Search for: All records

Evidence-accumulation models (EAMs) are powerful tools for making sense of human and animal decision-making behavior. EAMs have generated significant theoretical advances in psychology, behavioral economics, and cognitive neuroscience and are increasingly used as a measurement tool in clinical research and other applied settings. Obtaining valid and reliable inferences from EAMs depends on knowing how to establish a close match between model assumptions and features of the task/data to which the model is applied. However, this knowledge is rarely articulated in the EAM literature, leaving beginners to rely on the private advice of mentors and colleagues and inefficient trial-and-error learning. In this article, we provide practical guidance for designing tasks appropriate for EAMs, relating experimental manipulations to EAM parameters, planning appropriate sample sizes, and preparing data and conducting an EAM analysis. Our advice is based on prior methodological studies and the our substantial collective experience with EAMs. By encouraging good task-design practices and warning of potential pitfalls, we hope to improve the quality and trustworthiness of future EAM research and applications. 

Human learning and decision-making are supported by multiple systems operating in parallel. Recent studies isolating the contributions of reinforcement learning (RL) and working memory (WM) have revealed a trade-off between the two. An interactive WM/RL computational model predicts that although high WM load slows behavioral acquisition, it also induces larger prediction errors in the RL system that enhance robustness and retention of learned behaviors. Here, we tested this account by parametrically manipulating WM load during RL in conjunction with EEG in both male and female participants and administered two surprise memory tests. We further leveraged single-trial decoding of EEG signatures of RL and WM to determine whether their interaction predicted robust retention. Consistent with the model, behavioral learning was slower for associations acquired under higher load but showed parametrically improved future retention. This paradoxical result was mirrored by EEG indices of RL, which were strengthened under higher WM loads and predictive of more robust future behavioral retention of learned stimulus–response contingencies. We further tested whether stress alters the ability to shift between the two systems strategically to maximize immediate learning versus retention of information and found that induced stress had only a limited effect on this trade-off. The present results offer a deeper understanding of the cooperative interaction between WM and RL and show that relying on WM can benefit the rapid acquisition of choice behavior during learning but impairs retention. SIGNIFICANCE STATEMENT Successful learning is achieved by the joint contribution of the dopaminergic RL system and WM. The cooperative WM/RL model was productive in improving our understanding of the interplay between the two systems during learning, demonstrating that reliance on RL computations is modulated by WM load. However, the role of WM/RL systems in the retention of learned stimulus–response associations remained unestablished. Our results show that increased neural signatures of learning, indicative of greater RL computation, under high WM load also predicted better stimulus–response retention. This result supports a trade-off between the two systems, where degraded WM increases RL processing, which improves retention. Notably, we show that this cooperative interplay remains largely unaffected by acute stress.