Perception loves exaggeration
Across vision and sound alike, exaggerating key features directs attention to what differentiates one object from another, making recognition faster and memory stronger.
Colors are the perceptual sugar of the brain
Vivid colors act like attention magnets that consume cognitive resources, activating reward pathways and bypassing logical circuits to stimulate emotional systems.
Crowding turns clarity into clutter
Nearby elements interfere with visual recognition: an item clear in isolation may become unrecognizable in context as the brain aggregates competing features into noise.
Readability isn’t one-size-fits-all
Cognitive and perceptual differences shape how individuals process text. Readers labeled as struggling may simply be mismatched with default reading formats.
Agentic systems trigger moral expectations
The moment a system appears to understand, we expect it to be accountable. When perceived agency exceeds actual responsibility, trust collapses and missteps feel like betrayal.
Great creative partnership is built on good calibration
When a user knows exactly what they want, misalignment feels like resistance. But when intention is uncertain, boldness and exploration are welcomed— even preferred.
The best AI assistants read your intents, not your thoughts
When an algorithm predicts something so specific that it seems to know your private thoughts, it triggers a sense of being watched rather than helped.
Anchoring pulls judgment toward first impressions
In model evaluation, humans and LLMs alike judge later outputs in the shadow of earlier context, a pervasive bias that shapes scores in ways that may not reflect intrinsic quality.
Understanding human perceptual blind spots is key to safe AI
The “invisible gorilla” effect illustrates that human perception has systematic blind spots. Understanding these human limits becomes critical for safe AI development.
In AI, small errors scale fast. Design for human limits, or pay exponential costs
The “Rule of Ten” tells us that the cost of fixing an error increases by an order of magnitude the later it is detected. In the AI era, that multiplier grows steeper.