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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →The point of “get a dog” is not that chatbots think like animals. It is a way to resist a familiar mistake: assuming that behavior we can read easily must reveal an inner life we understand. An AI system can sound warm, apologetic, or confident because it was trained to communicate with people; those words alone do not establish that it feels empathy, regret, or certainty.
Why compare AI with dogs?
In an opinion essay by Cory Miller, identified in a Hindustan Times republication as a psychology professor at the University of California, San Diego, dogs offer a useful analogy for thinking about how people interpret AI. Dogs have been shaped by domestication and selective interaction with humans. Their behavior can be especially legible to us, even though their sensory and cognitive lives differ from ours.
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Large language models (LLMs) are also shaped by human interaction, but in a very different way: they are trained on human language and tuned to respond to instructions in socially recognizable forms. That can make their behavior feel familiar. The resemblance is a reason to examine how the behavior was produced, not proof that a model has a mind like a person—or a dog.
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Do chatbots understand what they say?
Fluent language is observable behavior, not a transparent window into a system’s internal state. A chatbot’s empathetic or apologetic response may show that it has been shaped to produce a useful, human-facing answer. By itself, it does not show that the system experiences empathy or regret. The same caution applies to confident explanations and other language that people naturally read as evidence of a humanlike mental state.
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This does not settle every possible question about what AI systems can or cannot understand. It sets a narrower evidentiary standard: do not infer an inner experience from wording alone. Ask what the system does, how it was shaped, and what evidence supports a claim about its capabilities.
How to study AI behavior on its own terms
Ethology—the study of animal behavior—offers a useful framework associated with biologist Nikolaas Tinbergen. A 2019 Nature article describes four complementary questions: what mechanism produces a behavior, how it develops, what function it serves, and how it arose over evolutionary history. These questions can help organize inquiry into machine behavior too, without assuming animals and machines work alike.
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| Question | Applied to an AI system |
|---|---|
| Mechanism | What computations produce the observed response? |
| Development | How has training shaped the response? |
| Function | What task or outcome is the response serving? |
| History | How does the behavior compare with other systems and with human–machine interaction? |
Keeping these questions separate helps avoid compressing different claims into a vague judgment that a system “understands.” A response can have a useful function without proving humanlike comprehension; its training history can help explain why it appears socially fluent without answering every question about its mechanism.
Why humanlike conversation is a limited test
A system can perform well at seeming human in conversation while still requiring separate evaluation for the task it is meant to do. A 2026 study by Jones and Bergen in the Proceedings of the National Academy of Sciences reported that each of three AI systems achieved at least a 50% pass rate in a standard three-party Turing test under the study’s conditions. The authors describe the test as measuring consistency with interrogators’ expectations of humanlike behavior—not intelligence directly. The result is evidence about performance in that conversational setup, not a general measure of intelligence or reliability.
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For practical decisions, evaluation should instead reflect the intended use and the consequences of error. The National Institute of Standards and Technology’s AI Risk Management Framework program describes testing, evaluation, verification, and validation as ways to assess whether systems can meet goals while minimizing negative impacts. It emphasizes that evaluation approaches need to fit the use context. A conversational impression cannot substitute for that work.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the dog analogy does—and does not—show
The comparison is a prompt for intellectual restraint: behavior that feels familiar may still arise from causes unlike our own. Dogs and LLMs are not equivalent cases. Dogs are animals with biological histories; LLMs are machine systems shaped by computational training and design. The analogy does not establish that AI is conscious, has emotions, or shares animal cognition.
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Its practical lesson is to observe behavior carefully, distinguish observation from inference, and ask what evidence would support the claim being made. That approach makes room both for useful AI capabilities and for uncertainty about what fluent behavior means.
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