Nature Human Behaviour

Compulsivity has been associated with variable behavior under uncertainty. However, previous work has not distinguished between two main sources of behavioral variability: the stochastic selection of choice options that do not maximize expected reward (choice variability), and random noise in the reinforcement learning process that updates option values from choice outcomes (learning variability). Here we studied the relation between dimensional compulsivity and behavioral variability, using a computational model which dissociates its two sources. We found that compulsivity is associated with more frequent switches between options, triggered by increased choice variability but no change in learning variability. This effect of compulsivity on the trait component of choice variability is observed even in conditions where this source of behavioral variability yields no cognitive benefits. These findings indicate that compulsive individuals make variable and maladaptive choices under uncertainty, but do not hold degraded representations of option values.

Expectations about the quality of a medical treatment influence how much an inert treatment helps to improve patient well-being. Similarly, expectations about the quality of products influence how identical goods and services are evaluated differently after their consumption. One driver for such "placebo effects" in medical treatments is social cognition in the form of trust, which may be influenced by the hormone oxytocin. An open question is whether trust and oxytocin play similar roles in marketing placebo effects. To answer this question, we combined oxytocin administration (24 IU) and trust questionnaires in a pre-registered double-blind randomized between-subjects study design (Nfood tasting task = 223; Ncognitive performance task = 202). We could not find evidence that oxytocin and trust play a role in placebo effects of marketing actions. Together with other recent null findings from oxytocin administration studies, these findings question the role trust might play in different types of placebo effects.

The percentage of women born 1965-1975 remaining childless is [~]20% in many Western European and [~]30% in some East Asian countries. Around a quarter of childless women do that voluntary, suggesting a remaining role for disease. Single diseases have been linked to childlessness, mostly in women, yet we lack a comprehensive picture of the effect of early-life diseases on lifetime childlessness. We examined all individuals born 1956-1968 (men) and 1956-1973 (women) in Finland (n=1,035,928) and Sweden (n=1,509,092) to completion of reproduction in 2018 (age 45 women; 50 men). Leveraging nationwide registers, we associated sociodemographic and reproductive information with 414 diseases across 16 categories, using a population and matched pair case-control design of siblings discordant for childlessness (71,524 full-sisters, 77,622 full-brothers). The strongest associations were mental-behavioural, particularly amongst men (schizophrenia, acute alcohol intoxication), congenital anomalies and endocrine-nutritional-metabolic disorders (diabetes), strongest amongst women. We identified novel associations for inflammatory (e.g., myocarditis) and autoimmune diseases (e.g., juvenile idiopathic arthritis). Associations were dependent on age at onset, earlier in women (21-25 years) than men (26-30 years). Disease association was mediated by singlehood, especially in men and by educational level. Evidence can be used to understand how disease contributes to involuntary childlessness. O_TEXTBOXText box:Defining Childlessness We use the term childlessness to describe individuals that have had no live-born children by the end of their reproductive lifespan (age 45 for women; 50 for men). Childlessness is defined in the literature as being both involuntary, related to biology and fecundity (e.g., infertility, inability to find a partner) and voluntary or childfree1 (e.g., active choice, preference2). It has been estimated that 4-5% of the current 15-20% women who are childless in Europe are voluntary childless3. Childless individuals are subjected to discrimination and marginalization in many societies4, with infertile women globally experiencing multiple types of violence and coercion5. A parallel line of work, which is not the position of this paper or authors, is to problematize and stigmatize childless individuals as egoistic and place blame on this group for producing a so-called demographic disaster of shrinking and ageing populations and collapse of social security systems6. The approach of this paper is to provide a neutral, data-driven, and factual examination of early-life diseases related to childlessness, with the aim to design a better understanding of health to prevent childlessness among those who want to have children. C_TEXTBOX

The ability to determine how much the environment can be controlled through our actions has long been viewed as fundamental to adaptive behavior. While traditional accounts treat controllability as a fixed property of the environment, we argue that real-world controllability often depends on the effort, time and money we are able and willing to invest. In such cases, controllability can be said to be elastic to invested resources. Here we propose that inferring this elasticity is essential for efficient resource allocation, and thus, elasticity misestimations result in maladaptive behavior. To test this hypothesis, we developed a novel treasure hunt game where participants encountered environments with varying degrees of controllability and elasticity. Across two pre-registered studies (N=514), we first demonstrate that people infer elasticity and adapt their resource allocation accordingly. We then present a computational model that explains how people make this inference, and identify individual elasticity biases that lead to suboptimal resource allocation. Finally, we show that overestimation of elasticity is associated with elevated psychopathology involving an impaired sense of control. These findings establish the elasticity of control as a distinct cognitive construct guiding adaptive behavior, and a computational marker for control-related maladaptive behavior.

Adaptive learning requires distinguishing volatility, changes in the latent state of the environment, from moment-to-moment stochasticity of observations. The two demand opposite adjustments to the learning rate: volatility calls for faster updating, stochasticity for slower. Disentangling them is computationally difficult because both inflate experienced variance, leaving the inference prone to systematic individual differences with potential consequences for psychopathology. Three computational phenotypes capture this variation: intact learners; stochasticity-blind learners, who over-update by treating noise as change; and volatility-blind learners, who under-update by treating change as noise. In two large online samples and across three tasks, we found a double dissociation between these phenotypes and transdiagnostic psychiatric dimensions: stochasticity-blind learners scored higher on Internalizing (anxiety, depression), volatility-blind learners on Externalizing (behavioral addiction, compulsivity). Distinct symptom dimensions thus correspond to distinct failures of inference about uncertainty, supporting a selective rather than generalized account of learning-under-uncertainty deficits in psychopathology.

Serotonin fosters cognitive flexibility, but how, exactly, remains unclear. We show that serotonin reduces belief stickiness: the tendency to get "stuck" in a belief about the state of the world despite incoming contradicting evidence. Participants performed a task assessing belief stickiness in a randomized, double-blind, placebo-controlled study using a single dose of the selective serotonin reuptake inhibitor (SSRI) escitalopram. In the escitalopram group, higher escitalopram plasma levels reduced belief stickiness more, resulting in better inference about the state of the world. Moreover, participants with sufficiently high escitalopram plasma levels had less belief stickiness, and therefore better state inference, than participants on placebo. Exaggerated belief stickiness is exemplified by obsessions: "sticky" thoughts that persist despite contradicting evidence. Indeed, participants with more obsessions had greater belief stickiness, and therefore worse state inference. The opposite relations of escitalopram and obsessions with belief stickiness may explain the therapeutic effect of SSRIs in obsessive-compulsive disorder.

The development of personality traits is often viewed as evolutionarily adaptive. Current neuroticism research, however, predominantly highlights its negative health impacts, neglecting its potential evolutionary advantages. We propose that neuroticisms inter-individual variability can be structured into two distinct geometric dimensions. One, named the Emotional Reactivity-Instability/Distress Spectrum (ERIS), correlates strongly with longevity and is associated with chronic diseases and risk-averse lifestyle. This dimension is underpinned by evolutionarily conserved subcortical brain regions and genes. The other, resembling the overall neuroticism score, is primarily linked to mental and stress-related disorders, as well as life satisfaction. It involves higher-order emotional brain regions and is genetically enriched in human-accelerated regions. Collectively, these dimensions represent a dual-strategy personality framework that optimizes survival and well-being, with the former being evolutionarily conservative and the latter potentially a unique human adaptation.

Significance StatementAdaptive behavior depends on knowing when to persist and when to let go; even when letting go appears as avoidance. While classical accounts of avoidance emphasize reward-effort trade-offs, we show that these decisions are critically guided by meta-control and inferences about outcome controllability and agency. Using a novel paradigm, we dissociate drivers of avoidance and demonstrate that threat does not uniformly promote disengagement. When outcome control is preserved, threat instead increases persistence, particularly following experiences that build agency in failure-safe contexts. We formalize these dynamics in the Meta-Arbitration of Control and Agency Q-learning (MACA-Q) model, which captures how experience-dependent beliefs about agency guide learning and choice across contexts. Our results show that similar avoidance behaviors can arise from distinct computational pathways. This shifts the focus from global avoidance biases to the dynamic regulation of agency as a core principle of adaptive behavior, with implications for neuroscience, psychiatry, and adaptive artificial intelligence. Adaptive behavior requires deciding when to persist and when to disengage under uncertainty and partial outcome control. Avoidance has often been studied as a response to threat or cost, yet existing paradigms cannot disentangle whether disengagement reflects threat sensitivity, expected failure, or reduced perceived control. We introduce a persistence-escape paradigm that independently manipulates incentive structures, effort demands, and outcome controllability. In a large online sample (N = 457), we show that avoidance is context-dependent rather than a stable, global trait. When outcome control was preserved under threat, the typical avoidance response reversed, promoting persistence rather than withdrawal. At the individual level, high-performing individuals were not uniformly more persistent, but more selective, disengaging when control was low. Moreover, higher anxiety symptoms were linked to cost-dominant evaluation and reduced use of accumulated competence. Conversely, higher depressive symptoms were linked to diminished sensitivity to effort and higher expected failure. To explain these behavioral patterns, we developed the Meta-Arbitration of Control and Agency Q-learning (MACA-Q) model, which embeds value learning and affective evaluation within a meta-control architecture. Critically, we formalize agency as a dynamically inferred learning gate, distinct from self-efficacy, that determines whether outcomes are treated as informative based on controllability and feedback reliability. The model explains context-specific avoidance and reveals that similar behaviors can arise from distinct computational pathways. It further shows how experience in failure-safe contexts guides subsequent behavior in adverse contexts. Our findings show that avoidance is guided by the dynamic regulation of engagement based on inferred controllability and competence. By combining a novel paradigm with a computational model, we provide a formal account of agency and a unifying framework in which meta-control regulates adaptive and maladaptive engagement across contexts, with implications for neuroscience, psychiatry, and adaptive artificial intelligence.

Deciding when to stop gathering information and commit to a choice is a fundamental challenge in decision-making under uncertainty. Normative characterizations such as Partially Observable Markov Decision Processes (POMDPs) prescribe mathematically optimal stopping rules; however, human evidence gathering systematically departs from optimality. Pathological departures -- such as the excessive indecisiveness characteristic of obsessive-compulsive disorder (OCD) -- offer an important opportunity to investigate the cognitive mechanisms involved in stopping. We extend a POMDP framework to incorporate key candidate suboptimalities: a biased prior belief, transient evidence exaggeration, progressive forgetting, boosted costs of error, temporal regulation (patience and urgency), and misperception of a deadline. We evaluate this model in a pre-existing dataset comprising 105 participants spanning healthy controls, generalised anxiety disorder, and the OCD spectrum performing an information gathering task with controlled, stochastic, deadlines. Model comparison reveals that human sequential choices are broadly governed by subjective risk penalties and time-dependent urgency, with a smaller and less certain contribution from an over-weighting of recent evidence, which a random-effects comparison does not support at the population level. Individuals differ in how that over-weighting is implemented: in one deadline condition, subjects divide almost evenly between models carrying a transient exaggeration of the newest sample, models carrying progressive forgetting of older evidence, and models carrying no recency mechanism at all. Crucially, while risk sensitivity and choice stochasticity act as shared mechanisms across conditions, mechanisms such as belief bias and patience are more variable. Finally, using OCD as a clinical case study, we demonstrate that simulating choices from the fitted exaggeration model reproduces model-agnostic regression signatures of clinical indecision, which the forgetting and no-recency accounts do not. These findings offer a generative foundation for dissecting clinical departures in information gathering across the obsessive-compulsive spectrum.

People often use recognizable features to infer the value of novel consumables. This "generalization" strategy is known to be beneficial in stable environments, such that individuals can use previously learned rules and values in efficiently exploring new situations. However, it remains unclear whether and how individuals adjust their generalization strategy in volatile environments where previously learned information becomes obsolete. We hypothesized that individuals adaptively use generalization by continuously updating their beliefs about the credibility of the feature-based reward generalization model at each state. Our data showed that participants used generalization more when the novel environment remained consistent with the previously learned monotonic association between feature and reward, suggesting efficient utilization of prior knowledge. Against other accounts, we found that individuals incorporated an arbitration mechanism between feature-based value generalization and model-based learning based on volatility tracking. Notably, our suggested model captured differential impacts of generalization dependent on the context-volatility, such that individuals who were biased the most toward generalization showed the lowest learning errors when the value of stimuli are generalized along the recognizable feature, but showed the highest errors in a volatile environment. This work provides novel insights into the adaptive usage of generalization, orchestrating two distinctive learning mechanisms through monitoring their credibility, and highlights the potential adverse effects of overgeneralization in volatile contexts.

In complex environments, available information does not uniquely define state, requiring attention learning to identify features relevant for learning and decision-making. As a result, human decisions often reflect reasoning that cannot be directly observed from choice. This dual opacity, at the level of agent and observer, poses a fundamental challenge for understanding naturalistic behavior. We inferred latent attention during learning and decision-making by training recurrent neural networks on synthetic data generated from two classes of attention learning models: feature-based reinforcement learning (FRL), in which attention emerges through retrospective value updating, and serial hypothesis testing (SHT), in which discrete hypotheses are prospectively sampled. A network trained on hybrid (FRL+SHT) synthetic data outperformed single-model networks, decoding latent human attention with more than 80% accuracy. This work provides a new approach for decoding latent attention and suggests a mechanism of attention learning wherein value-derived hypotheses are continuously tested against incoming evidence.

Decision-making in noisy, changing, and partially observable environments entails a basic tradeoff between immediate reward and longer-term information gain, known as the exploration-exploitation dilemma. Computationally, an effective way to balance this tradeoff is by leveraging uncertainty to guide exploration. Yet, in humans, empirical findings are mixed, from suggesting uncertainty-seeking to indifference and avoidance. In a novel bandit task that better captures uncertainty-driven behavior, we find multiple roles for uncertainty in human choices. First, stable and psychologically meaningful individual differences in uncertainty preferences actually range from seeking to avoidance, which can manifest as null group-level effects. Second, uncertainty modulates the use of basic decision heuristics that imperfectly exploit immediate rewards: a repetition bias and win-stay-lose-shift heuristic. These heuristics interact with uncertainty, favoring heuristic choices under higher uncertainty. These results, highlighting the rich and varied structure of reward-based choice, are a step to understanding its functional basis and dysfunction in psychopathology.

Prospective outcomes bias behavior in a "Pavlovian" manner: Reward prospect invigorates action, while punishment prospect suppresses it. Theories have posited Pavlovian biases as global action "priors" in unfamiliar or uncontrollable environments. However, this account fails to explain the strength of these biases--causing frequent action slips--even in well-known environments. We propose that Pavlovian control is additionally useful if flexibly recruited by instrumental control. Specifically, instrumental action plans might shape selective attention to reward/ punishment information and thus the input to Pavlovian control. In two eye-tracking samples (N = 35/ 64), we observed that Go/ NoGo action plans influenced when and for how long participants attended to reward/ punishment information, which in turn biased their responses in a Pavlovian manner. Participants with stronger attentional effects showed higher performance. Thus, humans appear to align Pavlovian control with their instrumental action plans, extending its role beyond action defaults to a powerful tool ensuring robust action execution.

In both risky choice and perception, people overweight small and underweight large probabilities. While prospect theory models this with a probability weighting function, and Bayesian noisy coding models attribute it to specific encoding functions or priors, we propose a more general account: Probability distortions arise from cognitive noise being repelled by the natural boundaries of probability (0,1). This boundary repulsion occurs in any encoding-decoding system that efficiently encodes, or Bayesian-decodes, bounded quantities, independent of specific priors or encoding functions. Our theory predicts: new, experimentally-induced boundaries should cause additional distortions; increasing cognitive noise should amplify distortions; and boundaries should reduce behavioral variability near them. We confirmed all predictions in three pre-registered experiments spanning risky choice and probability perception. Our findings further suggest that these changes originate largely during decoding. Our work provides a unified explanation for distorted and variable probability judgments, reframing them as consequences of bounded, noisy cognitive inference. SignificanceThe origin of probability weighting--a central feature of decision-making under risk--remains a longstanding puzzle. Does it arise from processes unique to risk, or does it reflect a more general cognitive mechanism? Here, we show that the classic probability weighting pattern is not domain-specific but instead emerges from a general property of noisy inference over bounded quantities, such as probabilities. Our account formalizes how resource-rational encoding and Bayesian optimal decoding naturally lead to interactions between cognitive noise and the 0-1 bounds of probability, giving rise to systematic distortions. Using pre-registered experimental manipulations across both risky lottery valuation and probability perception, we demonstrate that distortions in probability weighting and estimation are not fixed, intrinsic features, but rather predictable consequences of the interaction between noise and boundaries. This provides a mechanistic account of probability weighting and suggests a unifying explanation for its emergence across different cognitive domains. Similar mechanisms should extend to other naturally or contextually bounded quantities.

Romantic (typically sexual) relationships are important to personal, physical, mental, social, and economic wellbeing, and to human evolution. Yet little is known about factors contributing to long-term lack of intimate relationships. We investigated phenotypic and genetic correlates of never having had sex in [~]400,000 UK residents aged 39 to 73 and [~]13,500 Australian residents aged 18 to 89. The strongest associations revealed that sexless individuals were more educated, less likely to use alcohol and smoke, more nervous, lonelier, and unhappier. Sexlessness was more strongly associated with physical characteristics (e.g. upper body strength) in men than in women. Sexless men tended to live in regions with fewer women, and sexlessness was more prevalent in regions with more income inequality. Common genetic variants explained 17% and 14% of variation in sexlessness in men and women, with a genetic correlation between sexes of 0.56. Polygenic scores predicted a range of related outcomes in the Australian dataset. Our findings uncover multifaceted correlates of human intimacy of evolutionary significance.

The human brain organizes fragmented information into internal maps that support inference of latent relational structure. Vulnerabilities in this mapping process have been hypothesized to contribute to psychosis-spectrum disorders. Whether internal maps arise as readily from visual input as from linguistic description remains unknown. Here, participants learned food items associated with two abstract attributes, presented as images or language, organized along two abstract dimensions within a two-dimensional conceptual space. Participants then completed similarity judgments and reward-learning tasks that required inference of the underlying relational structure. We show that cognitive disorganization, a hallmark of psychosis-spectrum traits, selectively impairs spatial sensitivity and reward generalization when relational structure must be inferred from visual input, but not when matched structure is provided by linguistic descriptions. These findings support the shallow cognitive map hypothesis and suggest that cognitive disorganization selectively disrupts the transformation of perceptual input into stable relational representations.

Positive overgeneralization - the tendency to generalize from specific successes to broad expectations of future reward - has been linked to vulnerability to mania. Because positive overgeneralization has primarily been assessed using self-report measures, we have limited insight into the underlying cognitive process. Here, we introduce a behavioral paradigm designed to quantify how learned value generalizes to novel stimuli. We quantify individual generalization profiles by fitting psychometric functions to choice data. In an online transdiagnostic study (N=163), we show that task-based breadth of reward generalization is associated with both higher self-reported positive overgeneralization and subclinical bipolar symptoms. To provide a computational account of positive overgeneralization, we implement a reinforcement-learning model in which self-efficacy modulates the influence of anticipated future value during learning. We show that increasing this modulation reproduces the broader value propagation observed empirically. Together, these findings provide a behavioral and computational framework for studying positive overgeneralization, and suggest a mechanistic pathway by which success-related shifts in value representations may bias learning in ways relevant to bipolar risk.

In recent years, increasing skepticism regarding oxytocins (OT) influence on social behavior arose. Low power, HARKing (hypothesizing after the results are known), and replication failures have clouded the field. Here, we directly address these concerns with a high-powered, preregistered study that offers robust evidence for a causal effect of OT on trust among individuals with a low disposition to trust. We recruited 359 low-trusting individuals who participated in a trust game under strict anonymity conditions. Results show that OT administration significantly increased trusting behavior by roughly 15%, with consistent effects across regression models with and without controls for personality traits. A pooled data analysis incorporating a previous sample (n=219) of low-trusting individuals further strengthens this conclusion, yielding a statistically significant 16.9% increase in trust. Crucially, no interaction effect was found between OT and the degree of dispositional trust, suggesting OTs effect is uniform across the low-trusting spectrum. These findings present a strong case for OTs selective trust-enhancing role. By isolating OTs impact within a well-defined subpopulation and experimental context, this study provides a critical pivot in the debate over neurobiological mechanisms of trust.

Brain-Wide Association Studies (BWAS) have become a dominant method for linking mind and brain over the past 30 years. Univariate models test tens to hundreds of thousands of brain voxels individually, whereas multivariate models ( multivariate BWAS) integrate signals across brain regions into a predictive model. Numerous problems have been raised with univariate BWAS, including lack of power and reliability and an inability to account for pattern-level information embedded in distributed neural circuits1-3. Multivariate predictive models address many of these concerns, and offer substantial promise for delivering brain-based measures of behavioral and clinical states and traits2,3. In their recent paper4, Marek et al. evaluated the effects of sample size on univariate and multivariate BWAS in three large-scale neuroimaging dataset and came to the general conclusion that "BWAS reproducibility requires samples with thousands of individuals". We applaud their comprehensive analysis, and we agree that (a) large samples are needed when conducting univariate BWAS of individual differences in trait measures, and (b) multivariate BWAS reveal substantially larger effects and are therefore more highly powered. However, we disagree with Marek et al.s claims that multivariate BWAS provide "inflated in-sample associations" that often fail to replicate (i.e., are underpowered), and that multivariate BWAS consequently require thousands of participants when predicting trait-level individual differences. Here we substantiate that (i) with appropriate methodology, the reported in-sample effect size inflation in multivariate BWAS can be entirely eliminated, and (ii) in most cases, multivariate BWAS effects are replicable with substantially smaller sample sizes (Figure 1). O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=198 SRC="FIGDIR/small/497072v1_fig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@180764borg.highwire.dtl.DTLVardef@d64a2forg.highwire.dtl.DTLVardef@a0865aorg.highwire.dtl.DTLVardef@d4b14c_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO Multivariate BWAS provide unbiased effect sizes and high replicability with low-moderate sample sizes. (a) In-sample effects in multivariate BWAS are only inflated if estimates are obtained without cross-validation. (b) Cross-validation fully eliminates in-sample effect size inflation and, as a consequence, provides higher replicability. Each point in (a) and (b) corresponds to one bootstrap subsample, as in Fig. 4b of Marek et al. Dotted lines denote the threshold for p=0.05 with n=495. (c) The inflation of in-sample effect size obtained without cross-validation (red) is reduced, but does not disappear, at higher sample sizes. Conversely, cross-validated estimates (blue) are slightly pessimistic with low sample sizes and become quickly unbiased as sample size is increased. (d) Without cross-validation, in-sample effect size estimates are non-zero (r{approx}0.5, red) even when predicting permuted outcome data. Cross-validation eliminates systematic bias across all sample sizes (blue). Dashed lines in (c) and (d) denote 95% parametric confidence intervals, and shaded areas denote bootstrap and permutation-based confidence intervals. (e-f) Cross-validated analysis reveals that sufficient in-sample power (e) and out-of-sample replication probability (P(rep)) (f) can be achieved for a variety of phenotypes at low or moderate sample sizes. 80% power and P(rep) are achievable in <500 participants for half the phenotypes tested (colored bars) using the prediction algorithm in Marek et al. (top panels in (e) and (f), sample size required for 80% power or P(rep) shown). Other phenotypes require sample sizes >500 (bars with arrows). Power and P(rep) can be substantially improved with a ridge regression-based model recommended in some comparison studies10,11 (bottom panels in (e) and (f)), with 80% power and P(rep) with sample sizes as low as n=100 and n=75, respectively, when predicting cognitive ability, and sample sizes between 75 and 375 for other investigated variables, except inhibition assessed with the flanker task. (g) We estimated interactions between sample size and publication bias by computing effect size inflation (rdiscovery - rreplication) only for those bootstrap cases where prediction performance was significant (p>0.05) in the replication sample. Our analysis shows that the effect size inflation due to publication bias is modest (<10%) with <500 participants for half the phenotypes using the Marek et al. model and all phenotypes but the flanker using the ridge model. C_FIG

Learning from experience is central to human decision making, yet research on experience-based choice remains fragmented across paradigms and disciplines. We present the Decision-from-Experience Database (DfE-DB), a standardized, openly accessible resource comprising 3.8 million trial-level decisions from 11,921 participants across 168 studies and 13 paradigms. By harmonizing raw behavioral data and classifying studies along 13 key design features, the database enables quantitative comparisons previously obscured by heterogeneous task and data structures. Using this resource, we show that choice tendencies--toward higher risk, expected value, or experienced mean--vary substantially across paradigms and are strongly shaped by core design features such as feedback type, outcome structure, stationarity, and sampling. These features explain substantial cross-study variability and reveal underexplored paradigm variants. DfE-DB provides the empirical infrastructure necessary to test the generality of behavioral phenomena and computational models, fostering a more integrated science of decisions from experience.