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  <title>erratio</title>
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  <lastBuildDate>Tue, 21 Feb 2012 00:46:13 GMT</lastBuildDate>
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  <pubDate>Tue, 21 Feb 2012 00:46:13 GMT</pubDate>
  <title>Recent invited speakers</title>
  <link>https://erratio.dreamwidth.org/134102.html</link>
  <description>There&apos;s been some interesting talks lately, but today was the first one in a while that made me think &amp;quot;I should blog about that&amp;quot;. But since I also would like records of the other talks, I&apos;m going to start trying to summarise the ones I found interesting.&lt;br /&gt;&lt;br /&gt;Julie Van Dyke - on language processing using cue retrieval&lt;br /&gt;* Language processing is really heavily dependent on working memory. &lt;br /&gt;&lt;br /&gt;* But we don&apos;t actually know much about working memory (eg. how much of it we have), so to be safe let&apos;s assume that a hypothetical person can only remember the last item they heard/read. This isn&apos;t as insane as it sounds - computer models have indicated that processing can do pretty well even with such an impoverished working memory. Everything that isn&apos;t in active working memory is absorbed passively and can be called upon (albeit not as easily)&lt;br /&gt;&lt;br /&gt;* So let&apos;s consider a few hypothetical sentences: 1) the book ripped 2) the book recommended by the editor ripped 3) the book from Michigan by Anne Rice that was recommended by the editor ripped. How does a listener tell if &apos;ripped&apos; forms a grammatical sentence with &apos;the book&apos;? There are a few ways: they could search forwards or backwards through the sentence, in which case you would expect processing times to reflect the amount of material between &amp;quot;the book&amp;quot; and &amp;quot;ripped&amp;quot;. Or you could do cue-based retrieval, where you filter the sentence for words that have the features you&apos;re looking for, in which case you wouldn&apos;t expect there to be significant time difference in retrieval. As the name of the talk might suggest, people use cue-based retrieval.&lt;br /&gt;&lt;br /&gt;* So now we have a model where we store words as bundles of semantic/phonological/etc features and then retrieve them by using those features. But what if the sentence has several possible words that have the features you&apos;re looking for? In that case, retrieval might get blocked due to interference from the other items. This, according to Julie Van Dyke, is why people forget. (I don&apos;t know whether she meant in general or when processing sentences. Hopefully the latter)&lt;br /&gt;&lt;br /&gt;* And the main difference between people who are good at processing (eg. fast readers) vs those who aren&apos;t, is almost entirely based on how detailed your representations are. Because if your word representations are super detailed with lots of features, it&apos;s easier to zero in on them. And, good news, the main factor in how good your representations are (after controlling for IQ and a bunch of other bothersome details) is practice. So if you suck at reading, all you need to do to fix it is read more.&lt;br /&gt;&lt;br /&gt;&lt;img src=&quot;https://www.dreamwidth.org/tools/commentcount?user=erratio&amp;ditemid=134102&quot; width=&quot;30&quot; height=&quot;12&quot; alt=&quot;comment count unavailable&quot; style=&quot;vertical-align: middle;&quot;/&gt; comments</description>
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  <category>memory</category>
  <category>cogsci</category>
  <category>talks</category>
  <category>linguistics</category>
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