Learning decouples accuracy and reaction time for rapid decisions in a transitive inference task
Fabián Muñoz, Greg Jensen, Maxwell Shinn, Yelda Alkan, John D. Murray, H. S. Terrace, Vincent P. Ferrera · bioRxiv (Cold Spring Harbor Laboratory) · 2025
Transitive inference (TI) is a cognitive process in which decisions are guided by internal representations of abstract relationships. While the mechanisms underlying transitive learning have been well studied, the dynamics of the decision-making process during learning and inference remain less clearly understood. In this study, we investigated whether a modeling framework traditionally applied to perceptual decision-making-the drift diffusion model (DDM)-can account for performance in a TI transfer task involving rapid decisions that deviate from standard accuracy and response time (RT) patterns. We trained three macaque monkeys on a TI transfer task, in which they learned the implied order of a novel list of seven images in each behavioral session. Monkeys indicated their decisions with saccadic eye movements. Consistent learning of the list structure was achieved within 200-300 trials per session, with asymptotic accuracies reaching approximately 80-90%. Behavioral performance exhibited a symbolic distance effect, with accuracy increasing as the ordinal distance between items grew. Notably, RTs remained relatively stable across learning, despite improvements in accuracy. We applied a generalized DDM implementation (PyDDM; Shinn et al., 2020) to jointly fit accuracy and RT data. Model fits were achieved by incorporating both an increasing evidence accumulation rate and a collapsing decision bound, successfully capturing the RT distribution shapes observed during learning. These findings suggest that decision-making during serial learning and transfer in a TI task can be characterized by a "variable collapsing bound" DDM. Our results highlight a distinct dynamical regime of the DDM framework, extending its applicability to cognitive domains involving symbolic reasoning and serial relational learning.