture effects that the curvature measure missed entirely, particularly on trials where the distractor appeared far from the target.

Those attraction scores also allowed the team to reconstruct the time course of capture using cluster-based permutation analyses. When the distractor was adjacent to the target, the hand was pulled toward the distractor from the very first moments of movement, with the effect persisting for up to 78 percent of the trajectory before the movement was corrected. Even when the target and distractor sat on opposite sides of the display, the hand still showed significant early attraction to the distractor, though the effect was briefer and weaker. This graded pattern mirrors earlier findings that capture costs shrink as the distance between target and distractor grows, and it extends them into a fully three-dimensional movement space.

The comparison between the sparse room and the rich greenhouse produced a subtler story. Participants were generally slower and moved their eyes more in the detailed greenhouse, consistent with prior work showing that cluttered environments impair visual search. Yet the size of the attentional capture effect itself did not differ between environments. Bayesian analyses provided moderate evidence for this null result on several measures, though the evidence was mixed on others. One complication emerged: participants who started in the sparse room performed nearly identically in both environments, while those who started in the rich greenhouse were markedly faster in the sparse room afterward, hinting at practice or adaptation effects that future studies will need to disentangle.

The authors are candid about the limitations. Many participants adopted a rigid strategy of looking first at the top location, which complicated the analysis of initial eye movements. The task’s fixed layout may have allowed participants to learn that the bottom object revealed the target’s color, potentially shrinking the distractor effect. And VR hardware brings its own caveats: the headset’s eye tracker is less precise than professional laboratory systems, and the motion-to-photon lag between a real hand movement and its virtual rendering, estimated at a few tens of milliseconds, means VR-derived timing should be compared cautiously with data from electromagnetic or optical trackers.

Even so, the study makes a compelling case that a commercially available VR headset, costing a fraction of traditional tracking rigs, can deliver laboratory-grade measurements of how distraction plays out simultaneously in the hand and the eye. Because the equipment is portable and the software tools are increasingly accessible, the approach could spread quickly through labs studying attention, perception, and motor control. The data and analysis scripts have been released openly, and the team plans to publish user-friendly processing software. The gap between the psychology laboratory and the cluttered, three-dimensional world we actually inhabit may finally be starting to close.

Subject of Research: Measuring attentional capture dynamics in hand and eye movements using virtual reality

Article Title: Measuring the dynamics of attentional capture in hand and eye movements with virtual reality

Article References: Gurr, M. D., Moher, J., Egbert, M. D., & Erb, C. D. (2026). Measuring the dynamics of attentional capture in hand and eye movements with virtual reality. Behavior Research Methods, 58(11), Article 302. https://doi.org/10.3758/s13428-026-03177-9

Image Credits: AI Generated

DOI: 10.3758/s13428-026-03177-9

Keywords: attentional capture, virtual reality, eye tracking, hand tracking, additional singleton task, visual attention, reaching movements, distractors, Behavior Research Methods, sensorimotor processing, visual search, immersive environments