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The Cognitive Science of Deduction
Philip N . Johnson-Laird and Ruth M . J. Byrne
The late Lord Adrian, the distinguishedphysiologist , once remarked
that if you want to understandhow the mind works then you had
better first askwhat it is doing. This distinction e familiar in
cognitive scienceas one that Marr (1982) drew between a theory at
the "computationallevel " and a theory at the "algorithmic level." A
theory at putational level characterizeswhat is -
puted, why it is puted, and what constraintsmay assistthe
process. Sucha theory, to borrow from Chomsky (1965), is an account
of petence. And, as he emphasizes, it should also explain
how petenceis acquired. A theory at the algorithmic level
specifieshow putation is carried out, and ideally it should be
preciseenough for puter program to simulatethe process. The
algorithmic theory, to borrow again from Chomsky, should explain
the characteristicsof human performance- where it breaksdown and
leadsto error, where it runs smoothly, and how it is integratedwith
other mental abilities.
We have two goalsin this chapter. Our first goal is to charac-
terize deduction at putationallevel . Marr criticized researchers
for trying to erect theories about mental processeswithout having
stopped to think about what the processeswere -
pute. The same criticism can be levelled againstmany accountsof
deduction, and so we shall take pains to think about its function:
what the putes, what purpose is served, and what con-
straintsthere areon the process. Our secondgoal is to examineexisting
algorithmic theories. Here, experts in several domains of enquiry
have something to say. Linguists have consideredthe logical form
of sentencesin natural language. Computer scientistshave devised
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programs that make deductions , and , like philosophers , they have
confronted discrepancies between everyday inference and formal
logic . Psychologists have proposed algorithmic theories based on thei