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2,039 papers
EEG, pupillometry, and behavioral data from 28 healthy adults performing an auditory fear conditioning paradigm with voice stimuli. Participants localized voices (left or right) across Pre-Conditioning, Conditioning, and Extinction phases, with conditioned voices paired with an aversive loud white noise using 50% parti...
EEG recordings from a three-armed bandit reinforcement learning task in which alcohol-related and non-alcohol beverage cues were presented to 53 community participants, including hazardous drinkers and controls. The dataset supports investigation of electrophysiological markers of cue-specific exploration in hazardous...
Magnetoencephalography (MEG) recordings from participants with major depressive disorder and non-depressed controls performing a probabilistic selection reinforcement learning task. The dataset was collected to examine the Reward Positivity and related ventromedial frontal cortex activity during feedback processing. It...
EEG recordings from 53 community participants, including hazardous drinkers and controls, performing a reinforcement learning task (3-armed bandit) with alcohol-related versus beverage cue images. The dataset supports investigation of electrophysiological markers of cue-specific exploration in hazardous drinking. Data...
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EEG recordings from 54 community participants classified as light or heavy drinkers performing an affective-state reinforcement learning task involving alcohol imagery. The dataset supports investigation of reward-related neural responses, such as the reward positivity, in relation to drinking behavior. Data were colle...
EEG recordings from a Probabilistic Selection Task (PST) collected in two studies: 80 healthy participants plus 5 pilot placebo sessions, and a double-blind cabergoline (1.25 mg) challenge study with 27 completers. Data were collected circa 2012-2013 at Brown University and share a sample with a previously published st...
EEG dataset from a reinforcement learning task (probabilistic selection task, PST) performed by 50 healthy controls after either a sad or neutral mood manipulation (25 per group). The task has a training phase and a testing phase, and the mood induction took place separately beforehand. Data were collected between 2019...
EEG dataset from a reinforcement learning study of 50 healthy controls, 25 tested after a sad mood manipulation and 25 after a happy mood manipulation. Participants completed a probabilistic selection task with training and testing phases. The task was adapted from a Parkinsonism reinforcement learning paradigm, and th...
EEG dataset from twelve participants who completed three reinforcement learning tasks that differed in average task value. In each task, participants learned cue-response mappings for six coloured-shape cues, using left or right key presses. Cues had feedback validity of 0.5 (low-value) or 0.8 (high-value), and the tas...
EEG recordings from 25 college-age participants performing a probabilistic reinforcement learning task with affective feedback (Experiment 2). The dataset was collected to examine how the Reward Positivity (RewP) event-related potential is sensitive to affective liking of feedback stimuli. Data were acquired circa 2019...
EEG dataset from a reinforcement learning task performed by 28 patients with Parkinson's disease and 28 matched healthy controls. The task included volitional and instructed choices. Patients were tested twice, ON and OFF dopaminergic medication, one week apart, while controls were tested once. Accelerometer data from...
EEG recordings from 25 college-aged participants performing a probabilistic reinforcement learning task with affective feedback. Data were collected around 2018 in the CRCL at the University of New Mexico to examine how the Reward Positivity is influenced by affective liking of feedback. Additional scripts are included...
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
The effort aims to help U.S. learners from kindergarten to community college, with an emphasis on math, making, and the constructive use of AI.
Associate Professor Cathy Wu uses reinforcement learning to help map out improvements to transportation and other multifaceted systems.
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