Why Simple Robots Feel More "Human": The "Less is More" Paradox of AI Perception

Why Simple Robots Feel More "Human": The "Less is More" Paradox of AI Perception

Have you ever watched a robotic vacuum clean a room or a single-minded automated arm and felt like it had a mind of its own?

When we design robots and AI agents, the common assumption is that more human-like behaviors = more perceived human "selfhood." Add learning abilities! Add goal-seeking! Add efficiency! The more complex the behavior, the more we should see it as a conscious entity... right?

I our recent study, published in 2026 in Frontiers in Cognition, we turned this logic on its head. We found a surprising "less is more" effect: A robot displaying just one clear selfhood cue is often perceived as having more selfhood than a robot displaying multiple cues.

The Experiment: Testing Robots with Single vs. Multiple Cues

We ran three experiments comparing how people evaluate artificial agents based on specific behavioral indicators of selfhood:

1. Efficiency (moving directly and smoothly toward a target)

2. Learning Sensitivity (adapting behavior over time after errors)

3. Equifinality (reaching the same goal through different paths or methods when obstructed)

Participants watched robots perform tasks exhibiting either:

• A single cue: Just efficiency, just learning, or just equifinality.

• Multiple cues: A combination of these intelligent behaviors together.

Participants then rated the degree of selfhood, agency, and intentionality they attributed to each robot.

Key Findings: Counter-Intuitive Results

• The Single-Cue Advantage: In Experiments 1 (efficiency) and 3 (equifinality), participants attributed significantly higher selfhood to the robot showing a single distinct behavioral trait than to the robot displaying multiple traits.

• The Complexity Penalty: Combining multiple intelligent traits did not boost the robot's perceived "inner life." In fact, it seemed to dilute the illusion of selfhood.

• The Exception (Learning): In Experiment 2, a robot displaying only learning sensitivity scored higher on context sensitivity, but overall selfhood attribution remained dominated by single-focused behavior.

Why Does "Less Is More" Happen?

We explain this phenomenon through the lens of cognitive bootstrapping and psychological attribution theory:

1. Focused Intentionality: When an agent consistently applies a single skill to solve a problem (e.g., relentlessly pursuing one goal via equifinality), observers easily assign a clear, singular "mind" or "purpose" to it.

2. Over-Generalization: Humans tend to take a single strong cue and mentally fill in the rest ("It’s efficient, so it must be smart and conscious!").

3. Noise & Clutter in Multiple Cues: When a robot tries to do too many smart things at once (learning and switching paths and being efficient), human observers may perceive the behavior as an intricate algorithm rather than a focused, living individual.

Key Takeaways for AI & Robotics Designers

• Simplicity Wins Trust: You don't need to overload an AI or service robot with dozens of human-like traits to make users feel it has intentionality or "personality."

• Focus on Key Cues: One persistent, clear behavioral indicator (like adaptively reaching a goal despite obstacles) is more powerful at eliciting empathy and perceived selfhood than a jack-of-all-trades feature set.

• Rethink Human-Robot Interaction (HRI): Designing for perceived agency is less about layering features and more about honing singular, readable behavioral cues.

The original paper can be found here NB: The summary was originally prepared by Gemini

Pohl J, Nikolovska K, Maurelli F, Kappas A and Hommel B (2026) When less is more: single selfhood-related cues elicit higher selfhood ratings than multiple cues. Front. Cognit. 5:1815906. doi: 10.3389/fcogn.2026.1815906