By: Alireza Minagar, MD, MBA, MS (Bioinformatics) Software Engineer
Imagine this: a person eats only the right half of their plate, shaves only the right side of their face, and draws only the right half of a clock. They’re not lazy or careless—they literally don’t perceive the left.
This is hemispatial neglect—a neurological condition that reveals just how bizarre and incomplete human attention can be.
And surprisingly, it’s not just the human brain that suffers from neglect.
Our AI systems do too—in ways that are eerily similar.
🧠 What Is Neglect, Really?
Neglect happens when damage to the right parietal lobe causes a person to ignore half of space—not consciously, but perceptually. The left side of their world just ceases to exist in their awareness.
They bump into doors. Miss half of what’s written on a page. And yet, they think they’re seeing everything just fine.
Sound familiar?
🤖 How AI Mirrors This Neurological Blindness
Today’s large language models and computer vision systems suffer from a different kind of neglect:
Contextual neglect (ignoring long-range dependencies)
Spatial neglect (cropping or overfitting on image edges)
Ethical neglect (overlooking bias in training data)
Semantic neglect (missing nuance in sarcasm, tone, or intent)
AI isn’t conscious, but it mimics awareness—and like the damaged brain, it confidently reports the world as it sees it, even when it’s missing half the picture.
🧬 The Brain as a Codebase with Bugs
Neurologically, neglect is a failure of attention routing—not perception itself. The eyes work. The data’s there. But the brain’s indexing system is broken.
In code, it’s like:
if not attention_routed(input_segment):
return None # silently ignore critical data
Neural networks may often do this too. Dropout layers, vanishing gradients, or architectural bias in attention heads can cause models to miss entire threads of logic.
💡 What Can Coders and AI Designers Learn?
Absence is not emptiness
Just because your model doesn’t see it doesn’t mean it isn’t there. Think about adversarial inputs and edge cases.
Introspection matters
The human brain has metacognitive layers—areas that monitor and redirect attention. Why don’t our models?
Perception is filtered
Whether it’s a stroke patient or a transformer model, attention is selective. Bias isn’t just a social issue—it’s an architectural one.
🧠 What If We Built Systems that Noticed What They Ignore?
Imagine AI systems with built-in neglect detectors—circuits that measure what’s not being attended to and alert the model or user.
Or compilers that warn:
“You haven’t touched this input space in 10K iterations. Are you sure?”
Maybe it’s time we code like neuroscientists.
🔄 The Mirror Between Brain Bugs and Code Bugs
Neurology isn’t just a source of metaphors—it’s a roadmap for debugging machine minds.
Neglect shows us that confidence isn’t comprehension
—and that the absence of attention can be dangerous in any intelligent system
💬 Let’s Talk:
Have you ever seen a machine “confidently fail”?
What blind spots do you think today’s AI models are neglecting?
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