{"doi":"10.1121/1.421990","title":"Categorization of temporal intervals","abstract":"<jats:p>The rhythms of music and speech comprise sequences of temporal intervals separating the onsets of component events (e.g., notes and syllables). The perception of rhythmic patterning in such sequences involves the identification of distinct interval categories. Notating a musical rhythm, for example, relies upon the perception of nominal relationships between time intervals (e.g., 2:1, 3:1). Such categorization appears robust to perturbations that alter the absolute duration of temporal intervals. Thus it makes sense to ask whether the perception of categorical temporal relationships is ‘‘categorical perception.’’ The answer depends not only on the ability to make category judgments, but also on the differential discriminability of temporal intervals. Entrainment models propose that perceived onset time depends upon onset phase relative to an internal oscillation [Large and Kolen, Conn. Sci. 6, 177–280 (1995); McAuley, Proc. Cog. Sci. Soc., 615–620 (1996)]. Such models predict discrimination performance, however, they do not address categorization. This paper presents an extension of entrainment models that addresses categorization: In addition to relative phase dynamics, it explicitly considers the amplitude dynamics of internal oscillations. It captures existing data on interval categorization and makes new predictions regarding categorization and discrimination performance. Tests of the predictions are discussed.</jats:p>","journal":"The Journal of the Acoustical Society of America","year":1998,"id":684348,"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":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":943343,"name":"Edward W. Large","orcid":"0000-0003-3909-3518","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Categorization of temporal intervals","abstract":"<jats:p>The rhythms of music and speech comprise sequences of temporal intervals separating the onsets of component events (e.g., notes and syllables). The perception of rhythmic patterning in such sequences involves the identification of distinct interval categories. Notating a musical rhythm, for example, relies upon the perception of nominal relationships between time intervals (e.g., 2:1, 3:1). Such categorization appears robust to perturbations that alter the absolute duration of temporal intervals. Thus it makes sense to ask whether the perception of categorical temporal relationships is ‘‘categorical perception.’’ The answer depends not only on the ability to make category judgments, but also on the differential discriminability of temporal intervals. Entrainment models propose that perceived onset time depends upon onset phase relative to an internal oscillation [Large and Kolen, Conn. Sci. 6, 177–280 (1995); McAuley, Proc. Cog. Sci. Soc., 615–620 (1996)]. Such models predict discrimination performance, however, they do not address categorization. This paper presents an extension of entrainment models that addresses categorization: In addition to relative phase dynamics, it explicitly considers the amplitude dynamics of internal oscillations. It captures existing data on interval categorization and makes new predictions regarding categorization and discrimination performance. Tests of the predictions are discussed.</jats:p>","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"21097893","pmcid":null,"openalex_id":"https://openalex.org/W2014797649","authors":[],"funders":[],"total_grants":0,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[],"oa_status":"closed","license":null,"oa_locations":[{"url":"https://pubs.aip.org/jasa/article/103/5_Supplement/2853/560111/Categorization-of-temporal-intervals","host_type":"publisher"},{"url":"https://doi.org/10.1121/1.421990","host_type":"journal"}],"fields_of_study":["Music and Audio Processing"],"mesh_terms":[],"keywords":["Categorization","Categorical variable","Categorical perception","Rhythm","Perception","Interval (graph theory)","Time perception","Duration (music)","Entrainment (biomusicology)","Mathematics","Speech recognition","Computer science","Artificial intelligence","Speech perception","Psychology","Statistics","Acoustics","Physics"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Reduced inequalities"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-18T13:51:49.422917Z","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":[]}