Participants performed an additional-singleton visual-search task while their eye movements were recorded. Distractor presence followed either high- or low-volatility sequences, and the overall prevalence of distractors remained constant across the two regimes. That difference disappeared when comparisons were matched by position in the run.

The pooled effect was therefore consistent with a structural consequence of the manipulation: high-volatility blocks contained more early-run positions, where capture is intrinsically more frequent. On distractor-absent trials, which isolated the effect of volatility without a competing item, capture showed no detectable difference between regimes. Behavioral differences emerged later in the decision process.

Under high volatility, participants produced longer response times, made more fixations, spent more time dwelling on the final target and committed fewer errors. Target-acquisition latency—the time needed to reach the target—remained unchanged. Together, these observations locate the volatility effect after the first saccade, when observers were deciding whether the available visual evidence justified a response.

The authors evaluated the data with a hierarchical drift-diffusion model and a complementary volatility Kalman-filter model. Both analyses indicated that manual responses contained boundary-related and drift-related structure, linking environmental volatility to the accumulation of evidence and the thresholds used for decision-making. With distractor frequency held constant, the findings support increased response caution rather than a direct change in early oculomotor capture.

The authors describe this pattern as consistent with precision-weighted adjustment to environmental uncertainty. Because the evidence comes from a controlled laboratory task with eye tracking, the study establishes an association within that search paradigm rather than demonstrating how volatility influences decisions in everyday settings.

Subject of Research: How environmental volatility affects oculomotor capture and cautious decisions in visual search

Article Title: Environmental volatility is associated with adaptive caution in visual search

Article References: Qiu, N., Allenmark, F., Chen, S., Müller, H. J., & Shi, Z. (2026). Environmental volatility is associated with adaptive caution in visual search. PLOS Computational Biology, 22(10), e1014310. https://doi.org/10.1371/journal.pcbi.1014310

Image Credits: AI Generated

DOI: 10.1371/journal.pcbi.1014310

Keywords: visual search, environmental volatility, eye tracking, response caution, decision-making, drift-diffusion model, Kalman filtering, oculomotor capture, visual uncertainty, Environmental, volatility, associated

According to the abstract, the researchers combined TVP-VAR and DCC-GARCH methodologies to examine connections driven by returns and volatility. TVP-VAR estimates indicated significant but changing return spillovers, including notable surges during the COVID-19 pandemic and the Russia–Ukraine conflict. Within the modeled return network, WTI and heating oil were identified as enduring transmitters, while natural gas and carbon futures were described as subject to continuous shocks. The authors characterized these patterns as indicative of regional segmentation and policy-driven pricing.

The clean-energy indices had time-varying connections and primarily functioned as transmitters, which the abstract interprets as evidence of their increasing systemic significance. The DCC-GARCH analysis indicated volatility contagion across the markets, with WTI identified as the primary volatility transmitter and PBW as the most susceptible recipient. Robustness checks across forecast horizons, frequencies and oil benchmarks supported the authors’ conclusion that these patterns were structural rather than contingent on the model used.

The abstract concludes that fossil fuels continue to act as systemic risk transmitters. It also states that carbon and clean-energy assets require improved liquidity and hedging mechanisms, while energy-transition strategies must reconcile financial stability with decarbonization objectives. Because the supplied evidence consists only of the abstract and an early accepted version subject to further edits, the full methods, numerical estimates, model diagnostics and study limitations could not be independently assessed. The findings therefore should be understood as results summarized in the abstract rather than a full evaluation of the published article.

Subject of Research: Time-series analysis of return and volatility spillovers among fossil-fuel, clean-energy and carbon-emissions markets

Article Title: Dynamic connectedness and volatility spillovers across energy markets

Article References: Esmaeilpour Moghadam, H., & Karami, A. (2026). Dynamic connectedness and volatility spillovers across energy markets. Discover Sustainability. https://doi.org/10.1007/s43621-026-05046-z

Image Credits: AI Generated

DOI: 10.1007/s43621-026-05046-z

Keywords: energy markets, volatility spillovers, return connectedness, WTI crude oil, clean-energy indices, carbon emissions futures, TVP-VAR, DCC-GARCH, financial contagion, Dynamic, connectedness, volatility

To disentangle the two kinds of uncertainty, the team designed a three-option probabilistic reversal-learning task framed as a fishing game, in which participants chose among three islands to maximize their catch. The researchers independently manipulated two dimensions: stochasticity, the random variability of outcomes under stable rules, and volatility, the rate at which the underlying reward contingencies actually changed. In the low-stochasticity environment, the best patch paid off on 80 percent of trials; in the high-stochasticity environment, it paid off only 60 percent of the time, making individual outcomes far less informative. Volatility was manipulated by reversing which patch was best every 25 trials in slow conditions and every 10 trials in fast ones. Because normative learning theory predicts that learning rates should rise under volatility but fall under high stochasticity, this design allowed the researchers to test whether anxious individuals violate these prescriptions in a selective way.

In the first experiment, fifty participants completed six 50-trial blocks spanning all combinations of stochasticity and volatility, and trait anxiety was measured with the State-Trait Anxiety Inventory. The results were unambiguous: participants with high trait anxiety, defined as a trait score of 45 or above, earned significantly fewer rewards than their low-anxiety peers, but only in environments that were stable yet noisy, that is, under slow volatility combined with medium or high stochasticity. When the environment genuinely changed rapidly, the two groups performed identically. This selectivity is the study’s central surprise. It rules out the idea that anxiety produces a blanket overestimation of environmental change; instead, anxious individuals remain sensitive to true volatility but fail to discount random outcome noise appropriately when the environment is actually stable.

Computational modeling revealed the mechanism behind this performance gap. The researchers fit a hierarchical Bayesian reinforcement-learning model with separate learning rates for rewards and punishments, allowing them to quantify how strongly each participant updated their beliefs after positive and negative feedback. Contrary to the widely held view that anxiety amplifies learning from negative outcomes, the elevated learning rates in high trait anxiety were reward-driven. Under slow volatility, higher trait anxiety predicted increasing reward learning rates as stochasticity rose, with 95 percent highest density intervals well above zero in both medium and high stochasticity conditions. Under fast volatility, reward learning rates did not vary with anxiety at all. Punishment learning rates, meanwhile, remained below task-optimal levels in both groups and did not differ between them, indicating that punishment sensitivity could not explain the performance differences.

To determine whether these elevated learning rates were actually harmful, the team simulated reinforcement-learning agents across nearly sixty thousand parameter combinations to identify the reward-maximizing learning rate for each environment. As normative theory predicts, the optimal learning rate increased with volatility and decreased with stochasticity. Against this benchmark, high trait anxious individuals significantly exceeded the optimal reward learning rate in the stable, noisy environments, with Bayes factors exceeding ten billion in the medium-stochasticity condition. Higher learning rates were in turn associated with lower reward accumulation, and anxious participants clustered in the region of high learning rates and low earnings. In other words, anxious learners were overfitting noise, updating their beliefs on the basis of random reward fluctuations as if each surprise signaled a genuine shift in the world.

The decision process itself was also altered. Using hierarchical drift-diffusion modeling, which decomposes reaction times into parameters such as drift rate, the speed of evidence accumulation, and boundary separation, the threshold of information required before committing to a choice, the researchers found that in the slow-volatility, high-stochasticity environment, high trait anxious participants showed drift rates centered near zero, dramatically slower than those of low-anxiety peers, along with elevated decision boundaries. Under fast volatility, none of these differences appeared. This means anxious individuals were not only updating their beliefs too aggressively but also accumulating evidence more slowly and cautiously when valuing the most rewarding option, a double computational burden confined precisely to environments that demand a clean separation of signal from noise.

The second experiment asked whether sleep, a physiological state tightly intertwined with anxiety, could modulate these computational phenotypes. Forty participants performed the task before and after sleeping in the laboratory, with overnight sleep recorded using a validated wearable EEG headband. Greater N3 sleep, the deepest stage of non-REM sleep characterized by slow delta oscillations and strongly linked to synaptic plasticity, was associated with a larger evening-to-morning reduction in state anxiety. Contrary to the researchers’ initial prediction, it was the high trait anxious participants who derived the greatest anxiolytic benefit, showing the steepest dissipation of morning state anxiety as their N3 duration increased. No comparable associations emerged for lighter N1 or N2 sleep or for REM sleep.

The restorative benefit of deep sleep extended directly into the realm of learning. Post-sleep reward accumulation was higher than pre-sleep performance overall, and greater N3 sleep specifically predicted higher post-sleep earnings and more accurate identification of the most rewarding patch in high trait anxious individuals, particularly under slow volatility. At the computational level, reward learning rates decreased from pre- to post-sleep under high stochasticity across all participants, consistent with normative adjustment, and greater N3 sleep was associated with significantly reduced post-sleep reward learning rates in the anxious group. When compared against the simulated optimal benchmarks, participants with more N3 sleep showed learning rates that were statistically indistinguishable from optimal values after sleep, whereas those with less N3 sleep continued to exceed the optimal benchmark. Post-sleep drift rates also rose, indicating faster, more confident evidence accumulation favoring the best option.

Taken together, the two experiments sketch a coherent picture in which trait anxiety selectively corrupts the inference that separates stochastic noise from genuine environmental change, inflating reward learning rates in stable but noisy settings and slowing the accumulation of decision evidence, while N3 sleep acts as a state-dependent corrective that nudges these parameters back toward their task-optimal values. The authors caution that N3 sleep was measured rather than experimentally manipulated, so its effects remain partly correlational, and that related constructs such as intolerance of uncertainty were not assessed, leaving open whether the observed effects are unique to trait anxiety. The sample was also drawn from a single campus community, and circadian factors were not controlled. Even so, because the trade-off between stochasticity and volatility is fundamental to countless real-world learning problems, from financial markets to social relationships, the study raises the provocative possibility that protecting deep sleep could be a tangible, physiological lever for improving how anxious minds learn under uncertainty.

Subject of Research: How trait anxiety and N3 sleep shape reward learning and decision-making under stochasticity and volatility

Article Title: Trait anxiety selectively amplifies reward learning under uncertainty, while N3 sleep attenuates it

Article References: Deshmukh, R., & Ramakrishnan, A. (2026). Trait anxiety selectively amplifies reward learning under uncertainty, while N3 sleep attenuates it. iScience, 29(10), Article 117745. https://doi.org/10.1016/j.isci.2026.117745

Image Credits: AI Generated

DOI: 10.1016/j.isci.2026.117745

Keywords: trait anxiety, reward learning, uncertainty, stochasticity, volatility, N3 sleep, reinforcement learning, drift-diffusion model, reversal learning, decision-making, sleep EEG, computational psychiatry

Strategic foresight differs fundamentally from conventional forecasting. Where forecasting extrapolates present trends forward and assumes a broadly stable environment, foresight deliberately constructs multiple plausible futures, each shaped by different combinations of driving forces. These can include climate extremes, energy price swings, geopolitical disruption, trade fragmentation, technological breakthroughs and shifts in consumer demand. By developing scenarios that span this possibility space, researchers and policymakers can stress-test transition strategies before committing scarce public and private resources, identifying which elements of a climate-neutral farming pathway are robust across many futures and which are fragile bets on a single expected outcome.

The core insight of the research is that resilience and foresight are inseparable concerns for agricultural transformation. Farming sits at the intersection of ecological, economic and social systems, each with its own thresholds and feedback loops. A transition strategy that reduces greenhouse gas emissions on paper may nevertheless prove brittle if it depends on uninterrupted supply chains, stable subsidy regimes or benign weather. Strategic foresight provides a systematic way to expose these dependencies, revealing how plausible disruptions, from drought sequences to fertilizer market shocks, could interact with the transition process itself and either accelerate, slow or reverse progress toward climate neutrality.

Technically, the foresight approach typically proceeds through a sequence of steps. Analysts first scan for driving forces, categorizing them by their certainty and their potential impact on the system. The most consequential and most uncertain forces become the axes of scenario construction, producing a small set of internally coherent future worlds. Within each world, the dynamics of agricultural transition are explored: how farmers might adopt practices such as reduced tillage, cover cropping, improved nutrient management, agroforestry, precision fertilization or renewable-energy integration, and how those adoption patterns respond to the economic and institutional conditions of each scenario. The resilience of the transition is then assessed by comparing outcomes across scenarios and locating the points of common vulnerability.

One of the most important contributions of this framing is its treatment of time. Climate neutrality is usually expressed as a target date, but the journey toward that date is uneven and path-dependent. Early choices, such as which practices receive public support or which supply chains are reorganized first, can lock in certain configurations and foreclose others. Foresight makes these lock-in risks visible. It can show, for example, that a transition strategy optimized for a future of high carbon prices and stable trade may collapse under a future of price volatility and protectionism, whereas a more diversified strategy, combining multiple mitigation practices and revenue streams, retains functionality across both worlds.

The volatility emphasis is particularly timely. Recent years have confronted agriculture with a compound stress test: pandemic-era supply disruptions, energy and fertilizer price spikes linked to geopolitical conflict, recurrent droughts and floods, and shifting trade relationships. Each of these events strained farm businesses and policy frameworks alike. A transition to climate neutrality adds new layers of dependence, on carbon accounting systems, on emerging markets for low-emission products, and on technologies still moving down their cost curves. The research underscores that planning for the transition without accounting for such volatility would be a category error, because volatility is not an aberration but a defining feature of the operating environment.

Resilience, in this context, is unpacked rather than assumed. The analysis draws on the established conceptual vocabulary of resilience research, distinguishing the capacity of farming systems to absorb shocks, to adapt their structures and practices in response, and, where necessary, to transform into fundamentally new configurations. Applied to the climate-neutral transition, these capacities imply different design principles. Absorbency favors buffers such as financial reserves, diversified rotations and soil organic matter that cushions drought. Adaptability favors flexible policy instruments, learning networks among farmers, and monitoring systems that detect stress early. Transformability favors institutional space for experimentation, so that if climate or market conditions shift beyond what incremental change can handle, the sector can reorganize rather than collapse.

Strategic foresight also changes who is involved in planning. Because scenarios are built from assumptions about driving forces, the process benefits from the participation of a wide range of actors: farmers whose livelihoods embody the practical constraints, scientists who model biophysical processes, industry actors who control supply chains, and policymakers who set incentives. Participatory foresight exercises generate a shared vocabulary for discussing uncertain futures, which can reduce polarization and help stakeholders commit to transition strategies even when they disagree about which future is most likely. The research suggests this shared understanding is itself a resilience asset, enabling faster and more coordinated responses when real-world shocks arrive.

The implications for policy design are concrete. Strategies emerging from foresight-informed analysis tend to favor portfolios over silver bullets, combining emissions-reduction measures with adaptation measures and explicit contingency planning. They favor reversible and modular interventions, which can be scaled up or down as conditions change, over irreversible commitments whose value depends on a single forecast. They favor investment in information infrastructure, including monitoring, scenario updating and early-warning capacity, so that plans can be revised as evidence accumulates. And they favor attention to distributional consequences, because a transition that concentrates risk on vulnerable farms or regions is unlikely to sustain the social support it needs through a decade of turbulence.

The study also acknowledges the limits of foresight. Scenarios are not predictions, and there is a persistent risk that decision-makers treat the most comfortable scenario as the default. Foresight works best when it is iterative, revisited as conditions change, and when its outputs are explicitly linked to decision processes rather than filed away as reports. Maintaining that discipline requires institutional commitment, but the payoff, the authors argue, is a climate-neutral farming transition that is not merely planned but genuinely robust, one that can bend under pressure without breaking and can seize unexpected opportunities as the global environment continues to shift.

Beyond the immediate design of transition strategies, the foresight perspective carries implications for how agricultural research itself is organized. Much of agronomic science is built around optimizing individual practices under relatively controlled conditions, yet the resilience questions raised here concern combinations of practices interacting with turbulent external conditions. A scenario-based framing suggests value in research portfolios that evaluate practices not only for their average performance but for their performance under stress, including how cover cropping, nutrient management and energy integration behave when input prices, labor availability or weather patterns deviate sharply from historical norms.

The connection between soil processes and transition resilience deserves particular attention. Practices such as reduced tillage, diversified rotations and organic matter accumulation are frequently promoted for their mitigation benefits, but they also function as biophysical buffers. Soils with greater organic content hold more water during dry periods and recover more quickly from extreme rainfall, which means the same interventions that reduce emissions can simultaneously dampen the impact of climate shocks on yields. This dual character complicates simple cost-benefit accounting, because a practice that appears marginal when valued only for carbon may be clearly worthwhile once its risk-reduction role is included, a point that scenario analysis is well suited to surface.

Economic heterogeneity across the farming sector is another dimension that foresight exercises tend to expose. Farms differ enormously in size, capital access, tenure arrangements and exposure to international markets, so a transition pathway that is robust for a well-capitalized arable operation may be fragile for a small mixed farm carrying debt. When scenarios are populated with this heterogeneity rather than a representative average farm, the analysis can identify which policy instruments, such as targeted credit, insurance design or transition payments, determine whether the whole sector moves together or whether vulnerable segments fall behind and undermine collective targets.

The temporal structure of shocks also matters in ways that single-scenario planning obscures. Sequences of stressful years, rather than isolated extreme events, can deplete the financial and biological buffers that farms rely on, pushing systems past thresholds that individual disturbances would not. Foresight methods that explicitly model event sequences, including back-to-back droughts or coincident market and weather disruptions, therefore provide a more demanding and more informative resilience test than average-condition analysis, and they align closely with the absorb-adapt-transform vocabulary the study employs.

Finally, the iterative character of foresight connects naturally to emerging monitoring capacity in agriculture. Satellite observation, farm-level data platforms and improved biophysical models make it increasingly feasible to track indicators of transition health, such as adoption rates, soil carbon trends and input dependencies, and to compare them against scenario assumptions. When such signals diverge from the future world a strategy was designed for, that divergence becomes an early trigger for revision rather than a crisis discovered late. In this sense, foresight is less a one-time planning exercise than an ongoing navigation discipline, one that treats the climate-neutral transition as a course to be continuously corrected through volatile conditions rather than a route to be plotted once and followed regardless of weather.

Subject of Research: Using strategic foresight methods to assess the resilience of climate-neutral agricultural transition pathways under global volatility

Article Title: Strategic foresight provides insight into the resilience of climate-neutral farming transitions in a volatile world

Article References: Styles, D., Henn, D., Duffy, C., Black, K., & Martinez-Arce, A. (2026). Strategic foresight provides insight into the resilience of climate-neutral farming transitions in a volatile world. npj Sustainable Agriculture, 4(1), Article 73. https://doi.org/10.1038/s44264-026-00185-2

Image Credits: AI Generated

DOI: 10.1038/s44264-026-00185-2

Keywords: strategic foresight, climate-neutral agriculture, farming transitions, resilience, scenario analysis, sustainable agriculture, agricultural policy, volatility, food systems, climate mitigation, adaptive capacity, agroecology