No subject

of rules, variables, and dynamic bindings using temporal synchrony.
Behavioral and Brain Sciences 16 (3) 417--494.

R. Sun, (1992). On Connectionist variable binding. Connection Science.

R. Sun, (1995). 
Robust reasoning: integrating rule-based and similarity-based reasoning.
Artificial Intelligence. Vol.75, No.2, pp.241-296.  June, 1995.

Lange and Dyer (1989). High Level Inferencing  in a Connectionist Network. {\it Connection Science}, 181-217.
(See also Lange's chapter in Sun and Bookman (1994).)

R. Lacher, S. Hruska, and D. Kunciky, 1992.
Backpropagation learning in Expert Networks.
Technical Report 91-015. Florida State University.
also in IEEE TNN.

Barnden,  Complex Symbol-Processing in Conposit, in:
Sun and Bookman (eds.), Architectures incorporating
neural and symbolic processes. Kluwer. 1994.

J. Barnden and K. Srinivas, Overcoming Rule-Based Rigidity and Connectionist Limitations Through Massively Parallel Case-based Reasoning, {\it International Journal of Man-Machine Studies}, 1992. 


------------------------
NATURAL LANGUAGE (Syntactic and semantic processing)

Bailey, D., J. Feldman, S. Narayanan, G. Lakoff (1997). 
Embodied Lexical Development, Proceedings of the Nineteenth Annual Meeting of
the Cognitive Science Society COGSCI-97, Aug 9-11, Stanford:
Stanford University Press, 1997. 

J. Henderson. Journal of Psycholinguistic Research, 23(5):353--379, 1994. 
Connectionist Syntactic Parsing Using Temporal Variable Binding.

T. Regier, Cambridge, MA: MIT Press. 1996.
The Human Semantic Potential: Spatial Language and Constrained Connectionism,

L. Bookman, A Framework for Integrating Relational and Associational
Knowledge for Comprehension, in Sun and Bookman (eds.), Architectures incorporating
neural and symbolic processes. Kluwer. 1994.

S. Wermter, (ed.) Connectionist language processing (?).
Springer.

------------------------
LEARNING OF SYMBOLIC KNOWLEDGE (from NNs)

Fu, AAAI-91. and IEEE SMC, 1995, 1997.

Towell and Shavlik, Machine Learning. 1995.

Giles, et al, (1993). in: 
Connection Science,1993. special issue on hybrid models.

(some of these models involve somewhat distributed representation, 
but that's not the point.)

Sun et al (1998), A bottom-up model of skill learning.
CogSci'98 proceedings.

(Justification:
In some instances, such learning/extraction from NNs is better than
learning symbolic knowledge directly using symbolic algorithms,
in algorithmic or cognitive terms.)

------------------------
RECOGNITION, RECALL

Jacobs, A.M. & Grainger, J. (1992). Testing a semistochastic variant of
the interactive activation model in different word recognition
experiments. Journal of Experimental Psychology: Human Perception and
Performance, 18, 1174-1188.

Jacobs, A. M., & Grainger, J. (1994). Models of visual word
recognition: Sampling the state of the art. Journal of Experimental
Psychology: Human Perception and Performance, 20, 1311-1334.

McClelland, J. L. & Rumelhart, D. E. (1981). An interactive activation
model of context effects in letter perception: Part I. An account of
basic findings. Psychological Review, 88, 375-407.

Page, M. & Norris, D. (1998). Modeling immediate serial recall with a
localist implementation of the primacy model. In J. Grainger & A.M.
Jacobs (Eds.), Localist connectionist approaches to human cognition.
Mahwah, NJ.: Erlbaum.
------------------------

MEMORY

There are many existing models. 
See Hintzman 1996 for a review (in Annual Review of Psychology)

-------------------------
SKILL LEARNING

R. Sun and T. Peterson,
A subsymbolic+symbolic model for learning sequential navigation.
{\it Proc. of the Fifth International
Conference of Simulation of Adaptive Behavior (SAB'98).}
Zurich, Switzerland. 1998.  MIT Press.

R. Sun, E. Merrill, and T. Peterson,
A bottom-up model of skill learning.
{\it Proc.of 20th Cognitive Science Society Conference}, pp.1037-1042,
Lawrence Erlbaum Associates, Mahwah, NJ.  1998.


Thompson, Cohen, and Shastri's work (yet to be published, I believe).

------------------------

I cannot even begin to enumerate all the rationales for using
localist models for symbolic processing discussed in these pieces of work.
The reasons may include

(1) localist connectionist models are an apt description framework for
a variety of cognitive processing,
(See J. Grainger & A.M. Jacobs (Eds.), Localist
connectionist approaches to human cognition. Mahwah, NJ.: Erlbaum.)

(2) the inherent processing characteristics of connectionist
models (such as similarity-based processing, which can also be explored
in localist models) make them suitable for cognitive processing, 

(3) learning processes can naturally be applied to
localist models (as opposed to learning LISP code),  
such as gradient descent, EM, etc.

(As has been pointed out by many recently,  localist models share many 
features with Bayesian networks. This actually
has been recognized very early on, see for example, Sun (1990 INNC), 
Sun (1992), in which a localist network
is defined from a collection of hidden Markov models, and 
the Baum-Welch algorithm was used in learning.)

Regards,
--Ron

p.s.
See also the recently published edited collection:
R. Sun and  F. Alexandre (eds.),
{\it Connectionist Symbolic Integration}.
Lawrence Erlbaum Associates, Hillsdale, NJ.  1997.

-----------------------------------------
Dr. Ron Sun
NEC Research Institute
4 Independence Way
Princeton, NJ 08540
phone: 609-520-1550
fax: 609-951-2482
email: rsun at cs.ua.edu,  rsun at research.nj.nec.com 
-----------------------------------------
Prof. Ron Sun                                      http://cs.ua.edu/~rsun
Department of Computer Science 
 and Department of Psychology                       phone: (205) 348-6363
The University of Alabama                           fax:   (205) 348-0219
Tuscaloosa, AL 35487                                email: rsun at cs.ua.edu