>> The first argument emphasizes learning (Bechtel and Abrahamsen 2002: 51). A
vast range of cognitive phenomena involve learning from experience. Many
connectionist models are explicitly designed to model learning, through
backpropagation or some other algorithm that modifies the weights between
nodes. By contrast, connectionists often complain that there are no good
classical models of learning [4.2 Arguments for connectionism].
There is no special need for a specific model of "learning" in a classical
setting. Given an inference procedure, such as induction, adbuction or
deduction, that can derive new facts and rules in a logical language from
observations and a pre-existing theory (i.e. a pre-existing set of facts and
rules), all it takes to "learn" is to store the newly derived facts and rules
to a database.
I mean "learning" in the sense of Mitchell's definition of _machine_ learning,
as (informally) the ability of a system to improve its performance from
experience. In this sense, a system that starts with a database of logical
facts and rules and adds new facts and rules derived from new observations
is "learning".
You can find many examples of learning in a classical, logic setting in the
early ('70s and '80s) machine learning literature, particularly with
propositional logic learners such as decision list and decision tree learners,
the most famous of which are J. Ross Quinlan's ID3 and C4.5 decision tree
learners. The field of Inductive Logic Programming studies learning in
First-Order Logic languages, especially logic programming languages such as
Prolog and Answer Set Programming, and includes early systems such as
Shapiro's Model Inference System, Quinlan's FOIL (First-Order Inductive
Learner, essentialy a relational version of ID3), Muggleton's Progol and
Srinivasan's Aleph (based on inverse entailment), and more recently ASP
learners such as ASPAL (Mark Law), or Statistical Relational Learning
techniques, e.g. by De Raedt, Kerstig, Getoor, Taschar and others; etc etc.
Bottom line- there is a huge body of work on learning in a classical, logic
setting. There is no serious objection that "there _are_ good classical models
of learning". Such models are all over the place in machine learning. In fact,
they tend to be the most carefully characterised models of machine learning.