# A Quantitative Symbolic Approach to Individual Human Reasoning

arXiv: https://arxiv.org/abs/2205.05030 · HTML: https://mlsanity.com/paper/2205.05030/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 70 references that are in the map.
- First posted on arXiv 10 May 2022.

## Builds on (most cited first, 5 of 5)

- [The Model Counting Competition 2020](https://mlsanity.com/paper/2012.01323/) (2020)
- [The Seventh Answer Set Programming Competition: Design and Results](https://mlsanity.com/paper/1904.09134/) (2019)
- [Rushing and Strolling among Answer Sets -- Navigation Made Easy](https://mlsanity.com/paper/2112.07596/) (2021)
- [Exploiting Database Management Systems and Treewidth for Counting](https://mlsanity.com/paper/2001.04191/) (2020)
- [Cognitive Argumentation and the Suppression Task](https://mlsanity.com/paper/2002.10149/) (2020)

## Built on by (most cited first, 2 of 2)

- [Human Conditional Reasoning in Answer Set Programming](https://mlsanity.com/paper/2311.04412/) (2023)
- [The Dual Role of Abstracting over the Irrelevant in Symbolic Explanations: Cognitive Effort vs. Understanding](https://mlsanity.com/paper/2602.03467/) (2026)

## Abstract (arXiv)

Cognitive theories for reasoning are about understanding how humans come to conclusions from a set of premises. Starting from hypothetical thoughts, we are interested which are the implications behind basic everyday language and how do we reason with them. A widely studied topic is whether cognitive theories can account for typical reasoning tasks and be confirmed by own empirical experiments. This paper takes a different view and we do not propose a theory, but instead take findings from the literature and show how these, formalized as cognitive principles within a logical framework, can establish a quantitative notion of reasoning, which we call plausibility. For this purpose, we employ techniques from non-monotonic reasoning and computer science, namely, a solving paradigm called answer set programming (ASP). Finally, we can fruitfully use plausibility reasoning in ASP to test the effects of an existing experiment and explain different majority responses.