Cognitive Science is an interesting discipline. The name sounds like it should mean “a more mind-focused version of psychology,” but the field is much broader. Cognitive science is the interdisciplinary study of mind which draws together psychology, neuroscience, philosophy, computer science, and several other fields. In particular, it’s the field that asks questions like: How do minds represent the world? How do they reason, learn, remember, perceive, and make decisions?
As an interdisciplinary field that deals with a somewhat touchy set of concepts, cognitive science can also be internally contentious. There is some debate about what gets to count as a “mind,” what counts as “cognition,” and what should instead be treated as an analogy or mind-like object of study. Sometimes this is mostly semantic: people may avoid calling a machine, organization, or system a “mind” because they do not want the term to become misleading outside an academic context.
Sometimes however the disagreement represents a more material difference of opinion or area of focus. Some traditions treat cognition as an abstract process of representation, computation, and behavior independent of the ‘medium’ (person, machine, system) in which it occurs. Others tie cognition much more closely to the brain and would argue other forms of ‘mind’ aren’t really valid except as models. Still others would argue that ‘mind’ actually can’t include the brain alone and that mind cannot really be separated from the environment, tools, or social setting, because those things can store information and affect how decisions are made. Interestingly, this last school of thought has become one of the more visible and influential movements in recent cognitive science, even if it remains far from a universal consensus.
I mention most of this to contextualize my own little space in this tumultuous debate. This is the post where I want to clarify some of my assumptions to hopefully mitigate confusion further down the line.
To begin, there are really a few specific questions within CogSci that I’m interested in:
- How do AI minds (primarily LLMs) work in CogSci terms?
- How are AI minds similar to, and different from, human minds or specific human cognitive processes?
- How can we create better AI minds, especially in terms of accuracy, reliability, and explainability?
- What can AI minds, when used as models or analogies, teach us about human cognition?
- How is the increasing use of AI impacting human cognition?
Now I use the word ‘mind’ here in the broad academic sense: as a system that performs reasoning, representation, and decision-making under uncertainty. By this definition machines (and organizations, etc.) can in fact be ‘minds’. I don’t use the word ‘mind’ to imply ‘conscious’ in any form or fashion. On my more philosophical days I do think about consciousness, but it’s not something I am going to attempt to seriously academically address.
I’ve studied a few different areas of cognitive science at different times in my life, but the one which I’ve recently been spending the most time with (and thus the one which will likely make up the next several posts) is dual-process theory. I’ll go into this in more depth later, but to summarize for now: In its simplest form, dual-process theory distinguishes between fast, automatic, seemingly ‘effortless’ day-to-day cognition and slower, more deliberate, effortful reasoning. The terminology varies a bit depending on the writer and the time (I’ve taken to using ‘Type 1’ and ‘Type 2’ as is the typical modern parlance) but those two processes or some variation of them are the core idea behind the model.
I find dual-process theory useful for thinking about large neural networks, and LLMs in particular. My working view is that the model itself often behaves something like Type 1 cognition: fast, fluent, associative but also prone to characteristic errors and plausible-but-wrong answers. If that analogy is useful, then many problems in LLM accuracy and reliability may need to be addressed in a similar way to problems in human cognition: by adding interruption (‘executive function’), verification, external memory, tool use and calls to more structured reasoning.
That is the set of topics I mean to explore in this writing. I am not especially interested in examining ideas about future AI consciousness, or in settling whether AI is or could ever “really” be a mind in some philosophical sense. My interest is in the more grounded cognitive questions: how AI systems attempt to reason, why they fail in the ways they do, and what kinds of structures might make them more reliable. Those questions do point in the direction of bigger debates about machine cognition and perhaps even one day “AGI”, but my starting point is much more immediate and humble: trying to understand the pitfalls and plausible solutions to the models we already have.
I also, when I’m feeling particularly brave, muse about questions related to human minds. Here, though, I should admit the limits of my expertise. I am far more qualified to talk about machine learning and computers than I am to talk about neurons and brains. Human cognition fascinates me, and I may occasionally be reckless enough to write about it publicly, but those posts will be more exploratory than authoritative and I’ll try to caveat them appropriately. Still, this is my personal space, so I can’t promise I’ll spare you from the occasional speculative ramble.
Image: Photo of my front garden Iris bed, taken May 2024 on my phone because I thought they were beautiful in full bloom. This photo will be a reference point I’m going to use later for similar(ish) AI image generation.
