Reinstated episodic context guides sampling-based decisions for reward.

How does experience inform decisions? In episodic sampling, decisions are guided by a few episodic memories of past choices. This process can yield choice patterns similar to model-free reinforcement learning; however, samples can vary from trial to trial, causing decisions to vary. Here we show that context retrieved during episodic sampling can cause choice behavior to deviate sharply from the predictions of reinforcement learning. Specifically, we show that, when a given memory is sampled, choices (in the present) are influenced by the properties of other decisions made in the same context as the sampled event. This effect is mediated by fMRI measures of context retrieval on each trial, suggesting a mechanism whereby cues trigger retrieval of context, which then triggers retrieval of other decisions from that context. This result establishes a new avenue by which experience can guide choice and, as such, has broad implications for the study of decisions

Recall termination in free recall

Although much is known about the dynamics of memory search in the free recall task, relatively little is known about the factors related to recall termination. Reanalyzing individual trial data from 14 prior studies (1,079 participants in 28,015 trials) and defining termination as occurring when a final response is followed by a long nonresponse interval, we observed that termination probability increased throughout the recall period and that retrieval was more likely to terminate following an error than following a correct response. Among errors, termination probability was higher following prior-list intrusions and repetitions than following extralist intrusions. To verify that this pattern of results can be seen in a single study, we report a new experiment in which 80 participants contributed recall data from a total of 9,122 trials. This experiment replicated the pattern observed in the aggregate analysis of the prior studies

Using fMRI Brain Activation to Identify Cognitive States Associated with Perception of Tools and Dwellings

Previous studies have succeeded in identifying the cognitive state corresponding to the perception of a set of depicted categories, such as tools, by analyzing the accompanying pattern of brain activity, measured with fMRI. The current research focused on identifying the cognitive state associated with a 4s viewing of an individual line drawing (1 of 10 familiar objects, 5 tools and 5 dwellings, such as a hammer or a castle). Here we demonstrate the ability to reliably (1) identify which of the 10 drawings a participant was viewing, based on that participant's characteristic whole-brain neural activation patterns, excluding visual areas; (2) identify the category of the object with even higher accuracy, based on that participant's activation; and (3) identify, for the first time, both individual objects and the category of the object the participant was viewing, based only on other participants' activation patterns. The voxels important for category identification were located similarly across participants, and distributed throughout the cortex, focused in ventral temporal perceptual areas but also including more frontal association areas (and somewhat left-lateralized). These findings indicate the presence of stable, distributed, communal, and identifiable neural states corresponding to object concepts

Generative Embedding for Model-Based Classification of fMRI Data

Decoding models, such as those underlying multivariate classification algorithms, have been increasingly used to infer cognitive or clinical brain states from measures of brain activity obtained by functional magnetic resonance imaging (fMRI). The practicality of current classifiers, however, is restricted by two major challenges. First, due to the high data dimensionality and low sample size, algorithms struggle to separate informative from uninformative features, resulting in poor generalization performance. Second, popular discriminative methods such as support vector machines (SVMs) rarely afford mechanistic interpretability. In this paper, we address these issues by proposing a novel generative-embedding approach that incorporates neurobiologically interpretable generative models into discriminative classifiers. Our approach extends previous work on trial-by-trial classification for electrophysiological recordings to subject-by-subject classification for fMRI and offers two key advantages over conventional methods: it may provide more accurate predictions by exploiting discriminative information encoded in ‘hidden’ physiological quantities such as synaptic connection strengths; and it affords mechanistic interpretability of clinical classifications. Here, we introduce generative embedding for fMRI using a combination of dynamic causal models (DCMs) and SVMs. We propose a general procedure of DCM-based generative embedding for subject-wise classification, provide a concrete implementation, and suggest good-practice guidelines for unbiased application of generative embedding in the context of fMRI. We illustrate the utility of our approach by a clinical example in which we classify moderately aphasic patients and healthy controls using a DCM of thalamo-temporal regions during speech processing. Generative embedding achieves a near-perfect balanced classification accuracy of 98% and significantly outperforms conventional activation-based and correlation-based methods. This example demonstrates how disease states can be detected with very high accuracy and, at the same time, be interpreted mechanistically in terms of abnormalities in connectivity. We envisage that future applications of generative embedding may provide crucial advances in dissecting spectrum disorders into physiologically more well-defined subgroups

Finding the engram.

Many attempts have been made to localize the physical trace of a memory, or engram, in the brain. However, until recently, engrams have remained largely elusive. In this Review, we develop four defining criteria that enable us to critically assess the recent progress that has been made towards finding the engram. Recent \u27capture\u27 studies use novel approaches to tag populations of neurons that are active during memory encoding, thereby allowing these engram-associated neurons to be manipulated at later times. We propose that findings from these capture studies represent considerable progress in allowing us to observe, erase and express the engram

Retrieval induces adaptive forgetting of competing memories via cortical pattern suppression

Remembering a past experience can, surprisingly, cause forgetting. Forgetting arises when other competing traces interfere with retrieval and inhibitory control mechanisms are engaged to suppress the distraction they cause. This form of forgetting is considered to be adaptive because it reduces future interference. The effect of this proposed inhibition process on competing memories has, however, never been observed, as behavioral methods are 'blind' to retrieval dynamics and neuroimaging methods have not isolated retrieval of individual memories. We developed a canonical template tracking method to quantify the activation state of individual target memories and competitors during retrieval. This method revealed that repeatedly retrieving target memories suppressed cortical patterns unique to competitors. Pattern suppression was related to engagement of prefrontal regions that have been implicated in resolving retrieval competition and, critically, predicted later forgetting. Thus, our findings demonstrate a cortical pattern suppression mechanism through which remembering adaptively shapes which aspects of our past remain accessible

RECAPP-XPR: A smartphone application for presenting and recalling experimentally controlled stimuli over longer timescales

We report two experiments that used smartphone applications for presenting and recalling verbal stimuli over extended timescales. In Experiment 1, we used an iPhone application that we had developed, called RECAPP-XPR, to present 76 participants with a single list of eight words presented at a rate of one word every hour, followed by a test of free recall an hour later. The experiment was exceptionally easy to schedule, taking only between 5 and 10 min to set up using a web-based interface. RECAPP-XPR randomly samples the stimuli, presents the stimuli, and collects the free recall data. The stimuli disappear shortly after they have been presented, and RECAPP-XPR collects data on when each stimulus was viewed. In Experiment 2, the study was replicated using the widely used image-sharing application Snapchat. A total of 197 participants were tested by 38 student experimenters, who manually presented the stimuli as “snaps” of experimentally controlled stimuli using the same experimental rates that had been used in Experiment 1. Like all snaps, these stimuli disappeared from view after a very short interval. In both experiments, we observed significant recall advantages for the first and last list items (primacy and recency effects, respectively), and there were clear tendencies to make more transitions at output between near-neighboring items, with a forward-ordered bias, consistent with temporal contiguity effects. The respective advantages and disadvantages of RECAPP-XPR and Snapchat as experimental software packages are discussed, as is the relationship between single-study-list smartphone experiments and long-term recency studies of real-world events