![]() ![]() Received: MaAccepted: SeptemPublished: October 15, 2020Ĭopyright: © 2020 Nagy et al. We show that compression of experiences through a generative model gives rise to systematic distortions that qualitatively correspond to a diverse range of observations in the experimental literature.Ĭitation: Nagy DG, Török B, Orbán G (2020) Optimal forgetting: Semantic compression of episodic memories. In this paper we tackle these challenges by assuming that a latent variable generative model of the environment is maintained in semantic memory. 2, Information theory does not specify how different distortions of original experiences should be penalised. 1, The environmental statistics is not known for the brain, rather these have to be learned over time from limited observations. In this paper we recruit information theory, which establishes how to optimally lose information based on prior and complete knowledge of environmental statistics. While constraints on memory resources necessarily imply a loss of information, it is possible to do well or badly in relation to available memory resources. ![]() Human memory performs surprisingly poorly in many everyday tasks, which have been richly documented in laboratory experiments. Our model accounts for memory distortions related to domain expertise, gist-based distortions, contextual effects, and delayed recall. We use three datasets, chess games, natural text, and hand-drawn sketches, to demonstrate the effects of semantic compression on memory performance. We harness recent advances in learning deep generative models, that yield powerful tools to approximate generative models of complex data. We show that this semantic compression framework can provide a unifying explanation of a wide variety of memory phenomena. We argue that the form of distortions is characteristic of relying on a generative model adapted to the environment for compression. Resource constraints on memory can be formalised in the normative framework of lossy compression, however traditional lossy compression algorithms result in qualitatively different distortions to those found in experiments with humans. I plan to update it to a newer version soon and that update should bring in a bunch of new word senses for many words (or more accurately, lemma).It has extensively been documented that human memory exhibits a wide range of systematic distortions, which have been associated with resource constraints. Special thanks to the contributors of the open-source code that was used in this project: the UBY project (mentioned above), and express.js.Ĭurrently, this is based on a version of wiktionary which is a few years old. I simply extracted the Wiktionary entries and threw them into this interface! So it took a little more work than expected, but I'm happy I kept at it after the first couple of blunders. ![]() The researchers have parsed the whole of Wiktionary and other sources, and compiled everything into a single unified resource. That's when I stumbled across the UBY project - an amazing project which needs more recognition. However, after a day's work wrangling it into a database I realised that there were far too many errors (especially with the part-of-speech tagging) for it to be viable for Word Type.įinally, I went back to Wiktionary - which I already knew about, but had been avoiding because it's not properly structured for parsing. This caused me to investigate the 1913 edition of Websters Dictionary - which is now in the public domain. I initially started with WordNet, but then realised that it was missing many types of words/lemma (determiners, pronouns, abbreviations, and many more). The dictionary is based on the amazing Wiktionary project by wikimedia. And since I already had a lot of the infrastructure in place from the other two sites, I figured it wouldn't be too much more work to get this up and running. I had an idea for a website that simply explains the word types of the words that you search for - just like a dictionary, but focussed on the part of speech of the words. ![]() Both of those projects are based around words, but have much grander goals. For those interested in a little info about this site: it's a side project that I developed while working on Describing Words and Related Words. ![]()
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