{"title":"识别记忆中的空表强度效应:环境统计和联结主义的解释","authors":"S. Dennis","doi":"10.4324/9781315789354-42","DOIUrl":null,"url":null,"abstract":"In recognition paradigms, increasing the number of occurrences or presentation time in a study list of some words improves performance on these words (the item strength eeect), but does not aaect the performance on other words (null list strength eeect). In contrast, adding new items results in a deterioration of performance on the other words (list length eeect). Taken together these results place strong constraints on models of recognition memory. To explain these data an account based on optimisation to the environment is presented. A summary is given of environmental analyses which suggest that (1) the likelihood of recurrence of a word within a context increases as the number of occurrences increases; (2) the repetition rates of other words in a context has no signiicant eeect on the recurrence probability of a word; and (3) the recurrence probability of a word drops as a function of the number of words since the last occurrence of that word. A training set which reeected these constraints was constructed and presented to an optimising connectionist network which was designed to extract recurrence statistics (the Heb-bian Recurrent Network). The resultant model is able to model all three of the eeects outlined above.","PeriodicalId":393936,"journal":{"name":"Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Null List Strength Effect in Recognition Memory: Environmental Statistics and Connectionist Accounts\",\"authors\":\"S. Dennis\",\"doi\":\"10.4324/9781315789354-42\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In recognition paradigms, increasing the number of occurrences or presentation time in a study list of some words improves performance on these words (the item strength eeect), but does not aaect the performance on other words (null list strength eeect). In contrast, adding new items results in a deterioration of performance on the other words (list length eeect). Taken together these results place strong constraints on models of recognition memory. To explain these data an account based on optimisation to the environment is presented. A summary is given of environmental analyses which suggest that (1) the likelihood of recurrence of a word within a context increases as the number of occurrences increases; (2) the repetition rates of other words in a context has no signiicant eeect on the recurrence probability of a word; and (3) the recurrence probability of a word drops as a function of the number of words since the last occurrence of that word. A training set which reeected these constraints was constructed and presented to an optimising connectionist network which was designed to extract recurrence statistics (the Heb-bian Recurrent Network). The resultant model is able to model all three of the eeects outlined above.\",\"PeriodicalId\":393936,\"journal\":{\"name\":\"Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-05-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4324/9781315789354-42\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4324/9781315789354-42","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Null List Strength Effect in Recognition Memory: Environmental Statistics and Connectionist Accounts
In recognition paradigms, increasing the number of occurrences or presentation time in a study list of some words improves performance on these words (the item strength eeect), but does not aaect the performance on other words (null list strength eeect). In contrast, adding new items results in a deterioration of performance on the other words (list length eeect). Taken together these results place strong constraints on models of recognition memory. To explain these data an account based on optimisation to the environment is presented. A summary is given of environmental analyses which suggest that (1) the likelihood of recurrence of a word within a context increases as the number of occurrences increases; (2) the repetition rates of other words in a context has no signiicant eeect on the recurrence probability of a word; and (3) the recurrence probability of a word drops as a function of the number of words since the last occurrence of that word. A training set which reeected these constraints was constructed and presented to an optimising connectionist network which was designed to extract recurrence statistics (the Heb-bian Recurrent Network). The resultant model is able to model all three of the eeects outlined above.