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[stefano nolfi]

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                   RECALL OF SEQUENCES OF ITEMS
                       BY A NEURAL NETWORK

                         Stefano Nolfi*
                        Domenico Parisi*
                        Giuseppe Vallar**
                        Cristina Burani*

                *Inst. of Psychology - C.N.R. - Rome
                   **University of Milan - Italy


                             ABSTRACT

  A network architecture of the forward type but with additional 'memory'
  units that store the hidden units activation at time 1 and re-input this
  activation to the hidden units at time 2 (Jordan, 1986; Elman, 1990) is
  used to train a network to free recall sequences of items. The network's
  performance exhibits some features that are also observed in humans,
  such as decreasing recall with increasing sequence length and better
  recall of the first and the last items compared with middle items. An
  analysis of the network's behavior during sequence presentation can ex-
  plain these results.

                           INTRODUCTION

Human beings possess the ability to recall a set of items that are presented
to them in a sequence. The overall capacity of the memory systems used in
this task is limited and the probability of recall decreases with increasing
sequence length. A second relevant  feature of human performance in this task
is that the last (recency effect) and the initial (primacy effect) items of
the sequence tend to be recalled better than the middle items. These serial
position effects have been observed both in a free recall condition, in which
subjects may recall the stimuli in any order they wish, and in a serial recall
condition,  in  which subjects must preserve the presentation order. (See
reviews concerning free and serial recall of sequences and the recency ef-
fect in: Glanzer, 1972; Crowder, 1976; Baddeley and Hitch, 1977; Shallice
and Vallar, 1990).

In this paper we report the results of a simulation experiment in which we
trained neural networks to recall sequences of items. Our purpose was to
explore if a particular network architecture could function as a memory
store for generating free recall of sequences of items. Furthermore, we
wanted to determine if the recall performances of our networks exhibited
the two features of human free recall that we have mentioned, that is,
decreasing probability of recall with increasing sequence length and an
U-shaped recall curve (for related works see: Schneider and Detweiler, 1987;
Schreter and Pfeifer, 1989; Schweickert, Guentert and Hersberger, 1989).


To appear in:
In D.S.Touretzky, J.L. Elman, T.J. Sejnowski and G.E. Hinton (eds.),
Proceedings of the 1990 Connectionist Models Summer School. San Matteo,
CA: Morgan Kaufmann.


                                REFERENCES

Baddeley  A.D., Hitch G.J. (1974). Recency re-examined. In S. Dornic (Ed.).
Attention and performance (Vol. 6). Hillsdale, NJ:Erlbaum, pp. 647-667.

Crowder R.G. (1976). Principles of learning and   memory. Hillsdale,
NJ: Erlbaum.

Glanzer  M. (1972). Storage mechanisms in recall.  In G.H. Bower (Ed.).
The Psychology of learning and motivation. Advances in research and theory.
(Vol. 5). New York: Academic Press, pp.  129-193.

Elman, J.L. Finding structure in time. (1990). Cognitive Science, 14, 179-211.

Jordan, M.I. (1986). Serial order: A parallel distributed processing approach.
Institute for Cognitive Science. Report 8604. University of California,
San Diego.

Shallice T., Vallar G. (1990). The impairment of  auditory-verbal  short-term
storage. In: G. Vallar and T. Shallice  (Eds.). Neuropsychological impairments
of short-term memory. New York: Cambridge University Press, pp.11-53.

Schneider, W., & Detweiler, M. (1987). A connectionist control architecture
for working memory. In G.H. Bower (Ed.) The Psychology of learning and
motivation vol 21. New York: Academic Press.

Schreter, Z., & Pfeirer, R. (1989). Short term memory and long term memory
interactions in connectionist simulations of psychological experiments on
list learning. In L. Personnaz and G. Dreyfus (Eds.), Neural Network: From
models to applications. Paris: I.D.S.E.T.

Schweickert, R., Guentert, L., & Hersberger, L. (1989). Neural Network Models
of Memory Span. Preceedings of the Eleventh Annual Conference of the Cognitive
Science Society. Ann Arbor, Michigan.