# Analysis of the Neglect-Zero Effect in Large Language Models

arXiv: https://arxiv.org/abs/2606.05864 · HTML: https://mlsanity.com/paper/2606.05864/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 4 Jun 2026.

## Builds on (most cited first, 4 of 4)

- [Large Language Models and Cognitive Science: A Comprehensive Review of Similarities, Differences, and Challenges](https://mlsanity.com/paper/2409.02387/) (2024)
- [Do Language Models Exhibit Human-like Structural Priming Effects?](https://mlsanity.com/paper/2406.04847/) (2024)
- [Inflecting when there's no majority: Limitations of encoder-decoder neural networks as cognitive models for German plurals](https://mlsanity.com/paper/2005.08826/) (2020)
- [Experimental Pragmatics with Machines: Testing LLM Predictions for the Inferences of Plain and Embedded Disjunctions](https://mlsanity.com/paper/2405.05776/) (2024)

## Abstract (arXiv)

We investigate the extent to which the language processing of LLMs resembles human cognitive processes, focusing on a human cognitive bias called the textitneglect-zero effect. This effect refers to the human tendency to ignore textitzero-models, which are configurations that render a proposition vacuously true by virtue of an empty set. We focus on two types of inferences driven by the neglect-zero effect, and examine how LLMs process these inferences by comparing their behavior with that in an inference that does not involve the neglect-zero effect. For this purpose, we employ a paradigm based on textitstructural priming, where recent exposure to a preceding sentence (the textitprime) facilitates the processing of a subsequent sentence (the textittarget) due to their structural similarity. We prepare primes to force LLMs to consider the zero-model, and analyze whether they also consider it in the target. The results suggest that the neglect-zero effect may not occur in the LLMs analyzed in this study. Our code is available at https://github.com/ynklab/neglectzero