grammers of Deep Junior, while leaving the job of how Deep Junior or Deep
Senior functions (or ought to function) to AI researchers.^6
ACKNOWLEDGMENTS
We thank Gerald Matthews, David Perkins, Michael Posner, and Dan Rea
for their thoughtful comments on earlier drafts of this article.
This work was made possible by a grant from the National Science Foun-
dation to the first author (#0296062), and grants from the National Science
Foundation (REC-9979843) and Department of Education (R206R000001)
to the second author.
REFERENCES
Abelson, R. P. (1963). Computer simulation of “hot” cognition. In S. S. Tomkins & S. Messick
(Eds.),Computer simulation of personality: Frontier of psychological theory(pp. 277–298).
New York: Wiley.
Ackerman, P. L. (1999). Traits and knowledge as determinants of learning and individual differ-
ences: Putting it all together. In P. L. Ackerman, P. C. Kyllonen, & R. D. Roberts (Eds.),
Learning and individual differences: Process, traits, and content determinants(pp. 437–460).
Washington, DC: American Psychological Association.
Ackerman, P. L., & Heggestad, E. D. (1997). Intelligence, personality, and interest: Evidence for
overlapping traits.Psychological Bulletin, 121, 219–245.
Allman, J. M., Hakeem, A., Erwin, J. M., Nimchinsky, E., & Hof, P. (2001). The anterior
cingulate cortex: The evolution of an interface between emotion and cognition. In A. R.
Damasio, A. Harrington, J. Kagan, B. S. McEwen, H. Moss, & R. Shaikh (Eds.),Unity of
knowledge: The convergence of natural and human science(pp. 107–117). New York: The New
York Academy of Sciences.
Allport, G. (1961).Pattern and growth in personality. New York: Holt, Rinehart & Winston.
Apter, M. J. E. (2001).Motivational styles in everyday life: A guide to reversal theory. Washing-
ton, DC: American Psychological Association.
30 DAI AND STERNBERG
6 6 This is not to say computational modeling cannot be done to simulate human mental proc-
esses, as long as such simulations provide psychologically viable accounts of mental processes in-
volved in intellectual functioning, including affective ones (e.g., Picard, 1997). Our point is that
advances in artificial intelligence and computational modeling provide no direct evidence as to
how human intelligence works because the isomorphism between the two should not be as-
sumed. Building a successful chess program like Deep Blue or Deep Junior does not provide a
clear understanding of how Kasparov made his way to chess stardom. Likewise, how Deep Blue
makes a move does not intrinsically provide any insights into the mind of Kasparov, although
their levels of performance are comparable. In other words, making claims about human intelli-
gence and intellectual functioning purely based on empirical evidence from computational simu-
lation without sound theoretical justification and corroborating human data is problematic. This
is where the Turing test fails (see Chomsky, 1997 for a similar position; see also Searle, 2001, for
a description of the Chinese Room Argument and the Turing test).