There are three great and abiding mysteries: how and why is there something, rather than nothing; how and why does life emerge from inanimate matter; how and why does consciousness arise from animate matter. The question of mind evolves directly from the third of these mysteries, if mind is to be taken as consciousness that recognises itself; but speaks to them all, insofar as it is only consequent to mind that they eventuate as mysteries at all: without mind, there is no mystery. Mind is both emergent and revelatory — it belongs to the world yet opens the world to meaning and, as AN Whitehead would have it, to the adventure of ideas.

It’s an historical oddity, then, that a phase in the history of psychology dedicated itself to eradicating mind. Behaviourism was a school of thought, pioneered by John B. Watson, that suggested—no, insisted—that psychology must move beyond introspectionism, whereby researchers tried to capture and describe the contents of their own consciousness, because, said Watson, their methods were unreliable, unreplicable, highly subjective, and contaminated by language, preconception, training, and bias. Watson’s proposal was to make psychology an objective, experimental science focused purely on a narrow empiricism: what can be dispassionately observed and measured. He didn’t deny that humans have interiority; he merely judged it inconsequential, other than in the ways in which it manifested as behaviour. For Watson, the interesting thing about people was how they acted, not what they imagined they felt or thought.

For perhaps forty years, Watson’s project dominated psychology in the United States, as taught in university departments and practised in applied contexts such as industrial management, school‑based education, political science, consumer advertising, and control of crowd behaviour. Humans were conceived and treated as mechanical ‘black boxes’, whose internal workings could be manipulated, finessed, conditioned, and tweaked, with the success of these interventions evidenced in subsequent performance ‘feedback’.

Behaviourism’s insistence on the observable produced admirable empirical discipline but at a cost: the exile of mind itself. When the late‑twentieth‑century “cognitive revolution” arrived, it presented itself as a rescue of our interior life, but its liberation was curiously bloodless. The black box was reopened only to reveal circuitry inside. Consciousness became code; cognition, computation. The mind was granted mechanism but denied a soul. What resulted was not the resurrection of mind but its digitisation. Thought was reconceived as the rule‑based manipulation of symbols, and felt experience was declared epistemologically irrelevant.

In contemporary terms, this mechanistic inheritance reaches its apotheosis in the astonishing power of symbolic recombination now perfected by what we call Artificial Intelligence. Its performance is, indeed, brilliant—but also essentially lifeless. It accomplishes what machines do best: rearranging the tokens of knowledge without ever Knowing, calculating the probability of meaning without undergoing. If the behaviourists treated people as black boxes, the era of AI—better described as Combinatorial and Calculative Acumen (CCA)—turns people into boxes who judge themselves computers.

A distinction between Big K Knowledge and small k knowledge clarifies the loss. Big K refers to the lived, tacit, psychosomatic hinterland that accumulates through experience: everything we have undergone and by which we have been formed, sculpted, and changed. Small k denotes what can be represented symbolically—conceptualised, written, transmitted, tested. Big K is the ecosphere; small k the taxonomy. If Behaviourism confined itself to the ecosphere’s measurable surfaces, Cognitivism, while recasting its meaning-potential in greater detail, for the most part retained the same obsession with the metrics of capture and representation.

To know in the Big K sense is not to possess information but to suffer transformation. It requires subjectivity, vulnerability—a body that feels its way through the world. Even the amoeba, in its minuscule way, Knows more than a supercomputer: it dialogues with environment, interprets change, acts from within a point of view. Artificial systems, however prodigious their reach, remain affectless simulations, scraping material from the recorded text of life but never living it.

For schools the consequences of this distinction are immediate. Focused exclusively on what can be articulated, encoded, and examined—small k—we risk hollowing out the learner’s interior landscape. Knowledge that fails to engage as transformative potential is soon forgotten, passing through without trace. The criterion of learning is resonance: does material reverberate, stirring curiosity, discomfort, recognition, vexation, or joy? Resonance need not be sudden insight; it can be the grit that forms the oyster, the irritant that promotes thought, the idea that must, in time, be worked with precisely because it cannot be either expelled or actioned without consideration.

Schools therefore face a double task: maintaining the safe‑fail conditions in which benign big K knowledge grows—trust, safety, openness to the world—and linking small k content to lived experience so that symbols remain alive. The teacher’s craft is alchemical: turning representation back into felt meaning, transforming the dross of mere data into the gold of reverberant being.

Behaviourist schooling, by contrast, would treat learners as input‑output machines, training them in the habits of algorithmic response. The danger now is that we enact this model through our infatuation with digital systems of measurement, where performance metrics substitute for understanding and dashboards replace dialogue. In this light, Behaviourism’s legacy is less historical than ongoing. Its fantasy of the controllable human—efficient, optimised, measurable—has been renewed via our devotion to digital technocracy, promising “personalised” precision only by narrowing the field of human meaning and understanding to what can be incrementalised and tracked. What persists is the old hunger: the wish to objectify being until it yields to calculation, resulting in a world increasingly complicated—saturated with data, models, rubrics, and metrics—yet determinedly not complex. The reductionist impulse endures, cloaked now in code, seeking still to render life manageable by making it less vital.

By contrast, the reclamation of Big K requires patience for what is unquantifiable: experience, relation, affect, wonder. It insists that learning remains an encounter between living beings, not between data streams.