The authors conceptualize these memory systems from both behavioural and neurobiological perspectives, guided by three related principles: * First, that our understanding of a wide range of memory phenomena can be advanced by breaking down memory into multiple forms with different operating characteristics. * Second, that different forms of memory representation are supported by distinct brain pathways with circuitry and neural coding properties. * Third, that the contributions of different brain systems can be compared and contrasted by distinguishing between dedicated (or specific) and elaborate (or general) memory systems. A primary goal of this work is to relate the neurobiological properties of dedicated and elaborate systems to their neuropsychological counterparts, and in so doing, account for the phenomenology of memory, from conditioning to conscious recollection.

From Conditioning to Conscious Recollection Memory Systems of the Brain. Oxford Psychology Series, Volume 35.

Depression and anxiety are the most common psychiatric conditions in late life. Despite their prevalence, we know relatively little about their unique manifestation in older adults. And, Although the most common intervention for late-life depression and anxiety continues to be medication, research on psychosocial interventions for late-life depression and anxiety has burgeoned in the past several years. Unfortunately, this growing body of intervention research has yet to be widely translated into improved systems of care for late-life depression. This article is one step toward synthesizing the knowledge in this growing area of research. The review of literature presents the conclusions of several meta-analyses that have reviewed psychosocial interventions for late-life depression and anxiety. In addition, intervention studies concerning the effectiveness of cognitive behavioral therapy, interpersonal therapy, reminiscence therapy, and alternative therapies with depressed and/or anxious older adults are reviewed. A brief description of various approaches to psychosocial intervention with anxious and/or depressed older adults is also presented.

Depression and anxiety.

The authors present a new model of free recall on the basis of M. W. Howard and M. J. Kahana's (2002a) temporal context model and M. Usher and J. L. McClelland's (2001) leaky-accumulator decision model. In this model, contextual drift gives rise to both short-term and long-term recency effects, and contextual retrieval gives rise to short-term and long-term contiguity effects. Recall decisions are controlled by a race between competitive leaky accumulators. The model captures the dynamics of immediate, delayed, and continual distractor free recall, demonstrating that dissociations between short- and long-term recency can naturally arise from a model in which an internal contextual state is used as the sole cue for retrieval across time scales.

A Context-Based Theory of Recency and Contiguity in Free Recall. Commentary. Authors' reply

Episodic memory reflects the ability to recollect the temporal and spatial context of past experiences. Episodic memories depend on the hippocampus but have been proposed to undergo rapid forgetting unless consolidated during offline periods such as sleep to neocortical areas for long-term storage. Here, we propose an alternative to this standard systems consolidation theory (SSCT) - a contextual binding account - in which the hippocampus binds item-related and context-related information. We compare these accounts in light of behavioural, lesion, neuroimaging and sleep studies of episodic memory and contend that forgetting is largely due to contextual interference, episodic memory remains dependent on the hippocampus across time, contextual drift produces post-encoding activity and sleep benefits memory by reducing contextual interference.

A contextual binding theory of episodic memory: systems consolidation reconsidered

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.

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Learning task-state representations

Reward-prediction errors track the extent to which rewards deviate from expectations, and aid in learning. How do such errors in prediction interact with memory for the rewarding episode? Existing findings point to both cooperative and competitive interactions between learning and memory mechanisms. Here, we investigated whether learning about rewards in a high-risk context, with frequent, large prediction errors, would give rise to higher fidelity memory traces for rewarding events than learning in a low-risk context. Experiment 1 showed that recognition was better for items associated with larger absolute prediction errors during reward learning. Larger prediction errors also led to higher rates of learning about rewards. Interestingly we did not find a relationship between learning rate for reward and recognition-memory accuracy for items, suggesting that these two effects of prediction errors were caused by separate underlying mechanisms. In Experiment 2, we replicated these results with a longer task that posed stronger memory demands and allowed for more learning. We also showed improved source and sequence memory for items within the high-risk context. In Experiment 3, we controlled for the difficulty of reward learning in the risk environments, again replicating the previous results. Moreover, this control revealed that the high-risk context enhanced item-recognition memory beyond the effect of prediction errors. In summary, our results show that prediction errors boost both episodic item memory and incremental reward learning, but the two effects are likely mediated by distinct underlying systems. (PsycINFO Database Record

Dissociable effects of surprising rewards on learning and memory.

Theories of episodic memory have proposed that individual memory traces are linked together by a representation of context that drifts slowly over time. Recent data challenge the notion that contextual drift is always slow and passive. In particular, changes in one's external environment or internal model induce discontinuities in memory that are reflected in sudden changes in neural activity, suggesting that context can shift abruptly. Furthermore, context change effects are sensitive to top-down goals, suggesting that contextual drift may be an active process. These findings call for revising models of the role of context in memory, in order to account for abrupt contextual shifts and the controllable nature of context change.

Does mental context drift or shift

Reward prediction errors create event boundaries in memory.

Memory helps guide behavior, but which experiences from the past are prioritized? Classic models of learning posit that events associated with unpredictable outcomes as well as, paradoxically, predictable outcomes, deploy more attention and learning for those events. Here, we test reinforcement learning and subsequent memory for those events, and treat signed and unsigned reward prediction errors (RPEs), experienced at the reward-predictive cue or reward outcome, as drivers of these two seemingly contradictory signals. By fitting reinforcement learning models to behavior, we find that both RPEs contribute to learning by modulating a dynamically changing learning rate. We further characterize the effects of these RPE signals on memory and show that both signed and unsigned RPEs enhance memory, in line with midbrain dopamine and locus-coeruleus modulation of hippocampal plasticity, thereby reconciling separate findings in the literature.

Signed and unsigned reward prediction errors dynamically enhance learning and memory

Depression is a disorder characterized by sustained negative affect and blunted positive affect, suggesting potential abnormalities in reward learning and its interaction with episodic memory. This study investigated how reward prediction errors experienced during learning modulate memory for rewarding events in individuals with depressive and non-depressive symptoms. Across three experiments, participants learned the average values of two scene categories in two learning contexts. Each learning context had either high or low outcome variance, allowing us to test the effects of small and large prediction errors on learning and memory. Participants were later tested for their memory of trial-unique scenes that appeared alongside outcomes. We compared learning and memory performance of individuals with self-reported depressive symptoms (N = 101) to those without (N = 184). Although there were no overall differences in reward learning between the depressive and non-depressive group, depression severity within the depressive group predicted greater error in estimating the values of the scene categories. Similarly, there were no overall differences in memory performance. However, in depressive participants, negative prediction errors enhanced episodic memory more so than did positive prediction errors, and vice versa for non-depressive participants who showed a larger effect of positive prediction errors on memory. These results reflected differences in memory both within group and across groups. Individuals with self-reported depressive symptoms showed relatively intact reinforcement learning, but demonstrated a bias for encoding events that accompanied surprising negative outcomes versus surprising positive ones. We discuss a potential neural mechanism supporting these effects, which may underlie or contribute to the excessive negative affect observed in depression.

Nina Rouhani

Papers

Dissociable effects of surprising rewards on learning and memory.

Does mental context drift or shift

Reward prediction errors create event boundaries in memory.

Signed and unsigned reward prediction errors dynamically enhance learning and memory

Depressive symptoms bias the prediction-error enhancement of memory towards negative events in reinforcement learning.