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<span style="font-size: 16pt; font-family: Helvetica, serif, EmojiFont;">The <b>Mindvoyage</b> lectu</span><span style="font-size: 16pt; font-family: Helvetica, serif, EmojiFont; color: rgb(33, 33, 33);">re series features prominent scholars from different
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novel, distinct and often controversial topics. </span><span style="color: rgb(33, 33, 33); font-family: Calibri, sans-serif, serif, EmojiFont;"></span></p>
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<span style="font-size: 16pt; font-family: Helvetica, serif, EmojiFont; color: rgb(33, 33, 33);">The </span><span lang="EN-US" style="font-size: 16pt; font-family: Helvetica, serif, EmojiFont; color: rgb(33, 33, 33);">next</span><span style="font-size: 16pt; font-family: Helvetica, serif, EmojiFont; color: rgb(33, 33, 33);"> talk </span><span lang="EN-US" style="font-size: 16pt; font-family: Helvetica, serif, EmojiFont; color: rgb(33, 33, 33);">will
feature</span><span style="font-size: 16pt; font-family: Helvetica, serif, EmojiFont; color: rgb(33, 33, 33);"> <b>U</b></span><span style="font-family: Calibri, sans-serif, serif, EmojiFont;"><b><span lang="EN-US" style="font-size: 16pt; font-family: Helvetica, serif, EmojiFont; color: rgb(33, 33, 33);">ri</span></b></span><b><span lang="EN-US" style="font-size: 16pt; font-family: Helvetica, serif, EmojiFont; color: rgb(33, 33, 33);"> Hasson</span></b><span style="font-size: 16pt; font-family: Helvetica, serif, EmojiFont; color: rgb(33, 33, 33);"> on <u>December
8, 2023 @ 10:00 CET</u></span><span style="color: rgb(33, 33, 33); font-family: Calibri, sans-serif, serif, EmojiFont;"></span></p>
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<p style="margin: 0cm; font-size: 11pt; font-family: Calibri, sans-serif;"><span style="font-size: 14pt; font-family: Helvetica, serif, EmojiFont;">Abstract <br>
</span><span style="font-size: 12pt; color: rgb(33, 33, 33); font-family: Calibri, sans-serif, serif, EmojiFont;"></span></p>
<p style="margin: 0cm; font-size: 11pt; font-family: Calibri, sans-serif;"><span style="font-family: Calibri, sans-serif, serif, EmojiFont;"><b><span style="font-size: 16pt; font-family: Helvetica, serif, EmojiFont;">Deep language models as a cognitive model
for natural language processing in the human brain</span></b></span></p>
<p style="margin: 0cm; font-size: 11pt; font-family: Calibri, sans-serif;"><span style="font-family: Calibri, sans-serif, serif, EmojiFont;"><b><span style="font-size: 16.5pt; font-family: Helvetica, serif, EmojiFont;"> </span></b></span></p>
<p style="margin: 0cm; font-size: 11pt; font-family: Calibri, sans-serif;"><span style="font-size: 12pt; font-family: Helvetica, serif, EmojiFont;">Naturalistic experimental paradigms in cognitive neuroscience arose from a pressure to test, in real-world contexts,
the validity of models we derive from highly controlled laboratory experiments. In many cases, however, such efforts led to the realization that models (i.e., explanatory principles) developed under particular experimental manipulations fail to capture many
aspects of reality (variance) in the real world. Recent advances in artificial neural networks provide an alternative computational framework for modeling cognition in natural contexts. In contrast to the simplified and interpretable hypotheses we test in
the lab, these models learn how to act in the world from massive amounts of real-world examples (big data) by optimizing big models with millions to billions of parameters. Surprisingly, such models' performance matches human performances on many cognitive
tasks, including visual perceptions, language processing, and motor control. At the same time, these models sacrifice understanding in favor of competence by being able to act without knowing why their choices are optimal or preferable in a given context.</span></p>
<p style="margin: 0cm; font-size: 11pt; font-family: Calibri, sans-serif;"><span style="font-size: 12pt; font-family: Helvetica, serif, EmojiFont;">In this talk, I will ask whether the human brain's underlying computations are similar or different from the
underlying computations in deep neural networks. The ability to think and reason using natural language separates us from other animals and machines. In the talk, I will focus on the underlying neural process that supports natural language processing and language
development in children. Our study aims to model natural language processing in the wild. I will provide evidence that our neural code shares some computational principles with deep language models. This indicates that, to some extent, the brain relies on
overparameterized optimization methods to comprehend and produce language. At the same time, I will present evidence that the brain differs from deep language models as speakers try to convey new ideas and thoughts. Together, our findings expose some unexpected
similarities to deep neural networks while pointing to crucial human-centric missing properties in these machines.</span><span lang="EN-US" style="font-size: 12pt; font-family: Helvetica, serif, EmojiFont;"> </span><span style="font-size: 12pt; color: rgb(33, 33, 33); font-family: Calibri, sans-serif, serif, EmojiFont;"></span></p>
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<p style="font-family:Calibri,Helvetica,sans-serif; font-size:12pt"><span style="font-size:14pt; font-family:Arial,Helvetica,sans-serif"><b>Tanya Brown</b></span></p>
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<p style="font-family:Calibri,Helvetica,sans-serif; font-size:12pt"><span style="font-size:10pt"><span style="font-family:Arial,Helvetica,sans-serif">Scientific Project Manager | </span><a href="https://arc-cogitate.com" class="OWAAutoLink" id="LPNoLP"><span style="font-family:Arial,Helvetica,sans-serif">ARC</span><span style="font-family:"Trebuchet MS",Trebuchet,sans-serif"></span><span style="font-family:Arial,Helvetica,sans-serif">-Cogitate</span></a><span style="font-family:Arial,Helvetica,sans-serif"></span></span></p>
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