@inproceedings{yamamoto-etal-2026-timesteps,
title = "Timesteps of Mamba Align with Human Reading Times",
author = "Yamamoto, Yuji and
Isono, Shinnosuke and
Kawahara, Yoshinobu and
Yokoi, Sho",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1592/",
pages = "31820--31832",
ISBN = "979-8-89176-395-1",
abstract = "This study demonstrates an alignment of per-word processing time in a popular state-space language model Mamba and human readers. In Mamba, the recurrent state transition at each layer conceptually takes some duration of time, the discretization timestep $\Delta_t$, determined dynamically in response to the input. Using a naturalistic reading dataset, we show that the per-word timestep from Mamba is a powerful predictor of human reading times, comparable to strong baselines such as word frequency and GPT-2 surprisal and significant even when they are controlled for. We further suggest, through formal analysis of Mamba{'}s architecture and internal dynamics, that Mamba can serve as a new, valuable lens to look at human real-time language processing with ever-updated memory, because it allows us to look at how each module (layer) weighs short- and long-term information retention, and how noise may interact with dynamic, continuous memory representation. Code is available via an (anonymized) link."
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<abstract>This study demonstrates an alignment of per-word processing time in a popular state-space language model Mamba and human readers. In Mamba, the recurrent state transition at each layer conceptually takes some duration of time, the discretization timestep Δ_t, determined dynamically in response to the input. Using a naturalistic reading dataset, we show that the per-word timestep from Mamba is a powerful predictor of human reading times, comparable to strong baselines such as word frequency and GPT-2 surprisal and significant even when they are controlled for. We further suggest, through formal analysis of Mamba’s architecture and internal dynamics, that Mamba can serve as a new, valuable lens to look at human real-time language processing with ever-updated memory, because it allows us to look at how each module (layer) weighs short- and long-term information retention, and how noise may interact with dynamic, continuous memory representation. Code is available via an (anonymized) link.</abstract>
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%0 Conference Proceedings
%T Timesteps of Mamba Align with Human Reading Times
%A Yamamoto, Yuji
%A Isono, Shinnosuke
%A Kawahara, Yoshinobu
%A Yokoi, Sho
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F yamamoto-etal-2026-timesteps
%X This study demonstrates an alignment of per-word processing time in a popular state-space language model Mamba and human readers. In Mamba, the recurrent state transition at each layer conceptually takes some duration of time, the discretization timestep Δ_t, determined dynamically in response to the input. Using a naturalistic reading dataset, we show that the per-word timestep from Mamba is a powerful predictor of human reading times, comparable to strong baselines such as word frequency and GPT-2 surprisal and significant even when they are controlled for. We further suggest, through formal analysis of Mamba’s architecture and internal dynamics, that Mamba can serve as a new, valuable lens to look at human real-time language processing with ever-updated memory, because it allows us to look at how each module (layer) weighs short- and long-term information retention, and how noise may interact with dynamic, continuous memory representation. Code is available via an (anonymized) link.
%U https://aclanthology.org/2026.findings-acl.1592/
%P 31820-31832
Markdown (Informal)
[Timesteps of Mamba Align with Human Reading Times](https://aclanthology.org/2026.findings-acl.1592/) (Yamamoto et al., Findings 2026)
ACL
- Yuji Yamamoto, Shinnosuke Isono, Yoshinobu Kawahara, and Sho Yokoi. 2026. Timesteps of Mamba Align with Human Reading Times. In Findings of the Association for Computational Linguistics: ACL 2026, pages 31820–31832, San Diego, California, United States. Association for Computational Linguistics.