A recurring issue for all psychedelic neuroscience is the reliance on very small samples. Early fMRI studies with psilocybin or LSD often involved fewer than 20 participants. While understandable given the regulatory and logistical challenges of psychedelic research, such sample sizes are insufficient to reliably detect brain-behaviour relationships. Brain-wide association studies suggest that hundreds, if not thousands, of participants are necessary to achieve robust and generalisable findings. So-called ‘translational psychiatry’, or the discipline of converting the findings of neurobiology to clinical practice, has largely failed to deliver any clinically meaningful biomarkers. Even large-scale studies of depression yield only minute and inconsistent changes in cortical thickness or connectivity. And since most cognitive processes involve multiple brain regions, and most regions participate in multiple processes, inference to particular brain regions is rarely justified and may constitute a ‘neuro-phrenology’.
Another thorny methodological issue is the difficulty of maintaining blinding in psychedelic trials. The subjective effects of substances like LSD and psilocybin are unmistakable, making it nearly impossible for participants – or researchers – to remain unaware of who has received the active drug. This introduces powerful expectancy effects: participants who know they have ingested a psychedelic may report ego dissolution, therapeutic insights, or heightened openness simply because they expect such outcomes. Carhart-Harris has offered some interesting solutions. He advocates the use of active placebos (e.g. DXM) to improve experimental control. He also suggests replacing boring, repetitive ‘button push’ tasks that frustrate the tripper subjects with ‘experience sampling’ through highly calibrated questions about their phenomenologies, as well as continuous brain scanning during affectively immersive activities – like listening to music – to ‘draw out’ and deepen the richness of the experience.
That said, EBH encounters another obstacle: “entropy” is not a single, unified measure, but rather a collection of different metrics – Shannon entropy, Lempel–Ziv complexity, sample entropy, fractal dimensionality, and more – each emphasising distinct aspects of signal diversity. In practice, these metrics often diverge in outcomes. In one recent study assessing 12 different brain-entropy measures following psilocybin, only a subset showed significant effects, and many others did not. Moreover, the inter-metric correlations were weak: different metrics often did not line up with one another as one might expect. That suggests they may not be tapping into a single underlying property.
Another issue arises from the use of the Default Mode Network (DMN) as a neural linchpin in EBH: some critics argue there is no persuasive evidence that the DMN exists as a stable, coherent functional unit. The DMN is typically inferred from resting-state fMRI data as a set of brain regions whose activity fluctuates together when a person is not engaged in a task. But critics point out that these observed correlations may arise from methodological artefacts (such as vascular coupling or scanner noise) or from arbitrary choices in data processing, rather than reflecting a genuine, functionally unified network.
Empirically, the constitution of the DMN is unstable across individuals, sessions, and analysis methods, and its boundaries shift depending on how the data are filtered, aligned, or decomposed. Moreover, the brain is never truly “at rest” – even in so-called resting-state scans, people’s minds wander, think, remember, plan, and adjust breathing. This means that what is labelled as “default mode” may simply be the accumulation of uncontrolled cognitive content, not a specially privileged network.
Critics such as John Horgan have elsewhere emphasised the vagueness of complexity science altogether. The very terms the field relies on – entropy, emergence, criticality, and, of course, complexity – have always carried dozens of overlapping and often incompatible definitions that, when applied to all disciplines, soon lose application to any one in particular. Showing that the brain actually reaches this delicate balance of criticality is challenging. The markers of true criticality – such as patterns of neural activity that repeat across different scales – are hard to capture with tools like fMRI, which have limited precision in time.
In his revisitation of EBH, Carhart-Harris describes a more complex picture of criticality. Psychedelics may ‘normalise’ pathologically subcritical states of consciousness, nudging them back toward criticality. This is proposed as one mechanism behind their therapeutic benefits. He also stresses context dependence: music, for instance, can enhance criticality and shape the direction of psychedelic effects. But psychedelics may instead overshoot, tipping it into a supercritical regime: a state where activity becomes excessively excitable and unstable, similar to hypomania and psychosis, during which criticality may increase during onset but decrease again as rigid delusions consolidate. Moreover, until psychedelics are directly compared to stimulants, we cannot know whether “entropy increases” are not simply the by-products of arousal or alertness (caffeine, for instance, boosts entropy).
This complexified interpretation would make space for reports of impaired working memory, disorganised thought, and reduced reality testing that often accompany high-dose psychedelic experiences. Here, two researchers instead propose a multidimensional model, in which different domains – perception, cognition, selfhood – can be modulated independently. Psychedelics, for example, may intensify visual perception while simultaneously impairing working memory, and labelling such a state as “higher” consciousness obscures these trade-offs.
As for studies showing that caffeine can elevate entropy, the researcher Gaige Clark proposes that high entropy may simply reflect the brain’s chaotic search for solutions when faced with novel or unresolved problems, in contrast to low-entropy spaces, in which efficient, reflexive patterns are formed through learning. From this perspective, entropy may index arousal or motivation rather than the distinctive phenomenology of psychedelics. For example, caffeine might increase entropy by heightening motivation, prompting problem-oriented thinking and activity, while ADHD’s reduced entropy could reflect hypo-arousal and reduced problem engagement.
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Super interesting point Of view realy