The new psychology is the dangerous idea that traditional philosophy, psychology, psychotherapy and neuroscience has got it largel wrong. Connectionists see their theories grounded in the idea of the brain as a biological computer, operating computational processes, not the same as but showing similarities and parallels to nonbiological computing systems. They draw their primary inspiration from the work done from Hebbes onwards, in seeing the brain as a neural network. Both AI and the brain have as their basis huge, collective, connected networks.
They claim that ideas of the flat mind, with no hidden depths and computational models of the mind, fractures the self and the stories it tells itself, in favour of a more dynamic predictive improvising process. It is early days but already there are several serious thinkers who have emerged, building on existing thought, inspired by recent successes in artificial intelligence.
It is not Behaviourism but has something in common with that approach. Behaviourism, school of thought is often seen as being stupidly narrow and blunt in focussing upon observable behaviour only, ignoring the richness of internal reflection and experience. This is a caricature and they were far from stupid. There saw introspection as suspect and self-reported ideas about cognition unreliable. The new psychology is similarly suspicious, seeing such introspection as fatally flawed, especially when it involves language, as the mind creates stories and narratives about things that may not exist. Indeed, that is what we have evolved to do.
But they differ from the Behaviourists in having a much more substantial idea of how the brain works based on evidence from philosophy, neuroscience, mathematics and artificial intelligence. This is not a world of stimuli and responses but a complex interaction between the brain and world, which is predictive, inventive and improvisational. It is, like Behaviourism, critical of the explosion of theoretical constructs around
This is an important movement, as it takes evidence from philosophy, psychology, neuroscience, then the computation models that took root in the 1950s leading to recent advances in AI to do nothing less than redefine what it is to be human. It also brings together what is happening in our brains and in our use of language. It is nothing less than a refresh.
This is more of a fledgling school of thought, certainly accelerated by the success of Generative AI, but by no means fully fledged in terms of evidence. Many claim it underestimates the complexity and depth of human thought, particularly in areas like abstract reasoning, long-term planning, and deep emotional experiences. Neither is it clear that it can account for the rich internal lives that individuals report and the extensive research supporting the existence of complex mental structures and processes.
The movement focuses more on System 1 than System 2 but confirm what Kahneman and others say about this, that System 1 is dominant. Note that what is being achieved in AI is not al LLMs and generative AI. There are also advanced in AI along planning and reason, using the ‘energy’ model put forward by Karl Friston to optimise inference, also the work of Josh Tenenbaum.
Nevertheless, as a cross-disciplinary effort, it is gathering momentum with its explanatory breadth. We can learn a lot about what the brain is, how the brain learns and how we perceive, operate in the world and use language by considering what is clearly assumed to be a computational theory of the mind. In creating something inspired by our own minds, we have created entities that seem, not only to do what we do but in cases surpass us. It may well turn out to be a theory that sees us as cognitively capped, compared to systems with a different substrate that escape those limitations. It is vital that we study and consider these issues as artificial intelligence progresses, as it surely will.
AI inspired psychology
This is a new frontier in psychology. The trajectory is towards a computational theory of the mind; a neural networked, intentioned, seeking, Bayesian, predictive brain. The group denies deeper cognitive entities such as the subject, self or soul as well as cognitive psychotherapeutic structures in favour of a flatter, shallower, more dynamic, adaptive and improvisational forms of cognition. It takes its inspiration from Enlightenment empirical philosophy, such as Hume who sees the self as a fiction throwing psychology back to the flow of experience by a perceiving brain, a theory refined by Parfit and Strawson.
From the early 20th century, Hebb took inspiration from firing and wiring neurons to give us a theory of learning in the brain. This was given mathematical and logical form as neural nets by McCulloch and Pitts, an idea was turned into a working, learning perceptron by Rosenblatt and the race was on to find a way to make networks learn and perform in an intelligent fashion. Rumelhart and Hinton were to use backpropagation and layered neural networks along with LeCunn and Hassabis to push into Deep Learning. Large Language Models and their Multimodal offspring have produced astounding results that suggest we are track to at least find explanations that at last explain how the brain works.
During the 20th century there were also philosophers, neuroscientists and theorists that looked at language in detail, including Vygotsky and Wittgenstein, some with a focus on dialogue, such as Bakhtin and Pask. This is another area of inquiry that has been opened up with success in AI. Large Language and other multimodal models sprung into life, trained by enormous data sets, to predict a flow of language that seemed remarkably human, certainly useful. Then multimodal functionality appeared, where images, video and 3D worlds were used as both inputs and outputs and similarly successful generative capabilities were realised. This has confirmed the power of the connectionist approach. A number of connectionist theorists, now see cognition in a different way, more networked, diffuse, intentional, Bayesian, flat, active and predictive.
More recent computational theories of the mind have been influenced by the success of predictive, neural networks in artificial intelligence, with Dennett, Chater, Friston, Tenenbaum and Clark, among many others. They see the brain, not as a passive, receiving organ but as an active, predictive process.
The dialectic continues between the brain seen as a computational model and artificial intelligence which is definitely a computational model. We are seeing an explosion of progress and ideas on both fronts.
Flipped psychology
In a sense, the connectionist movement ‘flips’ psychology, seeing the brain as an evolved and far more active, dynamic, mercurial and improvising entity. Framing their work in the computational theory of mind, they call upon what we know about optimisation in AI to explain perception, thinking, language and consciousness.
Full conscious mind as receptive subject
Flatter computational mind, intentional and predictive
Consciousness is an output, the end result of inferencing, like the output from most digital technologies, from the display of a calculator to the generative output of an LLM. The computational processes are black-boxed, unconscious and not part of consciousness. What we see and understand when we read or hear speech speak are the end results of a nexus of computational processes taking place in our 100 billion neurons. We could never be conscious of such processes, no more than being conscious of the vast network of data servers, the internet, electricity production and transmission and processes taking place in the circuitry of television, is perceived when using generative AI. We don’t see this, as there is nothing to ‘see’. What we can do is understand the underlying mathematics of the process.
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