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arxiv: 2606.05864 · v1 · pith:SCLALU6Znew · submitted 2026-06-04 · 💻 cs.CL

Analysis of the Neglect-Zero Effect in Large Language Models

Jin Tanaka , Daiki Matsuoka , Ryoma Kumon , Hitomi Yanaka This is my paper

Pith reviewed 2026-06-28 01:34 UTC · model grok-4.3

classification 💻 cs.CL
keywords neglect-zero effectlarge language modelsstructural primingzero-modelsvacuous truthlogical inferencecognitive bias
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The pith

Large language models do not exhibit the neglect-zero effect that humans show when reasoning about empty sets.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tests whether LLMs display a human-like bias called the neglect-zero effect, in which reasoners ignore configurations that make a statement vacuously true because the relevant set is empty. Researchers apply a structural priming setup: they first present sentences that force attention to these zero-models, then measure whether that exposure changes how the model handles a follow-up inference that could also involve a zero-model. They compare this pattern against inferences that do not involve zero-models at all. The observed responses indicate that the LLMs continue to consider the zero-models even without the priming pressure that would be needed to overcome human neglect of them.

Core claim

Using structural priming, the study finds that the tested LLMs do not neglect zero-models in the manner humans do. Primes designed to highlight empty-set cases do not produce the facilitation pattern expected if the models were ignoring those cases on their own; instead, the models treat the zero-model inferences similarly to non-zero-model inferences throughout.

What carries the argument

Structural priming paradigm that presents a prime sentence forcing consideration of a zero-model before a target sentence whose interpretation could also rest on a zero-model.

If this is right

  • LLMs may treat vacuous truths as ordinary cases rather than defaulting to neglect.
  • Differences between LLM and human inference patterns appear even in tasks that rest on basic set emptiness.
  • The absence of the bias holds across the specific models and inference types examined in the experiments.
  • Training or architectural features may prevent the formation of the neglect-zero shortcut.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the result generalizes, LLMs could serve as test subjects for logical reasoning that avoids certain human shortcuts.
  • Prompt engineering that works on humans by countering neglect may be unnecessary for these models.
  • The finding leaves open whether the same pattern appears in zero-shot settings without any priming sentences.

Load-bearing premise

The structural priming setup cleanly reveals whether an LLM is already considering zero-models rather than being shaped by prompt wording or memorized training examples.

What would settle it

A replication in which the same LLMs produce reliably faster or more probable responses on zero-model targets after zero-model primes, matching the human priming pattern, would contradict the reported result.

Figures

Figures reproduced from arXiv: 2606.05864 by Daiki Matsuoka, Hitomi Yanaka, Jin Tanaka, Ryoma Kumon.

Figure 1
Figure 1. Figure 1: (a) is a zero-model of (1) and (2), and (b) is a non-zero-model of (1) and (2). structures (Bock, 1986; Pickering and Ferreira, 2008). The sentence processed first is called the prime, and the subsequent sentence is called the target. Structural priming is considered to result from the human tendency to reuse a mechanism during sentence processing. From this perspec￾tive, structural priming has been employ… view at source ↗
Figure 2
Figure 2. Figure 2: Examples of trials. structural priming for the prime trial in the latter experimental item. In what follows, we explain the details of these three kinds of trials. Prime trials, in which two open pictures are shown, are divided into two types based on whether the inference of interest is suppressed. One is the critical-prime trial, where Picture 1 does not match the sentence. Here, the subject is expected … view at source ↗
Figure 4
Figure 4. Figure 4: ). As the open picture shows a zero￾model, it is expected that if structural priming is effective, the open picture will be chosen more often after the critical-prime trials than after the control-prime trials. Finally, filler trials are designed so that the sentences in these trials do not induce the con￾versational implicatures of interest in this ex￾periment (see [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 3
Figure 3. Figure 3: Examples of a critical-prime trial and a control-prime trial (Ip = DIS). structural priming occurs. In target trials, one of the pictures is a better￾picture, and the sentence includes the phrase “fewer than three,” which induces ESQ (see [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: The regression curves for GLMM in each sub-experiment of Gemma-3-27B. It should [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The regression curves for GLMM in each sub-experiment of Llama-4. [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The regression curves for GLMM in each sub-experiment of GPT-5 nano. [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Illustration of A, A*, and B in Table [PITH_FULL_IMAGE:figures/full_fig_p013_8.png] view at source ↗
read the original abstract

We investigate the extent to which the language processing of LLMs resembles human cognitive processes, focusing on a human cognitive bias called the $\textit{neglect-zero effect}$. This effect refers to the human tendency to ignore $\textit{zero-models}$, 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 $\textit{structural priming}$, where recent exposure to a preceding sentence (the $\textit{prime}$) facilitates the processing of a subsequent sentence (the $\textit{target}$) 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/neglect_zero

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript investigates whether LLMs exhibit the neglect-zero effect (human tendency to ignore zero-models that render propositions vacuously true via empty sets) by comparing behavior on two types of zero-model inferences against a non-zero-model inference. It employs a structural priming paradigm in which primes are constructed to force consideration of zero-models, then tests for carry-over facilitation to structurally similar targets; the reported outcome is that the neglect-zero effect appears absent in the LLMs examined.

Significance. If the central claim is confirmed, the work would indicate a systematic divergence between LLM next-token processing and human logical inference on vacuous truths, with potential value for cognitive modeling and reasoning benchmarks. The public release of code at the cited GitHub repository is a clear strength that supports reproducibility.

major comments (2)
  1. [Methods] Methods section: the structural priming design does not report explicit controls for lexical overlap, prompt-formatting variants, or baseline priming strength in non-zero-model conditions; without these, the observed lack of priming cannot be unambiguously attributed to absence of the neglect-zero bias rather than failure of the prime to engage the intended logical structure.
  2. [Results] Results: the abstract (and by extension the reported evidence) provides only high-level summaries without data tables, error bars, statistical details, or per-model breakdowns, rendering it impossible to verify the strength or robustness of the claim that the effect is absent.
minor comments (1)
  1. [Abstract] Abstract: notation for the neglect-zero effect and zero-models is introduced with LaTeX italics but without a concise operational definition that would allow readers to map the paradigm directly onto the logical property under test.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback on our manuscript investigating the neglect-zero effect in LLMs. The comments highlight important aspects of methodological transparency and results presentation that we address below.

read point-by-point responses

  1. Referee: [Methods] Methods section: the structural priming design does not report explicit controls for lexical overlap, prompt-formatting variants, or baseline priming strength in non-zero-model conditions; without these, the observed lack of priming cannot be unambiguously attributed to absence of the neglect-zero bias rather than failure of the prime to engage the intended logical structure.

    Authors: We agree that the methods section would benefit from greater explicitness on these points. Our experimental materials were constructed with distinct lexical items between primes and targets to minimize overlap, multiple prompt phrasings were tested during piloting, and non-zero-model baseline conditions were included to establish priming strength. These elements were part of the design but not described in sufficient detail. We will revise the methods section to add a paragraph explicitly documenting the lexical controls, prompt variants, and baseline results, thereby strengthening the link between the observed lack of priming and the absence of the neglect-zero effect. revision: yes

  2. Referee: [Results] Results: the abstract (and by extension the reported evidence) provides only high-level summaries without data tables, error bars, statistical details, or per-model breakdowns, rendering it impossible to verify the strength or robustness of the claim that the effect is absent.

    Authors: The full results section contains per-model accuracy figures, statistical tests, and error bars within the figures. Nevertheless, we recognize that the abstract is high-level and that a consolidated table would improve verifiability. We will add a summary table reporting key metrics and statistics to the main text and revise the abstract to reference these quantitative details, allowing readers to assess the robustness of the claim directly. revision: yes

Circularity Check

0 steps flagged

Empirical behavioral study with no circular derivation chain

full rationale

The paper reports an empirical investigation comparing LLM responses to human neglect-zero bias via structural priming experiments. No equations, fitted parameters, or self-citations are used to derive the central claim; results follow directly from observed model outputs on primes and targets. This is a standard self-contained behavioral comparison against external benchmarks, with no reduction of predictions to inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Review based on abstract only; no mathematical content or new postulates present.

axioms (1)
  • domain assumption Structural priming paradigm isolates consideration of zero-models
    Method assumes primes force zero-model consideration and facilitation measures it.

pith-pipeline@v0.9.1-grok · 5754 in / 828 out tokens · 35473 ms · 2026-06-28T01:34:44.062236+00:00 · methodology

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Reference graph

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