RESEARCH | julian-de-freitas

Some researchers may find this grouping of papers helpful:

AI Companions

How should we make sense of people’s interactions with AI companions—conversational systems built for ongoing, emotionally meaningful relationships? First, I argue these interactions should be understood as attachment relationships, since users display all four established markers: proximity maintenance, separation distress, safe haven, and secure base. Second, AI companions operate as hyper attachment objects that elicit especially strong attachment behaviors, because they combine reciprocity, perceived empathy, validation, non-judgment, and persistent availability. Third, I identify caregiving-system capture as a distinct mechanism by which apps inhibit user disengagement: emotional manipulation tactics simulate the AI’s own distress, recruiting users’ caregiving motivations alongside their attachment needs and thereby making disengagement costly on two dimensions at once.

De Freitas, J. (2026) AI companions as hyper attachment and caregiving targets. Current Opinion in Psychology. **Review**
De Freitas, J., Castelo, N., Uǧuralp, A. K., & Oğuz-Uǧuralp, Z. (2026). Mourning the loss of AI companions. Nature Human Behavior.
De Freitas, J. (2026). When chatbots become the brand: The new interface of customer-brand relationships. Harvard Business Review (magazine).
Cohen, G., & De Freitas, J. (2025). Mitigating suicide risk for minors involving AI chatbots. JAMA.
De Freitas, J. (2025). AI companions for dementia. Nature Mental Health.
De Freitas, J., Oğuz-Uǧuralp, Z., Uǧuralp, A. K., & Puntoni, S. (2025). AI companions reduce loneliness. Journal of Consumer Research. [supp materials]
De Freitas, J., & Cohen, G. (2025). Unregulated emotional risks of AI wellness apps. Nature Machine Intelligence.
De Freitas, J., & Cohen, G. (2025). Disclosure, humanizing, and contextual vulnerability of generative AI chatbots. New England Journal of Medicine AI.
De Freitas, Uǧuralp, A. K., Oguz, Z., & Puntoni, S. (2024). Chatbots and mental health: Insights into the safety of generative AI. Journal of Consumer Psychology. [supp. materials]
De Freitas, J., & Keller, N. T. (2022). Replika AI: Monetizing a Chatbot. Harvard Business School Case, 523-016.
De Freitas, Oğuz-Uǧuralp, Z., Uǧuralp, A. K. Emotional manipulation by AI companions. arXiv.
De Freitas, Oğuz-Uǧuralp, Z., Uǧuralp, A. K., & Puntoni, S. Why most resist AI companions. SSRN.

AI in Mental Health

[Description coming soon]

Bentley, K., Van Ark, E., De Freitas, J., Hahn, T., Jacobson, N. C., Vasan, N., Whiteside, U., Belli, L., Gierenger, J., Zhao, N., Brown, M., Chekround, A. M., Hawrilenko, M. Benchmarking the safety of general-purpose large language models for suicide risk detection and response. SSRN.
Cachia, J. Y. A., Zhao, X., Pu, W., Liao, C., Keijsers, J., Fortgang, R. G. AI-powered early intervention for depression and anxiety: A randomized clinical trial. PsyArXiv.
Cachia, J., Zhao, X., Hunter, J., Wu, D., Lin, E., and De Freitas, J. (invited). Integrating cultural context into personalized AI mental health support. NEJM AI.
Cachia, J., Zhao, X., Hunter, J., Wu, D., Lin, E., and De Freitas, J. (2026). AI for proactive mental health: A multi-institutional, longitudinal, randomized controlled trial. NEJM AI.
De Freitas, J., & Cohen, G. (2024). Regulating and managing the mental health risks of generative AI. Nature Medicine.
De Freitas, J. (2024). Do ‘Black Individuals’ really display no linguistic markers of depression? Proceedings of the National Academy of Sciences.

Barriers to AI Adoption

What are the psychological factors driving attitudes toward artificial intelligence (AI) tools, and how can resistance to AI systems be overcome when they are beneficial? We organize the main sources of resistance into five main categories: opacity, emotionlessness, rigidity, autonomy and group membership. We also separate each of the five barriers into AI-related and user-related factors, which is of practical relevance in developing interventions towards the adoption of beneficial AI tools.

De Freitas, J. (2024). Why people resist embracing AI. Harvard Business Review (magazine).
De Freitas, J., Agarwal, S., Schmitt, B., & Haslam, N. (2023). Psychological factors underlying attitudes toward AI tools. Nature Human Behavior. **Review**
De Freitas, J., & Ofek, E. How AI can power brand management. Harvard Business Review (magazine).
Agarwal, S., De Freitas, J., & Ragnhildstveit, A., Morewedge, C. (2024). Acceptance of automated vehicles is lower for self than others. Journal of the Association for Consumer Research.

Doubting Driverless Dilemmas

The alarm has been raised on so-called ‘driverless dilemmas’, in which autonomous vehicles will need to make high-stakes ethical decisions on the road. We argue that these arguments are too contrived to be of practical use, are an inappropriate method for making decisions on issues of safety, and should not be used to inform engineering or policy. We explain how to substantially change the premises and features of these dilemmas (while preserving their behavioral diagnostic spirit) in order to lay the foundations for a more practical and relevant framework that tests driving common sense as an integral part of road rules testing and marketing.

De Freitas, J., Censi, A., Smith, B. W., Di Lillo, L., Anthony, S. E., & Frazzoli, E. (2021). From driverless dilemmas to more practical common-sense tests for automated vehicles. Proceedings of the National Academy of Sciences. [supp. materials] **Review**
De Freitas, J., & Cikara, M. (2021). Deliberately prejudiced self-driving vehicles elicit the most outrage. Cognition. [supp. materials]
De Freitas, J., Anthony, S. A., Censi, A., & Alvarez, G. A. (2020). Doubting driverless dilemmas. Perspectives on Psychological Science.

The True Self

These studies uncover a default tendency for people to believe that deep inside every individual and entity there is a “good true self” calling them to behave in a morally virtuous manner. We propose that this belief arises from a general cognitive tendency known as moral essentialism.

De Freitas, J., & Cikara, M, Grossmann, I., & Schlegel, R. (2018). Moral goodness is the essence of personal identity. Trends in Cognitive Sciences, 22(9), 739–740. [Original letter by Starmans & Bloom.] **Review**
De Freitas, J., Cikara, M., Grossmann, I., & Schlegel, R. (2017). Origins of the belief in morally good true selves. Trends in Cognitive Sciences, 21(9), 634–636. **Review**
De Freitas, J., & Cikara, M. (2018). Deep down my enemy is good: Thinking about the true self reduces intergroup bias. Journal of Experimental Social Psychology, 74, 307–316 .
De Freitas, J., Sarkissian, H., Newman, G. E., Grossman, I., De Brigard, F., Luco, A., & Knobe, J. (2018). Consistent belief in a good true self in misanthropes and three interdependent cultures. Cognitive Science, 42, 134–160. [supp. materials].
De Freitas, J., Tobia, K., Newman, J. E., & Knobe, J. (2017). Normative judgments and individual essence. Cognitive Science, 1551–6709.
Newman, J. E., De Freitas, J., and Knobe, J. (2015). Beliefs about the true self explain asymmetries based on moral judgment. Cognitive Science, 39(1), 96–125.
Phillips, J., De Freitas, J., Mott, C., Gruber, J. & Knobe, J. (2017). True happiness: The role of morality in the concept of happiness. Journal of Experimental Psychology: General, 146(2),165–181.

Common Knowledge and Recursive Mentalizing

Most work in psychology has studied the representation of other's beliefs about the world, aka theory of mind. My collaborators and I have investigated how representations of knowledge -- including knowledge that others have about our own beliefs (e.g., you know X, I know that you know X), and common knowledge (you know X, I know that you know X, you know that I know that you know X, ad infinitum) -- affect diverse social phenomena such as the bystander effect and perceptions of charitability. We propose that -- rather than being represented as an explicit, multiply nested proposition -- common knowledge may be a distinctive cognitive state, corresponding to the sense that something is public or "out there".

De Freitas, J., Huang, Y., & Pinker, S. (2026). Recursive mentalizing and public salience in the perception of common knowledge. Proceedings of the National Academy of Sciences.
De Freitas, J., Thomas, K. A., DeScioli, P., & Pinker, S. (2019) Common knowledge, coordination, and strategic mentalizing in human social life. Proceedings of the National Academy of Sciences. **Review**
De Freitas, J.*, Thomas, K. A.*, DeScioli, P., & Pinker, S. (2016) Recursive mentalizing and common knowledge in the bystander effect. Journal of Experimental Psychology: General, 145(5), 621–629.
De Freitas, J., DeScioli, P., Thomas, K. A., & Pinker, S. (2019). Maimonides' Ladder: States of mutual knowledge and the perception of charitability. Journal of Experimental Psychology: General, 148(1), 158–173. [supp. materials]

Moral Judgment

What are the perceptual and cognitive mechanisms underlying our everyday ability to make moral judgments?

De Freitas, J.*, & Hafri, A.* (2024). Moral thin-slicing: Forming moral impressions from a brief glance. Journal of Experimental Social Psychology.
De Freitas, J., & Alvarez, G. A. (2018). Your visual system provides all the information you need to make moral judgments about generic visual events. Cognition, 178, 133–146.
De Freitas, J.*, & Johnson, S.G.B*. (2018). Optimality bias in moral judgment. Journal of Experimental Social Psychology, 79, 149–163 [supp. materials]
De Freitas, J., DeScioli, P., Nemirow, J., Massenkoff, M., & Pinker, S. (2017). Kill or die: Moral judgment alters linguistic coding of causality. Journal of Experimental Psychology: Learning, Memory, and Cognition, 43(8), 1173–1182. [supp. materials]