Ada Aka

All Publications

• A timeline of cognitive costs in decision-making. Trends in cognitive sciences Schulze, C., Aka, A., Bartels, D. M., Bucher, S. F., Embrey, J. R., Gureckis, T. M., Haubl, G., Ho, M. K., Krajbich, I., Moore, A. K., Oettingen, G., Ongchoco, J. D., Oprea, R., Reinholtz, N., Newell, B. R. 2025

  Abstract

  Recent research from economics, psychology, cognitive science, computer science, and marketing is increasingly interested in the idea that people face cognitive costs when making decisions. Reviewing and synthesizing this research, we develop a framework of cognitive costs that organizes concepts along a temporal dimension and maps out when costs occur in the decision-making process and how they impact decisions. Our unifying framework broadens the scope of research on cognitive costs to a wider timeline of cognitive processing. We identify implications and recommendations emerging from our framework for intervening on behavior to tackle some of the most pressing issues of our day, from improving health and saving decisions to mitigating the consequences of climate change.

  View details for DOI 10.1016/j.tics.2025.04.004

  View details for PubMedID 40393899

• Memory modeling of counterfactual generation. Journal of experimental psychology. Learning, memory, and cognition Wang, F., Aka, A., He, L., Bhatia, S. 2024

  Abstract

  We use a computational model of memory search to study how people generate counterfactual outcomes in response to an established target outcome. Hierarchical Bayesian model fitting to data from six experiments reveals that counterfactual outcomes that are perceived as more desirable and more likely to occur are also more likely to come to mind and are generated earlier than other outcomes. Additionally, core memory mechanisms such as semantic clustering and word frequency biases have a strong influence on retrieval dynamics in counterfactual thinking. Finally, we find that the set of counterfactuals that come to mind can be manipulated by modifying the total number of counterfactuals that participants are prompted to generate, and our model can predict these effects. Overall, our findings demonstrate how computational memory search models can be integrated with current theories of counterfactual thinking to provide novel insights into the process of generating counterfactual thoughts. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

  View details for DOI 10.1037/xlm0001335

  View details for PubMedID 38573720

• Semantic determinants of memorability. Cognition Aka, A., Bhatia, S., McCoy, J. 2023; 239: 105497

  Abstract

  We examine why some words are more memorable than others by using predictive machine learning models applied to word recognition and recall datasets. Our approach provides more accurate out-of-sample predictions for recognition and recall than previous psychological models, and outperforms human participants in new studies of memorability prediction. Our approach's predictive power stems from its ability to capture the semantic determinants of memorability in a data-driven manner. We identify which semantic categories are important for memorability and show that, unlike features such as word frequency that influence recognition and recall differently, the memorability of semantic categories is consistent across recognition and recall. Our paper sheds light on the complex psychological drivers of memorability, and in doing so illustrates the power of machine learning methods for psychological theory development.

  View details for DOI 10.1016/j.cognition.2023.105497

  View details for PubMedID 37442022

• Free Association in a Neural Network PSYCHOLOGICAL REVIEW Richie, R., Aka, A., Bhatia, S. 2023; 130 (5): 1360-1382

  Abstract

  Free association among words is a fundamental and ubiquitous memory task. Although distributed semantics (DS) models can predict the association between pairs of words, and semantic network (SN) models can describe transition probabilities in free association data, there have been few attempts to apply established cognitive process models of memory search to free association data. Thus, researchers are currently unable to explain the dynamics of free association using memory mechanisms known to be at play in other retrieval tasks, such as free recall from lists. We address this issue using a popular neural network model of free recall, the context maintenance and retrieval (CMR) model, which we fit using stochastic gradient descent on a large data set of free association norms. Special cases of CMR mimic existing DS and SN models of free association, and we find that CMR outperforms these models on out-of-sample free association data. We also show that training CMR on free association data generates improved predictions for free recall from lists, demonstrating the value of free association for the study of many different types of memory phenomena. Overall, our analysis provides a new account of the dynamics of free association, predicts free association with increased accuracy, integrates theories of free association with established models of memory, and shows how large data sets and neural network training methods can be used to model complex cognitive processes that operate over thousands of representations. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

  View details for DOI 10.1037/rev0000396

  View details for Web of Science ID 000864166700001

  View details for PubMedID 36201827