Cognitive Semantics : Meaning and Cognition

SITUATED EMBODIED SEMANTICS AND CONNECTIONIST MODELING 177
• Is it possible to achieve adequate linguistic categorization of situations,
without any explicit (symbolic) “semantic representations”?
• What kind of pre-structuring (i.e., “innate bias”) is required for this to
be achieved?
These two questions will be the primary focus of the remaining part of this
paper. I will deal with them in the context of a concrete connectionist model
described in the following section.
3. Experimenting with Regier’s system
Regier’s (1992) system, graphically presented in Figure 1, is a feed-forward
structured connectionist net, using the backpropagation learning rule
(Rumelhart et al. 1986). It takes perceptual input from its environment,
processes it within a number of internal modules, and “learns” to associate the
perceptual scenes with one or more output nodes by comparing the actual
output with an externally given “correct” output and gradually minimizing the
discrepancy.
The environment the system interacts with is one of 2-dimensional geometric
objects. The first major simplification with respect to embodiment is
that this interaction takes the form of perception only, with no motor activity
whatsoever. A second simplification is that the system “sees” only 2 objects at
a time and furthermore, in order to avoid the empiricist problem of deciding
which object is being related to which, one of the objects is explicitly labeled
trajector (TR) and the other landmark (LM), in correspondence with the
terminology from cognitive linguistics. 2 A third simplification is the way
situations in which there is motion are dealt with: as a sequence of “snapshots”
or “frames” during which the landmark remains static, while the trajector
varies its position with respect to it. A static scene is a special case where the
frames in the sequence are identical. Regier refers to each of these sequences
as a “movie”. I will be using the more general term situation since, despite
limitations such as the above, I will be using the model as an (impoverished)
illustration of the situated embodied approach.
The internal modules contain considerable pre-structuring, which I do
not intend to describe here, but it should be noted that their purpose is to
extract relatively high-level perceptual features such as contact, inclusion,