Biology subjects

Bornstein, A. M.

Publications and source records attributed to Bornstein, A. M..

10 recordsLinked to original sources

bioRxiv2026

A specific computational role for early-life unpredictability, and not lifelong stressful experience, in decision-making under uncertainty.

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.

neuroscience↗

bioRxiv2026

Anhedonia buffers the effects of early-life unpredictability on threat-reward decision-making.

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 anothers behavioral signatures and suggest that the heterogeneous expression of transdiagnostic constructs like anhedonia may reflect context-dependent adaptations to distinct underlying etiologies.

neuroscience↗

bioRxiv2025

A compressed code for memory discrimination

The ability to discriminate similar visual stimuli has been used as an important index of memory function. This ability is widely thought to be supported by expanding the dimensionality of relevant neural codes, such that neural representations for the similar stimuli are maximally distinct, or "separated." An alternative hypothesis is that discrimination is supported by lossy compression of visual inputs, efficiently coding sensory information by discarding seemingly irrelevant details. A benefit of compression, relative to expansion, is that it allows the individual to efficiently retain fewer essential dimensions underlying stimulus variation--a process linked to higher-order visual processing--without hindering discrimination. Under the compression hypothesis, pattern separation is facilitated when more information from similar stimuli can be discarded, rather than preserving more information about distinct stimulus dimensions. We test the compression versus expansion hypotheses by predicting performance on the canonical mnemonic similarity task. First, we train neural networks to compress perceptual and semantic factors of stimuli, and measure lossiness of those representations using the mathematical framework underlying compression. Consistent with the compression hypothesis, and not the expansion hypothesis, we find that greater lossiness predicts the ease and performance of lure discrimination, particularly in later layers of convolutional neural networks shown to predict brain activity in the higher-order visual stream. We then empirically confirm these predictions across two sets of images, four behavioral datasets, and alternative metrics of lossiness. Finally, using task fMRI data, we identify signatures of lossy compression--neural dimensionality reduction and information loss--in the higher-order visual stream regions V4 and IT as well as hippocampal subregions dentate gyrus/CA3 and CA1 associated with lure discrimination performance. These results suggest lossy compression may support mnemonic discrimination behavior by discarding redundant and overlapping information.

neuroscience↗

bioRxiv2022

Context-dependent choice and evaluation in real-world consumer behavior

A body of work spanning neuroscience, economics, and psychology indicates that decisionmaking is context-dependent, which means that the value of an option depends not only on the option in question, but also on the other options in the choice set--or the context. While context effects have been observed primarily in small-scale laboratory studies with tightly constrained, artificially constructed choice sets, it remains to be determined whether these context effects take hold in real-world choice problems, where choice sets are large and decisions driven by rich histories of direct experience. Here, we investigate whether valuations are context-dependent in real-world choice by analyzing a large restaurant rating dataset (Yelp.com) as well as two independent replication datasets which provide complementary operationalizations of restaurant choice. We find that users make fewer ratings-maximizing choices in choice sets with higher-rated options--a hallmark of context-dependent choice-- and that post-choice restaurant ratings also varied systematically with the ratings of unchosen restaurants. Furthermore, in a follow-up laboratory experiment using hypothetical choice sets matched to the real-world data, we find further support for the idea that subjective valuations of restaurants are scaled in accordance with the choice context, providing corroborating evidence for a general mechanistic-level account of these effects. Taken together, our results provide a potent demonstration of context-dependent choice in real-world choice settings, manifesting both in decisions and subjective valuation of options.

neuroscience↗

bioRxiv2019

Joy division: Reward prediction errors create event boundaries in memory

We remember when things change. Particularly salient are experiences where there is a change in rewards, eliciting reward prediction errors (RPEs). This feature of memory may be useful because it can help us find greater rewards and avoid lesser ones in the future. How do RPEs influence our memory of those experiences? One idea is that this signal directly enhances the encoding of memory. Another, not mutually exclusive, idea is that the RPE signals a deeper change in the environment, and leads to the mnemonic separation of subsequent experiences from what came before, thereby creating a new latent context and a more separate memory trace. We tested this in four experiments in which participants learned to predict rewards associated with a series of images within visually-distinct \"rooms.\" High magnitude RPEs indicated a change in the underlying distribution of rewards. To test whether these large RPEs created a new latent context, we first assessed recognition priming for sequential pairs that contained or did not contain a high-RPE event, as well as out-of-sequence pairs (Exp. 1: n=27 & Exp. 2: n=83). We found evidence of recognition priming for both sequential pair types, including the pair with the high-RPE event, indicating that the high-RPE event is bound to its predecessor in memory. Given that high-RPE events are themselves preferentially remembered (Rouhani et al, 2018), we next tested recognition priming for pairs that had one item in between them (i.e. the pairs were either across a high-RPE event or not), where none of the tested items were high-RPE items (Exp. 3: n=85). Here, sequential pairs across a high-RPE no longer showed recognition priming whereas pairs within the same latent reward state did, providing initial evidence for an RPE-modulated event boundary. We then investigated whether RPE event boundaries disrupt temporal memory of those events (Exp. 4). After reward learning, we asked participants to order and estimate the distance between two events that had either included a high-RPE event between them, or not. We found (n=49) and replicated (n=77) worse sequence memory for events across a high-RPE event. Altogether, these findings demonstrate that high-RPE events are both more strongly encoded and act as event boundaries that interrupt the sequential integration of events. We captured these effects in a variant of the Context Maintenance and Retrieval model (CMR; Polyn, Norman & Kahana, 2009), modified to incorporate RPEs into the encoding process.

neuroscience↗

bioRxiv2019

Individual differences in model-based planning are linked to the ability to infer latent structure

Humans appear to represent many forms of knowledge in associative networks whose nodes are multiply connected, including sensory, spatial, and semantic. Recent work has shown that explicitly augmenting artificial agents with such graph-structured representations endows them with more human-like capabilities of compositionality and transfer learning. An open question is how humans acquire these representations. Previously, it has been shown that humans can learn to navigate graph-structured conceptual spaces on the basis of direct experience with trajectories that intentionally draw the network contours (Schapiro et al., 2012;2016), or through direct experience with rewards that covary with the underlying associative distance (Wu et al., 2018). Here, we provide initial evidence that this capability is more general, extending to learning to reason about shortest-path distances across a graph structure acquired across disjoint experiences with randomized edges of the graph - a form of latent learning. In other words, we show that humans can infer graph structures, assembling them from disordered experiences. We further show that the degree to which individuals learn to reason correctly and with reference to the structure of the graph corresponds to their propensity, in a separate task, to use model-based reinforcement learning to achieve rewards. This connection suggests that the correct acquisition of graph-structured relationships is a central ability underlying forward planning and reasoning, and may be a core computation across the many domains in which graph-based reasoning is advantageous.

animal behavior and cognition↗

bioRxiv2018

A common deliberative process underlies model-based planning and patient intertemporal choice

Humans and animals consistently forego, or \"discount\" future rewards in favor of more proximal, but less valuable, options. This behavior is often thought of in terms of a failure of \"self-control\", a lack of inhibition when considering the possibility of immediate gratification. However, rather than overweighting the near-term reward, the same behavior can result from failing to properly consider the far-off reward. The capacity to plan for future gains is a core construct in Reinforcement Learning (RL), known as \"model-based\" planning. Both discounting and model-based planning have been shown to track everyday behaviors from diet to exercise habits to drug abuse. Here, we show that these two capacities are related via a common mechanism - people who are more likely to deliberate about future reward in an intertemporal choice task, as indicated by the time they spend considering the choice, are also more likely to make multi-step plans for reward in a sequential reinforcement learning task. In contrast, the degree to which peoples intertemporal choices were driven by a more automatic bias did not correspond to their planning tendency, and neither did the more standard measure of discounting behavior. These results suggest that the standard behavioral economic measure of discounting is more fruitfully understood by decomposing it into constituent parts, and that only one of these parts corresponds to the sort of multi-step thinking needed to make plans for the future.

animal behavior and cognition↗

bioRxiv2018

Rats exhibit similar biases in foraging and intertemporal choice tasks

Animals, including humans, consistently exhibit myopia in two different contexts: foraging, in which they harvest locally beyond what is predicted by optimal foraging theory, and intertemporal choice, in which they exhibit a preference for immediate vs. delayed rewards beyond what is predicted by rational (exponential) discounting. Despite the similarity in behavior between these two contexts, previous efforts to reconcile these observations in terms of a consistent pattern of time preferences have failed. Here, via extensive behavioral testing and quantitative modeling, we show that rats exhibit similar time preferences in both contexts: they prefer immediate vs. delayed rewards and they are sensitive to opportunity costs -- delays to future decisions. Further, a quasi-hyperbolic discounting model, a form of hyperbolic discounting with separate components for short-and long-term rewards, explains individual rats time preferences across both contexts, providing evidence for a common mechanism for myopic behavior in foraging and intertemporal choice.

animal behavior and cognition↗

bioRxiv2017

Perceptual decisions result from the continuous accumulation of memory and sensory evidence

Expectations can inform fast, accurate decisions. But what informs expectations? Here we test the hypothesis that expectations are set by dynamic inference from memory. Participants performed a cue-guided perceptual decision task with independently-varying memory and sensory evidence. Cues established expectations by reminding participants of past stimulus-stimulus pairings, which predicted the likely target in a subsequent noisy image stream. Participants responses used both memory and sensory information, in accordance to their relative reliability. Formal model comparison showed that the sensory inference was best explained when its parameters were set dynamically at each trial by evidence sampled from memory. Supporting this model, neural pattern analysis revealed that responses to the probe were modulated by the specific content and fidelity of memory reinstatement that occurred before the probe appeared. Together, these results suggest that perceptual decisions arise from the continuous sampling of memory and sensory evidence.

neuroscience↗

bioRxiv2017

Refresh my memory: Episodic memory reinstatements intrude on working memory maintenance

A fundamental question in memory research is how different forms of memory interact. Previous research has shown that people rely on working memory (WM) in short-term recognition tasks; a common view is that episodic memory (EM) only influences performance on these tasks when WM maintenance is disrupted. However, retrieval of memories from EM has been widely observed during brief periods of quiescence, raising the possibility that EM retrievals during maintenance-critically, before a response can be prepared-might affect short-term recognition memory performance even in the absence of distraction. We hypothesized that this influence would be mediated by the lingering presence of reactivated EM content in WM. We obtained support for this hypothesis in three experiments, showing that delay-period EM reactivation introduces incidentally-associated information (context) into WM, and that these retrieved associations negatively impact subsequent recognition, leading to substitution errors (Experiment 1) and slowing of accurate responses (Experiment 2). fMRI pattern analysis showed that slowing is mediated by the content of EM reinstatement (Experiment 3). These results expose a previously hidden influence of EM on WM, raising new questions about the adaptive nature of their interaction.

animal behavior and cognition↗