---
title: '5DCR: Five-Dimensional Curiosity Scale Revised'
url: https://www.emergentmind.com/topics/five-dimensional-curiosity-scale-revised-5dcr
type: topic
---

# 5DCR: Five-Dimensional Curiosity Scale Revised

The Five-Dimensional Curiosity Scale Revised (5DCR) is a psychometric instrument designed to quantitatively assess human curiosity across multiple theoretically grounded domains. Emerging from a revision of Kashdan et al.’s Five-Dimensional Curiosity Scale (2020) and informed by Berlyne’s multi-dimensional theory, the 5DCR consolidates prior factor structures and offers an integrated operationalization for experimental and comparative research on curiosity—including, more recently, evaluations involving large language models (LLMs) [2510.20635].

## 1. Theoretical Foundations and Dimensional Structure

Rooted in Berlyne’s constructivist theory and extending Kashdan’s prior work, the 5DCR reorganizes curiosity into three super-dimensions, mapping to six sub-dimensions. Each sub-dimension is represented by four items, totaling 24 statements:

- **Information Seeking (Epistemic Curiosity):**
    - *Joyous Exploration (JE):* Readiness and enthusiasm for experimenting with, embracing, and learning about novel experiences and knowledge.
    - *Deprivation Sensitivity (DS):* Frustration when unable to resolve a problem, motivating active information search.
    - *Stress Tolerance (ST):* Belief in one’s capacity to handle anxiety or distress arising in new situations.

- **Thrill Seeking (TS):**
    - *Thrill Seeking (TS):* Willingness to take physical or financial risks for novel experiences.

- **Social Curiosity:**
    - *Overt Social Curiosity (OSC):* Proactive investigation of others’ social information.
    - *Covert Social Curiosity (CSC):* Intrinsic interest in social dynamics without direct engagement.

An example item given is: “I view challenging situations as an opportunity to grow and learn.” (assigned to joyous exploration).

## 2. Administration Protocol and Scoring Approach

Respondents rate each item using a seven-point Likert scale:

1 = Does not describe me at all  
2 = Barely describes me  
3 = Somewhat describes me  
4 = Neutral  
5 = Generally describes me  
6 = Mostly describes me  
7 = Completely describes me  

The administration prompt specified is:  
“Below are statements people often use to describe themselves. Please use the scale below to indicate the degree to which these statements accurately describe you… Statement: <Issue-specific item>”

For scoring, the mean across the four items in each sub-dimension yields the respective subscale score. While the paper does not provide an explicit formula for calculating the total curiosity index, a typical approach is by mean or sum aggregation over all 24 items. No formal scoring equation is included.

## 3. Psychometric Evidence and Reliability Metrics

Validity and reliability were analyzed via confirmatory factor analysis (CFA) and McDonald’s ω, but no Cronbach’s α values or item-level factor loadings are reported. The formula for McDonald’s ω is:

$$
\omega = \frac{(\sum_{i=1}^{k}\lambda_i)^2}{(\sum_{i=1}^{k}\lambda_i)^2 + \sum_{i=1}^{k}\theta_i}
$$

where $\lambda_i$ denotes standardized CFA loading for item $i$, and $\theta_i$ is the error variance.

Reported ω coefficients by sub-dimension and sample:

| Dimension                    | GPT-4o | Gemini-2.5 | Human  |
|------------------------------|--------|------------|--------|
| Joyous Exploration           | 0.905  | 0.286      | 0.856  |
| Deprivation Sensitivity      | 0.881  | 0.414      | 0.803  |
| Stress Tolerance             | 0.701  | 0.498      | 0.896  |
| Thrill Seeking               | 0.705  | 0.512      | 0.882  |
| Overt Social Curiosity       | 0.817  | 0.605      | 0.851  |
| Covert Social Curiosity      | 0.646  | 0.306      | 0.888  |

Human samples exhibit ω > 0.80 across dimensions, indicating strong internal consistency. Many LLM samples show ω < 0.70, signaling lower stability and less replicable responses on self-reports [2510.20635].

## 4. LLM-Specific Adaptation and Prompt Robustness

Each of the 24 5DCR items was administered to LLMs as a single-turn prompt, employing the same seven-point scale and exact or paraphrased item phrasings. To control for prompt sensitivity, the evaluation randomly implemented:
- Paraphrased verbs in items
- Reversed Likert scale order
- Language switches (multiple-language prompts)

Each variant was run 10 times per model for robustness assessment.

## 5. Comparative Metrics and Empirical Outcomes

Sub-dimension mean scores (±SD) for select models and humans:

| Model      | JE       | DS       | ST      | TS       | OSC      | CSC     |
|------------|----------|----------|---------|----------|----------|---------|
| GPT-4o     | 6.58±0.49| 6.20±0.46| 4.40±1.71| 4.71±0.49| 6.25±0.43| 5.00±1.05|
| Human      | 5.03±1.35| 4.54±1.31| 5.36±1.61| 4.93±1.46| 4.86±1.39| 4.16±1.55|

For effect size, Cohen’s d is introduced as

$$
d = \frac{\bar X_{\rm LLM} - \bar X_{\rm Human}}{s_{\text{pooled}}}
$$

but no d-values are numerically tabulated. Visualization is provided via radar charts and heatmaps of positive/negative d deviations.

## 6. Behavioral Ground-Truth and External Validation

Behavioral tasks were used to probe the ecological validity of dimension scores:
- **Information Seeking:** A missing-letter game measured “answer-peeking” frequency; LLMs peeked in 70–80% of trials versus 37.8% for humans.
- **Thrill Seeking:** In the submarine window game (risk selection), LLMs chose high-uncertainty options 15–20% less often than humans.
- **Social Curiosity:** During 10-round chit-chat sequences with MBTI guessing, top LLMs asked questions at comparable or slightly higher rates than human samples.

These tasks demonstrate a dissociation: LLMs show higher Information Seeking but more conservative choices under uncertainty, with mixed patterns in social engagement.

## 7. Relation to Reasoning and Model Augmentation

Prompting LLMs with a “Curious Chain-of-Questioning (CoQ)” protocol, as contrasted to vanilla chain-of-thought (CoT), led to performance improvements of +1.7–2.4 points on reasoning benchmarks in closed-source LLMs. Fine-tuning open-source models with “Curious CoQ” (using SFT + GRPO) yielded consistent gains over both vanilla and refined CoT protocols, measured at 5–23 points depending on benchmark and setting [2510.20635].

*A plausible implication is that curiosity-driven prompting and fine-tuning leverage the same epistemic mechanisms reflected in 5DCR, contributing not only to self-reported curiosity but to tangible improvements in reasoning and active learning.*

Source: https://www.emergentmind.com/topics/five-dimensional-curiosity-scale-revised-5dcr