A Quantitative Symbolic Approach to Individual Human Reasoning

• URL: http://arxiv.org/abs/2205.05030v1
• Date: Tue, 10 May 2022 16:43:47 GMT
• Title: A Quantitative Symbolic Approach to Individual Human Reasoning
• Authors: Emmanuelle Dietz, Johannes K. Fichte, Florim Hamiti
• Abstract summary: We take findings from the literature and show how these, formalized as cognitive principles within a logical framework, can establish a quantitative notion of reasoning. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Conceptual and Unbiased Reasoning in Language Models [98.90677711523645]
  We propose a novel conceptualization framework that forces models to perform conceptual reasoning on abstract questions. We show that existing large language models fall short on conceptual reasoning, dropping 9% to 28% on various benchmarks. We then discuss how models can improve since high-level abstract reasoning is key to unbiased and generalizable decision-making.
  arXiv  Detail & Related papers  (2024-03-30T00:53:53Z)
• Inference of Abstraction for a Unified Account of Reasoning and Learning [0.0]
  We give a simple theory of probabilistic inference for a unified account of reasoning and learning. We simply model how data cause symbolic knowledge in terms of its satisfiability in formal logic.
  arXiv  Detail & Related papers  (2024-02-14T09:43:35Z)
• Implicit Chain of Thought Reasoning via Knowledge Distillation [58.80851216530288]
  Instead of explicitly producing the chain of thought reasoning steps, we use the language model's internal hidden states to perform implicit reasoning. We find that this approach enables solving tasks previously not solvable without explicit chain-of-thought, at a speed comparable to no chain-of-thought.
  arXiv  Detail & Related papers  (2023-11-02T17:59:49Z)
• From Heuristic to Analytic: Cognitively Motivated Strategies for Coherent Physical Commonsense Reasoning [66.98861219674039]
  Heuristic-Analytic Reasoning (HAR) strategies drastically improve the coherence of rationalizations for model decisions. Our findings suggest that human-like reasoning strategies can effectively improve the coherence and reliability of PLM reasoning.
  arXiv  Detail & Related papers  (2023-10-24T19:46:04Z)
• Minding Language Models' (Lack of) Theory of Mind: A Plug-and-Play Multi-Character Belief Tracker [72.09076317574238]
  ToM is a plug-and-play approach to investigate the belief states of characters in reading comprehension. We show that ToM enhances off-the-shelf neural network theory mind in a zero-order setting while showing robust out-of-distribution performance compared to supervised baselines.
  arXiv  Detail & Related papers  (2023-06-01T17:24:35Z)
• Towards Unifying Perceptual Reasoning and Logical Reasoning [0.6853165736531939]
  Recent study of logic presents a view of logical reasoning as Bayesian inference. We show that the model unifies the two essential processes common in perceptual and logical systems.
  arXiv  Detail & Related papers  (2022-06-27T10:32:47Z)
• Observing Interventions: A logic for thinking about experiments [62.997667081978825]
  This paper makes a first step towards a logic of learning from experiments. Crucial for our approach is the idea that the notion of an intervention can be used as a formal expression of a (real or hypothetical) experiment. For all the proposed logical systems, we provide a sound and complete axiomatization.
  arXiv  Detail & Related papers  (2021-11-25T09:26:45Z)
• A Qualitative Theory of Cognitive Attitudes and their Change [8.417971913040066]
  We show that it allows us to express a variety of relevant concepts for qualitative decision theory. We also present two extensions of the logic, one by the notion of choice and the other by dynamic operators for belief change and desire change.
  arXiv  Detail & Related papers  (2021-02-16T10:28:49Z)
• Inductive Biases for Deep Learning of Higher-Level Cognition [108.89281493851358]
  A fascinating hypothesis is that human and animal intelligence could be explained by a few principles. This work considers a larger list, focusing on those which concern mostly higher-level and sequential conscious processing. The objective of clarifying these particular principles is that they could potentially help us build AI systems benefiting from humans' abilities.
  arXiv  Detail & Related papers  (2020-11-30T18:29:25Z)
• Cognitive Argumentation and the Suppression Task [1.027974860479791]
  This paper addresses the challenge of modeling human reasoning, within a new framework called Cognitive Argumentation. The framework relies on cognitive principles, based on empirical and theoretical work in Cognitive Science, to adapt a general and abstract framework of computational argumentation from AI. We argue that Cognitive Argumentation provides a coherent and cognitively adequate model for human conditional reasoning.
  arXiv  Detail & Related papers  (2020-02-24T10:30:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.