  Background: Recent advancements in large language models have motivated the
practical use of such models in code generation and program synthesis. However,
little is known about the effects of such tools on code readability and visual
attention in practice.
  Objective: In this paper, we focus on GitHub Copilot to address the issues of
readability and visual inspection of model generated code. Readability and low
complexity are vital aspects of good source code, and visual inspection of
generated code is important in light of automation bias.
  Method: Through a human experiment (n=21) we compare model generated code to
code written completely by human programmers. We use a combination of static
code analysis and human annotators to assess code readability, and we use eye
tracking to assess the visual inspection of code.
  Results: Our results suggest that model generated code is comparable in
complexity and readability to code written by human pair programmers. At the
same time, eye tracking data suggests, to a statistically significant level,
that programmers direct less visual attention to model generated code.
  Conclusion: Our findings highlight that reading code is more important than
ever, and programmers should beware of complacency and automation bias with
model generated code.
