{"doi":"10.31234/osf.io/nu9gm_v1","title":"The diffusion model’s drift rate parameter primarily reflects efficiency, rather than speed, of evidence accumulation","abstract":"Applications of the diffusion decision model (DDM) to the study of cognitive individual differences consistently find that the model’s drift rate (v) parameter forms a cohesive factor across many tasks and relates to measures of higher-order cognitive functioning, including general cognitive ability and working memory. This parameter is often interpreted as a measure of “processing speed”, a traditional psychometric construct thought to reflect an individual’s basic speed of information processing across tasks. However, conceptual differences between v and traditional notions of processing speed make this mapping far from straightforward. Racing accumulator models, which provide a more flexible and comprehensive account of behavioral data than the DDM (Heathcote & Matzke, 2022), allow for the speed with which individuals accumulate evidence to be dissociated from the efficiency with which they accumulate task-relevant evidence (versus task-irrelevant evidence). We applied the DDM and a racing accumulator model to three tasks across three independent datasets to gauge the extent to which v parameter findings from the cognitive individual differences literature reflect speed of evidence accumulation (SEA) versus efficiency of evidence accumulation (EEA). Across all tasks, v was more strongly related to EEA than SEA. EEA was consistently related to measures of general cognitive ability, working memory, and executive function whereas SEA explained less than 1% of the variance in each. These findings suggest individual differences in the DDM’s v parameter, and its relations with higher-order cognitive abilities, primarily reflect EEA rather than SEA and challenge the widespread practice of equating v with the traditional “processing speed” construct.","journal":null,"year":2025,"id":572267,"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":0.9524,"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":1141256,"name":"M. Fiona Molloy","orcid":"0000-0002-1530-2867","position":1,"is_corresponding":false},{"id":304987,"name":"Chandra Sripada","orcid":"0000-0001-9025-6453","position":2,"is_corresponding":false},{"id":711345,"name":"Andrew Heathcote","orcid":"0000-0003-4324-5537","position":3,"is_corresponding":false},{"id":361170,"name":"Alexander Weigard","orcid":"0000-0003-3820-6461","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T02:57:23.653298Z","pmid":null,"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":[]}