Hypothesis Spaces: How Structural Beliefs Shape Uncertainty in the Anxious Mind
Abstract
While uncertainty is ubiquitous, not all uncertainty is the same. In this review, we dissociate between uncertainty in aspects of known problem spaces (e.g. how long are hospital waiting times; “parameter uncertainty”) and uncertainty arising from unknown structure or even the nature of a problem (e.g. how do I get medical help in a new country; “structural uncertainty”). We suggest that much everyday uncertainty is structural: uncertainty about which model of the world applies in the first place. We bring research on uncertainty and structure learning together through the concept of hypothesis spaces - sets of candidate generative structures that an individual considers plausible, together with distribution of beliefs over their likelihood. We propose that properties of these spaces, including their size, complexity and entropy, provide a systematic framework for understanding structural uncertainty and may help explain characteristic features of anxious thought such as worry, overthinking, catastrophizing and overgeneralization. This perspective also offers a way to refine broad constructs such as intolerance of uncertainty by linking them to specific representational and inferential processes and their properties. We suggest that revisiting the role of uncertainty in anxiety from the viewpoint of structural inference holds the key for mechanistic understanding of clinical observations and closer alignment with fundamental research in cognitive neuroscience.
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BibTeX
@misc{senfft2026hypothesis,
title = {{Hypothesis Spaces: How Structural Beliefs Shape Uncertainty in the Anxious Mind}},
author = {Julia Benita Senfft and Ondrej Zika},
howpublished = {PsyArXiv},
year = {2026},
month = sep,
doi = {10.31234/osf.io/bmp6g_v2},
url = {https://osf.io/preprints/psyarxiv/bmp6g_v2/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Hypothesis Spaces: How Structural Beliefs Shape Uncertainty in the Anxious Mind
AU - Julia Benita Senfft
AU - Ondrej Zika
PY - 2026
DA - 2026/09/16
PB - PsyArXiv
DO - 10.31234/osf.io/bmp6g_v2
UR - https://osf.io/preprints/psyarxiv/bmp6g_v2/
AB - While uncertainty is ubiquitous, not all uncertainty is the same. In this review, we dissociate between uncertainty in aspects of known problem spaces (e.g. how long are hospital waiting times; “parameter uncertainty”) and uncertainty arising from unknown structure or even the nature of a problem (e.g. how do I get medical help in a new country; “structural uncertainty”). We suggest that much everyday uncertainty is structural: uncertainty about which model of the world applies in the first place. We bring research on uncertainty and structure learning together through the concept of hypothesis spaces - sets of candidate generative structures that an individual considers plausible, together with distribution of beliefs over their likelihood. We propose that properties of these spaces, including their size, complexity and entropy, provide a systematic framework for understanding structural uncertainty and may help explain characteristic features of anxious thought such as worry, overthinking, catastrophizing and overgeneralization. This perspective also offers a way to refine broad constructs such as intolerance of uncertainty by linking them to specific representational and inferential processes and their properties. We suggest that revisiting the role of uncertainty in anxiety from the viewpoint of structural inference holds the key for mechanistic understanding of clinical observations and closer alignment with fundamental research in cognitive neuroscience.
ER -