![](bg6.png)
optimum by avoiding local optima. However, before
concluding his discussion, Thagard states explicitly
that serial algorithms are important for
understanding bounded rationality in humans.
Vertolli and Davies (2014) have shown that
functionally serial processing techniques can be
better than parallel algorithms when the feature set is
low level (e.g., conditional probabilities), low
dimensionality (e.g., only one feature), and high
combinatoric load. However, they leave open the
possibility that some combination of parallel and
serial techniques could explain how bounded
rationality approaches optimal functionality.
The current work supports and extends these
authors by implementing a parallelized serial
processing system with similarities with
connectionist approaches in its artificial neural
network representation: Coherence Net. As Thagard
predicted, greater parallelization increased the
optimality of the system as a whole. We have also
extended their work by providing a formal
description of the task and algorithms.
It is worth noting that, outside of the quantitative
testing metric, it is challenging to interpret the literal
output of each of the models. At times, it is clear
why Coherence Net outperformed Coherencer. For
example, given the query ‘robber,’ Coherence Net
returned ‘steal,’ ‘thief,’ ‘mask,’ and ‘jail’ while
Coherencer returned ‘steal,’ ‘thief,’ ‘money,’ and
‘square.’ Coherence Net’s result occurs in an image
and Coherencer’s does not. However, for the query
‘bank,’ Coherence Net returned ‘fruit,’ ‘away,’
‘keeps,’ and ‘an’ while Coherencer returned ‘hand,’
‘atm,’ ‘credit,’ and ‘keeps.’ Though Coherence
Net’s output does occur in an image and
Coherencer’s does not, it is not obvious which result
is actually more desirable as a model of imagination.
Another caveat is that many of the parameters
used, especially the number the nodes at each tier,
are arbitrary. Generally, more nodes improved the
search space but increased the search time. We used
1000 nodes for tiers 1 through 3 and 2500 nodes for
tiers 4 and 5 as we found these numbers worked well
in a reasonable amount of time. Future work will
evaluate many of these properties in greater detail.
REFERENCES
Breault, V., Ouellet, S., Somers, S., & Davies, J. (2013).
SOILIE: A computational model of 2D visual
imagination. In R. West & T. Stewart (eds.),
Proceedings of the 12th International Conference on
Cognitive Modeling, Ottawa, ON.
Brualdi, R. A. (2010). Introductory Combinatorics, Fifth
Edition. Pearson Education, Inc.
Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast
learning algorithm for deep belief nets. Neural
computation, 18(7), 1527-1554.
Holland, J. H. (1975), Adaptation in Natural and Artificial
Systems, University of Michigan Press, Ann Arbor.
Huang, S. J., Yu, Y., & Zhou, Z. H. (2012).Multi-label
hypothesis reuse. In Proceedings of the 18th ACM
SIGKDD international conference on Knowledge
discovery and data mining (pp. 525-533).ACM.
Maguire, E. A., & Mullally, S. L. (2013). The
hippocampus: A manifesto for change. Journal of
Experimental Psychology: General, 142(4), 1180.
Mckay, R. I., Hoai, N. X., Whigham, P. A., Shan, Y., &
O’Neill, M. (2010). Grammar-based genetic
programming: a survey. Genetic Programming and
Evolvable Machines, 11(3-4), 365-396.
Miller, B. L., & Goldberg, D. E. (1995).Genetic
Algorithms, Tournament Selection, and the Effects of
Noise. Urbana, 51, 61801.
Reiczigel, J. (2003). Confidence intervals for the binomial
parameter: some new considerations. Statistics in
Medicine, 22(4), 611-621.
Thagard, P. (2000). Coherence in thought and action.
Cambridge, MIT Press.
Vertolli, M. O., Breault, V., Ouellet, S., Somers, S.,
Gagné, J. & Davies, J. (2014). Theoretical assessment
of the SOILIE model of the human imagination,
Proceedings of the 36th Annual Conference of the
Cognitive Science Society. Quebec City, QC.
Vertolli, M. O. & Davies, J. (2013).Visual imagination in
context: Retrieving a coherent set of labels with
Coherencer. In R. West & T. Stewart (eds.),
Proceedings of the 12
th
International Conference on
Cognitive Modeling, Ottawa, ON.
Vertolli, M. O. & Davies, J. (2014). Coherence in the
visual imagination: Local hill search outperforms
Thagard’s connectionist model, Proceedings of the
36th Annual Conference of the Cognitive Science
Society. Quebec City, QC.
Vertolli, M. O., Kelly, M. A., & Davies, J. (2014).
Perception and generation as a compression-
decompression dyad, Proceedings of the 7th
Conference on Artificial General Intelligence (AGI),
Quebec City, QC: AGI.
Von Ahn, L., Liu, R., &Blum, M. (2006). Peekaboom: a
game for locating objects in images. In Proceedings of
the SIGCHI Conference on Human Factors in
Computing Systems (pp. 55-64).ACM.
Zhang, M. & Zhou, Z. (2013).A review on multi-label
learning algorithms. IEEE Transactions on Knowledge
and Data Engineering, PP(99), 1-59.
CoherenceNet-ANewModelofGenerativeCognition
313