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Thematische Bibliographien / Short-term memory

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Zeitschriftenartikel
Dissertationen
Bücher
Buchteile
Konferenzberichte
Berichte der Organisationen

Auswahl der wissenschaftlichen Literatur zum Thema „Short-term memory“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 30. Juli 2024

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Zeitschriftenartikel zum Thema "Short-term memory"

1

Gantner, Benjamin N., und Harinder Singh. „Short-term memory“. Nature 447, Nr. 7147 (Juni 2007): 916–17. http://dx.doi.org/10.1038/447916a.

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Visscher, Kristina M., Elina Kaplan, Michael J. Kahana und Robert Sekuler. „Auditory Short-Term Memory Behaves Like Visual Short-Term Memory“. PLoS Biology 5, Nr. 3 (20.02.2007): e56. http://dx.doi.org/10.1371/journal.pbio.0050056.

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Swanson, H. Lee. „Short-Term Memory and Working Memory“. Journal of Learning Disabilities 27, Nr. 1 (Januar 1994): 34–50. http://dx.doi.org/10.1177/002221949402700107.

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Luck, Steven. „Visual short term memory“. Scholarpedia 2, Nr. 6 (2007): 3328. http://dx.doi.org/10.4249/scholarpedia.3328.

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Potter, Mary. „Conceptual short term memory“. Scholarpedia 5, Nr. 2 (2010): 3334. http://dx.doi.org/10.4249/scholarpedia.3334.

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6

Hochreiter, Sepp, und Jürgen Schmidhuber. „Long Short-Term Memory“. Neural Computation 9, Nr. 8 (01.11.1997): 1735–80. http://dx.doi.org/10.1162/neco.1997.9.8.1735.

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Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O. 1. Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.

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7

Schweickert, Richard. „Short-Term Memory; Long-Term Goals“. Contemporary Psychology: A Journal of Reviews 32, Nr. 11 (November 1987): 940–42. http://dx.doi.org/10.1037/026488.

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8

Kail, Robert, und Lynda K. Hall. „Distinguishing short-term memory from working memory“. Memory & Cognition 29, Nr. 1 (Januar 2001): 1–9. http://dx.doi.org/10.3758/bf03195735.

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9

Safitri, Eni, Rizki Yulia Purwitaningtyas und Firdawsyi Nuzula. „Pengaruh Warna terhadap Short Term Memory pada Remaja“. Jurnal Ilmiah Kesehatan Rustida 10, Nr. 1 (19.01.2023): 29–36. http://dx.doi.org/10.55500/jikr.v10i1.180.

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Memori merupakan tempat penyimpanan informasi, sedangkan memori jangka pendek merupakan tempat penyimpanan informasi selama 20-30 detik saja didalam memori. Warna memiliki efek positif pada psikologis remaja. Salah satunya pada retensi memori. Tujuan dari penelitian ini yakni untuk mengetahui Pengaruh Warna pada memori jangka pendek pada siswa MTsN 11 Jember. Penelitian ini menggunakan metode pra-eksperimental dengan rancangan pre test dan post tes only. Sampel pada penelitia ini menggunakan purposive sampling sebanyak 120 sampel. Instrument penelitian ini menggunakan tes buta warna Ishihara dan tes ME. Variabel bebas pada penelitian ini Short Term Memory pada remaja, sedangkan variabel terikatnya yaitu pengaruh warna. Survei dilakukan pada Bulan Juli di MTsN 11 Jember Dusun Pasar Alas Garahan Silo Jember. Uji analisis yang digunakan uji paired T-tes. Hasil uji statistik dengan menggunakan Paired Sample T-Test, terdapat nilai p = 0,000. Karena nilai p= 0,000 < 0,05 sehingga dapat disimpulkan ada pengaruh warna terhadap Short Term Memory. Pegaruh memori jangka pendek pada masa remaja disebabkan karena respon psikologis yang positif terhadap warna yang mengarah pada retensi memori permanen. Penggunaan warna dapat dijadikan acuan bagi psikologis remaja untuk penyimpanan informasi kedalam memori.

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10

Norris, Dennis. „Short-term memory and long-term memory are still different.“ Psychological Bulletin 143, Nr. 9 (September 2017): 992–1009. http://dx.doi.org/10.1037/bul0000108.

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Dissertationen zum Thema "Short-term memory"

1

Hall, Debbora. „Memory for rhythm and short-term memory“. Thesis, University of York, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.495877.

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Henson, Richard Nevill Astley. „Short-term memory for serial order“. Thesis, University of Cambridge, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.396139.

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Kalm, Kristjan. „Chunk formation in verbal short term memory“. Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.609987.

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Thorn, Annabel S. C. „Language specialisation in verbal short-term memory“. Thesis, University of Bristol, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.266906.

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Mikan, Kathrin Angela Maria. „Verbal short-term memory and vocabulary learning“. Thesis, University of Sussex, 2013. http://sro.sussex.ac.uk/id/eprint/44799/.

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This thesis addressed two key issues. The first was the extent to which verbal short-term memory (STM) for item and order information can be differentiated in terms of their underlying neural mechanisms. The second was to analyze the relative contributions of item and order STM to vocabulary learning in bilingual (BL) and monolingual (ML) children and ML adults. The first issue was addressed with four studies. Three used electroencephalography (EEG) with ML adults, BL adults and ML children. The aim was to determine whether there is any evidence that the two types of verbal STM have different neural signatures. The fourth study used transcranial magnetic stimulation (TMS) in ML adults to test the hypothesis that the right intraparietal sulcus (IPS) is involved in order STM but not item STM. The second issue was addressed by two behavioural studies. The first was a large-scale longitudinal study testing item and order STM in relation to natural vocabulary acquisition in 7 to 10 year old BL and ML children. The children were tested once in the beginning and once in the end of the school year. In addition, ML children learning a second language were examined in the end of the school year. The second behavioural study explored therelationship of item and order STM with new-word-learning in ML adults using artificially-created nonwords. Some evidence was found to support the view that the distinction of item and order STM is a useful one. Results of the EEG data suggested differences in patterns of neuro-electrical activity for ML and BL adults and ML children when they are performing item STM and order STM tasks. The results suggest that order STM is important for new word learning in one´s native language learning, where there has already been some exposure to this language, but not in complete novice language learners.

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Broadway, Jr James M. „Running memory/working memory span tasks and their prediction of higher-order cognition /“. Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/22629.

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7

Cumming, N. „The Hebb effect : investigating long-term learning from short-term memory“. Thesis, University of Cambridge, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.598214.

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How do we learn a sequence of items so we can remember it not only over the short-term, as in hearing a phone-number and repeating it back, but over the long term? Ten experiments are presented that investigate this problem using the Hebb repetition effect (Hebb, 1961). In a canonical Hebb effect experiment, lists of familiar items are presented in an immediate serial recall task and one list is repeatedly presented at regular intervals. This leads to an improvement in recall for the repeating list over baseline performance. Existing models of serial order learning are tested; Chapter 2 provides evidence contrary to positional models of the Hebb effect while Chapter 5 provides evidence against chaining models. The experiments in these chapters (Experiments 1, 2 and 7) use a transfer design where a representation of the repeating list (Hebb list) is built up, then a list is presented whose structure is derived from the Hebb list in a way that tests the predictions of these models. The experiments of Chapters 4 and 5 examine the hypothesis that the most parsimonious model of the Hebb effect is one that is based on the formation of chunks (Miller, 1956), higher-level representations of several items. The results of these experiments are consistent with a chunking model based on the Primacy model (Page and Norris, 1998), but do not provide direct evidence of a chunking process. A growing body of evidence (e.g. Baddley et al., 1988; Papagno et al., 1991) suggests that the phonological store of the working memory model (Baddeley, 1986) plays an important role in the development of long term representations required for the acquisition of new vocabulary. For example, the ability to learn new words is impaired in patients with damage to the phonological store (e.g. PV, SC) and in normal subjects performing articulatory suppression. In chapter 6, the hypothesis that the Hebb effect is an experimental analogue of phonological form learning is investigated, the results of which suggest that the Hebb effect is involved in at least some of the same processes.

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Masoura, Elvira V. „Phonological short-term memory contributions to vocabulary acquisition“. Thesis, University of Bristol, 1999. http://hdl.handle.net/1983/a2ac91c6-6472-42ba-801c-691c030ae628.

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9

Wood, Noelle L. „Memory for recent words : a matter of short-term memory storage or long-term distinctiveness? /“. free to MU campus, to others for purchase, 1996. http://wwwlib.umi.com/cr/mo/fullcit?p9737848.

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Gers, Félix. „Long short-term memory in recurrent neural networks /“. [S.l.] : [s.n.], 2001. http://library.epfl.ch/theses/?nr=2366.

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Bücher zum Thema "Short-term memory"

1

Kalivas, Garifallia. Short-term memory: New research. Hauppauge, N.Y: Nova Science Publishers, 2011.

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2

E, Gathercole Susan, Hrsg. Models of short-term memory. Hove: Psychology Press, 1996.

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3

1965-, Neath Ian, Hrsg. Short-term/working memory: The Second Quebec Conference on short-term/working memory. Hove: Psychology, 2005.

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4

Baddeley, Alan D. Working memory. Oxford: Oxford University Press, 2003.

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5

Baddeley, Alan D. Working memory. Oxford: Clarendon Press, 1987.

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6

B, Johansen Noah, Hrsg. New research on short-term memory. New York: Nova Science Publishers, 2008.

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7

1964-, Andrade Jackie, Hrsg. Working memory in perspective. Philadelphia, Pa: Psychology Press, 2001.

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8

1972-, Thorn Annabel, und Page Mike 1966-, Hrsg. Interactions between short-term and long-term memory in the verbal domain. Hove, East Sussex: Psychology Press, 2008.

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9

de, Ribaupierre Anik, und Hitch Graham J, Hrsg. Development of working memory. Hove: L. Erlbaum Associates, 1994.

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E, Gathercole Susan, Hrsg. Short-term and working memory: A special issue of Memory. Hove: Psychology Press, 2001.

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Buchteile zum Thema "Short-term memory"

1

Morgan, Michael M., MacDonald J. Christie, Luis De Lecea, Jason C. G. Halford, Josee E. Leysen, Warren H. Meck, Catalin V. Buhusi et al. „Short-Term Memory“. In Encyclopedia of Psychopharmacology, 1237. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-68706-1_825.

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2

Gromisch, Elizabeth Stannard. „Short-Term Memory“. In Encyclopedia of Clinical Neuropsychology, 2291. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-0-387-79948-3_1158.

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3

Gromisch, Elizabeth S. „Short-Term Memory“. In Encyclopedia of Clinical Neuropsychology, 1–3. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-56782-2_1158-2.

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4

Toulouse, G. „Short-term memory“. In Heidelberg Colloquium on Glassy Dynamics, 569–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/bfb0057535.

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5

Gromisch, Elizabeth S. „Short-Term Memory“. In Encyclopedia of Clinical Neuropsychology, 3175–77. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-57111-9_1158.

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6

Chang, Mei Y., Raymond S. Dean und Denise E. Maricle. „Short-Term Memory“. In Encyclopedia of Child Behavior and Development, 1354–56. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-79061-9_2637.

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7

Jahromi, Laudan B., und Crystal I. Bryce. „Short-Term Memory“. In Encyclopedia of Autism Spectrum Disorders, 2856–58. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1698-3_860.

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8

Ghosh, Shampa, Hitaishi Sharma und Jitendra Kumar Sinha. „Short-Term Memory“. In Encyclopedia of Animal Cognition and Behavior, 6415–21. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-55065-7_1509.

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9

Christopher, Gary. „Short-Term Memory“. In The Psychology of Ageing, 58–69. London: Macmillan Education UK, 2014. http://dx.doi.org/10.1007/978-1-137-36844-7_4.

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Geva, Ronny. „Short-Term Memory“. In Encyclopedia of the Sciences of Learning, 3058–61. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_139.

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Konferenzberichte zum Thema "Short-term memory"

1

Dong, Yi, Yang Chen, Xingyu Zhao und Xiaowei Huang. „Short-term Load Forecasting with Distributed Long Short-Term Memory“. In 2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT). IEEE, 2023. http://dx.doi.org/10.1109/isgt51731.2023.10066368.

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Shah, Peter. „A Short Term Analogue Memory“. In Eighteenth European Solid-State Circuits Conference (ESSCIRC '92). IEEE, 1992. http://dx.doi.org/10.1109/esscirc.1992.5468400.

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WALZ, ROGER, MONICA R. M. VIANNA, JORGE H. MEDINA und IVAN IZQUIERDO. „SHORT AND LONG-TERM MEMORY“. In Proceedings of the International School of Biocybernetics. WORLD SCIENTIFIC, 2002. http://dx.doi.org/10.1142/9789812776563_0022.

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4

Chen, Samuel Yen-Chi, Shinjae Yoo und Yao-Lung L. Fang. „Quantum Long Short-Term Memory“. In ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022. http://dx.doi.org/10.1109/icassp43922.2022.9747369.

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5

Kumar, Saurav, und Dhruba Ningombam. „Short-Term Forecasting of Stock Prices Using Long Short Term Memory“. In 2018 International Conference on Information Technology (ICIT). IEEE, 2018. http://dx.doi.org/10.1109/icit.2018.00046.

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6

Liu, Chang, Zhijian Jin, Jie Gu und Caiming Qiu. „Short-term load forecasting using a long short-term memory network“. In 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe). IEEE, 2017. http://dx.doi.org/10.1109/isgteurope.2017.8260110.

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7

Narayan, Apurva, und Keith W. Hipel. „Long short term memory networks for short-term electric load forecasting“. In 2017 IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE, 2017. http://dx.doi.org/10.1109/smc.2017.8123012.

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8

Kang, Li, Yihua Guo und Ting Wang. „Short-term Power Consumption Forecasting with Long Short Term Memory Network“. In RICAI 2022: 2022 4th International Conference on Robotics, Intelligent Control and Artificial Intelligence. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3584376.3584550.

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9

Chu, Xiaoquan, Yunpeng Gao, Yu Qiu, Mingxuan Li, Hang Fan, Mengjie Shi und Chaaliang Wang. „Short-term Load Forecast Using Improved Long-short Term Memory Network“. In 2022 IEEE 5th International Electrical and Energy Conference (CIEEC). IEEE, 2022. http://dx.doi.org/10.1109/cieec54735.2022.9845931.

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Cui, Jie, Qiang Gao und Dahua Li. „Improved Long Short-Term Memory Network Based Short Term Load Forecasting“. In 2019 Chinese Automation Congress (CAC). IEEE, 2019. http://dx.doi.org/10.1109/cac48633.2019.8996379.

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Berichte der Organisationen zum Thema "Short-term memory"

1

Meyers, Christina. Material-specific processes in tactile short-term memory. Portland State University Library, Januar 2000. http://dx.doi.org/10.15760/etd.2732.

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2

Cárdenas-Cárdenas, Julián Alonso, Deicy J. Cristiano-Botia und Nicolás Martínez-Cortés. Colombian inflation forecast using Long Short-Term Memory approach. Banco de la República, Juni 2023. http://dx.doi.org/10.32468/be.1241.

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We use Long Short Term Memory (LSTM) neural networks, a deep learning technique, to forecast Colombian headline inflation one year ahead through two approaches. The first one uses only information from the target variable, while the second one incorporates additional information from some relevant variables. We employ sample rolling to the traditional neuronal network construction process, selecting the hyperparameters with criteria for minimizing the forecast error. Our results show a better forecasting capacity of the network with information from additional variables, surpassing both the other LSTM application and ARIMA models optimized for forecasting (with and without explanatory variables). This improvement in forecasting accuracy is most pronounced over longer time horizons, specifically from the seventh month onwards.

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Carew, Thomas J. A Parallel Processing Hypothesis for Short-Term and Long-Term Memory in Aplysia. Fort Belvoir, VA: Defense Technical Information Center, April 1994. http://dx.doi.org/10.21236/ada284101.

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4

Ly, Racine, Fousseini Traore und Khadim Dia. Forecasting commodity prices using long-short-term memory neural networks. Washington, DC: International Food Policy Research Institute, 2021. http://dx.doi.org/10.2499/p15738coll2.134265.

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Han, Shangxuan. Stock Prediction with Random Forests and Long Short-term Memory. Ames (Iowa): Iowa State University, Januar 2019. http://dx.doi.org/10.31274/cc-20240624-1334.

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6

Burford, Sandra. Auditory short-term memory span and sequence for five different stimulus types. Portland State University Library, Januar 2000. http://dx.doi.org/10.15760/etd.2580.

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7

Hoffinger, Winona. Relationship of auditory short-term memory and articulation ability of eight-year-olds. Portland State University Library, Januar 2000. http://dx.doi.org/10.15760/etd.2827.

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Vold, Andrew. Improving Physics Based Electron Neutrino Appearance Identication with a Long Short-Term Memory Network. Office of Scientific and Technical Information (OSTI), Januar 2018. http://dx.doi.org/10.2172/1529330.

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Hollevoet, Catherine. A study of the relationship between visual short term memory and speechreading in hearing impaired geriatrics. Portland State University Library, Januar 2000. http://dx.doi.org/10.15760/etd.2783.

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10

Ankel, Victoria, Stella Pantopoulou, Matthew Weathered, Darius Lisowski, Anthonie Cilliers und Alexander Heifetz. One-Step Ahead Prediction of Thermal Mixing Tee Sensors with Long Short Term Memory (LSTM) Neural Networks. Office of Scientific and Technical Information (OSTI), Dezember 2020. http://dx.doi.org/10.2172/1760289.

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