How Past Experiences Shape Decisions: A Psychology of Reward Learning

March 6, 2026 Ananya Mittal - World Editor

For over a century, psychologists have been working to understand the complex processes that drive human decision-making and how we learn from our experiences. Recent research is now exploring how a novel, hybrid approach – one informed by artificial intelligence – could refine our understanding of reward-based learning and, potentially, offer new avenues for studying and addressing mood disorders.

The Foundations of Reward-Based Learning

The core idea behind reward-based learning is relatively straightforward: we tend to repeat behaviors that lead to positive outcomes and avoid those that result in negative ones. This principle, observed across species, is fundamental to survival. Still, the human brain’s implementation of this system is far more nuanced than a simple stimulus-response mechanism. Researchers have long known that the brain’s meso-limbic dopamine reward circuitry plays a critical role in this process, but the precise interplay of cognitive functions remains a subject of intense investigation.

A study published in January 2026 by UC Berkeley challenges the traditional view that humans primarily learn through direct rewards. Led by Psychology Professor Anne Collins, the research, titled “A habit and working memory model as an alternative account of human reward-based learning,” suggests that our brains rely on a combination of working memory and habit formation, rather than solely on anticipating rewards. This represents a significant shift in perspective, as it implies that learning isn’t always about consciously calculating the best outcome, but also about the automatic repetition of behaviors.

Working Memory and Habit: A Complex Interaction

Working memory, the ability to hold information in mind for short periods, allows us to craft informed choices based on recent experiences. Think of remembering a phone number long enough to dial it. Habits, are more automatic and develop through repeated actions, regardless of whether those actions consistently lead to positive results. Collins’s team found that when people were asked to learn only a few things, their behavior aligned with traditional reward-based learning models – they stopped repeating choices that didn’t work. However, when the task became more complex, involving four, five, or six items, the pattern changed. People were more likely to repeat mistakes, a phenomenon that standard reward-based learning struggles to explain.

This suggests that when faced with more complex scenarios, our brains rely more heavily on working memory and habit, creating a learning system that is both efficient and surprisingly resilient to immediate rewards or punishments. “Neither process on its own is really a good learner,” Collins explained in a UC Berkeley news release. “Working memory is too capacity limited, and habit doesn’t care about rewards. But we show that together, they end up allowing us to learn efficiently, and mimicking more complex reward-based learning abilities.”

The Role of AI in Modeling Human Learning

Building on these insights, researchers are now exploring how AI can help model these intricate learning processes. An AI-informed model of human reward-based learning, as described in a recent report, aims to create a more accurate representation of how the brain processes rewards and makes decisions. This isn’t about replicating human intelligence, but rather using the computational power of AI to analyze vast amounts of data and identify patterns that might be missed by traditional research methods.

The potential applications of this research extend beyond simply understanding how we learn. A more nuanced understanding of reward-based learning could be particularly valuable in the study of mood disorders, such as depression and anxiety. These conditions are often characterized by disruptions in the brain’s reward system, leading to a diminished ability to experience pleasure or motivation. By creating more accurate models of how the reward system functions in healthy individuals, researchers hope to gain insights into what goes wrong in these disorders and develop more effective treatments.

What Does This Mean for Understanding Mood Disorders?

Disruptions in the brain’s reward circuitry are increasingly recognized as a core feature of many mental health conditions. For example, individuals with depression often exhibit a reduced response to rewarding stimuli, leading to a loss of interest in activities they once enjoyed. Similarly, anxiety can interfere with the ability to accurately assess rewards and punishments, leading to avoidance behaviors and heightened fear responses.

The hybrid approach, combining insights from psychology and AI, offers a promising avenue for investigating these complex interactions. AI models can help researchers analyze brain imaging data, identify biomarkers associated with mood disorders, and predict treatment outcomes. However, it’s important to remember that these models are only as good as the data they are trained on, and there are inherent limitations to any computational approach.

Looking Ahead: Refining Models and Expanding Research

The development of AI-informed models of reward-based learning is an ongoing process. Researchers are continually refining these models, incorporating new data and insights from both psychological and neuroscientific studies. Future research will likely focus on several key areas, including:

  • Individual Variability: Recognizing that reward-based learning processes can vary significantly from person to person.
  • The Role of Context: Investigating how environmental factors and social interactions influence learning and decision-making.
  • Long-Term Learning: Exploring how reward-based learning changes over time and contributes to the development of habits and beliefs.

The ultimate goal is to translate these research findings into more effective treatments for mood disorders and other conditions that are linked to disruptions in the brain’s reward system. This will require continued collaboration between psychologists, neuroscientists, and AI experts, as well as a commitment to rigorous scientific investigation. The process of refining our understanding of the brain’s learning mechanisms is a long one, but the potential benefits for human health and well-being are immense.

Next Steps: Ongoing research will focus on validating these models with larger and more diverse populations, and on exploring their potential for personalized treatment approaches. Clinicians should continue to rely on established diagnostic and treatment guidelines, while staying informed about emerging research in this rapidly evolving field.