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<p>The cognitive science literature suggests that human reasoning is both a similarity machine and a probability engine, but a clear picture of the relationship between these mechanisms is still missing. Recent work combines Conceptual Spaces (CS) – a similarity-based theory about the structure of conceptual content – with probabilistic, or Bayesian theories of reasoning. This synthesis not only reframes the problem but also prompts a broader question – namely, whether the CS account is compatible with Predictive Processing (PP), which is a thriving research paradigm that aims to derive a grand unified theory of cognition from Bayesian principles. If so, then CS can serve as components of predictive models. This paper articulates compatibility constraints, engages critically with ideas from the aforementioned literature, and evaluates their significance for the compatibility question. The literature provides several mechanisms through which similarity and probability may interact in the human mind, including deriving priors from relative region size, distance to prototype, and updating probability distributions over CS using Gaussian mixtures. These mechanisms support what I call partial Marr-horizontal methodological compatibility. This approach is contrasted with the possibility of reducing psychological similarity to probability.</p>

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Conceptual spaces as predictive models

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  • 发表日期：

    2026年5月28日

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  • Sebastian Scholz

摘要

The cognitive science literature suggests that human reasoning is both a similarity machine and a probability engine, but a clear picture of the relationship between these mechanisms is still missing. Recent work combines Conceptual Spaces (CS) – a similarity-based theory about the structure of conceptual content – with probabilistic, or Bayesian theories of reasoning. This synthesis not only reframes the problem but also prompts a broader question – namely, whether the CS account is compatible with Predictive Processing (PP), which is a thriving research paradigm that aims to derive a grand unified theory of cognition from Bayesian principles. If so, then CS can serve as components of predictive models. This paper articulates compatibility constraints, engages critically with ideas from the aforementioned literature, and evaluates their significance for the compatibility question. The literature provides several mechanisms through which similarity and probability may interact in the human mind, including deriving priors from relative region size, distance to prototype, and updating probability distributions over CS using Gaussian mixtures. These mechanisms support what I call partial Marr-horizontal methodological compatibility. This approach is contrasted with the possibility of reducing psychological similarity to probability.