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Chest X-rays are the most commonly performed medical imaging exam, yet they are often misinterpreted by physicians. Here, we present an FDA-cleared, artificial intelligence (AI) system which uses a deep learning algorithm to assist physicians in the comprehensive detection and localization of abnormalities on chest X-rays. We trained and tested the AI system on a large dataset, assessed generalizability on publicly available data, and evaluated radiologist and non-radiologist physician accuracy when unaided and aided by the AI system. The AI system accurately detected chest X-ray abnormalities (AUC: 0.976, 95% bootstrap CI: 0.975, 0.976) and generalized to a publicly available dataset (AUC: 0.975, 95% bootstrap CI: 0.971, 0.978). Physicians showed significant improvements in detecting abnormalities on chest X-rays when aided by the AI system compared to when unaided (difference in AUC: 0.101, p < 0.001). Non-radiologist physicians detected abnormalities on chest X-ray exams as accurately as radiologists when aided by the AI system and were faster at evaluating chest X-rays when aided compared to unaided. Together, these results show that the AI system is accurate and reduces physician errors in chest X-ray evaluation, which highlights the potential of AI systems to improve access to fast, high-quality radiograph interpretation., (© 2024. The Author(s).)

Memory for temporal structure enables both planning of future events and retrospection of past events. We investigated how the brain flexibly represents extended temporal sequences into the past and future during anticipation. Participants learned sequences of environments in immersive virtual reality. Pairs of sequences had the same environments in a different order, enabling context-specific learning. During fMRI, participants anticipated upcoming environments multiple steps into the future in a given sequence. Temporal structure was represented in the hippocampus and across higher-order visual regions (1) bidirectionally, with graded representations into the past and future and (2) hierarchically, with further events into the past and future represented in successively more anterior brain regions. In hippocampus, these bidirectional representations were context-specific, and suppression of far-away environments predicted response time costs in anticipation. Together, this work sheds light on how we flexibly represent sequential structure to enable planning over multiple timescales., (© 2024. The Author(s).)

Many experiences unfold predictably over time. Memory for these temporal regularities enables anticipation of events multiple steps into the future. Because temporally predictable events repeat over days, weeks, and years, we must maintain-and potentially transform-memories of temporal structure to support adaptive behavior. We explored how individuals build durable models of temporal regularities to guide multistep anticipation. Healthy young adults (Experiment 1: N = 99, age range = 18-40 years; Experiment 2: N = 204, age range = 19-40 years) learned sequences of scene images that were predictable at the category level and contained incidental perceptual details. Individuals then anticipated upcoming scene categories multiple steps into the future, immediately and at a delay. Consolidation increased the efficiency of anticipation, particularly for events further in the future, but diminished access to perceptual features. Further, maintaining a link-based model of the sequence after consolidation improved anticipation accuracy. Consolidation may therefore promote efficient and durable models of temporal structure, thus facilitating anticipation of future events.

Memory for temporal structure enables both planning of future events and retrospection of past events. We investigated how the brain flexibly represents extended temporal sequences into the past and future during anticipation. Participants learned sequences of environments in immersive virtual reality. Pairs of sequences had the same environments in a different order, enabling context-specific learning. During fMRI, participants anticipated upcoming environments multiple steps into the future in a given sequence. Temporal structure was represented in the hippocampus and across higher-order visual regions (1) bidirectionally, with graded representations into the past and future and (2) hierarchically, with further events into the past and future represented in successively more anterior brain regions. In hippocampus, these bidirectional representations were context-specific, and suppression of far-away environments predicted response time costs in anticipation. Together, this work sheds light on how we flexibly represent sequential structure to enable planning over multiple timescales.

Healthy aging is accompanied by well-characterized shifts in memory systems: episodic memory tends to decline with age while semantic memory remains relatively intact, with some knowledge domains strengthening. Beyond reflecting on the past, these distinct memory systems often guide decisions about the future. Yet how such age-related memory shifts influence simple value-based choices remain understudied. Here, younger (18-24 years) and older (61-75 years) adults completed a card game in which they could use task-relevant episodic memories to maximize the number of points they earned. Critically, they could also use task-irrelevant semantic memories to guide their choices. Both younger and older adults successfully used episodic memory to make decisions, but older adults did so less reliably than younger adults. Further, while younger adults strategically suppressed task-irrelevant semantic memories when a relevant episodic memory could be used, older adults used semantic memory to guide their decisions regardless of the relevance of episodic memory. We provide evidence that declining inhibitory control may play a role in how older adults arbitrate between competing memory sources when making decisions. These effects are consistent with the literature on age-related shifts in memory and cognitive control systems and add to a growing body of work on how episodic memories inform reinforcement learning and value-based decision-making. Our findings highlight how patterns of age-related memory differences can have consequences for value-based choices, which has implications for other types of decision-making, from the economic to the mundane. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Why do we sometimes easily retrieve memories, but other times appear to forget them? We often look to our external environment for retrieval cues, but another way to optimize memory retrieval is to be in a mental state, or mode, that prioritizes access to our internal representation of the world. Such a 'retrieval mode' was proposed by Endel Tulving (1983), who considered it a neurocognitive state in which one keeps the goal of memory retrieval in mind. Building on Tulving's proposal, we review converging evidence from multiple lines of research that emphasize the importance of internal states in the instantiation of retrieval modes that optimize successful remembering. We identify three key factors that contribute to a retrieval mode by modulating either the likelihood or the content of retrieval: (1) an intention to remember or forget (either in the present or the future), (2) attentional selection of goal-relevant memories and suppression of distractors, and (3) fluctuating levels of acetylcholine in the hippocampus. We discuss empirical evidence that these internal states individually influence memory retrieval and propose how they may interact synergistically. Characterizing these dynamic internal factors is an important key for unlocking our understanding of the organization and accessibility of our memories., (Copyright © 2020 Elsevier Ltd. All rights reserved.)