Abstract
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.
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