arXiv — NLP / Computation & Language · · 3 min read

Psychological Constructs in Shared Semantic Space

Mirrored from arXiv — NLP / Computation & Language for archival readability. Support the source by reading on the original site.

Computer Science > Computation and Language

arXiv:2605.26801 (cs)
[Submitted on 26 May 2026]

Title:Psychological Constructs in Shared Semantic Space

View a PDF of the paper titled Psychological Constructs in Shared Semantic Space, by Hubert Plisiecki
View PDF HTML (experimental)
Abstract:Psychological constructs are often measured in separate instruments, datasets, and research traditions, which makes direct comparison difficult. This paper proposes a framework for making such constructs semantically commensurate by representing and comparing them as directions in a shared word-embedding space. Using Supervised Semantic Differential, we estimate construct-specific semantic gradients from text-outcome associations and project them onto theoretically motivated reference axes. As an initial test case, we use Valence, Arousal, and Dominance (VAD) as an affective coordinate system. First, we recover interpretable VAD directions from English word-level affective norms. Second, we project semantic gradients for 27 GoEmotions categories into this space and recover the expected organization of emotions, especially along valence and arousal. Third, we apply the same procedure to Big Five personality domains and facets derived from IPIP-NEO-300 item-factor associations. Domain-level placements are broadly coherent, while facet-level results are more exploratory because they rely on sparse questionnaire text. The results suggest that embedding spaces can support construct-level comparison across otherwise incommensurable psychological measurements, provided that semantic placements are assessed for stability and interpretability.
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2605.26801 [cs.CL]
  (or arXiv:2605.26801v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2605.26801
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Hubert Plisiecki PhD [view email]
[v1] Tue, 26 May 2026 10:16:24 UTC (99 KB)
Full-text links:

Access Paper:

Current browse context:

cs.CL
< prev   |   next >
Change to browse by:
cs

References & Citations

Loading...

BibTeX formatted citation

loading...
Data provided by:

Bookmark

BibSonomy Reddit
Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos

Demos

Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers

Recommenders and Search Tools

Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Discussion (0)

Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.

Sign in →

No comments yet. Sign in and be the first to say something.

More from arXiv — NLP / Computation & Language