Work on neural networks certainly fell out of favor following the publications of Minsky and Papert in 1966-67. I have only skimmed portions of the sociology paper referenced in the question, but other circumstances suggest that the topic might have diminished in popularity even without their results.
Rosenblatt's perceptrons had certainly created a lot of enthusiasm,
perhaps the first big wave of excitement about AI (later followed by
the 1980's excitement about rule-based "expert systems" and the
current excitement about neural-net and other machine learning
techniques). One story involves a conference report on the
development of a perceptron that could accurately detect the presence
of army tanks in photographs of fields and forests -- the inputs involved digitizing each photograph into 16 pixels.
Much of the enthusiasm around perceptrons accrued to convergence theorems guaranteeing that if a perceptron could decide some question, basic learning
procedures would converge on suitable network weights. These theorems all played off of the linear threshold for perceptrons. I would guess that many perceptron
publications amounted to retelling of some standard result about
linear transformations in the perceptron setting.
Then, Minsky and Papert's major paper aimed at mathematicians appeared, bearing the title "Linearly unrecognizable patterns". This title is both accurate and misleading in its own right.
The title is accurate because the primary result was that some patterns are not recognizable by linear threshold machines. But obviously, this result alone does not doom neural networks. If linear transformations are not adequate to characterize some patterns of interest, how about polynomials? Trigonometric and exponential functions? There was no lack of alternatives available to study.
The title is misleading in that it
makes no mention of Minsky and Papert's second main result, that even
when considering only linearly-recognizable patterns, the learned
weights would require astronomically-sized precision, and hence
astronomical amounts of data to determine these weights.
So neural network researchers faced two problems:
Computers were laughably small compared to machines today, and the
amount of data one could obtain was tiny compared to the vast
quantities available today. For decades, lack of sufficient data, and
of machines capable of manipulating this data, impeded many parts of
AI, both symbolic and nonsymbolic.
Also, neural network methods were not explicable. Minsky and others championed heuristic and symbolic methods, whose answers and actions can be
explained in terms that mean something to people. In contrast, neural
networks operated as black boxes, systems trained to provide an
answer, but incapable of providing any explanation meaningful to
people. This limitation persists to this day. For decades, it
provided an easy retort to neural-net proponents, for in many fields
such as medical diagnosis, no one will follow machine recommendations
without reasonable explanations.
Circumstances have changed since then. Hinton and others persisted in developing techniques for multilayer networks, investigating ideas based on nonlinear methods from classical physics. Processing speeds and data availability have increased. Also the recent solutions of interesting problems, such as non-trivial Go, have given much of a reason to consider using neural networks even without explicability.
So it seems to me that the
inattention to neural network research had a lot to do with waiting for
computational progress, and that the unrecognizability
result on its own does not provide a sufficient explanation.