Aaron M. Bornstein

Background and Aims: Alcohol choice is governed by its subjective value, which can be experimentally modified. However, less is known about the moment-to-moment dynamics through which experimental manipulations of value influence observable choice behaviour. We tested whether an experimental manipulation intended to elevate alcohol value would alter computational parameters of value-based decision-making (VBDM) and alcohol choice, and whether these outcomes would mediate ad libitum alcohol consumption. Design: Pre-registered, randomised between-subjects experiment comparing two personalised experimental conditions: alcohol value (exposure to alcohol memories and multisensory alcohol cues) vs. control (exposure to non-alcohol memories and multisensory soft-drink cues), conducted over two separate sessions with baseline measures included as covariates. Setting: University laboratory in the United Kingdom (UK). Participants: 129 adults (63% male; mean age = 44 years; mean Alcohol Use Disorders Identification Test [AUDIT] score = 16) who regularly consume alcohol (mean Estimated Weekly Alcohol Consumption [EWAC] = 42 UK units/week), randomised to alcohol value (n = 65) or control (n = 64) conditions. Measurements: At baseline and post-exposure, participants completed self-reported measures of alcohol craving and a value-based decision-making (VBDM) task where they chose between pairs of previously rated alcohol-related and non-alcohol images. A drift-diffusion model was fitted to behavioural data to estimate evidence accumulation rates (value sensitivity) and response thresholds (response caution). Participants also completed a concurrent choice task (alcohol vs. non-alcohol) that measured percentage alcohol relative to non-alcohol reward (enlargement; index of relative alcohol value), and a taste test to measure alcohol consumption. Findings: Some indices of subjective craving were responsive to the experimental manipulation, but there were no significant between-group differences at post-manipulation (ps ≥ .17). Baseline-adjusted analyses showed that response thresholds for non-alcohol decisions were higher in the alcohol value condition compared to control (p = .03, d = 0.40); however, Bayesian analysis quantified only anecdotal evidence for this effect (BF10 = 1.84). Other VBDM parameters, percentage alcohol choice, and alcohol consumed were unaffected by the manipulation (ps ≥ .19, BF10s ≤ 0.40). Furthermore, no value indices (computational or percentage choice) mediated the effect of the experimental manipulation on alcohol consumed. Conclusions: A personalised experimental manipulation intended to elevate alcohol value had modest effects on self-reported craving, but did not alter computational parameters of VBDM, relative alcohol choice, or ad-libitum alcohol consumption.

We build upon Haueis and Colaço’s account in two ways. First, we argue that the backgrounding of metabolism was a pragmatic response to a data granularity problem faced by traditional cognitive modelers. Then, we suggest that equivalence demonstrations offer a promising pathway for incrementally introducing metabolic considerations into extant modeling practice.

Expectations—prior knowledge of environmental statistics—support adaptive behavior by reducing uncertainty during inference. However, the questions of where expectations come from and how that ought to impact their integration has remained largely unexplored. Here, we present a novel model formalizing the role of memory retrieval in the process of integrating expectations with uncertain sensory information. To do this, we first articulate how uncertainty about expectations —omitted from existing normative models—ought to induce memory retrieval processes. We then model expectations as a dynamic source of internal evidence that observers sample from in parallel to vision, and that are weighed according to a time-evolving estimate of their reliability relative to sensory evidence. Finally, we show that this single, principled model can reproduce several previously disparate observations of time-varying effects of expectations in neural and behavioral data, each of which was previously explained using different neural and computational mechanisms. These findings suggest that our model identifies a basic mechanism governing the integration of expectations with sensory evidence on assumptions that more closely approximate the dynamic, heterogeneous, and uncertain nature of real-world decision settings.

Most widely studied option evaluation strategies rely on knowledge accumulated across repeated experiences. But how should options be evaluated in unfamiliar environments, in which knowledge is sparse? In these situations, how do decision makers make efficient use of limited past experience to guide their choices? One possible strategy is episodic sampling, in which a decision maker retrieves a small number of past decisions from memory to estimate the value of present options. By virtue of their age, children and adolescents have less experience than adults, making episodic sampling a particularly useful strategy for them. At the same time, the effectiveness of episodic sampling derives from memory’s precision and context sensitivity—properties that continue to develop into adolescence and young adulthood. This tension raises a key question: are developmental differences in episodic memory associated with differences in episodic sampling? To address this question, 106 participants, ages 8 to 25, completed a 2-d choice task that dissociated the influence of a single episodic memory from the influence of multiple episodes sharing a common context. At all ages, single episodes biased choices. But, only adults were sensitive to the broader evoked context. Further clarifying the relationship between episodic memory and decision making, differences in memory precision predicted differences in episodic sampling, even after taking into account age, while episodic sampling, in turn, accounted for individual differences in forward planning. Together, these findings suggest that episodic memory guides decision making throughout development, but the character of its influence evolves as memory becomes more precise and richly structured.

It has been shown that early-life adversity (ELA) shapes how individuals learn, remember, and make decisions, yet the precise computations altered by these experiences remain unclear. Here, we combine a structured foraging task with computational modeling to test a recently developed theory for how a particular kind of ELA, early-life unpredictability (ELU), specifically influences choice under uncertainty. Adult participants (N=297) performed a sequential foraging task requiring continuous trade-offs between exploiting depleting resources and exploring alternatives. Subsets also completed assessments of early-life unpredictability (QUIC) and for trauma symptoms arising from lifelong stressors (PCL). We fit participants’ behavior with a Bayesian learning-and-planning model in which uncertainty modulates the valuation of leaving a current resource patch. Critically, the subjective influence of local uncertainty was allowed to vary freely between participants. Consistent with theoretical proposals, computational model fits and mediation analyses revealed a robust indirect pathway: ELU predicted increased discounting in the face of uncertainty, which in turn predicted greater overharvesting. This pattern was consistent across environmental conditions, indicating that early-life unpredictability primarily influences behavior through a general influence on uncertainty processing. Importantly, although PCL scores were also correlated with uncertainty adaptation, these effects were fully accounted for by shared variance with ELU, offering a clear dissociation between developmental unpredictability and lifelong traumatic experience. Together, our results show that early-life unpredictability causes long-lasting changes in decision-making by amplifying the subjective experience of uncertainty.

Anhedonia – the diminished capacity to experience or anticipate pleasure – is among the most common consequences of early-life unpredictability, yet how these co-occurring conditions jointly shape real-world decision-making remains unknown. Here, we use a sequential foraging-under-threat task to probe motivational conflict decisions in 357 individuals varying in early-life unpredictability and anhedonia symptoms. We find that unpredictability and anhedonia exert opposing influences on choice: unpredictability shifts behavior away from the survival-optimal policy in a sex-dependent manner, while anhedonia promotes adherence to it, partly through heightened sensitivity to unexpected threatening outcomes. A mediation analysis reveals that anhedonia partially buffers the deleterious effects of unpredictability on decision quality. These results demonstrate that co-occurring conditions can mask one another’s behavioral signatures and suggest that the heterogeneous expression of transdiagnostic constructs like anhedonia may reflect context-dependent adaptations to distinct underlying etiologies.

Early in development, children can infer latent structure in the world from sparse and ambiguous evidence. Through a process known as structure learning, they extract statistical regularities, construct causal models from those regularities, and use those models to arbitrate between exploiting known options and exploring novel alternatives. In turn, each decision and its outcomes refine the model that produced them. Despite the clear reciprocal relationship between structure learning and decision‐making in the real world, developmental research has largely examined these processes separately. To address this gap, we compared how children, adolescents, and adults behaved in a patch‐foraging task designed to reveal how structure learning shapes exploratory decisions in a richly structured, dynamic environment. We found that younger participants left patches sooner than adults, enabling them to explore the environment more broadly within the fixed time window of the study. Computational modeling demonstrated that this difference in exploration arose from differences in participants’ causal models of the environments. Younger participants grouped all patches into a single category despite large differences in richness, whereas older participants separated them into distinct categories. Despite differences in representation, participants of all ages used their uncertainty about the environment to guide their decisions. Together, our findings suggest that structure learning undergoes protracted development, but uncertainty‐sensitive decision‐making emerges earlier and can support adaptive behavior even when representations remain imprecise. Summary: Children and adolescents formed less granular representations of environmental structure than adults. Despite these imprecise representations, younger participants showed adult‐like sensitivity to uncertainty, planning further ahead when more confident in their internal models of the environment.

Objectives: Effective goal-directed decision making relies on memory and planning–processes that are known to decline with age. We tested the hypothesis that these declines stem from a common mechanism by focusing on mnemonic discrimination, a measure of memory precision that shows unique vulnerability to age-related decline. Methods: We used a latent learning task that measures the ability to learn and make judgments about associations among interconnected stimuli, assessing performance across the adult life span. This task allows us to measure multistep inference judgments that reflect how individuals organize relational structure, previously shown to capture the internal model-construction processes that support model-based planning. In Experiment 1, we examined relationships between judgment performance and memory precision. In Experiment 2, we tested whether a “blocked” learning schedule designed to reduce memory interference by separating overlapping objects could improve performance for individuals with weaker memory abilities. Results: Across the life span, both younger and older adults showed evidence of successful latent learning and inference, but variability in judgment performance was explained by mnemonic discrimination ability. In Experiment 2, mnemonic discrimination interacted with training condition: intermixed training benefited those with high memory precision, whereas blocked training benefited those with low memory precision. We also implemented artificial neural network simulations, which reproduced these qualitative patterns. Discussion: These findings suggest that age-related declines in complex judgments stem from declines in mnemonic discrimination and demonstrate that individualized, memory-based training interventions can improve learning and reasoning processes that support goal-directed planning, offering a promising approach to preserving decision-making abilities across the life span.

Social interactions can be deeply rewarding, yet they are marked by social uncertainty. The uncertainty arises because we can never truly know another’s mind. As a species, humans find uncertainty aversive and excessive. Intolerance of uncertainty is a risk factor for emotional disorders. One way to resolve uncertainty in novel situations is to rely on memories of past similar experiences, yet this process is largely unexplored in social contexts. A novel social decision-making task showed that individuals recruit both memories and incremental experiences of social rewards to guide decisions under social uncertainty. Further, individuals with elevated depressive symptoms showed better integration of positive social memories if they also had high levels of autobiographical memory specificity. These findings suggest that successfully recalling past positive social experiences improves navigation of social uncertainty, and may confer preventative advantages for those with high levels of affective disorder symptoms.