Moral Decisions & Rational Altruism
- Faculty Sponsor: Dr. Falk Lieder
- Department: Psychology
- Contact Name: Zahra Tahmasebi
- E-mail: altruismlab@g.ucla.edu
- Room Number: 7525 Pritzker Hall
- Phone: (424) 259-5300
- Website: https://ralab.psych.ucla.edu/
Description of Research Project
Time Commitment per Week: 12 hours or more
Position:
This position is voluntary and does not carry a salary. However, it provides a valuable opportunity for you to gain hands-on research experience and contribute to scientific articles. UCLA students may receive course credit for their work.
The Rational Altruism Lab is a purpose-driven team with the mission to strengthen the scientific foundations for improving society and the future of humanity. We conduct fundamental research on crucial questions about morality, altruism, rationality, learning, and decision-making. Our core values include altruism, rationality, scientific rigor, precise theories and models, intellectual humility and open-mindedness, working hard and smart, taking responsibility, open communication, seeking and providing constructive feedback, continuous learning and improvement, publishing our findings, and open science.
The Rational Altruism Lab combines rigorous experiments, computational modeling, and AI to understand and improve moral decision-making. Our current research focuses on the following topics:
1. Moral decision-making in social and intergenerational dilemmas
2. Moral learning, moral circle expansion, and moral progress
3. Impact of LLMs on moral decision-making and moral learning
4. Moral education and tools for lifelong moral learning
We are looking for research assistants who will contribute to one of the following projects:
Promoting prosocial decision-making through systematic moral reflection. This project develops and evaluates digital interventions that help people learn moral lessons from the consequences of their past decisions. We are currently evaluating the effectiveness of an online survey that asks people Socratic questions. Later on, we will develop and evaluate a chatbot.
Moral Advice from AI. This project examines the impact of seeking advice from large language models, such as ChatGPT, on how people resolve social dilemmas.
Moral learning and moral circle expansion. This line of research uses behavioral experiments and computational modeling to elucidate how human morality is shaped by learning from experience.
Description of Student Responsibilities
Research Assistants will be routed to one or more of the lab research projects based on their interests and needs of the projects. Primary mentor will depend on the project assignment. Responsibilities (depending on the projects) may include attending weekly lab meetings, attending project meetings, creating experimental materials, setting up Qualtrics surveys, literature research, data collection, study coordination, analyzing participants' verbal responses, developing computational models, conducting computer simulations, statistical data analysis, and manuscript preparation. All projects are designed to produce or contribute to scientific articles. All projects will give you the opportunity to earn a co-authorship on one or more publications that your work contributes to.
At least a basic knowledge of psychological research methodology and statistics are necessary for the position. Prior experience working in lab settings is beneficial but not required. The research assistants should be professional and organized, with good communication skills, a desire to learn and dedication to the Rational Altruism Lab core values. A commitment of at least two quarters is expected.
By joining the Rational Altruism Lab, you will benefit from working with a team of skilled and passionate researchers. This will give you the chance to expand your knowledge of psychological research methodologies while providing hands-on experience in various research domains, such as designing and developing research materials, data collection, data analysis, and reporting.
How This Relates to Cognitive Science
Our lab studies how people think, learn, and make decisions, especially in moral and social situations. We use psychological theories and experiments to understand the cognitive processes that shape people’s judgments and behavior.
If you are interested to join the Rational Altruism Lab as a Research Assistant, pllease complete the Rational Altruism lab interest form to get notified when you can apply: https://docs.google.com/forms/d/e/1FAIpQLSfeKP84Z73vIt1sk29FJmm6zHRUMqhZhRXn1lXVsJoArf41zg/viewform
For questions, contact altruismlab@g.ucla.edu and see the Rational Altruism Lab website to learn more about us.
Human-Computer Interaction Research
- Faculty Sponsor: Xiang 'Anthony' Chen
- Department: Electrical & Computer Engineering; Computer Science
- Contact Name: Xiang 'Anthony' Chen
- E-mail: xac@ucla.edu
- Website: https://hci.ucla.edu
Description of Research Project
UCLA HCI Research studies how people understand, use, and collaborate with emerging interactive technologies, especially AI-enabled systems. We combine human-centered design, cognitive and behavioral research, and interactive system building. Current topics include human–AI collaboration and reliance, generative interfaces, accessibility, and technology for specialized domains such as health. Students join an active project and collaborate with faculty, graduate researchers, and other lab members.
Description of Student Responsibilities
Students participate in one of two complementary roles:
1. UX Research Assistant: Help review literature and refine research questions; prepare study materials; pilot and conduct IRB-approved interviews, surveys, usability studies, or experiments; organize and analyze qualitative or quantitative data; and communicate findings through lab discussions and research writing. This role is well suited to students interested in human behavior and empirical research methods.
2. UX Engineering Assistant: Translate research questions and design ideas into testable interactive prototypes; implement and instrument web or AI-enabled interfaces; support pilot studies and deployments; debug and document research software; and iterate on prototypes using participant feedback and study findings. Some programming or prototyping experience is expected.
All students work at least seven hours per week, attend weekly project meetings, maintain clear research documentation, and complete the Psychology 196B paper and presentation requirements.
How This Relates to Cognitive Science
This work investigates how perception, attention, memory, learning, mental models, decision-making, and social factors shape people’s interactions with intelligent systems. Students use empirical methods to study human behavior and apply the resulting evidence to the design and evaluation of new interfaces, connecting psychological theory with computation and human-centered engineering.
Social Identity and Social Cognition: How Social Context Shapes Perception, Judgment, and Behavior
- Faculty Sponsor: Margaret Shih
- Department: Management and Psychology
- Contact Name: Margaret Shih
- E-mail: margaret.shih@anderson.ucla.edu
- Website: https://shihlab.psych.ucla.edu/
Description of Research Project
The Social and Identity Lab (SAIL) studies how social identities and social contexts shape the ways people perceive themselves and others, make judgments and decisions, and behave in interpersonal, educational, and organizational settings. Our research focuses on social identities such as race, gender, and socioeconomic status and examines how these identities influence cognitive and behavioral processes.
Current projects investigate questions related to social categorization, stereotypes, identity, inequality, belonging, political polarization, and intergroup interactions. For example, we examine how social identity cues and contextual information influence people's perceptions and judgments; how stereotypes and expectations affect cognition, performance, and behavior; and how individuals interpret and respond to members of different social groups. We also study the psychological antecedents and consequences of gender, racial, and economic inequality and how multiple social identities intersect to shape people's experiences.
Using theories and methods from social psychology and cognitive science, our research seeks to better understand how social information is represented, interpreted, and used in human judgment and behavior, as well as how these processes contribute to consequential outcomes in educational and workplace settings.
Description of Student Responsibilities
Undergraduate research assistants will participate in multiple stages of the research process and work closely with faculty, graduate students, and other members of the research team. Depending on the needs and stage of individual projects, responsibilities may include conducting literature reviews; developing and pretesting experimental materials and stimuli; assisting with survey and experimental design; programming and testing online studies; recruiting and interacting with research participants; collecting, coding, organizing, and managing behavioral data; conducting data quality checks; and assisting with basic statistical analyses and interpretation of findings.
Students will attend regular research meetings in which they discuss relevant scientific literature, research design, hypotheses, methodological issues, and findings from ongoing studies. Through these activities, students will gain experience with the scientific process, including translating theoretical questions into testable hypotheses, operationalizing psychological constructs, designing controlled studies, evaluating empirical evidence, and communicating research findings.
Research assistants are expected to work carefully and responsibly, maintain participant confidentiality, follow human-subjects research and laboratory protocols, communicate reliably with their research team, and contribute collaboratively to ongoing projects.
How This Relates to Cognitive Science
This research is directly related to cognitive science because it investigates how people perceive, represent, categorize, and interpret social information and how these cognitive processes influence judgment, decision-making, and behavior. Social identities such as race, gender, and socioeconomic status provide important sources of information in the social environment. Our research examines how people attend to and process these cues, how they activate existing knowledge structures and stereotypes, and how these mental representations subsequently shape perceptions of the self and others.
The research also examines how cognition is influenced by context. Rather than treating perception and judgment as occurring independently of the social environment, our work investigates how situational cues, identity salience, prior knowledge, expectations, and interactions between social groups influence information processing and behavioral outcomes. Questions about social categorization, stereotype activation, person perception, self-representation, attribution, judgment, and decision-making are therefore central to our research.
Students participating in these projects will apply core cognitive science principles through hypothesis development, experimental design, behavioral measurement, data analysis, and interpretation of empirical findings. The research provides an opportunity to understand human cognition as an interaction between fundamental information-processing mechanisms and the social environments in which those mechanisms operate.
Research in Computational Cognition
- Faculty Sponsor: Hongjing Lu
- Department: Psychology
- Contact Name: Hongjing Lu
- E-mail: hongjing@ucla.edu
Description of Research Project
We study human perception and reasoning from a computational perspective. We are especially interested in what prior knowledge humans assume in making an inference from few examples. Our research aims to develop computational models and test AI models for a range of perception and reasoning experiments, and assess the validity of computational models by comparing their predictions with human performance in controlled experiments.
Our current areas of active study include action understanding, object recognition, causal reasoning, and analogical reasoning. See check our lab website https://cvl.psych.ucla.edu/publications/ for papers related to recent projects.
Description of Student Responsibilities
Students selected for this project should have a strong interest in computational cognition, with interests spanning both human cognition and AI, as well as strong programming skills. They should be familiar with implementing AI models, including using APIs for commercial AI models and Python programming for open-source models. A strong ability to read and understand research papers with mathematical formulations is also expected. Students are expected to have strong organizational skills and enjoy working collaboratively on research projects.
Students will be involved in designing human experiments, collecting and analyzing data, running computational models, and comparing human and model performance.
Priority will be given to students who can commit to the project for two quarters. In addition to completing a final paper each quarter, students will be expected to present their research project at a lab meeting at the end of the second quarter.
How This Relates to Cognitive Science
The central goal of our research is to understand how humans learn and reason, and how intelligent machines might emulate these abilities. In both perception and reasoning, humans can often make surprisingly successful inferences from limited or imperfect data. The information available to us is frequently sparse (with very few examples), ambiguous (with multiple possible interpretations), and noisy (with a low signal-to-noise ratio). This ability to learn from sparse data stands in sharp contrast to current large-scale AI models, which typically rely on massive amounts of training data.
Humans and AI models sometimes achieve similar performance, but they can also differ substantially across tasks. Why do these similarities and differences arise? Do humans and AI models rely on similar or different representations? What factors shape the formation of these representations? Answering these questions can provide new insights into human cognition while also advancing the development of more intelligent and human-like AI systems. By combining cognitive science with computational modeling and modern AI techniques, our research seeks to understand the principles underlying learning, representation, and reasoning in both humans and machines.
Eye Tracking for Studying Memory and Cognition
- Faculty Sponsor: Maureen Ritchey
- Department: Psychology
- Contact Name: Maureen Ritchey
- E-mail: mritchey@ucla.edu
- Room Number: Psychology A349
- Website: www.thememolab.org
- Title of Rsearch: Eye Tracking for Studying Memory and Cognition
Description of Research Project
Eye movements can reveal how people attend to visual information, giving insight into what they will remember. We are running a series of eye-tracking studies examining how language and attention interact to influence memory. If interested, please submit an application here: https://forms.gle/qKAZ9z4ZfZigF7dRA
Description of Student Responsibilities
Students will assist with experimental design and data collection on a series of studies using eye-tracking methods to study memory and other cognitive processes. Other responsibilities may include scheduling participants, scoring participant responses, or developing experimental stimuli. Prior programming and/or data analysis experience is preferred. This is an in-person position within the Psychology building.
How This Relates to Cognitive Science
Through their involvement in a cognitive science research lab, students will develop skills in experimental design, human subjects data collection, and behavioral data analysis. Students will learn to operate the eye-tracker to obtain high-quality data. They will also read scientific research articles related to human memory and cognition.
Developmental Cognitive Signatures of Risk and Resilience in Psychosis
- Faculty Sponsor: Brittany Wolff
- Department: Psychiatry and Biobehavioral Sciences
- Contact Name: Brittany Wolff
- E-mail: bwolff@mednet.ucla.edu
- Room Number: C9-456
- Phone: (310) 447-2004
- Website: https://www.semel.ucla.edu/initiatives/seedlab/
- Title of Rsearch: Developmental Cognitive Signatures of Risk and Resilience in Psychosis
Description of Research Project
The SEED Laboratory (Signatures of Early Emergence and Divergence) investigates why individuals at familial and clinical high risk for psychosis and bipolar spectrum disorders follow different developmental trajectories, with some progressing to serious mental illness while others remain resilient. Supported by an NIMH K99/R00 Pathway to Independence Award, this research integrates neurocognitive, clinical, psychosocial, and multimodal longitudinal data to identify early developmental signatures that predict illness onset, remission, and resilience. Advanced statistical and machine learning approaches are used to characterize individual differences and translate these findings toward earlier identification and personalized intervention.
Description of Student Responsibilities
The student will contribute to ongoing SEED Laboratory and K99/R00 research through literature review, data organization and quality assurance, coding and management of neurocognitive and clinical data, and preparation of research materials. Depending on experience and project needs, the student may assist with neuropsychological data analysis, longitudinal datasets, data visualization, and computational or machine learning approaches. Responsibilities may also include participant recruitment for digital cognitive studies. The student will participate in laboratory meetings and scientific discussions and may contribute to conference presentations, manuscripts, and other research products.
How This Relates to Cognitive Science
This research directly examines cognition as a core component of neuropsychiatric development, including individual differences in attention, memory, executive functioning, processing speed, and variability in cognitive performance. It integrates cognitive psychology and neuropsychology with developmental science, neuroscience, computational modeling, and artificial intelligence to understand how cognitive processes relate to emerging psychopathology and resilience. The project therefore provides students with experience applying cognitive science methods and theory to clinically meaningful questions about human behavior, brain function, and mental health.