Planning is one of the most computationally demanding things the human mind does. To decide on a course of action, people must mentally simulate future states of the world, evaluate the consequences of each possible move, and compare the expected values of different sequences of choices. In a perfectly predictable environment, this simulation can be carried out with confidence: if you take action A, outcome B will follow. But the real world is stochastic. Outcomes are corrupted by noise, the rules governing them shift over time, and sometimes our own actions fail to produce the effects we intend. Each of these forms of randomness, the authors note, makes exact planning exponentially harder, because the decision-maker must average over many possible futures rather than tracing a single one. The theoretical optimal solution to such problems quickly becomes intractable, which raises a fundamental question that has lingered in cognitive science: how do people balance the cognitive cost of planning against its potential benefits when the environment is uncertain?

To answer this question, the team designed a laboratory planning task in which participants encountered one of three distinct forms of stochasticity that are commonly found in real-world environments. The first was reliability, meaning the degree to which outcomes consistently reflect the underlying value of an action. The second was volatility, meaning the rate at which the environment’s structure changes over time. The third was controllability, meaning the extent to which a person’s own actions reliably determine what happens next. These three manipulations capture, in simplified form, the kinds of unpredictability people face when navigating unreliable transportation systems, volatile financial markets, or social situations where other people’s behavior only partially responds to our own. By varying each form of stochasticity independently, the researchers could ask whether different types of randomness affect planning in different ways, or whether the brain responds to all of them with a common strategy.

The central behavioral measure was deceptively simple: the time participants took to make their first choice in each planning episode. First-choice response times are widely used as a proxy for planning depth, because a longer deliberation typically reflects more mental simulation of future possibilities. Across all three manipulations, the researchers found a robust and consistent pattern. As stochasticity increased, people reduced their planning effort, as indexed by faster first-choice response times. In other words, whether the randomness came from unreliable outcomes, a shifting environment, or diminished control over events, the response was the same: participants spent less time deliberating. This consistency across three conceptually distinct sources of uncertainty is one of the study’s most important contributions, suggesting that the brain may treat different forms of environmental randomness through a common computational lens when deciding how much to think.

To understand what was happening inside the mind during these decisions, the team went beyond response times and built a family of computational cognitive models designed to account for participants’ choices. This modeling approach is a hallmark of the Ma laboratory’s work on resource-rational cognition, the idea that the brain optimizes not for perfect performance but for the best performance achievable given limited time, energy, and neural machinery. The models allowed the researchers to distinguish between competing hypotheses about how people cope with stochasticity. One possibility is that people perform something close to the correct calculation, computing expected values by averaging over possible outcomes weighted by their probabilities. Another possibility is that people adopt a much simpler strategy, one that sacrifices accuracy in exchange for a dramatic reduction in mental effort.

The modeling results pointed decisively toward the simpler strategy. Rather than calculating expected values optimally, people behaved as if the world were deterministic, a phenomenon the authors call determinizing. Under this scheme, the planner ignores the probability distribution over outcomes and plans as though each action leads to a single, certain result. Determinizing is computationally cheap: it converts a difficult problem of averaging over many futures into an easy problem of following one imagined path. The cost, of course, is accuracy, because in a genuinely stochastic world the single imagined path will sometimes be wrong. But the benefit is a substantial saving of cognitive effort, and the study suggests that when randomness rises, the brain judges that saving to be worth the loss in precision. This finding connects to a broader theme in recent cognitive science, in which people construct simplified mental representations to make planning tractable, rather than brute-forcing optimal solutions their neural hardware cannot afford.

Within this determinizing framework, the researchers identified a second, subtler signature of reduced effort. As stochasticity increased, people decreased their sensitivity to values, a pattern the authors describe as policy compression. In a fully sensitive policy, small differences in the value of different options translate into large differences in choice behavior, reflecting careful, fine-grained evaluation. When sensitivity to values decreases, choices become less discriminating, as though the planner is working from a coarser, more compressed summary of the decision landscape. Policy compression is a form of lossy compression of a behavioral policy: the brain retains the broad shape of what is worth doing but discards the fine distinctions that would require more computation to resolve. Consistent with the response time findings, this compression deepened as environmental randomness grew, providing converging evidence at both the level of deliberation time and the level of choice structure that planning effort was being actively withdrawn.

The study also carries a methodological warning for the field. The authors emphasize that their results reveal the limitations of studying stochasticity solely through single-shot decisions, the brief one-off gambles that dominate much of the decision-making literature. In a single-shot choice, the computational burden of planning is minimal, and the effects of stochasticity on effort may be invisible. It is only in tasks that require genuine multi-step planning, where the decision-maker must simulate sequences of future actions, that the withdrawal of effort under uncertainty becomes measurable. This suggests that conclusions about how humans handle uncertainty drawn from simple gambling paradigms may not generalize to the richer, sequential decisions that fill everyday life, from choosing a commute route to planning a career move. The planning task developed here offers a template for probing those richer decisions under controlled variation in environmental structure.

The broader implications of the work extend beyond the laboratory. If people systematically reduce planning effort as their environments become less reliable, more volatile, or less controllable, then the cognitive consequences of living in unstable conditions may be more nuanced than commonly assumed. It is tempting to interpret poor planning under uncertainty as a failure of attention or motivation, but this study reframes it as a potentially rational allocation of a scarce cognitive resource: when the expected payoff of careful planning falls because randomness will erode the value of any plan anyway, the brain sensibly stops paying for it. At the same time, the finding raises questions about contexts where reduced deliberation is harmful, such as volatile but high-stakes environments where careful planning remains valuable despite the noise. Understanding when determinizing serves us well and when it leads us astray is a natural next step. The research was funded by the National Science Foundation and the National Institutes of Health, and the authors acknowledge guidance from Marcelo Mattar, Todd Gureckis, and Cristina Savin. As stochasticity shapes everything from economic turbulence to climate variability, this work highlights an often overlooked truth: the mind’s response to an unpredictable world is not to think harder, but to think less.

Subject of Research: How environmental stochasticity affects human planning effort and strategy

Article Title: Environmental stochasticity reduces human planning effort

Article References: Lei, J., Olieslagers, J., Arfaei, N., Lin, D. X., & Ma, W. J. (2026). Environmental stochasticity reduces human planning effort. Nature Communications. https://doi.org/10.1038/s41467-026-78023-9

Image Credits: AI Generated

DOI: 10.1038/s41467-026-78023-9

Keywords: planning, stochasticity, decision-making, cognitive effort, determinizing, policy compression, computational modeling, response times, uncertainty, Nature Communications, resource rationality, human behavior

The core insight behind VRrec is deceptively simple. Instead of embedding motion tracking inside a purpose-built experiment, the tool runs quietly in the background on a Windows PC, harvesting positional and rotational data from the headset, controllers, and optional body trackers while participants engage with whatever VR software the researcher has chosen — a commercial game, a mature third-party simulation, or an off-the-shelf training application. This design inverts the conventional all-in-one model, in which the same program both delivers the VR experience and records the data. With VRrec, the recording layer is decoupled entirely, meaning a study can be designed around existing VR content rather than around a bespoke application that must be coded, debugged, and rebuilt whenever the experiment changes.

Technically, VRrec is a Windows desktop application written in C# for the .NET 6 runtime. It taps into the OpenVR software development kit and relies on the SteamVR runtime to obtain tracking information from connected devices. The reference configuration validated in the paper consisted of an HTC Vive Pro headset, two Vive controllers, and six Vive trackers attached to a user’s body, but Valve’s documentation lists broader SteamVR-compatible families — including the Valve Index, Oculus Rift, and Windows Mixed Reality headsets, and, via the Steam Link streaming route, Meta Quest 2, 3, and Pro — as platform-side compatibility examples. The researchers caution that these broader configurations have not been individually validated and should be pilot-tested before research use.

The tool’s graphical interface is deliberately minimal. Researchers specify where the data file should be saved, name the file, and select a sampling frequency from six options ranging from 30 to 500 hertz. A configuration window displays the serial numbers of every tracked device the system recognizes, allowing users to assign human-readable aliases such as LeftHand to make later analysis easier. Pressing START begins recording; pressing STOP finalizes the file. Perhaps most importantly for experimental design, any keyboard key can be used to insert an event marker into the data stream — but only when Caps Lock is switched on, a safeguard that prevents accidental keystroke logging and is clearly indicated in the interface.

The output is a tab-separated text file in which every row corresponds to one recorded sample. Each row carries a sequential event number, a millisecond-precision timestamp, and, for every connected device, a rich set of fields: connection and pose-validity flags, three-dimensional position coordinates, linear and angular velocities, orientation expressed as a quaternion, and a full three-by-four transformation matrix for advanced applications such as inverse kinematics. Column names follow a self-explanatory scheme that encodes the device category, alias, serial number, parameter, and data type, so a column labeled Controller_LeftHand_LHR-493D7BY62_PosX_Float unambiguously identifies the left hand’s X-coordinate. Because the file is plain text, it can be imported directly into analysis pipelines built in Python, R, or MATLAB.

The authors are candid about the trade-off inherent in this background-recording approach. Because VRrec never reads the target application’s internal state, it cannot know what is happening inside the virtual world — no scene changes, no stimulus onsets, no distances to dynamic avatars. Paradigms that require millisecond-accurate synchronization with in-app events, such as reaction-time studies, fall outside its native capabilities. Manual keyboard markers partially compensate, and researchers can sometimes approximate spatial measures by calibrating static object locations beforehand or placing a tracker at a known reference point. The team also stresses that raw motion data are context-free: they become meaningful psychological indicators only when combined with a study’s theoretical framework and experimental conditions.

Why does body motion matter so much for behavioral science? The paper surveys a rich tradition. Classic VR studies showed that the amount a participant moves — particularly head rotation and bending — correlates with their sense of presence in a virtual environment, and that people maintain larger personal-space buffers around virtual humans than around similarly sized objects, with gender and culture shaping those distances. More recent work has pushed further: researchers have detected implicit prejudice in how closely participants approach avatars of different ethnicities, predicted racial bias from subtle head and hand movements during shooting decisions, and shown that fear can be read from motion-capture replays of people walking a plank at a simulated 80-story height — even from minimal point-light animations.

Clinical applications add further weight. A 2024 study fed VR movement patterns — headset and controller tracking, reaction times, and distractibility metrics collected during everyday tasks such as organizing a room or packing a backpack — into a machine-learning model that distinguished children with ADHD from those without with high accuracy. Other researchers have proposed combining VR simulations of daily activities with artificial intelligence to detect mild cognitive impairment in older adults through gait analysis. Against this backdrop, a tool that removes the coding barrier could substantially widen who can run such studies and how many paradigms become feasible.

The team put VRrec through punishing stress tests. In ten two-hour sessions totaling twenty hours of active use, a participant played the fast-paced rhythm game Beat Saber while VRrec recorded a headset, two controllers, and six body trackers simultaneously; the tool never crashed and never interrupted data collection. A separate 24-hour test with stationary devices likewise completed without failure, producing roughly 30 gigabytes of output. An R validation script, included in the project repository, confirmed that no rows failed parsing in any of the 34 test files. At the nominal 125-hertz setting, the median interval between samples was a stable 8 milliseconds, with the exact value spanning the 5th to 95th percentile in the long-term files. Rare timing outliers — occasional delays followed by catch-up bursts — were recorded transparently rather than hidden, and the authors note that many whole-body movement analyses do not require millisecond-level precision anyway.

The validation also surfaced a subtlety that reflects on the upstream hardware rather than the tool itself. In the stationary test, positional anomalies clustered tightly around moments when the tracking system’s pose-validity flag dropped — consistent with prior findings that consumer VR tracking can exhibit offsets after tracking loss and recovery. VRrec, by design, neither smooths nor corrects the incoming stream; it records exactly what the VR stack delivers, preserving device states, missingness, and timing so that problems can be diagnosed afterward. The researchers argue this transparency is a feature: even when a tracker’s battery died mid-session, the output file made the disconnection reconstructable. VRrec remains, by its authors’ own description, a recorder rather than an analyzer — it eliminates coding for data acquisition but not for interpretation. Still, with source code and executables freely available on GitHub, the tool invites community-driven improvement, and its authors hope it will make VR-based behavioral research accessible to teams that have until now watched from outside the headset.

Subject of Research: An open-source, zero-coding tool for capturing body-motion data in PC-based virtual reality for behavioral research

Article Title: VRrec: A zero-coding open-source VR body-motion capture tool for behavioral research

Article References: Kobylinski, P., Muczynski, B., Cnotkowski, D., Wierzbowski, M., & Biele, C. (2026). VRrec: A zero-coding open-source VR body-motion capture tool for behavioral research. Behavior Research Methods, 58(11), Article 308. https://doi.org/10.3758/s13428-026-03169-9

Image Credits: AI Generated

DOI: 10.3758/s13428-026-03169-9

Keywords: virtual reality, motion capture, open source, behavioral research, VRrec, SteamVR, body tracking, research methods, psychology, HTC Vive, data recording, human behavior

The research addresses a long-standing puzzle in the science of cooperation. Human societies sustain remarkably high levels of collaboration among genetically unrelated individuals, and one of the most influential theoretical explanations is indirect reciprocity. Under indirect reciprocity, people decide whether to help someone based on that person’s reputation: those known to help others earn a good standing, which makes third parties more willing to assist them in the future, while defectors acquire bad standing and are shunned. Because reputational information circulates socially, an individual can benefit from a good reputation even with people they have never met. Decades of theoretical modeling and behavioral experiments have explored how such reputation-based systems can stabilize cooperation, but most of this work has focused on socially shared information—what everyone in a group supposedly knows about everyone else.

Everyday life, however, rarely presents reputation and experience in neat agreement. A coworker may be widely praised yet have treated you badly, or a neighbor may carry a poor reputation despite having personally helped you when you needed it. Yamamoto and his colleagues set out to examine precisely this conflict: how do people evaluate another person’s decision to cooperate or refuse when the public standing of the individual involved contradicts what the evaluator has directly experienced? The question matters because evaluations are the currency of indirect reciprocity. If people’s judgments are swayed by private history rather than shared reputation, the mechanics of reputation-based cooperation may be more complicated than standard models assume.

To investigate, the team conducted two scenario-based experiments with participants in Japan. The scenarios were deliberately grounded in familiar social settings: one involved coworkers at a restaurant, and the other involved neighbors in a residential community. In each scenario, participants read about a situation in which one person either cooperated with or refused a request made by another person. The researchers systematically varied two factors. The first was the reputation of the person making the request—whether that individual was publicly regarded as good or bad. The second was the participant’s own prior experience with the person responding to the request—whether that responder had previously cooperated with or refused to cooperate with the participant personally. Participants then evaluated the person who had responded, allowing the researchers to measure how reputation and personal experience each shaped moral judgment.

The results revealed a striking asymmetry between cooperation and non-cooperation. When the responder refused the request, evaluations of that refusal depended heavily on the participant’s own prior experience with the responder. Even when the responder carried a bad public reputation, the refusal was judged more negatively if that responder had previously helped the participant. Conversely, even when the responder enjoyed a good reputation, the refusal was judged more positively if that responder had previously refused to help the participant. In other words, when public reputation and personal experience pointed in opposite directions, it was the private, firsthand history that strongly colored how participants rated the act of non-cooperation. The shared social record, which much of the indirect reciprocity literature treats as the decisive input, was effectively pushed aside.

Cooperative behavior told a different story. Acts of cooperation were generally evaluated positively regardless of the other person’s reputation and regardless of how that person had previously treated the participant. This suggests that the human evaluative system does not process cooperation and defection symmetrically. Cooperation appears to be rewarded almost automatically, insulated from the evaluator’s private grievances and from the target’s public standing. Defection, by contrast, invites a more contextualized judgment—one in which the evaluator asks not simply whether the refusal was socially acceptable, but how the person on the receiving end had previously treated them personally.

Perhaps the most sobering finding concerned a scenario in which refusing seemed entirely justified. When the person being refused had both a bad public reputation and a personal history of refusing to cooperate with the participant—conditions under which there appeared to be good reason to withhold help—the refusal itself was still evaluated neutrally rather than positively. Refusing, in other words, was not converted into a virtuous act even when the circumstances seemed to warrant it. This indicates that while people may tolerate or excuse non-cooperation under certain conditions, they do not necessarily celebrate it as positively good. The moral ledger of everyday cooperation appears to reserve its positive marks for helping, with refusal occupying at best a neutral zone even when justified.

From a technical perspective, the findings challenge a simplifying assumption embedded in many formal models of indirect reciprocity. Standard models typically treat reputational information as a single, socially shared signal that determines who deserves help. The new results suggest that human evaluators integrate at least two distinct channels—socially transmitted reputation and privately accumulated experience—and that the weighting between these channels depends on the type of behavior being judged. For defection, the private channel dominates; for cooperation, the evaluation is largely insensitive to either channel. This kind of asymmetric integration could have important consequences for how reputation-based cooperation actually operates in groups, because it implies that shared reputational records may be less powerful in governing sanctions and forgiveness than theorists have often assumed. It also hints at why personal grudges and personal loyalties can persist within communities even when everyone has access to the same public information about one another.

The study also clarifies the scope of previous research. Earlier work on indirect reciprocity concentrated mainly on how socially shared reputational information guides decisions about whom to cooperate with, leaving open the question of what happens when that shared information conflicts with lived experience. By manipulating reputation and personal experience independently within controlled scenarios, Yamamoto and his colleagues were able to isolate the contribution of each source and demonstrate that direct experience is not merely one input among many but can override reputation altogether in judgments of defection. The use of two distinct social contexts—workplace and neighborhood—suggests the pattern is not confined to a single type of relationship, although the scenarios were hypothetical and conducted with participants in Japan, so further work will be needed to test how general the effect is across cultures and real-world interactions.

Professor Yamamoto framed the work as a step toward understanding how people combine heterogeneous information when judging others. “In everyday life, we judge others by combining many different kinds of information, including what we hear about them from others and what we have experienced ourselves,” he said. “Our study provides one clue to understanding how people make judgments when these sources of information do not agree.” He also outlined where the research program is headed next: “In future research, we would like to examine how people use reputational information in environments where direct personal experience is limited, such as online rating systems and AI-mediated judgments.” That direction is increasingly urgent. On digital platforms, ratings and reviews function as public reputations, yet users often have little or no direct experience with the sellers, drivers, or service providers they evaluate. If human judgment naturally privileges firsthand experience where it exists, the design of reputation systems for environments that lack such experience becomes a genuinely open scientific and engineering question—one that this study helps to define.

Subject of Research: How direct personal experience and public reputation interact in human evaluations of cooperation and defection in indirect reciprocity

Article Title: “Public reputation” or “Personal experience”?

Article References: “Public reputation” or “Personal experience”?. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: indirect reciprocity, cooperation, reputation, defection, social evaluation, human behavior, evolutionary psychology, moral judgment, personal experience, scenario experiments, Evolution and Human Behavior, prosocial behavior

The research, published in the journal Parasites & Vectors, was led by Tyler Maire and colleagues at the Florida Medical Entomology Laboratory, part of the University of Florida’s Institute of Food and Agricultural Sciences, working with the Indian River Mosquito Control District. The team set out to close a stubborn gap in mosquito-borne risk assessment. Standard surveillance relies heavily on entomological indicators—trap counts, species composition, infection rates—but these measures never capture the behavioral half of the equation: the human routines, habits, and protective choices that determine whether an infected mosquito ever encounters skin.

To measure bite exposure directly, the researchers built a smartphone-based reporting system called Bite Diary, a progressive web application that allowed participants to log bites and outdoor activity in near real time. The study unfolded in suburban neighborhoods of Indian River County, Florida, between July 2024 and June 2025, a full year that captured seasonal swings in mosquito pressure. Each participant recorded bites and time spent outside over a four-day reporting period. In parallel, the team deployed mosquito traps in participants’ own yards during the first 24 hours of enrollment, using CO2-baited BG-Sentinel traps alongside ultraviolet-light-based devices to sample the local mosquito population where people actually live.

Recruitment brought 83 individuals into the study, 71 of whom registered for the app, and 32 of whom completed a follow-up online survey. Over the study period, participants submitted 70 bite records. The geographic pattern was unambiguous: 92.9 percent of reported bites occurred outdoors, and 73.6 percent happened at home. Rather than lurking indoors or striking far from the house, biting mosquitoes were overwhelmingly a backyard and doorstep phenomenon, encountered during everyday outdoor life rather than on exotic excursions.

The headline number is deceptively modest. Across all participants, the average bite exposure rate was 0.29 bites per person per day, with a 95 percent confidence interval of 0.09 to 0.49. But the average conceals enormous individual variation, and it is precisely this heterogeneity that matters for disease transmission. In epidemiological terms, transmission is driven not by the population mean but by the small fraction of people who receive a disproportionate share of bites. A self-reported average near zero can coexist with individuals experiencing frequent, repeated exposure—the people most likely to encounter an infected mosquito first.

The trapping effort painted an equally detailed picture of the biting population. Across 78 residential yards, the team collected 3,788 female mosquitoes, dominated by species with well-established public health credentials: Aedes aegypti, the yellow fever mosquito and primary urban vector of dengue; Aedes albopictus, the Asian tiger mosquito; Aedes taeniorhynchus, the black salt marsh mosquito notorious along Florida’s coasts; and the southern house mosquito complex members Culex nigripalpus and Culex quinquefasciatus, key vectors of West Nile virus and St. Louis encephalitis. This diversity is typical of suburban Florida, where container-breeding Aedes species and wetland-associated Culex species coexist within a few hundred meters of one another.

Then came the analytical twist. Using generalized linear mixed models—a statistical framework that can account for repeated measures from the same individuals and clustered data across neighborhoods—the researchers tested whether the number of mosquitoes trapped in a person’s yard predicted the number of bites that person reported. It did not. Yard-level mosquito abundance, the very metric that drives much routine surveillance, showed no association with self-reported bite exposure. What did predict bites was behavior: time spent outdoors during the evening hours of 5:00 p.m. to 9:00 p.m. was positively associated with bite exposure, consistent with the crepuscular and nocturnal activity patterns of many local vector species, particularly Culex mosquitoes that feed most actively from dusk onward.

The activity logs added texture to this finding. Dog walking and gardening emerged as frequently reported activities during bite events—unremarkable, mundane pursuits that nonetheless place people outdoors, moving slowly, with exposed skin, during the precise window when hungry female mosquitoes are foraging. This detail matters for intervention design. A resident who douses themselves in repellent before a hiking trip but walks the dog bare-legged at dusk is exposed in ways that conventional risk messaging may not address. The protective behaviors that matter most may be those woven into daily routines, not those reserved for special occasions.

Why should yard abundance and personal bites decouple? The authors point to several plausible mechanisms. Traps sample a fixed point in a yard for 24 hours, while humans move through a mosaic of microhabitats—the front porch, the neighbor’s yard, the sidewalk, the dog park. Mosquitoes themselves disperse, so the insects biting a person at 6 p.m. may not have emerged anywhere near that person’s property. And bite risk depends on the intersection of vector abundance with human presence, protective behavior, and species-specific biting preferences. A yard can teem with trapped mosquitoes that rarely bite humans, or host a handful of anthropophilic Aedes aegypti that deliver nearly all the risk. Trap counts alone cannot distinguish these scenarios.

The study also carries a methodological message. Bite Diary demonstrates that smartphone-based participatory surveillance—essentially citizen science for biting events—is feasible, and that ordinary residents can contribute usable, temporally fine-grained exposure data. Such data could, in principle, be integrated with existing mosquito control operations, which already collect extensive trap-based surveillance, to produce risk models grounded in real human–mosquito contact rather than proxy measures. The authors note that much of mosquito-borne disease research still relies primarily on entomological metrics, with limited integration of how people’s behaviors shape their contact with mosquitoes, and their findings highlight the limitations of that approach. They argue that linking human behavior to bite exposure is essential for understanding and predicting transmission risk.

There are, of course, caveats. The study was modest in size, conducted in one suburban Florida county over four-day reporting windows, and self-reported bites depend on participants noticing and logging each event—a small bite from a Culex at dusk may go unnoticed even as it transmits a pathogen. Seventy bite records across a year is a thin foundation for firm conclusions, and the authors are careful to frame the work as a feasibility demonstration as much as an epidemiological result. Still, the pattern is consistent and biologically sensible: bites cluster outdoors, at home, in the evening, during ordinary activities, and they do not track trap counts.

For residents of mosquito-rich regions, the practical takeaways are refreshingly concrete. Personal protection is most valuable in the early evening hours, during routine outdoor tasks rather than only during outdoor recreation. And for public health agencies, the study suggests that the next frontier in mosquito-borne disease surveillance may not be a better trap, but a better picture of the human half of the bite—the schedules, habits, and backyards where mosquitoes and people actually meet. In a warming, urbanizing world where dengue and other mosquito-borne viruses keep expanding their range, understanding contact, not just abundance, may prove the difference between watching an outbreak and preventing one.

Subject of Research: Human–mosquito contact and bite exposure patterns measured with a smartphone-based bite diary in suburban Florida.

Article Title: How often, when, and where do people get bitten by mosquitoes? Characterizing human–mosquito contact using Bite Diary

Article References: Maire, T., Futo, M., Tran, M., Snowden, S., Kosinski, K., Jiang, Y., Lord, C. C., & Thongsripong, P. (2026). How often, when, and where do people get bitten by mosquitoes? Characterizing human–mosquito contact using Bite Diary. Parasites & Vectors. https://doi.org/10.1186/s13071-026-07698-2

Image Credits: AI Generated

DOI: 10.1186/s13071-026-07698-2

Keywords: mosquito bites, bite exposure, Bite Diary app, human behavior, outdoor activity, mosquito surveillance, Aedes aegypti, Culex quinquefasciatus, Florida, citizen science, vector-borne disease, Parasites & Vectors