This annual meeting aims to provide a dedicated platform for researchers and practioners to discuss computational models that explain and predict human behavior in psycholinguistic experiments, bring together experts from different subfields to advance our understanding of language processing mechanisms, and analyze the successes and limitations of different modeling approaches.

We welcome contributions on any topic related to computational psycholinguistics, including both novel work and recently published research.

The meeting will cover a range of topics, including but not limited to:

◦ Exploring how models such as symbolic, Bayesian, connectionist, ACT-R, and others can explain and predict human behavior in language tasks.

◦ Analyzing where different types of models succeed or fall short in capturing human language processing.

◦ Investigating what linguistic information should be integrated across different levels (words, sentences, discourse) and how this affects comprehension and production.

◦ Examining the potential of models that combine neural and symbolic approaches to better mimic human language processing.

◦ Applying computational, algorithmic, and implementational levels of analysis to understand psycholinguistic phenomena.

◦ Focusing on recent developments in computational modeling of semantics, syntax, sentence processing, speech perception, production, and language acquisition.

This is the second edition of CPL. For the last year, see CPL2025.