{"doi":"10.1101/2025.02.11.636952","title":"Learning decouples accuracy and reaction time for rapid decisions in a transitive inference task","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.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2025,"id":561927,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.955,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":416257,"name":"Greg Jensen","orcid":"0000-0001-5050-4360","position":1,"is_corresponding":false},{"id":225635,"name":"Maxwell Shinn","orcid":"0000-0002-7424-4230","position":2,"is_corresponding":false},{"id":417119,"name":"Yelda Alkan","orcid":null,"position":3,"is_corresponding":false},{"id":225637,"name":"John D. Murray","orcid":"0000-0003-4115-8181","position":4,"is_corresponding":false},{"id":416259,"name":"H. S. Terrace","orcid":"0000-0003-0795-0159","position":5,"is_corresponding":false},{"id":416260,"name":"Vincent P. Ferrera","orcid":"0000-0002-6599-7740","position":6,"is_corresponding":false},{"id":416256,"name":"Fabián Muñoz","orcid":"0000-0002-8739-1985","position":0,"is_corresponding":true}],"reference_count":28,"raw_metadata":null,"created_at":"2026-07-19T02:56:01.883848Z","pmid":"39990363","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}