Post-hypnotic suggestion improves confidence and speed of memory access with long-lasting effects.

In our study, we use the post-hypnotic suggestion of easy remembering to improve memory with long-lasting effects. We tested 24 highly suggestible participants in an online study. Participants learned word lists and recalled them later in a recognition memory task. At the beginning of the study, participants were hypnotized and the post-hypnotic suggestion to remember easily was associated with a cue that participants used during the recognition memory task. In a control condition, the same participants used a neutral cue. One week later, participants repeated both conditions with new word lists. Participants were significantly faster and more confident in their recognition ratings in the easy-remembering condition compared to the control condition, and this effect persisted over one week. Crucially, the increased speed and confidence in the easy-remembering condition did not affect memory accuracy. That makes our hypnosis intervention promising for patients experiencing subjective memory impairments. APA PSYCINFO CODES: 2343 (Learning and Memory), 2380 (Consciousness States), 3351 (Clinical Hypnosis).

Bayesian Surprise Predicts Human Event Segmentation in Story Listening.

Event segmentation theory posits that people segment continuous experience into discrete events and that event boundaries occur when there are large transient increases in prediction error. Here, we set out to test this theory in the context of story listening, by using a deep learning language model (GPT-2) to compute the predicted probability distribution of the next word, at each point in the story. For three stories, we used the probability distributions generated by GPT-2 to compute the time series of prediction error. We also asked participants to listen to these stories while marking event boundaries. We used regression models to relate the GPT-2 measures to the human segmentation data. We found that event boundaries are associated with transient increases in Bayesian surprise but not with a simpler measure of prediction error (surprisal) that tracks, for each word in the story, how strongly that word was predicted at the previous time point. These results support the hypothesis that prediction error serves as a control mechanism governing event segmentation and point to important differences between operational definitions of prediction error.

A shared linguistic space for transmitting our thoughts from brain to brain in natural conversations.

Effective communication hinges on a mutual understanding of word meaning in different contexts. The embedding space learned by large language models can serve as an explicit model of the shared, context-rich meaning space humans use to communicate their thoughts. We recorded brain activity using electrocorticography during spontaneous, face-to-face conversations in five pairs of epilepsy patients. We demonstrate that the linguistic embedding space can capture the linguistic content of word-by-word neural alignment between speaker and listener. Linguistic content emerged in the speaker's brain before word articulation, and the same linguistic content rapidly reemerged in the listener's brain after word articulation. These findings establish a computational framework to study how human brains transmit their thoughts to one another in real-world contexts.

Large language models can segment narrative events similarly to humans.

Humans perceive discrete events such as "restaurant visits" and "train rides" in their continuous experience. One important prerequisite for studying human event perception is the ability of researchers to quantify when one event ends and another begins. Typically, this information is derived by aggregating behavioral annotations from several observers. Here we present an alternative computational approach where event boundaries are derived using a large language model, GPT-3, instead of using human annotations. We demonstrate that GPT-3 can segment continuous narrative text into events. GPT-3-annotated events are significantly correlated with human event annotations. Furthermore, these GPT-derived annotations achieve a good approximation of the "consensus" solution (obtained by averaging across human annotations); the boundaries identified by GPT-3 are closer to the consensus, on average, than boundaries identified by individual human annotators. This finding suggests that GPT-3 provides a feasible solution for automated event annotations, and it demonstrates a further parallel between human cognition and prediction in large language models. In the future, GPT-3 may thereby help to elucidate the principles underlying human event perception.

Evidence That Event Boundaries Are Access Points for Memory Retrieval.

When recalling memories, we often scan information-rich continuous episodes, for example, to find our keys. How does our brain access and search through those memories? We suggest that high-level structure, marked by event boundaries, guides us through this process: In our computational model, memory scanning is sped up by skipping ahead to the next event boundary upon reaching a decision threshold. In adult Mechanical Turk workers from the United States, we used a movie (normed for event boundaries; Study 1, N = 203) to prompt memory scanning of movie segments for answers (Study 2, N = 298) and mental simulation (Study 3, N = 100) of these segments. Confirming model predictions, we found that memory-scanning times varied as a function of the number of event boundaries within a segment and the distance of the search target to the previous boundary (the key diagnostic parameter). Mental simulation times were also described by a skipping process with a higher skipping threshold than memory scanning. These findings identify event boundaries as access points to memory.

Magnetoencephalographic spectral fingerprints differentiate evidence accumulation from saccadic motor preparation in perceptual decision-making.

The understanding of the neurobiological basis of perceptual decision-making has been profoundly shaped by studies in the monkey brain in tandem with mathematical models, providing the basis for the formulation of an intentional account of decision-making. Although much progress has been made in human studies, a characterization of the neural underpinnings of an integrative mechanism, where evidence accumulation and the selection and execution of responses are carried out by the same system, remains challenging. Here, by employing magnetoencephalographic recording in combination with an experimental protocol that measures saccadic response and leverages a systematic modulation of evidence levels, we obtained a spectral dissociation between evidence accumulation mechanisms and motor preparation within the same brain region. Specifically, we show that within the dorsomedial parietal cortex alpha power modulation reflects the amount of sensory evidence available while beta power modulations reflect motor preparation, putatively representing the human homolog of the saccadic-related LIP region.

RT-Cloud: A cloud-based software framework to simplify and standardize real-time fMRI.

Real-time fMRI (RT-fMRI) neurofeedback has been shown to be effective in treating neuropsychiatric disorders and holds tremendous promise for future breakthroughs, both with regard to basic science and clinical applications. However, the prevalence of its use has been hampered by computing hardware requirements, the complexity of setting up and running an experiment, and a lack of standards that would foster collaboration. To address these issues, we have developed RT-Cloud (https://github.com/brainiak/rt-cloud), a flexible, cloud-based, open-source Python software package for the execution of RT-fMRI experiments. RT-Cloud uses standardized data formats and adaptable processing streams to support and expand open science in RT-fMRI research and applications. Cloud computing is a key enabling technology for advancing RT-fMRI because it eliminates the need for on-premise technical expertise and high-performance computing; this allows installation, configuration, and maintenance to be automated and done remotely. Furthermore, the scalability of cloud computing makes it easier to deploy computationally-demanding multivariate analyses in real time. In this paper, we describe how RT-Cloud has been integrated with open standards, including the Brain Imaging Data Structure (BIDS) standard and the OpenNeuro database, how it has been applied thus far, and our plans for further development and deployment of RT-Cloud in the coming years.

The hippocampus as the switchboard between perception and memory.

Adaptive memory recall requires a rapid and flexible switch from external perceptual reminders to internal mnemonic representations. However, owing to the limited temporal or spatial resolution of brain imaging modalities used in isolation, the hippocampal-cortical dynamics supporting this process remain unknown. We thus employed an object-scene cued recall paradigm across two studies, including intracranial electroencephalography (iEEG) and high-density scalp EEG. First, a sustained increase in hippocampal high gamma power (55 to 110 Hz) emerged 500 ms after cue onset and distinguished successful vs. unsuccessful recall. This increase in gamma power for successful recall was followed by a decrease in hippocampal alpha power (8 to 12 Hz). Intriguingly, the hippocampal gamma power increase marked the moment at which extrahippocampal activation patterns shifted from perceptual cue toward mnemonic target representations. In parallel, source-localized EEG alpha power revealed that the recall signal progresses from hippocampus to posterior parietal cortex and then to medial prefrontal cortex. Together, these results identify the hippocampus as the switchboard between perception and memory and elucidate the ensuing hippocampal-cortical dynamics supporting the recall process.

Moment-by-moment tracking of naturalistic learning and its underlying hippocampo-cortical interactions.

Humans form lasting memories of stimuli that were only encountered once. This naturally occurs when listening to a story, however it remains unclear how and when memories are stored and retrieved during story-listening. Here, we first confirm in behavioral experiments that participants can learn about the structure of a story after a single exposure and are able to recall upcoming words when the story is presented again. We then track mnemonic information in high frequency activity (70-200 Hz) as patients undergoing electrocorticographic recordings listen twice to the same story. We demonstrate predictive recall of upcoming information through neural responses in auditory processing regions. This neural measure correlates with behavioral measures of event segmentation and learning. Event boundaries are linked to information flow from cortex to hippocampus. When listening for a second time, information flow from hippocampus to cortex precedes moments of predictive recall. These results provide insight on a fine-grained temporal scale into how episodic memory encoding and retrieval work under naturalistic conditions.

EEG and fMRI evidence for autobiographical memory reactivation in empathy.

Empathy relies on the ability to mirror and to explicitly infer others' inner states. Theoretical accounts suggest that memories play a role in empathy, but direct evidence of reactivation of autobiographical memories (AM) in empathy is yet to be shown. We addressed this question in two experiments. In Experiment 1, electrophysiological activity (EEG) was recorded from 28 participants. Participants performed an empathy task in which targets for empathy were depicted in contexts for which participants either did or did not have an AM, followed by a task that explicitly required memory retrieval of the AM and non-AM contexts. The retrieval task was implemented to extract the neural fingerprints of AM and non-AM contexts, which were then used to probe data from the empathy task. An EEG pattern classifier was trained and tested across tasks and showed evidence for AM reactivation when participants were preparing their judgement in the empathy task. Participants self-reported higher empathy for people depicted in situations they had experienced themselves as compared to situations they had not experienced. A second independent fMRI experiment replicated this behavioural finding and showed increased activation for AM compared to non-AM in the brain networks underlying empathy: precuneus, posterior parietal cortex, superior and inferior parietal lobule, and superior frontal gyrus. Together, our study reports behavioural, electrophysiological, and fMRI evidence that robustly supports AM reactivation in empathy.

The Sync-Fire/deSync model: Modelling the reactivation of dynamic memories from cortical alpha oscillations.

We propose a neural network model to explore how humans can learn and accurately retrieve temporal sequences, such as melodies, movies, or other dynamic content. We identify target memories by their neural oscillatory signatures, as shown in recent human episodic memory paradigms. Our model comprises three plausible components for the binding of temporal content, where each component imposes unique limitations on the encoding and representation of that content. A cortical component actively represents sequences through the disruption of an intrinsically generated alpha rhythm, where a desynchronisation marks information-rich operations as the literature predicts. A binding component converts each event into a discrete index, enabling repetitions through a sparse encoding of events. A timing component - consisting of an oscillatory "ticking clock" made up of hierarchical synfire chains - discretely indexes a moment in time. By encoding the absolute timing between discretised events, we show how one can use cortical desynchronisations to dynamically detect unique temporal signatures as they are reactivated in the brain. We validate this model by simulating a series of events where sequences are uniquely identifiable by analysing phasic information, as several recent EEG/MEG studies have shown. As such, we show how one can encode and retrieve complete episodic memories where the quality of such memories is modulated by the following: alpha gate keepers to content representation; binding limitations that induce a blink in temporal perception; and nested oscillations that provide preferential learning phases in order to temporally sequence events.

Pattern separation and pattern completion: Behaviorally separable processes?

Episodic memory capacity requires several processes, including mnemonic discrimination of similar experiences, termed pattern separation, and holistic retrieval of multidimensional experiences given a cue, termed pattern completion. Both computations seem to rely on the hippocampus proper, but they also seem to be instantiated by distinct hippocampal subfields. Thus, we investigated whether individual differences in behavioral expressions of pattern separation and pattern completion were correlated after accounting for general mnemonic ability. Young adult participants learned events comprised of a scene-animal-object triad. In the pattern separation task, we estimated mnemonic discrimination using lure classification for events that contained a similar lure element. In the pattern completion task, we estimated holistic recollection using dependency in retrieval success for different associations from the same event. Although overall accuracies for the two tasks correlated as expected, specific measures of individual variation in holistic retrieval and mnemonic discrimination did not correlate, suggesting that these two processes involve distinguishable properties of episodic memory.

Directional coupling of slow and fast hippocampal gamma with neocortical alpha/beta oscillations in human episodic memory.

Episodic memories hinge upon our ability to process a wide range of multisensory information and bind this information into a coherent, memorable representation. On a neural level, these 2 processes are thought to be supported by neocortical alpha/beta desynchronization and hippocampal theta/gamma synchronization, respectively. Intuitively, these 2 processes should couple to successfully create and retrieve episodic memories, yet this hypothesis has not been tested empirically. We address this by analyzing human intracranial electroencephalogram data recorded during 2 associative memory tasks. We find that neocortical alpha/beta (8 to 20 Hz) power decreases reliably precede and predict hippocampal "fast" gamma (60 to 80 Hz) power increases during episodic memory formation; during episodic memory retrieval, however, hippocampal "slow" gamma (40 to 50 Hz) power increases reliably precede and predict later neocortical alpha/beta power decreases. We speculate that this coupling reflects the flow of information from the neocortex to the hippocampus during memory formation, and hippocampal pattern completion inducing information reinstatement in the neocortex during memory retrieval.

Speed of time-compressed forward replay flexibly changes in human episodic memory.

Remembering information from continuous past episodes is a complex task1. On the one hand, we must be able to recall events in a highly accurate way, often including exact timings. On the other hand, we can ignore irrelevant details and skip to events of interest. Here, we track continuous episodes consisting of different subevents as they are recalled from memory. In behavioural and magnetoencephalography data, we show that memory replay is temporally compressed and proceeds in a forward direction. Neural replay is characterized by the reinstatement of temporal patterns from encoding2,3. These fragments of activity reappear on a compressed timescale. Herein, the replay of subevents takes longer than the transition from one subevent to another. This identifies episodic memory replay as a dynamic process in which participants replay fragments of fine-grained temporal patterns and are able to skip flexibly across subevents.

Data-driven re-referencing of intracranial EEG based on independent component analysis (ICA).

BACKGROUND: Intracranial recordings from patients implanted with depth electrodes are a valuable source of information in neuroscience. They allow for the unique opportunity to record brain activity with high spatial and temporal resolution. A common pre-processing choice in stereotactic EEG (S-EEG) is to re-reference the data with a bipolar montage. In this, each channel is subtracted from its neighbor, to reduce commonalities between channels and isolate activity that is spatially confined. NEW METHOD: We challenge the assumption that bipolar reference effectively performs this task. To extract local activity, the distribution of the signal source of interest, interfering distant signals, and noise need to be considered. Referencing schemes with fixed coefficients can decrease the signal to noise ratio (SNR) of the data, they can lead to mislocalization of activity and consequently to misinterpretation of results. We propose to use Independent Component Analysis (ICA), to derive filter coefficients that reflect the statistical dependencies of the data at hand. RESULTS: We describe and demonstrate this on human S-EEG recordings. In a simulation with real data, we quantitatively show that ICA outperforms the bipolar referencing operation in sensitivity and importantly in specificity when revealing local time series from the superposition of neighboring channels. COMPARISON WITH EXISTING METHOD(S): We argue that ICA already performs the same task that bipolar referencing pursues, namely undoing the linear superposition of activity and will identify activity that is local. CONCLUSIONS: When investigating local sources in human S-EEG, ICA should be preferred over re-referencing the data with a bipolar montage.

Replay of Stimulus-specific Temporal Patterns during Associative Memory Formation.

Forming a memory often entails the association of recent experience with present events. This recent experience is usually an information-rich and dynamic representation of the world around us. We here show that associating a static cue with a previously shown dynamic stimulus yields a detectable, dynamic representation of this stimulus. We further implicate this representation in the decrease of low-frequency power (∼4-30 Hz) in the ongoing EEG, which is a well-known correlate of successful memory formation. The reappearance of content-specific patterns in desynchronizing brain oscillations was observed in two sensory domains, that is, in a visual condition and in an auditory condition. Together with previous results, these data suggest a mechanism that generalizes across domains and processes, in which the decrease in oscillatory power allows for the dynamic representation of information in ongoing brain oscillations.

Learning processes underlying avoidance of negative outcomes.

Successful avoidance of a threatening event may negatively reinforce the behavior due to activation of brain structures involved in reward processing. Here, we further investigated the learning-related properties of avoidance using feedback-related negativity (FRN). The FRN is modulated by violations of an intended outcome (prediction error, PE), that is, the bigger the difference between intended and actual outcome, the larger the FRN amplitude is. Twenty-eight participants underwent an operant conditioning paradigm, in which a behavior (button press) allowed them to avoid a painful electric shock. During two learning blocks, participants could avoid an electric shock in 80% of the trials by pressing one button (avoidance button), or by not pressing another button (punishment button). After learning, participants underwent two test blocks, which were identical to the learning ones except that no shocks were delivered. Participants pressed the avoidance button more often than the punishment button. Importantly, response frequency increased throughout the learning blocks but it did not decrease during the test blocks, indicating impaired extinction and/or habit formation. In line with a PE account, FRN amplitude to negative feedback after correct responses (i.e., unexpected punishment) was significantly larger than to positive feedback (i.e., expected omission of punishment), and it increased throughout the blocks. Highly anxious individuals showed equal FRN amplitudes to negative and positive feedback, suggesting impaired discrimination. These results confirm the role of negative reinforcement in motivating behavior and learning, and reveal important differences between high and low anxious individuals in the processing of prediction errors.

The Temporal Signature of Memories: Identification of a General Mechanism for Dynamic Memory Replay in Humans.

Reinstatement of dynamic memories requires the replay of neural patterns that unfold over time in a similar manner as during perception. However, little is known about the mechanisms that guide such a temporally structured replay in humans, because previous studies used either unsuitable methods or paradigms to address this question. Here, we overcome these limitations by developing a new analysis method to detect the replay of temporal patterns in a paradigm that requires participants to mentally replay short sound or video clips. We show that memory reinstatement is accompanied by a decrease of low-frequency (8 Hz) power, which carries a temporal phase signature of the replayed stimulus. These replay effects were evident in the visual as well as in the auditory domain and were localized to sensory-specific regions. These results suggest low-frequency phase to be a domain-general mechanism that orchestrates dynamic memory replay in humans.

Hippocampal pattern completion is linked to gamma power increases and alpha power decreases during recollection.

How do we retrieve vivid memories upon encountering a simple cue? Computational models suggest that this feat is accomplished by pattern completion processes involving the hippocampus. However, empirical evidence for hippocampal pattern completion and its underlying mechanisms has remained elusive. Here, we recorded direct intracranial EEG as human participants performed an associative memory task. For each study (encoding) and test (retrieval) event, we derived time-frequency resolved representational patterns in the hippocampus and compared the extent of pattern reinstatement for different mnemonic outcomes. Results show that successful associative recognition (AR) yields enhanced event-specific reinstatement of encoding patterns compared to non-associative item recognition (IR). Moreover, we found that gamma power (50-90 Hz) increases - in conjunction with alpha power (8-12 Hz) decreases not only distinguish AR from IR, but also correlate with the level of hippocampal reinstatement. These results link single-shot hippocampal pattern completion to episodic recollection and reveal how oscillatory dynamics in the gamma and alpha bands orchestrate these mnemonic processes.