Lecture 1: Introduction

[Disclaimer: These informal lecture notes are not intended to be comprehensive - there are some additional ideas in the lectures and lecture slides, textbook, tutorial materials etc. As always, the lectures themselves are the best guide for what is and is not examinable content. However, I hope they are useful in picking out the core content in each lecture.]


The lecture began with a slightly long-winded way of introducing these two "definitions" for perception and cognition.

To illustrate this definition, we used some empirical data (taken from De Deyne, Navarro & Storms 2013) to map out the words that people associate with "perception" and "cognition". The reason for doing this was partly because it's kind of neat to see the "definition" emerge from commonsense intuition (e.g., the word "perception" is associated with a cluster of words about "sensation" and another cluster of words about "mind"), but it's also a bit of foreshadowing - this kind of semantic network will appear later on in the cognition lecture stream in Prof. Taft's section.

Historical background

“Psychology as the behaviorist views it is a purely objective experimental branch of natural science. Its theoretical goal is the prediction and control of behavior. Introspection forms no essential part of its methods, nor is the scientific value of its data dependent upon the readiness with which they lend themselves to interpretation in terms of consciousness. The behaviorist, in his efforts to get a unitary scheme of animal response, recognizes no dividing line between man and brute.”

Some difficulties with behaviourism

One of the most influential criticisms of the radical behaviourist position occurred in the late 1950s when B.F. Skinner published Verbal Behavior in 1957. The key idea in the book was an attempt to describe how language might be "trained" via a process of operant conditioning. Leaving the particulars of Skinner's theory to one side, the main thing to note here is that he treated language as being no different to any other behaviour, a view that didn't go down well in linguistics. Noam Chomsky, who had published his own book Syntactic Structures in 1957 (which emphasised the importance of understanding the extraordinary amound of grammatical structure in human language), published a scathing review of Verbal Behaviour in 1959. There's quite a lot of ideas in those two publications by Chomsky, but several key points that he made were:

The overall message underpinning these kinds of observations is that human language (and by exension, perhaps, many other phenomena in human behaviour) is highly structured, and is not completely under the control of "the environment". Instead of thinking about the production of behaviour a "simple" mapping from stimulus to response, perhaps we should be thinking about what happens in betweebn the S and the R. Perhaps... we need to understand the thought processes that lie in between the two!

Cognitivism and the "computational metaphor"

To contrast the two positions:

When we shift from a behaviourist to cognitivist perspective, the big question that we need to answer is "what kind of machine are we talking about here?" Our brains (and by extension, our minds) are terribly complicated things, and to make any progress in describing them in comprehensible ways, we'll need to rely on some simplifications, and maybe come up with a sensible "language" for describing what they're doing.

With that in mind, much of the cognitive literature relies on something known as the computational metaphor. The key idea is to analyse the behaviour of the mind by considering the "information processing steps" it goes through in order to produce a response to a particular stimulus. From the lecture:

[A toy example] In the lecture we gave a very simple example of an information processing theory, one that proposes that people do "mental multiplication" by repeatedly adding numbers together (e.g., 7 * 4 is solved by doing 7+7=14, then doing 14+14=28). Mental arithmetic is actually much more complicated than this, but the point we wanted to make is that although this theory is inconsistent with radical behaviourism (because it speculates about the internal mental processes that the mind uses to produce a response "28" to a stimulus "7 * 4"), it is consistent with methodological behaviourism because it yields testable empirical predictions. For instance, it predicts that people should respond faster to 7 * 2 than to 7 * 4, because it requires fewer steps. Etc.

Methodological examples

There are many different experimental methods used to investigate cognitive processes. Some common examples include:

The lecture slides have a bit more detail on this.

Beware "the laptop fallacy"

Because cognitive theories tend to be "information processing" theories, and digital computers are a kind of "information processing machine", there's a bit of a tendency for people to interpret the computational metaphor in an overly literal way.

In lecture, I referred to this description by Robert Epstein in 2016:

“Computers, quite literally, process information – numbers, letters, words, formulas, images. The information first has to be encoded into a format computers can use, which means patterns of ones and zeroes (‘bits’) organised into small chunks (‘bytes’). On my computer, each byte contains 8 bits, and a certain pattern of those bits stands for the letter d, another for the letter o, and another for the letter g. Side by side, those three bytes form the word dog.”

This is a very heavily simplified description of how modern digital computers encode the text "dog". However, there is a very extensive literature on how humans represent words, word spellings and word meanings that I won't go into here, but the short version of it is that the mind doesn't encode words the same way a computer does. On the basis of this, Epstein argues that the entirety of cognitive science is built on a terrible foundation:

“Our shoddy thinking about the brain has deep historical roots, but the invention of computers in the 1940s got us especially confused. For more than half a century now, psychologists, linguists, neuroscientists and other experts on human behaviour have been asserting that the human brain works like a computer.”

In other words, he argues that the computational metaphor is wrong because (among other things) the specific way in which a digital computer encodes "dog" is different from the way that the mind encodes "dog". Is he right? I don't believe so, and I think Epstein is falling for something that I refer to as "the laptop fallacy".

To understand what's going on here, it's important to understand the distinction between the theoretical idea of a computing machine, and the very specific instantiation that your laptop embodies. In the abstract, a computing machine is simply anything that is able to process information in a sufficiently "complicated" way. There's a whole branch of mathematics that is devoted to making that idea precise, but for our purposes let's just note that there's such a thing as a "Turing machine", an abstract notion of "a machine that can do computations". You can build Turing machines out of lego, you can build them out of biologically inspired networks, you can even implement them as a tiling scheme in your bathroom floor (see lecture slides for pictures). Superficially, none of these "machines" are particularly similar to each other, and their real world behaviour can be very, very different to one another. But they are all Turing machines, they all process information, and they are all doing computation.

There's a serious point to all this:

Marr's levels

All of that being said, there's a bit of a tension within the cognitive approach, between those who emphasise a "bottom up" view and those who emphasise a "top down" view:

The tension exists, but it's mostly amicable: I belong to the latter group, but many of the neuro folks around UNSW belong to the former. We get along pretty well.

One way of capturing the tension is via David Marr's (1980) three "levels of explanation"

  1. Abstract computation: What problem does cognition solve?
  2. Algorithm: What processing steps does it follow to do so?
  3. Implementation: How is this instantiated as a physical entity?

The lecture slides have a bit more detail on this.