The computational nature of memory modification

Improving Exposure Therapy: Rationale and Design of an International Consortium

The Exposure Therapy Consortium (ETC) was established to advance the science and practice of exposure therapy. To encourage participation from researchers and clinicians, this article describes the organizational structure and activities of the ETC. Initial research working group experiences and a proof-of-principle study underscore the potential of team science and larger-scale collaborative research in this area. Clinical working groups have begun to identify opportunities to enhance access to helpful resources for implementing exposure therapy effectively. This article discusses directions for expanding the consortium's activities and its impact on a global scale.

Computational models of intrinsic motivation for curiosity and creativity

We link Ivancovsky et al.'s novelty-seeking model (NSM) to computational models of intrinsically motivated behavior and learning. We argue that dissociating different forms of curiosity, creativity, and memory based on the involvement of distinct intrinsic motivations (e.g., surprise and novelty) is essential to empirically test the conceptual claims of the NSM.

Bayesian reinforcement learning: A basic overview

We and other animals learn because there is some aspect of the world about which we are uncertain. This uncertainty arises from initial ignorance, and from changes in the world that we do not perfectly know; the uncertainty often becomes evident when our predictions about the world are found to be erroneous. The Rescorla-Wagner learning rule, which specifies one way that prediction errors can occasion learning, has been hugely influential as a characterization of Pavlovian conditioning and, through its equivalence to the delta rule in engineering, in a much wider class of learning problems. Here, we review the embedding of the Rescorla-Wagner rule in a Bayesian context that is precise about the link between uncertainty and learning, and thereby discuss extensions to such suggestions as the Kalman filter, structure learning, and beyond, that collectively encompass a wider range of uncertainties and accommodate a wider assortment of phenomena in conditioning.

Memory persistence: from fundamental mechanisms to translational opportunities

Memory persistence is a double edge sword. Persistence of adaptive memories is essential for survival and even determines who we are. Neurodegenerative conditions with significant memory loss such as Alzheimer's disease, testify how defects of memory persistence have severe and irreversible effects on personality, among other symptoms. Yet, maintenance of overly strong maladaptive memories underlies highly debilitating psychiatric conditions including post-traumatic stress disorder, specific phobia, substance dependence and binge eating disorder. Here we review the neurobiological mechanisms supporting memory formation, persistence, inhibition and forgetting. We then shift the focus to how such mechanisms have been exploited to alter the persistence of laboratory-generated memories in human healthy volunteers as a proof of concept. Finally, we review the effect of behavioural and pharmacological interventions in anxiety and addiction disorder patients, highlighting key findings, gaps, and future directions for basic and translational research.

Fast adaptation to rule switching using neuronal surprise

In humans and animals, surprise is a physiological reaction to an unexpected event, but how surprise can be linked to plausible models of neuronal activity is an open problem. We propose a self-supervised spiking neural network model where a surprise signal is extracted from an increase in neural activity after an imbalance of excitation and inhibition. The surprise signal modulates synaptic plasticity via a three-factor learning rule which increases plasticity at moments of surprise. The surprise signal remains small when transitions between sensory events follow a previously learned rule but increases immediately after rule switching. In a spiking network with several modules, previously learned rules are protected against overwriting, as long as the number of modules is larger than the total number of rules-making a step towards solving the stability-plasticity dilemma in neuroscience. Our model relates the subjective notion of surprise to specific predictions on the circuit level.

Evaluating the effects of counterconditioning, novelty-facilitated, and standard extinction on the spontaneous recovery of threat expectancy and conditioned stimulus valence

Extinction training has proved effective to diminish the expectancy of the aversive unconditioned stimulus (US). However, the negative valence of the conditioned stimulus (CS) may still stay intact. In fact, several studies have suggested that the CS negative valence may be a factor that promotes the return of fear. Our study focuses on the role of changes in the CS valence as a potential mechanism to reduce the spontaneous recovery of threat expectancies. To do that, we evaluated counterconditioning (CC), a technique aimed to reduce the CS negative valence by paring it with a positive stimulus and compared its efficacy to that of a novelty-facilitated extinction (NFE) and a standard extinction interventions. Using a 2-day protocol, participants first learned the relationship between a figure and an aversive sound, using a differential conditioning paradigm, and were then randomly assigned to one of three different groups. For the CC group, CS+ or cue A was paired with a positive US. The standard extinction group was exposed to cue A alone. For a third NFE group, cue A was followed by a neutral US. Finally, on the second day, spontaneous recovery was tested. Our findings did not provide evidence to suggest that CC could be more effective to prevent or reduce the return of threat expectancies or influence valence ratings when compared with NFE and standard extinction.

Drug memory reconsolidation: from molecular mechanisms to the clinical context

Since its rediscovery at the beginning of the 21st Century, memory reconsolidation has been proposed to be a therapeutic target for reducing the impact of emotional memories that can go awry in mental health disorders such as drug addiction (substance use disorder, SUD). Addiction can be conceptualised as a disorder of learning and memory, in which both pavlovian and instrumental learning systems become hijacked into supporting drug-seeking and drug-taking behaviours. The past two decades of research have characterised the details of the molecular pathways supporting the reconsolidation of pavlovian cue-drug memories, with more recent work indicating that the reconsolidation of instrumental drug-seeking memories also relies upon similar mechanisms. This narrative review considers what is known about the mechanisms underlying the reconsolidation of pavlovian and instrumental memories associated with drug use, how these approaches have translated to experimental medicine studies, and the challenges and opportunities for the clinical use of reconsolidation-based therapies.

Minor Changes Change Memories: Functional Magnetic Resonance Imaging and Behavioral Reflections of Episodic Prediction Errors

Episodic memories can be modified, a process that is potentially driven by mnemonic prediction errors. In the present study, we used modified cues to induce prediction errors of different episodic relevance. Participants encoded episodes in the form of short toy stories and then returned for an fMRI session on the subsequent day. Here, participants were presented either original episodes or slightly modified versions thereof. Modifications consisted of replacing a single object within the episode and either challenged the gist of an episode (gist modifications) or left it intact (surface modifications). On the next day, participants completed a post-fMRI memory test that probed memories for originally encoded episodes. Both types of modifications triggered brain activation in regions we previously found to be involved in the processing of content-based mnemonic prediction errors (i.e., the exchange of an object). Specifically, these were ventrolateral pFC, intraparietal cortex, and lateral occipitotemporal cortex. In addition, gist modifications triggered pronounced brain responses, whereas those for surface modification were only significant in the right inferior frontal sulcus. Processing of gist modifications also involved the posterior temporal cortex and the precuneus. Interestingly, our findings confirmed the posterior hippocampal role of detail processing in episodic memory, as evidenced by increased posterior hippocampal activity for surface modifications compared with gist modifications. In the post-fMRI memory test, previous experience with surface modified, but not gist-modified episodes, increased erroneous acceptance of the same modified versions as originally encoded. Whereas surface-level prediction errors might increase uncertainty and facilitate confusion of alternative episode representations, gist-level prediction errors seem to trigger the clear distinction of independent episodes.

Why is the Rescorla-Wagner model so influential?

The influence of the Rescorla-Wagner model cannot be overestimated, despite that (1) the model does not differ much computationally from its predecessors and competitors, and (2) its shortcomings are well-known in the learning community. Here we discuss the reasons behind its widespread influence in the cognitive and neural sciences, and argue that it is the constant search for general-process theories by learning scholars which eventually produced a model whose application spans many different areas of research to this day. We focus on the theoretical and empirical background of the model, the theoretical connections that it has with later developments across Marr's levels of analysis, as well as the broad variety of research that it has guided and inspired.

Structure learning principles of stereotype change

Why, when, and how do stereotypes change? This paper develops a computational account based on the principles of structure learning: stereotypes are governed by probabilistic beliefs about the assignment of individuals to groups. Two aspects of this account are particularly important. First, groups are flexibly constructed based on the distribution of traits across individuals; groups are not fixed, nor are they assumed to map on to categories we have to provide to the model. This allows the model to explain the phenomena of group discovery and subtyping, whereby deviant individuals are segregated from a group, thus protecting the group's stereotype. Second, groups are hierarchically structured, such that groups can be nested. This allows the model to explain the phenomenon of subgrouping, whereby a collection of deviant individuals is organized into a refinement of the superordinate group. The structure learning account also sheds light on several factors that determine stereotype change, including perceived group variability, individual typicality, cognitive load, and sample size.

Enhancing reinforcement learning models by including direct and indirect pathways improves performance on striatal dependent tasks

A major advance in understanding learning behavior stems from experiments showing that reward learning requires dopamine inputs to striatal neurons and arises from synaptic plasticity of cortico-striatal synapses. Numerous reinforcement learning models mimic this dopamine-dependent synaptic plasticity by using the reward prediction error, which resembles dopamine neuron firing, to learn the best action in response to a set of cues. Though these models can explain many facets of behavior, reproducing some types of goal-directed behavior, such as renewal and reversal, require additional model components. Here we present a reinforcement learning model, TD2Q, which better corresponds to the basal ganglia with two Q matrices, one representing direct pathway neurons (G) and another representing indirect pathway neurons (N). Unlike previous two-Q architectures, a novel and critical aspect of TD2Q is to update the G and N matrices utilizing the temporal difference reward prediction error. A best action is selected for N and G using a softmax with a reward-dependent adaptive exploration parameter, and then differences are resolved using a second selection step applied to the two action probabilities. The model is tested on a range of multi-step tasks including extinction, renewal, discrimination; switching reward probability learning; and sequence learning. Simulations show that TD2Q produces behaviors similar to rodents in choice and sequence learning tasks, and that use of the temporal difference reward prediction error is required to learn multi-step tasks. Blocking the update rule on the N matrix blocks discrimination learning, as observed experimentally. Performance in the sequence learning task is dramatically improved with two matrices. These results suggest that including additional aspects of basal ganglia physiology can improve the performance of reinforcement learning models, better reproduce animal behaviors, and provide insight as to the role of direct- and indirect-pathway striatal neurons.

Computational Mechanisms of Addiction and Anxiety: A Developmental Perspective

A central goal of computational psychiatry is to identify systematic relationships between transdiagnostic dimensions of psychiatric symptomatology and the latent learning and decision-making computations that inform individuals' thoughts, feelings, and choices. Most psychiatric disorders emerge prior to adulthood, yet little work has extended these computational approaches to study the development of psychopathology. Here, we lay out a roadmap for future studies implementing this approach by developing empirically and theoretically informed hypotheses about how developmental changes in model-based control of action and Pavlovian learning processes may modulate vulnerability to anxiety and addiction. We highlight how insights from studies leveraging computational approaches to characterize the normative developmental trajectories of clinically relevant learning and decision-making processes may suggest promising avenues for future developmental computational psychiatry research.

Human generalization of internal representations through prototype learning with goal-directed attention

The world is overabundant with feature-rich information obscuring the latent causes of experience. How do people approximate the complexities of the external world with simplified internal representations that generalize to novel examples or situations? Theories suggest that internal representations could be determined by decision boundaries that discriminate between alternatives, or by distance measurements against prototypes and individual exemplars. Each provide advantages and drawbacks for generalization. We therefore developed theoretical models that leverage both discriminative and distance components to form internal representations via action-reward feedback. We then developed three latent-state learning tasks to test how humans use goal-oriented discrimination attention and prototypes/exemplar representations. The majority of participants attended to both goal-relevant discriminative features and the covariance of features within a prototype. A minority of participants relied only on the discriminative feature. Behaviour of all participants could be captured by parameterizing a model combining prototype representations with goal-oriented discriminative attention.

Reconsolidation and Fear Extinction: An Update

Fear memories can be updated behaviorally by delivering extinction trials during the reconsolidation window, which results in a persistent attenuation of fear memories (Monfils et al., Science 324:951-955, 2009). This safe and non-invasive paradigm, termed retrieval-extinction (or post-retrieval extinction), has also been found to be successful at preventing the return of fear in healthy fear conditioned humans (Schiller et al., Nature 463:49-53, 2010), and in the time since its discovery, there has been an explosion of research on the use of retrieval-extinction in fear memories in humans and other animals, some of which have found a long-term reduction in conditioned responding, and some who have not. These discrepant findings have raised concerns as to whether retrieval-extinction really results in updating of the original fear memory, or if it simply enhances extinction. We will first review the progress made on elucidating the cellular mechanisms underlying the fear attenuating effects of retrieval-extinction and how they differ from traditional extinction. Special attention will be paid to the molecular events necessary for retrieval-extinction to successfully occur and how these reconsolidated memories are represented in the brain. Next, we will examine the parameters that determine whether or not a memory will be updated via extinction during the reconsolidation window (also known as boundary conditions). These boundary conditions will also be discussed as possible explanations for discrepant findings of the retrieval-extinction effect. Then we will examine the factors that can determine whether an individual's fears will successfully be attenuated by retrieval-extinction. These individual differences include genetics, age, and psychopathology. Finally, we will discuss recent attempts to bring the retrieval-extinction paradigm from the bench to the bedside for the behavioral treatment of anxiety and trauma disorders.

Solidity Meets Surprise: Cerebral and Behavioral Effects of Learning from Episodic Prediction Errors

How susceptible a memory is to later modification might depend on how stable the episode has been encoded. This stability was proposed to increase when retrieving information more (vs. less) often and in a spaced (vs. massed) practice. Using fMRI, we examined the effects of these different pre-fMRI retrieval protocols on the subsequent propensity to learn from episodic prediction errors. After encoding a set of different action stories, participants came back for two pre-fMRI retrieval sessions in which they encountered original episodes either 2 or 8 times in either a spaced or a massed retrieval protocol. One week later, we cued episodic retrieval during the fMRI session by using original or modified videos of encoded action stories. Recurrent experience of modified episodes was associated with increasing activity in the episodic memory network including hippocampal and cortical areas, when leading to false memories in a post-fMRI memory test. While this observation clearly demonstrated learning from episodic prediction errors, we found no evidence for a modulatory effect of the different retrieval protocols. As expected, the benefit of retrieving an episode more often was reflected in better memory for originally encoded episodes. In addition, frontal activity increased for episodic prediction errors when episodes had been less frequently retrieved pre-fMRI. A history of spaced versus massed retrieval was associated with increased activation throughout the episodic memory network, with no significant effect on behavioral performance. Our findings show that episodic prediction errors led to false memories. The history of different retrieval protocols was reflected in memory performance and brain responses to episodic prediction errors, but did not interact with the brain's episodic learning response.

Appraising reconsolidation theory and its empirical validation

Re-exposure to elements of prior experiences can create opportunities for inducing amnesia for those events. The dominant theoretical framework posits that such re-exposure can result in memory destabilization, making the memory representation temporarily sensitive to disruption while it awaits reconsolidation. If true, such a mechanism that allows for memories to be permanently changed could have important implications for the treatment of several forms of psychopathology. However, there have been contradictory findings and elusive occurrences of replication failures within the "reconsolidation" field. Considering its potential relevance for clinical applications, the fact that this "hot" research area is being dominated by a single mechanistic theory, and the presence of unexplainable contradictory findings, we believe that it is both useful and timely to critically evaluate the reconsolidation framework. We discuss potential issues that may arise from how reconsolidation interference has typically been deducted from behavioral observations, and provide a principled assessment of reconsolidation theory that illustrates that the theory and its proposed boundary conditions are vaguely defined, which has made it close to impossible to refute reconsolidation theory. We advocate for caution, encouraging researchers not to blindly assume that a reconsolidation process must underlie their findings, and pointing out the risks of doing so. Finally, we suggest concrete theoretical and methodological advances that can promote a fruitful translation of reminder-dependent amnesia into clinical treatment.

Representations of Temporal Community Structure in Hippocampus and Precuneus Predict Inductive Reasoning Decisions

Our understanding of the world is shaped by inferences about underlying structure. For example, at the gym, you might notice that the same people tend to arrive around the same time and infer that they are friends that work out together. Consistent with this idea, after participants are presented with a temporal sequence of objects that follows an underlying community structure, they are biased to infer that objects from the same community share the same properties. Here, we used fMRI to measure neural representations of objects after temporal community structure learning and examine how these representations support inference about object relationships. We found that community structure learning affected inferred object similarity: When asked to spatially group items based on their experience, participants tended to group together objects from the same community. Neural representations in perirhinal cortex predicted individual differences in object grouping, suggesting that high-level object representations are affected by temporal community learning. Furthermore, participants were biased to infer that objects from the same community would share the same properties. Using computational modeling of temporal learning and inference decisions, we found that inductive reasoning is influenced by both detailed knowledge of temporal statistics and abstract knowledge of the temporal communities. The fidelity of temporal community representations in hippocampus and precuneus predicted the degree to which temporal community membership biased reasoning decisions. Our results suggest that temporal knowledge is represented at multiple levels of abstraction, and that perirhinal cortex, hippocampus, and precuneus may support inference based on this knowledge.

Demarcating the boundary conditions of memory reconsolidation: An unsuccessful replication

Disrupting memory reconsolidation provides an opportunity to abruptly reduce the behavioural expression of fear memories with long-lasting effects. The success of a reconsolidation intervention is, however, not guaranteed as it strongly depends on the destabilization of the memory. Identifying the necessary conditions to trigger destabilization remains one of the critical challenges in the field. We aimed to replicate a study from our lab, showing that the occurrence of a prediction error (PE) during reactivation is necessary but not sufficient for destabilization. We tested the effectiveness of a reactivation procedure consisting of a single PE, compared to two control groups receiving no or multiple PEs. All participants received propranolol immediately after reactivation and were tested for fear retention 24 h later. In contrast to the original results, we found no evidence for a reconsolidation effect in the single PE group, but a straightforward interpretation of these results is complicated by the lack of differential fear retention in the control groups. Our results corroborate other failed reconsolidation studies and exemplify the complexity of experimentally investigating this process in humans. Thorough investigation of the interaction between learning and memory reactivation is essential to understand the inconsistencies in the literature and to improve reconsolidation interventions.

Rapid neural reorganization during retrieval practice predicts subsequent long-term retention and false memory

Active retrieval can alter the strength and content of a memory, yielding either enhanced or distorted subsequent recall. However, how consolidation influences these retrieval-induced seemingly contradictory outcomes remains unknown. Here we show that rapid neural reorganization over an eight-run retrieval practice predicted subsequent recall. Retrieval practice boosted memory retention following a 24-hour (long-term) but not 30-minute delay, and increased false memory at both delays. Long-term retention gains were predicted by multi-voxel representation distinctiveness in the posterior parietal cortex (PPC) that increased progressively over retrieval practice. False memory was predicted by unstable representation distinctiveness in the medial temporal lobe (MTL). Retrieval practice enhanced the efficiency of memory-related brain networks, through building up PPC and MTL connections with the ventrolateral and dorsolateral prefrontal cortex that predicted long-term retention gains and false memory, respectively. Our findings indicate that retrieval-induced rapid neural reorganization together with consecutive consolidation fosters long-term retention and false memories via distinct pathways.

The Episodic Prototypes Model (EPM): On the nature and genesis of facial representations

Faces undergo massive changes over time and life events. We need a mental representation which is flexible enough to cope with the existing visual varieties, but which is also stable enough to be the basis for valid recognition. Two main theoretical frameworks exist to describe facial representations: prototype models assuming one central item comprising all visual experiences of a face, and exemplar models assuming single representations of each visual experience of a face. We introduce a much more ecological valid model dealing with episodic prototypes (the Episodic Prototypes Model—EPM), where faces are represented by a low number of prototypes that refer to specific Episodes of Life (EoL, e.g., early adulthood, mature age) during which the facial appearance shows only moderate variation. Such an episodic view of mental representation allows for efficient storage, as the number of needed prototypes is relatively low, and it allows for the needed variation within a prototype that keeps the everyday and steadily ongoing changes across a certain period of time. Studies 1–3 provide evidence that facial representations are highly dependent on temporal aspects which is in accord with EoL, and that individual learning history generates the structure and content of respective prototypes. In Study 4, we used implicit measures (RT) in a face verification task to investigate the postulated power of the EPM. We could demonstrate that episodic prototypes clearly outperformed visual depictions of exhaustive prototypes, supporting the general idea of our approach.

Can contingency rehearsal during the interval between a retrieval cue and extinction training change the effects of post-retrieval extinction?

Return of fear may be prevented by post-retrieval extinction (PRE), a procedure consisting of presenting a stimulus that was present during conditioning (retrieval cue) prior to extinction training. However, recent evidence suggests that there might be circumstances under which PRE is not effective to prevent the return of fear (boundary conditions), but some of these conditions remain unknown. We explored if rehearsing the CS, US or CS-US contingency during the interval between the retrieval cue and extinction training might change the effects of PRE. One day after differential fear conditioning, healthy human participants (n = 83) underwent either standard extinction (control condition, n = 31) or two different PRE procedures, one in which participants rehearsed the CS-US contingency during the interval between the retrieval cue and extinction (rehearsal condition, n = 25), or another in which they underwent a verbal fluency task directing their attention away from the experimental contingencies during this interval (nonrehearsal condition, n = 27). Return of fear in a reinstatement test was observed in both control and rehearsal conditions, whereas in the nonrehearsal condition there was a generalized increase in response to the CS+ and CS-. Differential response in the rehearsal condition had values slightly smaller than the control group with no significant differences from both control and nonrehearsal conditions. These results suggest that the overt behavior of participants during the interval between a retrieval cue and extinction training might change the effects of PRE in healthy human participants, but further manipulations of these variables are needed to confirm these findings.

Mnemonic prediction errors promote detailed memories

When our experience violates our predictions, it is adaptive to update our knowledge to promote a more accurate representation of the world and facilitate future predictions. Theoretical models propose that these mnemonic prediction errors should be encoded into a distinct memory trace to prevent interference with previous, conflicting memories. We investigated this proposal by repeatedly exposing participants to pairs of sequentially presented objects (A → B), thus evoking expectations. Then, we violated participants' expectations by replacing the second object in the pairs with a novel object (A → C). The following item memory test required participants to discriminate between identical old items and similar lures, thus testing detailed and distinctive item memory representations. In two experiments, mnemonic prediction errors enhanced item memory: Participants correctly identified more old items as old when those items violated expectations during learning, compared with items that did not violate expectations. This memory enhancement for C items was only observed when participants later showed intact memory for the related A → B pairs, suggesting that strong predictions are required to facilitate memory for violations. Following up on this, a third experiment reduced prediction strength prior to violation and subsequently eliminated the memory advantage of violations. Interestingly, mnemonic prediction errors did not increase gist-based mistakes of identifying old items as similar lures or identifying similar lures as old. Enhanced item memory in the absence of gist-based mistakes suggests that violations enhanced memory for items' details, which could be mediated via distinct memory traces. Together, these results advance our knowledge of how mnemonic prediction errors promote memory formation.

Limitations of occasional reinforced extinction to alleviate spontaneous recovery and reinstatement effects: Evidence for a trial-signalling mechanism

Fear extinction is not permanent but is instead more vulnerable than the original fear memory, as traditionally shown by the return of fear phenomena. Because of this, techniques to mitigate the return of fear are needed in the clinical treatment of related psychological conditions. One promising strategy is the occasional reinforced extinction treatment, introducing a gradual and sparse number of conditioned stimulus-unconditioned stimulus (CS-US) pairings within the extinction treatment. We present the results of three experiments in which we used a threat conditioning procedure in humans. Our main aim was to evaluate whether occasional reinforced extinction could reduce two different forms of relapse: spontaneous recovery (Experiments 1 and 2) and reinstatement (Experiment 3). Contrary to our predictions and previous literature, the results indicate that an occasional reinforcement treatment did not mitigate relapse compared with standard extinction. From a theoretical standpoint, these results are more consistent with the idea that extinction entails the acquisition of new knowledge than with the idea that there are conditions in which extinction leads to a weakening of the original fear memory. These findings also question the generality of the potential benefits of using occasional reinforced extinction in clinical settings.

Novelty is not surprise: Human exploratory and adaptive behavior in sequential decision-making

Classic reinforcement learning (RL) theories cannot explain human behavior in the absence of external reward or when the environment changes. Here, we employ a deep sequential decision-making paradigm with sparse reward and abrupt environmental changes. To explain the behavior of human participants in these environments, we show that RL theories need to include surprise and novelty, each with a distinct role. While novelty drives exploration before the first encounter of a reward, surprise increases the rate of learning of a world-model as well as of model-free action-values. Even though the world-model is available for model-based RL, we find that human decisions are dominated by model-free action choices. The world-model is only marginally used for planning, but it is important to detect surprising events. Our theory predicts human action choices with high probability and allows us to dissociate surprise, novelty, and reward in EEG signals.

Simulating the computational mechanisms of cognitive and behavioral psychotherapeutic interventions: insights from active inference

Cognitive-behavioral therapy (CBT) leverages interactions between thoughts, feelings, and behaviors. To deepen understanding of these interactions, we present a computational (active inference) model of CBT that allows formal simulations of interactions between cognitive interventions (i.e., cognitive restructuring) and behavioral interventions (i.e., exposure) in producing adaptive behavior change (i.e., reducing maladaptive avoidance behavior). Using spider phobia as a concrete example of maladaptive avoidance more generally, we show simulations indicating that when conscious beliefs about safety/danger have strong interactions with affective/behavioral outcomes, behavioral change during exposure therapy is mediated by changes in these beliefs, preventing generalization. In contrast, when these interactions are weakened, and cognitive restructuring only induces belief uncertainty (as opposed to strong safety beliefs), behavior change leads to generalized learning (i.e., "over-writing" the implicit beliefs about action-outcome mappings that directly produce avoidance). The individual is therefore equipped to face any new context, safe or dangerous, remaining in a content state without the need for avoidance behavior-increasing resilience from a CBT perspective. These results show how the same changes in behavior during CBT can be due to distinct underlying mechanisms; they predict lower rates of relapse when cognitive interventions focus on inducing uncertainty and on reducing the effects of automatic negative thoughts on behavior.

The Embodied-Enactive-Interactive Brain: Bridging Neuroscience and Creative Arts Therapies

The recognition and incorporation of evidence-based neuroscientific concepts into creative arts therapeutic knowledge and practice seem valuable and advantageous for the purpose of integration and professional development. Moreover, exhilarating insights from the field of neuroscience coincide with the nature, conceptualization, goals, and methods of Creative Arts Therapies (CATs), enabling comprehensive understandings of the clinical landscape, from a translational perspective. This paper contextualizes and discusses dynamic brain functions that have been suggested to lie at the heart of intra- and inter-personal processes. Touching upon fundamental aspects of the self and self-other interaction, the state-of-the-art neuroscientific-informed views will shed light on mechanisms of the embodied, predictive and relational brain. The conceptual analysis introduces and interweaves the following contemporary perspectives of brain function: firstly, the grounding of mental activity in the lived, bodily experience will be delineated; secondly, the enactive account of internal models, or generative predictive representations, shaped by experience, will be defined and extensively deliberated; and thirdly, the interpersonal simulation and synchronization mechanisms that support empathy and mentalization will be thoroughly considered. Throughout the paper, the cross-talks between the brain and the body, within the brain through functionally connected neural networks and in the context of agent-environment dynamics, will be addressed. These communicative patterns will be elaborated on to unfold psychophysiological linkage, as well as psychopathological shifts, concluding with the neuroplastic change associated with the formulation of CATs. The manuscript suggests an integrative view of the brain-body-mind in contexts relevant to the therapeutic potential of the expressive creative arts and the main avenues by which neuroscience may ground, enlighten and enrich the clinical psychotherapeutic practice.

Human Representation Learning

The central theme of this review is the dynamic interaction between information selection and learning. We pose a fundamental question about this interaction: How do we learn what features of our experiences are worth learning about? In humans, this process depends on attention and memory, two cognitive functions that together constrain representations of the world to features that are relevant for goal attainment. Recent evidence suggests that the representations shaped by attention and memory are themselves inferred from experience with each task. We review this evidence and place it in the context of work that has explicitly characterized representation learning as statistical inference. We discuss how inference can be scaled to real-world decisions by approximating beliefs based on a small number of experiences. Finally, we highlight some implications of this inference process for human decision-making in social environments.

Latent cause inference during extinction learning in trauma-exposed individuals with and without PTSD

BACKGROUND

Problems in learning that sights, sounds, or situations that were once associated with danger have become safe (extinction learning) may explain why some individuals suffer prolonged psychological distress following traumatic experiences. Although simple learning models have been unable to provide a convincing account of why this learning fails, it has recently been proposed that this may be explained by individual differences in beliefs about the causal structure of the environment.

METHODS

Here, we tested two competing hypotheses as to how differences in causal inference might be related to trauma-related psychopathology, using extinction learning data collected from clinically well-characterised individuals with varying degrees of post-traumatic stress (N = 56). Model parameters describing individual differences in causal inference were related to multiple post-traumatic stress disorder (PTSD) and depression symptom dimensions via network analysis.

RESULTS

Individuals with more severe PTSD were more likely to assign observations from conditioning and extinction stages to a single underlying cause. Specifically, greater re-experiencing symptom severity was associated with a lower likelihood of inferring that multiple causes were active in the environment.

CONCLUSIONS

We interpret these results as providing evidence of a primary deficit in discriminative learning in participants with more severe PTSD. Specifically, a tendency to attribute a greater diversity of stimulus configurations to the same underlying cause resulted in greater uncertainty about stimulus-outcome associations, impeding learning both that certain stimuli were safe, and that certain stimuli were no longer dangerous. In the future, better understanding of the role of causal inference in trauma-related psychopathology may help refine cognitive therapies for these disorders.

Neural Computations of Threat

A host of learning, memory, and decision-making processes form the individual's response to threat and may be disrupted in anxiety and post-trauma psychopathology. Here we review the neural computations of threat, from the first encounter with a dangerous situation, through learning, storing, and updating cues that predict it, to making decisions about the optimal course of action. The overview highlights the interconnected nature of these processes and their reliance on shared neural and computational mechanisms. We propose an integrative approach to the study of threat-related processes, in which specific computations are studied across the various stages of threat experience rather than in isolation. This approach can generate new insights about the evolution, diagnosis, and treatment of threat-related psychopathology.

The brain in motion: How ensemble fluidity drives memory-updating and flexibility

While memories are often thought of as flashbacks to a previous experience, they do not simply conserve veridical representations of the past but must continually integrate new information to ensure survival in dynamic environments. Therefore, 'drift' in neural firing patterns, typically construed as disruptive 'instability' or an undesirable consequence of noise, may actually be useful for updating memories. In our view, continual modifications in memory representations reconcile classical theories of stable memory traces with neural drift. Here we review how memory representations are updated through dynamic recruitment of neuronal ensembles on the basis of excitability and functional connectivity at the time of learning. Overall, we emphasize the importance of considering memories not as static entities, but instead as flexible network states that reactivate and evolve across time and experience.

Retrieval-Dependent Mechanisms Affecting Emotional Memory Persistence: Reconsolidation, Extinction, and the Space in Between

Maladaptive emotional memories contribute to the persistence of many mental health disorders, and therefore the prospect of disrupting these memories to produce long-term reductions in relapse is of great clinical appeal. Reducing the impact of maladaptive emotional memories on behaviour could be achieved by two retrieval-dependent manipulations that engage separate mnemonic processes: "reconsolidation disruption" and "extinction enhancement." Extinction occurs during a prolonged re-exposure session in the absence of the expected emotional outcome and is widely accepted as reflecting the formation of a new, inhibitory memory that prevents behavioural expression of the original trace. Reconsolidation, by contrast, involves the destabilisation of the original memory, allowing for subsequent updating and restabilisation in specific brain regions, unless the re-stabilization process is prevented through specific pharmacological or behavioural interventions. Both destabilisation of the original memory and memory extinction require that re-exposure induces prediction error-a mismatch between what is expected and what actually occurs-but the parameters that allow reconsolidation and extinction to occur, and control the transition between them, have not been well-characterised. Here, we review what is known about the induction of memory destabilisation and extinction, and the transition period that separates these mnemonic processes, drawing on preclinical and clinical examples. A deeper understanding of the processes that determine the alternative routes to memory persistence or inhibition is critical for designing new and more reliable clinical treatments targeting maladaptive emotional memories.

Hippocampal remapping as hidden state inference

Cells in the hippocampus tuned to spatial location (place cells) typically change their tuning when an animal changes context, a phenomenon known as remapping. A fundamental challenge to understanding remapping is the fact that what counts as a ‘‘context change’’ has never been precisely defined. Furthermore, different remapping phenomena have been classified on the basis of how much the tuning changes after different types and degrees of context change, but the relationship between these variables is not clear. We address these ambiguities by formalizing remapping in terms of hidden state inference. According to this view, remapping does not directly reflect objective, observable properties of the environment, but rather subjective beliefs about the hidden state of the environment. We show how the hidden state framework can resolve a number of puzzles about the nature of remapping.

Locus coeruleus-norepinephrine: basic functions and insights into Parkinson's disease

The locus coeruleus is a pontine nucleus that produces much of the brain's norepinephrine. Despite its small size, the locus coeruleus is critical for a myriad of functions and is involved in many neurodegenerative and neuropsychiatric disorders. In this review, we discuss the physiology and anatomy of the locus coeruleus system and focus on norepinephrine's role in synaptic plasticity. We highlight Parkinson's disease as a disorder with motor and neuropsychiatric symptoms that may be understood as aberrations in the normal functions of locus coeruleus.

An Active Inference Approach to Modeling Structure Learning: Concept Learning as an Example Case

Within computational neuroscience, the algorithmic and neural basis of structure learning remains poorly understood. Concept learning is one primary example, which requires both a type of internal model expansion process (adding novel hidden states that explain new observations), and a model reduction process (merging different states into one underlying cause and thus reducing model complexity via meta-learning). Although various algorithmic models of concept learning have been proposed within machine learning and cognitive science, many are limited to various degrees by an inability to generalize, the need for very large amounts of training data, and/or insufficiently established biological plausibility. Using concept learning as an example case, we introduce a novel approach for modeling structure learning-and specifically state-space expansion and reduction-within the active inference framework and its accompanying neural process theory. Our aim is to demonstrate its potential to facilitate a novel line of active inference research in this area. The approach we lay out is based on the idea that a generative model can be equipped with extra (hidden state or cause) "slots" that can be engaged when an agent learns about novel concepts. This can be combined with a Bayesian model reduction process, in which any concept learning-associated with these slots-can be reset in favor of a simpler model with higher model evidence. We use simulations to illustrate this model's ability to add new concepts to its state space (with relatively few observations) and increase the granularity of the concepts it currently possesses. We also simulate the predicted neural basis of these processes. We further show that it can accomplish a simple form of "one-shot" generalization to new stimuli. Although deliberately simple, these simulation results highlight ways in which active inference could offer useful resources in developing neurocomputational models of structure learning. They provide a template for how future active inference research could apply this approach to real-world structure learning problems and assess the added utility it may offer.

Impact of a reminder/extinction procedure on threat-conditioned pupil size and skin conductance responses

A reminder can render consolidated memory labile and susceptible to amnesic agents during a reconsolidation window. For the case of threat memory (also termed fear memory), it has been suggested that extinction training during this reconsolidation window has the same disruptive impact. This procedure could provide a powerful therapeutic principle for treatment of unwanted aversive memories. However, human research yielded contradictory results. Notably, all published positive replications quantified threat memory by conditioned skin conductance responses (SCR). Yet, other studies measuring SCR and/or fear-potentiated startle failed to observe an effect of a reminder/extinction procedure on the return of fear. Here we sought to shed light on this discrepancy by using a different autonomic response, namely, conditioned pupil dilation, in addition to SCR, in a replication of the original human study. N = 71 humans underwent a 3-d threat conditioning, reminder/extinction, and reinstatement, procedure with 2 CS+, of which one was reminded. Participants successfully learned the threat association on day 1, extinguished conditioned responding on day 2, and showed reinstatement on day 3. However, there was no difference in conditioned responding between the reminded and the nonreminded CS, neither in pupil size nor SCR. Thus, we found no evidence that a reminder trial before extinction prevents the return of threat-conditioned responding.

Retrieval-Extinction and Relapse Prevention: Rewriting Maladaptive Drug Memories?

Addicted individuals are highly susceptible to relapse when exposed to drug-associated conditioned stimuli (CSs; "drug cues") even after extensive periods of abstinence. Until recently, these maladaptive emotional drug memories were believed to be permanent and resistant to change. The rediscovery of the phenomenon of memory reconsolidation-by which retrieval of the memory can, under certain conditions, destabilize the previously stable memory before it restabilizes in its new, updated form-has led to the hypothesis that it may be possible to disrupt the strong maladaptive drug-memories that trigger a relapse. Furthermore, recent work has suggested that extinction training "within the reconsolidation window" may lead to a long-term reduction in relapse without the requirement for pharmacological amnestic agents. However, this so-called "retrieval-extinction" effect has been inconsistently observed in the literature, leading some to speculate that rather than reflecting memory updating, it may be the product of facilitation of extinction. In this mini review article, we will focus on factors that might be responsible for the retrieval-extinction effects on preventing drug-seeking relapse and how inter-individual differences may influence this therapeutically promising effect. A better understanding of the psychological and neurobiological mechanisms underpinning the "retrieval-extinction" paradigm, and individual differences in boundary conditions, should provide insights with the potential to optimize the translation of "retrieval-extinction" to clinical populations.

Shifting from fear to safety through deconditioning-update

Aversive memories are at the heart of psychiatric disorders such as phobias and post-traumatic stress disorder (PTSD). Here, we present a new behavioral approach in rats that robustly attenuates aversive memories. This method consists of ‘deconditioning’ animals previously trained to associate a tone with a strong footshock by replacing it with a much weaker one during memory retrieval. Our results indicate that deconditioning-update is more effective than traditional extinction in reducing fear responses; moreover, such effects are long lasting and resistant to renewal and spontaneous recovery. Remarkably, this strategy overcame important boundary conditions for memory updating, such as remote or very strong traumatic memories. The same beneficial effect was found in other types of fear-related memories. Deconditioning was mediated by L-type voltage-gated calcium channels and is consistent with computational accounts of mismatch-induced memory updating. Our results suggest that shifting from fear to safety through deconditioning-update is a promising approach to attenuate traumatic memories.

Spontaneous Task Structure Formation Results in a Cost to Incidental Memory of Task Stimuli

Humans are characterized by their ability to leverage rules for classifying and linking stimuli to context-appropriate actions. Previous studies have shown that when humans learn stimulus-response associations for two-dimensional stimuli, they implicitly form and generalize hierarchical rule structures (task-sets). However, the cognitive processes underlying structure formation are poorly understood. Across four experiments, we manipulated how trial-unique images mapped onto responses to bias spontaneous task-set formation and investigated structure learning through the lens of incidental stimulus encoding. Participants performed a learning task designed to either promote task-set formation (by “motor-clustering” possible stimulus-action rules), or to discourage it (by using arbitrary category-response mappings). We adjudicated between two hypotheses: Structure learning may promote attention to task stimuli, thus resulting in better subsequent memory. Alternatively, building task-sets might impose cognitive demands (for instance, on working memory) that divert attention away from stimulus encoding. While the clustering manipulation affected task-set formation, there were also substantial individual differences. Importantly, structure learning incurred a cost: spontaneous task-set formation was associated with diminished stimulus encoding. Thus, spontaneous hierarchical task-set formation appears to involve cognitive demands that divert attention away from encoding of task stimuli during structure learning.

Believing in dopamine

Midbrain dopamine signals are widely thought to report reward prediction errors that drive learning in the basal ganglia. However, dopamine has also been implicated in various probabilistic computations, such as encoding uncertainty and controlling exploration. Here, we show how these different facets of dopamine signalling can be brought together under a common reinforcement learning framework. The key idea is that multiple sources of uncertainty impinge on reinforcement learning computations: uncertainty about the state of the environment, the parameters of the value function and the optimal action policy. Each of these sources plays a distinct role in the prefrontal cortex-basal ganglia circuit for reinforcement learning and is ultimately reflected in dopamine activity. The view that dopamine plays a central role in the encoding and updating of beliefs brings the classical prediction error theory into alignment with more recent theories of Bayesian reinforcement learning.

Learning task-state representations

Arguably, the most difficult part of learning is deciding what to learn about. Should I associate the positive outcome of safely completing a street-crossing with the situation 'the car approaching the crosswalk was red' or with 'the approaching car was slowing down'? In this Perspective, we summarize our recent research into the computational and neural underpinnings of 'representation learning'-how humans (and other animals) construct task representations that allow efficient learning and decision-making. We first discuss the problem of learning what to ignore when confronted with too much information, so that experience can properly generalize across situations. We then turn to the problem of augmenting perceptual information with inferred latent causes that embody unobservable task-relevant information, such as contextual knowledge. Finally, we discuss recent findings regarding the neural substrates of task representations that suggest the orbitofrontal cortex represents 'task states', deploying them for decision-making and learning elsewhere in the brain.

Making translation work: Harmonizing cross-species methodology in the behavioural neuroscience of Pavlovian fear conditioning

Translational neuroscience bridges insights from specific mechanisms in rodents to complex functions in humans and is key to advance our general understanding of central nervous function. A prime example of translational research is the study of cross-species mechanisms that underlie responding to learned threats, by employing Pavlovian fear conditioning protocols in rodents and humans. Hitherto, evidence for (and critique of) these cross-species comparisons in fear conditioning research was based on theoretical viewpoints. Here, we provide a perspective to substantiate these theoretical concepts with empirical considerations of cross-species methodology. This meta-research perspective is expected to foster cross-species comparability and reproducibility to ultimately facilitate successful transfer of results from basic science into clinical applications.

A flexible and generalizable model of online latent-state learning

Many models of classical conditioning fail to describe important phenomena, notably the rapid return of fear after extinction. To address this shortfall, evidence converged on the idea that learning agents rely on latent-state inferences, i.e. an ability to index disparate associations from cues to rewards (or penalties) and infer which index (i.e. latent state) is presently active. Our goal was to develop a model of latent-state inferences that uses latent states to predict rewards from cues efficiently and that can describe behavior in a diverse set of experiments. The resulting model combines a Rescorla-Wagner rule, for which updates to associations are proportional to prediction error, with an approximate Bayesian rule, for which beliefs in latent states are proportional to prior beliefs and an approximate likelihood based on current associations. In simulation, we demonstrate the model's ability to reproduce learning effects both famously explained and not explained by the Rescorla-Wagner model, including rapid return of fear after extinction, the Hall-Pearce effect, partial reinforcement extinction effect, backwards blocking, and memory modification. Lastly, we derive our model as an online algorithm to maximum likelihood estimation, demonstrating it is an efficient approach to outcome prediction. Establishing such a framework is a key step towards quantifying normative and pathological ranges of latent-state inferences in various contexts.

In a Temporally Segmented Experience Hippocampal Neurons Represent Temporally Drifting Context But Not Discrete Segments

There is widespread agreement that episodic memory is organized into a timeline of past experiences. Recent work suggests that the hippocampus may parse the flow of experience into discrete episodes separated by event boundaries. A complementary body of work suggests that context changes gradually as experience unfolds. We recorded from hippocampal neurons as male Long-Evans rats performed 6 blocks of an object discrimination task in sets of 15 trials. Each block was separated by removal from the testing chamber for a delay to enable segmentation. The reward contingency reversed from one block to the next to incentivize segmentation. We expected animals to hold two distinct, recurring representations of context to match the two distinct rule contingencies. Instead, we found that overtrained rats began each block neither above nor below chance but by guessing randomly. While many units had clear firing fields selective to the conjunction of objects in places, a significant population also reflected a continuously drifting code both within block and across blocks. Despite clear boundaries between blocks, we saw no neural evidence for event segmentation in this experiment. Rather, the hippocampal ensemble drifted continuously across time. This continuous drift in the neural representation was consistent with the lack of segmentation observed in behavior.SIGNIFICANCE STATEMENT The neuroscience literature yet to reach consensus on how the hippocampus supports the organization of events across time in episodic memory. Initial studies reported stable hippocampal maps segmented by remapping events. However, it remains unclear whether segmentation is an artifact of cue responsivity. Recently, research has shown that the hippocampal code exhibits continuous drift. Drift may represent a continually evolving context; however, it is unclear whether this is an artifact of changing experiences. We recorded dCA1 in rats performing an object discrimination task designed to segment time. Overtrained rats could not anticipate upcoming context switches but used context boundaries to their advantage. Hippocampal ensembles showed neither evidence of alternating between stable contexts nor sensitivity to boundaries, but showed robust temporal drift.

Spatial distancing reduces emotional arousal to reactivated memories

Memories are able to update and adapt with new information about the world after they are reactivated. However, it is unknown whether the labile period following reactivation makes episodic memories more amenable to emotion regulation, an application that holds great clinical promise. Here, we investigated the efficacy of cognitive reappraisal to down regulate negative affect in response to reactivated memories. Healthy young adults (N = 119) rated the emotionality of negative pictures. After a partial reactivation of each picture 2 days later, participants voluntarily engaged in a spatial distancing regulation tactic by imagining the reactivated object extremely far away from them. Compared with no-regulation and no-reactivation controls, self-reported arousal for regulated pictures dropped significantly 2 days after the manipulation, despite no significant difference in memory accuracy or valence. These results open up a new line of work that capitalizes on reactivation-based lability to selectively alter enduring arousal responses to emotional memories.

Big-Loop Recurrence within the Hippocampal System Supports Integration of Information across Episodes

Recent evidence challenges the widely held view that the hippocampus is specialized for episodic memory, by demonstrating that it also underpins the integration of information across experiences. Contemporary computational theories propose that these two contrasting functions can be accomplished by big-loop recurrence, whereby the output of the system is recirculated back into the hippocampus. We use ultra-high-resolution fMRI to provide support for this hypothesis, by showing that retrieved information is presented as a new input on the superficial entorhinal cortex-driven by functional connectivity between the deep and superficial entorhinal layers. Further, the magnitude of this laminar connectivity correlated with inferential performance, demonstrating its importance for behavior. Our findings offer a novel perspective on information processing within the hippocampus and support a unifying framework in which the hippocampus captures higher-order structure across experiences, by creating a dynamic memory space from separate episodic codes for individual experiences.

Human representation of multimodal distributions as clusters of samples

Behavioral and neuroimaging evidence shows that human decisions are sensitive to the statistical regularities (mean, variance, skewness, etc.) of reward distributions. However, it is unclear what representations human observers form to approximate reward distributions, or probability distributions in general. When the possible values of a probability distribution are numerous, it is cognitively costly and perhaps unrealistic to maintain in mind the probability of each possible value. Here we propose a Clusters of Samples (CoS) representation model: The samples of the to-be-represented distribution are classified into a small number of clusters and only the centroids and relative weights of the clusters are retained for future use. We tested the behavioral relevance of CoS in four experiments. On each trial, human subjects reported the mean and mode of a sequentially presented multimodal distribution of spatial positions or orientations. By varying the global and local features of the distributions, we observed systematic errors in the reported mean and mode. We found that our CoS representation of probability distributions outperformed alternative models in accounting for subjects’ response patterns. The ostensible influence of positive/negative skewness on the over/under estimation of the reported mean, analogous to the “skewness preference” phenomenon in decisions, could be well explained by models based on CoS.

Life is full of uncertainties: An action may yield multiple possible consequences and a percept may imply multiple possible causes. To survive, humans and animals must compensate for the uncertainty in the environment and in their own perceptual and motor systems. However, how humans represent probability distributions to fulfill probabilistic computations for perception and action remains elusive. The number of possible values in a distribution is vast and grows exponentially with the dimension of the distribution. It would be costly, if not impossible, to maintain the probability of each possible value. Here we propose a sparse representation of probability distributions, which can reduce an arbitrary distribution to a small set of coefficients while still keeping important global and local features of the original distribution. Our experiments provide preliminary evidence for the use of such representations in human cognition.

When and How Implicit First Impressions Can Be Updated

Human perceivers continually react to the social world implicitly —that is, spontaneously and rapidly. Earlier research suggested that implicit impressions of other people are slower to change than self-reported impressions in the face of contradictory evidence, often leaving them miscalibrated from what one learns to be true. Recent work, however, has identified conditions under which implicit impressions can be rapidly updated. Here, we review three lines of work showing that implicit impressions are responsive to information that is highly diagnostic, believable, or reframes earlier experience. These findings complement ongoing research on mechanisms of changing implicit impressions in a wider variety of groups, from real people to robots, and provide support for theoretical frameworks that embrace greater unity in the factors that can impact implicit and explicit social cognition.

Memory boundaries: opening a window inspired by reconsolidation to treat anxiety, trauma-related, and addiction disorders

Pioneering research over the past two decades has shown that memories are far more malleable than we once thought, thereby highlighting the potential for new clinical avenues for treatment of psychopathology. We first briefly review the historical foundation of memory reconsolidation-a concept that refers to hypothetical processes that occur when a memory is retrieved and restored. Then, we provide an overview of the basic research on memory reconsolidation that has been done with humans and other animals, focusing on models of fear, anxiety-related disorders, and addiction, from the perspective that they all involve disorders of memory. This basic research has fuelled early stage developments of novel treatment techniques. More specifically, we consider behavioural interventions inspired by reconsolidation updating, namely retrieval-extinction techniques. We discuss the set of principles that would be needed for memory modifications within a putative reconsolidation time window, and review research that employs reconsolidation-based strategies with clinical populations. We conclude by highlighting current pitfalls and controversies surrounding the use of reconsolidation-based approaches, but end on an optimistic note for clinical research going forward. Despite the challenges, we believe that drawing on ideas from psychological science can help open up treatment innovation.

Episodic memory and Pavlovian conditioning: ships passing in the night

Research on emotional learning and memory is traditionally approached from one of two directions: episodic memory and classical conditioning. These approaches differ substantially in methodology and intellectual tradition. Here, we offer a new approach to the study of emotional memory in humans that involves integrating theoretical knowledge and experimental techniques from these seemingly distinct fields. Specifically, we describe how subtle modifications to traditional Pavlovian conditioning procedures have provided new insight into how emotional experiences are selectively prioritized in long-term episodic memory. We also speculate on future directions and undeveloped lines of research where some of the knowledge and principles of classical conditioning might advance our understanding of how emotion modifies episodic memory, and vice versa.