# Probabilistic Learning and Psychological Similarity

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## Abstract

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## 1. Introduction

## 2. Probabilistic Models of Cognition and Functional Explanation in Psychology: The Case of Concept Learning

#### 2.1. What Is Missing

## 3. Filling out Probabilistic Computation in Cognition: Two Similarity-Oriented Perspectives

#### 3.1. Similarity Spaces: Internal Magnitudes of Experience and Belief

#### 3.2. Structural Resemblance: How to Render Learning from Experience as Action-Guiding

## 4. Future Work

#### 4.1. Placing Probabilistic Constraints on Psychological Similarity Representation

#### 4.2. Drawing Conceptual Distinctions between Psychological Similarities and Probabilistic Dependence

#### 4.3. Refining Theoretical Connections between Psychological Similarity and Probabilistic Learning

#### 4.4. Connecting Psychological Similarity and Neural Representations

The lack of an answer to the content question does arouse suspicion that mental representation is a dubious concept. Some want to eliminate the notion of representational content from our theorizing entirely, perhaps replacing it with a purely neural account of behavioural mechanisms. If that were right, it would radically revise our conception of ourselves as reason-guided agents since reasons are mental contents. […] But even neuroscientists should want to hold onto the idea of representation, because their explanations would be seriously impoverished without it. Even when the causes of behaviour can be picked out in neural terms, our understanding of why that pattern of neural activity produces this kind of behaviour depends crucially on neural activity being about things in the organism’s environment.

## 5. Conclusions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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**Table 1.**Organization of probabilistic, geometric, and structural similarity approaches to specifying semantic content in probabilistic hypotheses spaces. The specification is according to three categories: the type of content these models represent (i.e., mathematical/probabilistic or cognitive/intentional) and the constraints and inference norms they place on learning and reasoning (e.g., internal or external norms).

Content-Type | Constraints | Norms | Approach |
---|---|---|---|

Cognitive | Second-order isomorphism, action-guidance | Coherence with external geometries | [32] |

Mathematical | Implicit assumptions about the data-generating (sampling) process | Internal coherence with probability axioms | [6,12,13,14] |

Cognitive | Cognitive design principles | Internal coherence with geometric axioms | [5,54] |

Cognitive | Second-order homomorphisms, action-guidance | Coherence with external environment statistics | [53,84] |

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Poth, N.
Probabilistic Learning and Psychological Similarity. *Entropy* **2023**, *25*, 1407.
https://doi.org/10.3390/e25101407

**AMA Style**

Poth N.
Probabilistic Learning and Psychological Similarity. *Entropy*. 2023; 25(10):1407.
https://doi.org/10.3390/e25101407

**Chicago/Turabian Style**

Poth, Nina.
2023. "Probabilistic Learning and Psychological Similarity" *Entropy* 25, no. 10: 1407.
https://doi.org/10.3390/e25101407