19 research outputs found
Reward feedback stimuli elicit high-beta EEG oscillations in human dorsolateral prefrontal cortex
Reward-related feedback stimuli have been observed to elicit a burst of power in the beta frequency range over frontal areas of the human scalp. Recent discussions have suggested possible neural sources for this activity but there is a paucity of empirical evidence on the question. Here we recorded EEG from participants while they navigated a virtual T-maze to find monetary rewards. Consistent with previous studies, we found that the reward feedback stimuli elicited an increase in beta power (20-30 Hz) over a right-frontal area of the scalp. Source analysis indicated that this signal was produced in the right dorsolateral prefrontal cortex (DLPFC). These findings align with previous observations of reward-related beta oscillations in the DLPFC in non-human primates. We speculate that increased power in the beta frequency range following reward receipt reflects the activation of task-related neural assemblies that encode the stimulus-response mapping in working memory
Frontal midline theta and N200 amplitude reflect complementary information about expectancy and outcome evaluation
Feedback ERN (fERN) and frontal midline theta have both been proposed to index a dopamine-like reinforcement learning signal in anterior cingulate cortex (ACC). We investigated these proposals by comparing fERN amplitude and theta power with respect to their sensitivities to outcome valence and probability in a previously collected EEG dataset. Bayesian model comparison revealed a dissociation between the two measures, with fERN amplitude mainly sensitive to valence and theta power mainly sensitive to probability. Further, fERN amplitude was highly correlated with the portion of theta power that is consistent in phase across trials (i.e., evoked theta power). These results suggest that although both measures provide valuable information about cognitive function of frontal midline cortex, fERN amplitude is specifically sensitive to dopamine reinforcement learning signals whereas theta power reflects the ACC response to unexpected events
Reward-based contextual learning supported by anterior cingulate cortex
The anterior cingulate cortex (ACC) is commonly associated with cognitive control and decision making, but its specific function is highly debated. To explore a recent theory that the ACC learns the reward values of task contexts (Holroyd & McClure in Psychological Review, 122, 54-83, 2015; Holroyd & Yeung in Trends in Cognitive Sciences, 16, 122-128, 2012), we recorded the event-related brain potentials (ERPs) from participants as they played a novel gambling task. The participants were first required to select from among three games in one "virtual casino," and subsequently they were required to select from among three different games in a different virtual casino; unbeknownst to them, the payoffs for the games were higher in one casino than in the other. Analysis of the reward positivity, an ERP component believed to reflect reward-related signals carried to the ACC by the midbrain dopamine system, revealed that the ACC is sensitive to differences in the reward values associated with both the casinos and the games inside the casinos, indicating that participants learned the values of the contexts in which rewards were delivered. These results highlight the importance of the ACC in learning the reward values of task contexts in order to guide action selection
Beta oscillations following performance feedback predict subsequent recall of task-relevant information
Reward delivery in reinforcement learning tasks elicits increased beta power in the human EEG over frontal areas of the scalp but it is unclear whether these 20-30 Hz oscillations directly facilitate reward learning. We previously proposed that frontal beta is not specific to reward processing but rather reflects the role of prefrontal cortex in maintaining and transferring task-related information to other brain areas. To test this proposal, we had subjects perform a reinforcement learning task followed by a memory recall task in which subjects were asked to recall stimuli associated either with reward feedback (Reward Recall condition) or error feedback (Error Recall condition). We trained a classifier on post-feedback beta power in the Reward Recall condition to discriminate trials associated with reward feedback from those associated with error feedback and then tested the classifier on post-feedback beta power in the Error Recall condition. Crucially, the model classified error-related beta in the Error Recall condition as reward-related. The model also predicted stimulus recall from post-feedback beta power irrespective of feedback valence and task condition. These results indicate that post-feedback beta power is not specific to reward processing but rather reflects a more general task-related process
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Large language models (LLMs) have been shown to be able to perform new tasks
based on a few demonstrations or natural language instructions. While these
capabilities have led to widespread adoption, most LLMs are developed by
resource-rich organizations and are frequently kept from the public. As a step
towards democratizing this powerful technology, we present BLOOM, a
176B-parameter open-access language model designed and built thanks to a
collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer
language model that was trained on the ROOTS corpus, a dataset comprising
hundreds of sources in 46 natural and 13 programming languages (59 in total).
We find that BLOOM achieves competitive performance on a wide variety of
benchmarks, with stronger results after undergoing multitask prompted
finetuning. To facilitate future research and applications using LLMs, we
publicly release our models and code under the Responsible AI License
EEG Dynamics of Self-Regulatory Strategies in Dietary Decision Making
Optimal decision making requires self-regulation (i.e., the use of attention, working memory, and executive control to alter one’s behaviour). For many individuals, choosing a plate of broccoli over a bar of KitKat is a challenging decision even when they want to maintain a healthy diet. Humans may use a number of different strategies to regulate their decisions in order to maintain their goals. For example, one can try to maintain a healthy diet by focussing on the healthiness of food items or by avoiding eating food in general. Previous functional magnetic resonance imaging studies suggest that self-regulation during decision making elicit changes in activation in the ventromedial and dorsolateral parts of the prefrontal cortex (Hare, Camerer, & Rangel, 2009) but how these regions interact and the precise roles they perform during self-regulation are still debated (Hare, Malmaud, & Rangel, 2011; Hutcherson, Plassmann, Gross, & Rangel, 2012; Tusche & Hutcherson, 2012). Studies on the temporal dynamics of dietary decision making using mouse-tracking, electroencephalogram (EEG), and diffusion drift-diffusion models (DDM) also provide evidence that different attributes of a food option such as its healthiness and tastiness are processed in different speeds (Sullivan, Hutcherson, Harris, & Rangel, 2015) and this process is likely modulated by regulation (Harris, Hare, & Rangel, 2013). However whether different regulation strategies are driven by distinct temporal dynamics is an open question.
In this project as a follow-up to Hutcherson et al. (2012) we aim to use EEG to study the neural and temporal dynamics underlying two different regulation strategies in dietary decision making: focusing on the healthiness of food versus more generally decreasing one’s desire for all food. We will use a food choice paradigm (Harris et al., 2013; Hutcherson et al., 2012) adapted to EEG and apply several analysis methods (see analysis section for details) to investigate the neural and temporal dynamics of these decision strategies. Our goal is to answer two complementary questions: 1) How and why do different regulatory strategies differ in their implementation and effectiveness during choice? 2) Do these regulatory strategies result in sustained changes to food valuation beyond the moment of regulation, and if so, what predicts such change