کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10459782 | 923097 | 2006 | 14 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Differentiating the differentiation models: A comparison of the retrieving effectively from memory model (REM) and the subjective likelihood model (SLiM)
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
موضوعات مرتبط
علوم زیستی و بیوفناوری
علم عصب شناسی
علوم اعصاب شناختی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
The subjective likelihood model [SLiM; McClelland, J. L., & Chappell, M. (1998). Familiarity breeds differentiation: a subjective-likelihood approach to the effects of experience in recognition memory. Psychological Review, 105(4), 734-760.] and the retrieving effectively from memory model [REM; Shiffrin, R. M., & Steyvers, M. (1997). A model for recognition memory: REM-Retrieving effectively from memory. Psychonomic Bulletin & Review, 4, 145-166.] are often considered indistinguishable models. Indeed both share core assumptions including a Bayesian decision process and differentiation during encoding. We give a brief tutorial on each model and conduct simulations showing cases where they diverge. The first two simulations show that for foils that are similar to a studied item, REM predicts higher false alarms rates than SLiM. Thus REM is not able to account for certain associative recognition data without using emergent features to represent pairs. Without this assumption, rearranged pairs have too strong an effect. In contrast, this assumption is not required by SLiM. The third simulation shows that SLiM predicts a reversal in the low frequency hit rate advantage as a function of study time. This prediction is tested and confirmed in an experiment.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Memory and Language - Volume 55, Issue 4, November 2006, Pages 447-460
Journal: Journal of Memory and Language - Volume 55, Issue 4, November 2006, Pages 447-460
نویسندگان
Amy H. Criss, James L. McClelland,