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

Statistical Computations Underlying the Dynamics of Memory Updating

Psychophysical and neurophysiological studies have suggested that memory is not simply a carbon copy of our experience: Memories are modified or new memories are formed depending on the dynamic structure of our experience, and specifically, on how gradually or abruptly the world changes. We present a statistical theory of memory formation in a dynamic environment, based on a nonparametric generalization of the switching Kalman filter. We show that this theory can qualitatively account for several psychophysical and neural phenomena, and present results of a new visual memory experiment aimed at testing the theory directly. Our experimental findings suggest that humans can use temporal discontinuities in the structure of the environment to determine when to form new memory traces. The statistical perspective we offer provides a coherent account of the conditions under which new experience is integrated into an old memory versus forming a new memory, and shows that memory formation depends on inferences about the underlying structure of our experience.Templeton FoundationAlfred P. Sloan Foundation (Fellowship)National Science Foundation (U.S.) (NSF Graduate Research Fellowship)National Institute of Mental Health (U.S.) (NIH Award Number R01MH098861

Recollection, Familiarity, and Cortical Reinstatement: A Multivoxel Pattern Analysis

SummaryEpisodic memory retrieval is thought to involve reinstatement of the neurocognitive processes engaged when an episode was encoded. Prior fMRI studies and computational models have suggested that reinstatement is limited to instances in which specific episodic details are recollected. We used multivoxel pattern-classification analyses of fMRI data to investigate how reinstatement is associated with different memory judgments, particularly those accompanied by recollection versus a feeling of familiarity (when recollection is absent). Classifiers were trained to distinguish between brain activity patterns associated with different encoding tasks and were subsequently applied to recognition-related fMRI data to determine the degree to which patterns were reinstated. Reinstatement was evident during both recollection- and familiarity-based judgments, providing clear evidence that reinstatement is not sufficient for eliciting a recollective experience. The findings are interpreted as support for a continuous, recollection-related neural signal that has been central to recent debate over the nature of recognition memory processes

The principles underlying the use of powder diffraction data in solving pharmaceutical crystal structures

Solving pharmaceutical crystal structures from powder diffraction data is discussed in terms of the methodologies that have been applied and the complexity of the structures that have been solved. The principles underlying these methodologies are summarized and representative examples of polymorph, solvate, salt and cocrystal structure solutions are provided, together with examples of some particularly challenging structure determinations

Enabling Factor Analysis on Thousand-Subject Neuroimaging Datasets

The scale of functional magnetic resonance image data is rapidly increasing as large multi-subject datasets are becoming widely available and high-resolution scanners are adopted. The inherent low-dimensionality of the information in this data has led neuroscientists to consider factor analysis methods to extract and analyze the underlying brain activity. In this work, we consider two recent multi-subject factor analysis methods: the Shared Response Model and Hierarchical Topographic Factor Analysis. We perform analytical, algorithmic, and code optimization to enable multi-node parallel implementations to scale. Single-node improvements result in 99x and 1812x speedups on these two methods, and enables the processing of larger datasets. Our distributed implementations show strong scaling of 3.3x and 5.5x respectively with 20 nodes on real datasets. We also demonstrate weak scaling on a synthetic dataset with 1024 subjects, on up to 1024 nodes and 32,768 cores

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

The cognitive neurosciences of constructive memory.

ABSTRACT Numerous empirical and theoretical observations point to the constructive nature of human memory. This paper reviews contemporary research pertaining to two major types of memory distortions that illustrate such constructive processes: (a) false recognition and (b) intrusions and confabulations. A general integrative framework that outlines the types of problems that the human memory system must solve in order to produce mainly accurate representations of past experience is first described. This constructive memory framework (CMF) emphasizes processes that operate at encoding (initially binding distributed features of an episode together as a coherent trace; ensuring sufficient pattern separation of similar episodes) and also at retrieval (formation of a sufficiently focused retrieval description with which to query memory; postretrieval monitoring and verification). The framework is applied to findings from four different areas of research: cognitive studies of young adults, neuropsychological investigations of brain-damaged patients, neuroimaging studies, and studies of cognitive aging. CONTENT