Different kinds of cognitive plausibility: why are transformers better
than RNNs at predicting N400 amplitude?

Different kinds of cognitive plausibility: why are transformers better than RNNs at predicting N400 amplitude?

20 July 2021

Megan D. Bardolph

Benjamin Bergen

Papers citing "Different kinds of cognitive plausibility: why are transformers better than RNNs at predicting N400 amplitude?"

11 / 11 papers shown

Title
Decomposition of surprisal: Unified computational model of ERP components in language processing Jiaxuan Li Richard Futrell 28 1 0 10 Sep 2024
Revenge of the Fallen? Recurrent Models Match Transformers at Predicting Human Language Comprehension Metrics J. Michaelov Catherine Arnett Benjamin Bergen 32 3 0 30 Apr 2024
Psychometric Predictive Power of Large Language Models Tatsuki Kuribayashi Yohei Oseki Timothy Baldwin LM&MA 24 3 0 13 Nov 2023
When Language Models Fall in Love: Animacy Processing in Transformer Language Models Michael Hanna Yonatan Belinkov Sandro Pezzelle 22 11 0 23 Oct 2023
Are words equally surprising in audio and audio-visual comprehension? Pranava Madhyastha Ye Zhang G. Vigliocco 14 1 0 14 Jul 2023
Can Peanuts Fall in Love with Distributional Semantics? J. Michaelov S. Coulson Benjamin Bergen MILM 21 8 0 20 Jan 2023
Rarely a problem? Language models exhibit inverse scaling in their predictions following few-type quantifiers J. Michaelov Benjamin Bergen 17 17 0 16 Dec 2022
Collateral facilitation in humans and language models J. Michaelov Benjamin Bergen 17 11 0 09 Nov 2022
Context Limitations Make Neural Language Models More Human-Like Tatsuki Kuribayashi Yohei Oseki Ana Brassard Kentaro Inui 44 29 0 23 May 2022
Testing the limits of natural language models for predicting human language judgments Tal Golan Matthew Siegelman N. Kriegeskorte Christopher A. Baldassano 22 15 0 07 Apr 2022
So Cloze yet so Far: N400 Amplitude is Better Predicted by Distributional Information than Human Predictability Judgements J. Michaelov S. Coulson Benjamin Bergen 16 41 0 02 Sep 2021