Latent-state and model-based learning in PTSD

Cisler, Josh M.; Dunsmoor, Joseph E.; Fonzo, Gregory A.; Nemeroff, Charles B.

doi:10.1016/j.tins.2023.12.002

Cited by 3 publications

(1 citation statement)

References 114 publications

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“…The relationship between stress and LCs, while also preliminary, is more complex and reflects a burgeoning area of scientific inquiry ( Cisler et al, 2024 ). Both anatomical overlap of regions involved in inferring latent causes and those disrupted in PTSD (e.g., OFC and hippocampus), together with latent cause models providing strong fits to physiological markers of threat learning in PTSD, strongly implicate the importance of considering latent structure when examining memory biases in PTSD ( Cisler et al, 2024 ; Letkiewicz et al, 2022 ). Some models of PTSD theorize that individuals with PTSD form more, and more event-specific, LCs ( Rigoli, 2022 ).…”

Section: Extra-event Bindingmentioning

confidence: 99%

Integrating and fragmenting memories under stress and alcohol

Loetscher,

Goldfarb

2024

Neurobiology of Stress

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Section: Extra-event Bindingmentioning

confidence: 99%

Integrating and fragmenting memories under stress and alcohol

Loetscher,

Goldfarb

2024

Neurobiology of Stress

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Building and Breaking the Chain: A Model of Reward Prediction Error Integration and Segmentation of Memory

Rouhani,

Clewett,

Antony

2024

Journal of Cognitive Neuroscience

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Prediction errors drive reinforcement learning and organize episodic memory into distinct contexts, but do these effects interact? Here, we review the roles of midbrain dopamine, the locus coeruleus, and the hippocampus in event cognition to propose and simulate the theoretical influence of two prediction error signals in integrating versus segmenting events in memory. We suggest that signed reward prediction errors can build mental models of reward environments, increasing the contextual similarity (integration) of experiences with stronger, more stable reward expectations. On the other hand, unsigned reward prediction errors can signal a new model of the environment, generating a contextual shift (segmentation) between experiences that crossed them. We moreover predicted that these differences in contextual similarity give rise to distinct patterns of temporal-order memory. We combined these ideas in a computational model to account for a seemingly paradoxical pattern of temporal-order memory where greater representational distance helps order memory within context but impairs it across contexts. We found that simulating signed reward prediction error integration and unsigned reward prediction error segmentation differentially enabled the model to perform associative chaining, which involved reactivating items between two tested probes to assist with sequential retrieval. In summary, our simulations provide a unifying explanation for the varied ways that neuromodulatory systems alter event cognition and memory.

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The Ubiquity of Time in Latent-cause Inference

Mirea,

Shin,

DuBrow

et al. 2024

Journal of Cognitive Neuroscience

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Humans have an outstanding ability to generalize from past experiences, which requires parsing continuously experienced events into discrete, coherent units, and relating them to similar past experiences. Time is a key element in this process; however, how temporal information is used in generalization remains unclear. Latent-cause inference provides a Bayesian framework for clustering experiences, by building a world model in which related experiences are generated by a shared cause. Here, we examine how temporal information is used in latent-cause inference, using a novel task in which participants see “microbe” stimuli and explicitly report the latent cause (“strain”) they infer for each microbe. We show that humans incorporate time in their inference of latent causes, such that recently inferred latent causes are more likely to be inferred again. In particular, a “persistent” model, in which the latent cause inferred for one observation has a fixed probability of continuing to cause the next observation, explains the data significantly better than two other time-sensitive models, although extensive individual differences exist. We show that our task and this model have good psychometric properties, highlighting their potential use for quantifying individual differences in computational psychiatry or in neuroimaging studies.

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Latent-state and model-based learning in PTSD

Cited by 3 publications

References 114 publications

Integrating and fragmenting memories under stress and alcohol

Integrating and fragmenting memories under stress and alcohol

Building and Breaking the Chain: A Model of Reward Prediction Error Integration and Segmentation of Memory

The Ubiquity of Time in Latent-cause Inference

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