A5067816902- Decision-Making and Behavioral Economics
- Mental Health Research Topics
- Neural dynamics and brain function
- Neural and Behavioral Psychology Studies
- Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Health Systems, Economic Evaluations, Quality of Life
- Sentiment Analysis and Opinion Mining
- Psychology of Moral and Emotional Judgment
- Functional Brain Connectivity Studies
- Experimental Behavioral Economics Studies
- Memory and Neural Mechanisms
- Emotion and Mood Recognition
- Face Recognition and Perception
- Behavioral Health and Interventions
- Topic Modeling
- Music and Audio Processing
- Risk Perception and Management
- Complex Systems and Decision Making
- Humor Studies and Applications
- Mental Health via Writing
- Identity, Memory, and Therapy
- Explainable Artificial Intelligence (XAI)
- Machine Learning in Healthcare
- Stress Responses and Cortisol
- Posttraumatic Stress Disorder Research
University of California, Berkeley
2017-2022
Max Planck Institute for Biological Cybernetics
2020-2022
Max Planck Society
2020-2021
Brown University
2015
Boston College
2005-2014
What are the neural dynamics of choice processes during reinforcement learning? Two largely separate literatures have examined learning (RL) as a function experience but assuming static process, or conversely, in decision making based on values. Here we show that human RL well described by drift diffusion model (DDM) which learned trial-by-trial reward values sequentially sampled, with made when value signal crosses threshold. Moreover, simultaneous fMRI and EEG recordings revealed this...
Using a contingency volatility manipulation, we tested the hypothesis that difficulty adapting probabilistic decision-making to second-order uncertainty might reflect core deficit cuts across anxiety and depression holds regardless of whether outcomes are aversive or involve reward gain loss. We used bifactor modeling internalizing symptoms separate symptom variance common both from unique each. Across two experiments, modeled performance on under task using hierarchical Bayesian framework....
The prefrontal cortex (PFC) is central to flexible, goal-directed cognition, and understanding its representational code an important problem in cognitive neuroscience. In humans, multivariate pattern analysis (MVPA) of fMRI blood oxygenation level-dependent (BOLD) measurements has emerged as approach for studying neural representations. Many previous studies have implicitly assumed that MVPA BOLD just effective decoding information encoded PFC activity it visual cortex. However, had mixed...
The Multimodal Sentiment Analysis Challenge (MuSe) 2023 is a set of shared tasks addressing three different contemporary multimodal affect and sentiment analysis problems: In the Mimicked Emotions Sub-Challenge (MuSe-Mimic), participants predict continuous emotion targets. This sub-challenge utilises Hume-Vidmimic dataset comprising user-generated videos. For Cross-Cultural Humour Detection (MuSe-Humour), an extension Passau Spontaneous Football Coach (Passau-SFCH) provided. Participants...
The ACM Multimedia 2023 Computational Paralinguistics Challenge addresses two different problems for the first time in a research competition under well-defined conditions: In Emotion Share Sub-Challenge, regression on speech has to be made; and Requests Sub-Challenges, requests complaints need detected. We describe baseline feature extraction, classifiers based 'usual' ComPaRE features, auDeep toolkit, deep extraction from pre-trained CNNs using DeepSpectRum toolkit; addition, wav2vec2...
The replay of task-relevant trajectories is known to contribute memory consolidation and improved task performance. A wide variety experimental data show that the content replayed sequences highly specific can be modulated by reward as well other prominent variables. However, rules governing choice still remain poorly understood. One recent theoretical suggestion prioritization experiences in decision-making problems based on their effect action. We this implies subjects should sub-optimal...
Individuals prone to anxiety and depression often report beliefs make judgements about themselves that are more negative than those reported by others. We use computational modeling of a richly naturalistic task disentangle the role priors versus negatively biased belief updating investigate their association with different dimensions Internalizing psychopathology. Undergraduate participants first provided profiles for hypothetical tech internship. They then viewed pairs other selected...
Theoretical accounts have linked anxiety to intolerance of ambiguity. However, this relationship has not been well operationalized empirically. Here, we used computational and neuro-imaging methods characterize anxiety-related differences in aversive decision-making under ambiguity associated patterns cortical activity. Adult human participants chose between two urns on each trial. The ratio tokens ('O's 'X's) urn determined probability electrical stimulation receipt. A number above...
Detecting emotionally expressive nonverbal vocalizations is essential to developing technologies that can converse fluently with humans. The affective computing community has largely focused on understanding the intonation of emotional speech and language. However, advances in study vocal behavior suggest emotions may be more readily conveyed not by but such as laughs, sighs, shrieks, grunts – often occur lieu speech. task detecting been overlooked researchers, likely due limited...
Abstract Understanding the nature and form of prefrontal cortex representations that support flexible behavior is an important open problem in cognitive neuroscience. In humans, multi-voxel pattern analysis (MVPA) fMRI BOLD measurements has emerged as approach for studying neural representations. An implicit, untested assumption underlying many PFC MVPA studies base rate decoding information from activity patterns similar to other brain regions. Here we estimate these rates a meta-analysis...
This paper describes the 6th Affective Behavior Analysis in-the-wild (ABAW) Competition, which is part of respective Workshop held in conjunction with IEEE CVPR 2024. The ABAW Competition addresses contemporary challenges understanding human emotions and behaviors, crucial for development human-centered technologies. In more detail, focuses on affect related benchmarking tasks comprises five sub-challenges: i) Valence-Arousal Estimation (the target to estimate two continuous dimensions,...
The ability to quickly categorize visual scenes is critical daily life, allowing us identify our whereabouts and navigate from one place another. Rapid scene categorization relies heavily on the kinds of objects contain; for instance, studies have shown that recognition less accurate which incongruent been added, an effect usually interpreted as evidence objects' general capacity activate semantic networks categories they are statistically associated with. Essentially all real-world contain...
Large language models (LLMs) are being adopted in a wide range of applications, but an understanding other social-affective signals is needed to support effective human-computer-interaction (HCI) multimodal interfaces. In particular, robust, accurate measurements human emotional expression can be used tailor responses values and preferences. this paper, we present two available from API-based suite that measure nuanced facial vocal signals, providing rich, high-dimensional estimates (EEEs)....
Risk occupies a central role in both the theory and practice of decision-making. Although it is deeply implicated many conditions involving dysfunctional behavior thought, modern theoretical approaches to understanding mitigating risk, either one-shot or sequential settings, have yet permeate fully fields neural reinforcement learning computational psychiatry. Here we use one prominent approach, called conditional value-at-risk (CVaR), examine optimal risk-sensitive choice form optimal,...
Distributional reinforcement learning (RL) -- in which agents learn about all the possible long-term consequences of their actions, and not just expected value is great recent interest. One most important affordances a distributional view facilitating modern, measured, approach to risk when outcomes are completely certain. By contrast, psychological neuroscientific investigations into decision making under have utilized variety more venerable theoretical models such as prospect theory that...
The ACM Multimedia 2023 Computational Paralinguistics Challenge addresses two different problems for the first time in a research competition under well-defined conditions: In Emotion Share Sub-Challenge, regression on speech has to be made; and Requests Sub-Challenges, requests complaints need detected. We describe baseline feature extraction, classifiers based usual ComPaRE features, auDeep toolkit, deep extraction from pre-trained CNNs using DeepSpectRum toolkit; addition, wav2vec2 models...
The NeurIPS 2023 Machine Learning for Audio Workshop brings together machine learning (ML) experts from various audio domains. There are several valuable audio-driven ML tasks, speech emotion recognition to event detection, but the community is sparse compared other areas, e.g., computer vision or natural language processing. A major limitation with available data; being a time-dependent modality, high-quality data collection time-consuming and costly, making it challenging academic groups...
ABSTRACT The replay of task-relevant trajectories is known to contribute memory consolidation and improved task performance. A wide variety experimental data show that the content replayed sequences highly specific can be modulated by reward as well other prominent variables. However, rules governing choice still remain poorly understood. One recent theoretical suggestion prioritization experiences in decision-making problems based on their effect action. We this implies subjects should...