This critical review synthesizes empirical studies of algorithmically personalized and artificial-intelligence-generated music interventions reporting neurophysiological, neuroimaging, or objective behavioral outcomes. Literature was identified through structured searches of PubMed, Scopus, Google Scholar, IEEE Xplore, and the ACM Digital Library through 12 August 2026, with CNKI and Wanfang searched on 22 September 2026, supplemented by citation tracking. Twenty-one eligible studies (at least 1,005 participants) were identified. Five things the term artificial intelligence covers in this literature are separated: algorithmic selection from a corpus, rule-based sonification, parametric modulation of pre-existing material, machine-learning generative composition, and closed-loop delivery contingent on the listener’s own signal. Only three studies used a machine-learning generative model, so most of the evidence concerns algorithmic personalization of existing material. Studies of closed-loop neural systems reported the most consistent neurophysiological signal, and music-based alpha neurofeedback produced higher normalized alpha power than yoked random feedback. In an open-loop design, personalized EEG-derived music was reported to attenuate anxiety induction where self-selected preferred music and silence did not, with enhanced prefrontal theta oscillations as the associated correlate. Eight studies carried a comparator capable of isolating an algorithmic component from music exposure, and all eight favored the algorithmic condition. These eight address different causal questions, use different designs, and point to no single mechanism, so their results are best read as preliminary and hypothesis-generating. Evidence quality was very low throughout. The median reported sample size was 22, no study blinded participants or measured expectancy, and no specific finding has been independently replicated. GRADE certainty was very low for every outcome domain, covering EEG oscillatory power, sleep architecture, autonomic regulation, epileptiform activity, and cognitive, behavioral and affective outcomes. Neurophysiological change is reported more often than behavioral change, and behavioral change more often than clinical benefit. The evidence supports continued investigation and does not support clinical deployment. Eight reporting and design requirements for future trials are proposed.
Phonetic recalibration enables listeners to adjust their perception of ambiguous speech sounds based on visual contextual information. In line with a specific difficulty in forming letter-speech sound associations, adults with dyslexia have been found to show no text-based, but intact lipread-based, recalibration. The underlying neural mechanisms remain unclear however. This fMRI study examined cortical responses during text-based and lipread-based phonetic recalibration in 34 adults (18 with dyslexia, 16 typical readers). Participants were exposed to ambiguous speech sounds /a?a/ paired with disambiguating visual information (“aba”/“ada” text or lipread videos), followed by auditory-only categorization trials. Contrary to previous findings, both groups showed robust text-based and lipread-based behavioral recalibration effects, with no significant group differences between typical and dyslexic readers. Neuroimaging data did yield group differences in the underlying cortical activation. Most notably, dyslexic readers demonstrated increased bilateral fusiform activation compared to typical readers during exposure to lipread videos and ambiguous speech sounds, with left fusiform activation specifically correlating with increased phonological processing difficulties. Additionally, typical readers exhibited higher right frontal activation during text-based audiovisual stimuli. These findings suggest that while adults with dyslexia did not differ significantly from typical readers in behavioral recalibration effects, they employed different neural processing strategies. Unexpectedly, this was particularly the case for lipread audiovisual stimuli. The relatively increased fusiform and superior temporal activation to lipread audiovisual stimuli in dyslexic readers possibly relates to subtle difficulties in phonological processing of ambiguous speech signals.
Conscious processing is associated with coordinated interactions among distributed neural systems, yet the biological constraints that enable and sustain large-scale neural integration remain incompletely understood. Contemporary theories emphasize computational, informational, and network mechanisms, while the potential contribution of systems-level energetic organization has received comparatively less explicit theoretical attention. This Hypothesis & Theory article proposes that systems-level energetic organization may represent an additional biological constraint influencing the neural conditions under which large-scale neural integration associated with conscious processing can emerge and be sustained. Systems-level energetic organization is defined as the dynamic coordination of energy production, distribution, allocation, and utilization across interacting neural systems. The framework integrates established principles of brain energetics, metabolic regulation, and distributed neural organization, while the proposed relationship between their systems-level coordination and conscious neural integration remains hypothetical. It is intended to complement rather than replace existing theories of consciousness and does not propose that energy generates consciousness, determines conscious content, or explains subjective experience. The framework generates experimentally testable predictions concerning whether systems-level energetic measures provide explanatory value beyond regional metabolism and established neurophysiological and network variables. Explicit falsification criteria define observations that would weaken or reject the hypothesis. Multimodal human and non-human animal studies, computational modeling, and carefully controlled translational investigations provide complementary approaches for evaluating these predictions. By treating systems-level energetic organization as an empirically testable biological constraint rather than an explanation for consciousness itself, the proposed framework provides a falsifiable research program for investigating an unresolved dimension of large-scale neural integration while remaining agnostic regarding the ultimate origin and nature of subjective experience.
