Fear of Missing Out Predicts Distraction by Social Reward Signals Displayed on a Smartphone in Difficult Driving Situations

📜🔗LINK to the source

1. ANALYTICAL SUMMARY

Road distraction as an attention phenomenon, not just behaviour

Driver distraction is among the leading contributory factors of accidents. The smartphone occupies a unique place in this: beyond visual and manual distraction, it produces a cognitive, even without direct manipulation. The authors mobilise a recent theoretical framework — the selection history and the reward history — according to which a stimulus already associated with a reward captures attention even if it is neither salient nor relevant. The smartphone is a form of social reward learned. In this context, FoMO (the persistent fear of missing out on rewarding social experiences) is presented as a modulating individual factor, previously only associated with self-reported distraction, never tested on actual behaviour.

An experimental demonstration of the role of FoMO under attentional constraint

The study provides the first behavioural (and not declarative) evidence that FoMO predicts attentional capture by social reward signals while driving. The central result is conditional: the effect only appears when the driving task is difficult (heavy fog). In these conditions, the higher the FoMO score, the longer the reaction time in response to a distractor with high social reward. In an easy situation, FoMO plays no role. The operational contribution remains indirect: it does not provide a field tool, but a solid scientific argument to target prevention messages at risk profiles and contexts.

2. KEY POINTS OF THE DOCUMENT

1. The smartphone distracts even without direct interaction. The authors remind us that mere presence or a notification is enough to degrade performance on attentional tasks, mobilising resources to inhibit capture and intrusive thoughts (pp. 2-3). The mechanism is therefore not just "looking at your screen".
2. The attraction of the smartphone relates to the history of social reward. The text anchors the smartphone in a third attentional factor — the selection history / reward history — distinct from salience and relevance: a stimulus that has already been rewarded captures attention almost automatically (p. 2).
3. FoMO is defined and measured precisely. Trait measured by the FoMO scale of Przybylski et al. (2013), French version by Michot et al. (2016), 10 items, score from 1 to 5 (p. 3). The average score of the sample is low (M = 2.01; SD = 0.32), which limits the scope (p. 5).
4. The effect is a three-factor interaction, not a simple effect. The analysis reveals a significant interaction FoMO × fog density × type of distractor (b = 22; 95% CI [3, 41]; p = 0.023) (p. 5). In heavy fog only, each point of FoMO adds 28 ms reaction times in response to the high-reward distractor (p = 0.009 ; r = 0.48) ; in low fog, no effect (p. 5).
5. The difficulty of the task is the triggering condition. Dense fog generally lengthens reaction times (731 ms vs 683 ms ; main effect p < 0.001) and opens a temporal "window" where the distractor can intrude (pp. 5-6). Without attentional overload, social distraction does not result in measurable deficit.

3. ACTION POINTS FOR LOCAL ACTORS

1. Reformulate the road safety message beyond mere willpower. Rely on the mechanism of automatic attraction (pp. 2-3, 7) to explain that "muting the phone is not enough if it remains within sight" — the very presence acts.
2. Target audiences and contexts that accumulate risk factors. The data suggest that risk concentrates when high FoMO, difficult tasks, and social signals combine (p. 5). In animations, emphasise degraded driving situations (night, rain, fog, fatigue) rather than a general discourse.
3. Integrate FoMO as a framework for understanding, not as a diagnosis. The FoMO scale (p. 3) can serve as a pedagogical tool for self-questioning ("am I afraid of missing out by turning off my phone?") without making it a clinical measurement tool — this is not its validated use here.
4. Link road safety prevention and prevention of digital usage. The document bridges the gap between distraction while driving and mechanisms of behavioural addiction (p. 2): an opportunity to cross two fields often siloed in health promotion.
5. Encourage environmental measures rather than relying solely on willpower. Since inhibition consumes resources (p. 2), promoting material solutions (driving mode, phone out of sight) is consistent with the described mechanism.
6. Necessary adaptation / unmet need : the study does not test either experienced drivers or real or simulated driving with risk (p. 7). Any field transposition must be presented as working hypothesis, to be supplemented by recent French data in a realistic situation (see references §4).

4. ADDITIONAL REFERENCES 

1. Assurance Prévention (France Assureurs), "Calls, SMS and other distractions: what impact on driving vigilance?", 2024. Study on simulator with eye-tracking among regular drivers (16–25 April 2024): complement behavioural in a realistic French context to the laboratory setup of Matias et al. → https://www.assurance-prevention.fr/nos-etudes/distracteurs-volant-2024
2. Road safety (Delegation for Road Safety), "The phone and driving" (updated page 2025). Institutional reference resource detailing the four sources of distraction (visual, cognitive, auditory, physical), directly usable in animation. → https://www.securite-routiere.gouv.fr/dangers-de-la-route/le-telephone-et-la-conduite
3. Commission of experts "Children and screens — In search of lost time", report submitted to the President of the Republic, April 2024 (official summary, DAJ / economie.gouv.fr). Complementary on the aspect of addictive design and attention capture, which sheds light on the social reward mechanism exploited by platforms. → https://www.economie.gouv.fr/daj/exposition-jeunes-ecrans-recommandations-rapport

5. FREQUENTLY ASKED QUESTIONS (FAQ)

1. What exactly does this study test? If the fear of missing out (FoMO) predicts distraction real (reaction time) caused by a social signal displayed on a smartphone during a road detection task, depending on difficulty (pp. 3, 5).
2. What is the main finding? In difficult driving (heavy fog), the higher the FoMO, the more the high social reward distractor slows down the reaction (28 ms per point of FoMO). In easy driving, no effect (p. 5).
3. Is this a “real” driving situation? No. Participants observed images of road scenes on a screen, without simulated driving or collision risk (pp. 3-4, 7). This is a major limitation acknowledged by the authors.
4. How many people participated? 29 students (7 men), average age 20 years, mainly young women in psychology (p. 3). Sample not representative : the results are not generalisable (p. 7).
5. What is a “social reward” distractor here? A coloured circle previously associated, in the learning phase, with smiling faces (social reward) rather than neutral ones (p. 3, Figure 1). It has no salience or utility of its own.
6. Why does fog change everything? It makes the task demanding and lengthens decision time, opening a window where the distractor can intrude; it may also make the screen relatively more salient (pp. 5-6).
7. What precautions should be taken before citing this study in the field? Remind that this is a preliminary study, in the laboratory, on a small young and female sample, with a low average FoMO (p. 5): an interesting scientific signal, not an established proof nor a figure to brandish (p. 7).

6. REWRITING IN PLAIN LANGUAGE

What is this study about?

Researchers studied distraction while driving.

They looked at the role of the phone.

They also looked at an emotion: the fear of missing out.

This fear is called FoMO.

What they found

The phone attracts our attention on its own.

It doesn't need to ring to distract us.

An image related to something pleasant attracts us even more.

People who are very afraid of missing out are more distracted.

But be careful: it depends on the situation

When driving is easy, this fear makes no difference.

When driving is difficult, this fear distracts us a lot.

For example, when there is fog.

Then the person takes longer to react.

What to remember

This study is small. There were 29 young people.

It was on a screen, not in a real car.

The result is interesting. But it is not total proof.

More studies are needed to be sure.

7. CROSS-SECTIONAL ANALYSIS — VALUES OF HEALTH PRACTICES

Framing note: this is a fundamental laboratory study. Most health promotion principles are, by nature, absent — it is neither a participatory device nor a community intervention. The analysis below is therefore mainly a statement of absence, with an honest editorial aim.

• Literacy: low. The document is intended for a scientific readership; no tools suitable for varying levels of understanding are provided (the above easy-to-read version is an addition from us).
• Empowerment: absent. The participants are subjects of the experiment, not involved in the design or evaluation.
• Participation: no co-construction mechanism is described; protocol entirely defined by the researchers.
• Community health: absent. The approach is strictly individual and cognitive.
• Ethics: the study is validated by an ethics committee (IRB, p. 3) and declares the absence of conflict of interest; however, no cultural or social bias is thematised beyond the sampling limit.
• Human rights: informed consent obtained (p. 3). The issue of equity/inclusion is not addressed, except implicitly (homogeneous sample).
• Intersectorality: implicit via affiliations (academic research + CEREMA, mobility/infrastructure expertise), but no operational partnership recommended.
• Partnership: no formalised field collaboration model; purely scientific collaboration.
• Combating discrimination: untreated. The document mentions neither discrimination nor issues of non-judgment or diversity.

8. EVALUATION OF THE RELIABILITY OF THE RESOURCE

Scientific relevance — strong on method, cautious on scope. Peer-reviewed article, reviewed by a committee, rigorous protocol (Helmert contrasts, mixed confirmation models, transparent management of extreme values, raw data available on OSF). Current and well-referenced theoretical framework. But : very small sample (n = 29), homogeneous (young women, psychology students), low and little dispersed average FoMO, modest effect sizes (partial R² = 0.047 for the key interaction), and above all self-qualification as a preliminary study by the authors themselves. The key result is a third-order interaction — statistically fragile and to be replicated.

Operational relevance — indirect. No tools, checklists or intervention protocols are provided: the resource is not usable "as is" in the field. Its value is argumentative and educational (understanding and making understand a mechanism), provided it is presented as a scientific lead and not as conclusive evidence. The experimental setup (images, absence of real collision risk) is far from actual driving, which the authors acknowledge.

10. STRATEGIC HASHTAGS

#healthpractices #RoadSafety #DistractedDriving #FoMO #DigitalLiteracy #CognitiveAttention #ScreenUse #EvidenceBased