In Reinforcement Learning from Human Feedback (RLHF), explicit human feedback, such as rankings, is employed to align Natural Language Processing (NLP) models with human preferences. In contrast, the potential of implicit human feedback, encompassing cognitive processing signals like eye-tracking and brain activity, remains underexplored. These signals capture unconscious human responses but are often marred by noise and redundancy, complicating their application to specific tasks. To address this issue, we introduce the Cognitive Information Bottleneck (CIB), a method that extracts only the task-relevant information from cognitive processing signals. Grounded in the principles of the information bottleneck, CIB aims to learn representations that maximize the mutual information between the representations and targets while minimizing the mutual information between inputs and representations. By employing CIB to filter out redundant information from cognitive processing signals, our goal is to provide representations that are both minimal and sufficient. This approach enables more efficient fitting of models to inputs. Our results show that the proposed method outperforms existing methods in efficiently compressing various cognitive processing signals and significantly enhances performance on downstream tasks. Evaluated on public datasets, our model surpasses contemporary state-of-the-art models. Furthermore, by analyzing these compressed representations, we offer insights into how cognitive processing signals can be leveraged to improve performance.

For nearly the past forty years, there has been discussion regarding whether symbolic representations are involved in morphological inflection, a debate commonly known as the Past Tense Debate. The previous literature has extensively explored whether neural models, which do not use symbolic representations can process morphological inflection like humans. However, current research interest has shifted towards whether neural models can acquire morphological inflection like humans. In this paper, we trained neural models, the recurrent neural network (RNN) with attention and the transformer, and a symbolic model, the Minimal Generalization Learner (MGL), under a human-like learning environment. Evaluating the models from the perspective of language acquisition, we found that while the transformer and the MGL exhibited some human-like characteristics, the RNN with attention did not demonstrate human-like behavior across all the evaluation metrics considered in this study. Furthermore, none of the models accurately inflected verbs in the same manner as humans in terms of morphological inflection direction. These results suggest that these models fall short as cognitive models of morphological inflection.