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**gptr is the R package; Peter is your agent inside R.** Ask Peter with `peter()` to work
with your live objects. A conversational model returns a session; a typed decision model
returns a vector that ordinary R code can use. The name connects readable programs with
the study of reasoning.

## Peter Naur: make the program readable

Peter Naur was a Danish computer scientist and a central contributor to ALGOL 60. He edited
its language report, which used the syntax notation now known as **Backus-Naur Form** to
describe how programs are constructed. This was collaborative work, including John Backus;
Naur helped make the language's definition precise and readable.
The [revised ALGOL 60 report](https://www.cs.tufts.edu/comp/150FP/archive/peter-naur/algol60.pdf)
records this collaborative work and shows the notation in use.

For gptr, the connection is practical: an analysis should leave code you can read, inspect
and run. Peter works inside R, and a designated history document can retain the R code an
agent executed beside the prompt that requested it. Recording preserves work for review;
replay still depends on the code's inputs, packages and side effects.

## Peter Cathcart Wason: examine the reasoning

Peter Cathcart Wason studied human reasoning. With Jonathan St. B. T. Evans, he proposed a
dual-process account in
["Dual processes in reasoning?"](https://cogsci.ucsd.edu/~coulson/203/wason-evans.pdf).
Their study distinguished processes behind people's responses from the conscious reasons
people gave for them. An explanation could be a justification rather than an accurate account
of how a response arose.

That influence suggests a useful discipline for model-assisted analysis: examine the result
and the evidence, as well as the explanation.

## Two ideas in one R workflow

gptr uses **System 1** for typed logical decisions, choices and scores with probabilities, and
**System 2** for conversational agents that produce text and request tools. These are software
roles inspired by reasoning research; they do not establish that models reproduce human
cognition or that Wason introduced those labels.

Use [typed decisions](system-one.html) to screen or route inputs, keeping uncertainty visible.
Use [agent sessions](language-models.html) to inspect objects and carry out requested R work.
Keep useful work in a [readable history document](script-as-history.html), and verify the
calculations and outputs before relying on a model-generated explanation.