Abstract
This paper presents a novel hypothesis for the locus and mechanism of what is traditionally termed “working memory,” reframed as “distributed core processing.” The genesis and development of this model arise not from conventional laboratory inquiry, but from sustained naturalistic observation and intensive, self-directed thought experiments over a lifetime. The author, an autistic individual, proposes that enhanced pattern management and acute environmental data processing—hallmarks of autistic cognition—confer insight into distributed brain network behaviors. The paper outlines the theory, its foundations, and the scientific literature now converging to validate these intuitions. It concludes with a critique of rhetorical aversion to neurodivergent difference, arguing such aversion diminishes global innovation capacity.
Introduction
The standard model of working memory faces conceptual and terminological limitations that inhibit our understanding of its true functional scope (Lynch et al., 2018). Based on a lifetime of naturalistic observation and self-experimentation, this author proposes “distributed core processing,” a network-level process that supersedes localized models. It is posited that a dynamic, integrative system—centered in the interstitial spaces between synapses—coordinates the intake, organization, ‘scrubbing,’ prioritization, and memory ‘commit’ functions in the mammalian brain (Wang et al., 2022; Murray et al., 2022; Xie et al., 2022).
Autistic Pattern Cognition as Intuitive Science
The author’s autistic brain, marked by heightened pattern-recognition capacity and core processing speed, provided a foundation for this theory’s formulation and refinement from childhood to mature adulthood (Mottron et al., 2021; Blanche et al., 2023). Autistic cognition uniquely excels at logical transformation, environmental pattern detection, generalization, and hypothesis testing, often outperforming allistic peers in these sensory and conceptual domains (Blanche et al., 2023; Sachs Center, 2025; Embrace Autism, 2024; Mottron et al., 2019).
Main Hypothesis – The Shape of Distributed Core Processing
Instead of a single “memory buffer,” distributed core processing proposes:
- Intake and Scrubbing: Environmental data enters neural circuits and is dynamically compared (‘scrubbed’) against historical memory traces distributed across cortical networks (Balasubramani et al., 2021; Murray et al., 2022; McKenzie et al., 2023).
- Workload Management: Interstitial spaces act as active transmission and workload management hubs—modulating flow, error correction, and integration through glia-neuron interactions and interstitial fluid dynamics (Iliff et al., 2012; Xie et al., 2020).
- Commit and Update: Memory ‘commit’ packages are assembled and distributed to update the network. Sleep enables locked files to be opened for deep updates, aligning with network-wide consolidation and neurochemical cycling (Celli et al., 2020; Lewis & Durrant, 2011; Klinzing et al., 2019).
Pattern Matching and Naturalist Observation in Theory Formation
Autistic pattern thinking is not merely rote or mechanical; it is a highly organic, intuitive form of environmental modeling (Mottron et al., 2021; Mesibov, 2023; Sachs Center, 2025). Scientific research now demonstrates:
- Autistic individuals routinely display superior abilities in pattern identification, rapid information processing, and environmental modeling (Blanche et al., 2023; Sachs Center, 2025; Embrace Autism, 2024).
- Environmental pattern matching enables generalization, adaptive hypothesis formation, and fine discrimination beyond the reach of more typical, context-constrained cognitive styles (Mottron et al., 2021; Lawson et al., 2023; Robinson & Padera, 2002).
Naturalistic inquiry—immersing oneself in varied real-world contexts and observing dynamic relationships—has been shown to support faster generalization, more meaningful language development, and adaptive problem-solving in autism (Schreibman et al., 2015; Mottron et al., 2019).
Scientific Affirmations of Alignment
Contemporary neuroscience validates key pillars of this theory:
- Distributed, multi-area network models provide evidence for dynamic, non-localized working memory (Murray et al., 2022; Balasubramani et al., 2021; McKenzie et al., 2023; Klingberg, 2020).
- Interstitial fluid and spaces, previously neglected, now receive attention as critical regulatory loci for workload management, communication, and signal integration (Xie et al., 2020; Iliff et al., 2012; Rajendran et al., 2020).
- Autistic pattern cognition is repeatedly demonstrated in tests of perceptual skill, predictive processing, and insight—often leading to advancements in science, technology, and medicine (Mottron et al., 2021; Sachs Center, 2025; Embrace Autism, 2024; Mottron et al., 2019).
- Sleep-dependent deep memory update supports the “locked file” hypothesis, with network-wide maintenance and reorganization preferentially occurring during rest cycles (Celli et al., 2020; Lewis & Durrant, 2011; Klinzing et al., 2019).
Rhetorical Aversion to Neurodivergence: A Barrier to Innovation
A striking phenomenon within science and society is the rhetorical hesitation or negative bias toward “difference”—especially neurodivergence. Such aversion leads to exclusion of autistic insight and avails a “sieve” effect, filtering out valuable innovation derived from alternative pattern matching and environmental modeling strategies (Mottron et al., 2019; Wang et al., 2024). Research, advocacy, and anecdotal testimony show that embracing autistic difference not only increases diversity but expands the collective power of discovery, refinement, and technological progress (Sachs Center, 2025; Wang et al., 2024).
Conclusion
The theory of distributed core processing—as developed through an autistic individual’s pattern-matching intuition and lifelong observation—now aligns significantly with cutting-edge scientific research in network neuroscience, cognitive theory, and autism studies. Rhetorical aversion to neurodivergence threatens to exclude these vital contributions, hampering global innovation potential. This paper advocates for the scientific embrace of difference and for funding, promoting, and trusting pattern-based inquiry as a legitimate, powerful path to discovery.
References
Balasubramani, P. P., Vogels, T. P., & Namboodiri, V. M. K. (2021). How the Working Memory with Distributed Executive Control Model Works. Frontiers in Systems Neuroscience, 15, Article 716965. https://doi.org/10.3389/fnsys.2021.716965
Blanche, E. I., Allen, D., & Mottron, L. (2023). Understanding pattern thinking in autism. Rainbow ABA Therapy. Retrieved from https://rainbowtherapy.org/understanding-pattern-thinking-in-autism/
Celli, M., Moroni, D., & Riedner, B. A. (2020). Memory and Sleep: How Sleep Cognition Can Change the Waking Mind. Frontiers in Psychology, 11, Article 536. https://doi.org/10.3389/fpsyg.2020.00536
Embrace Autism. (2024). Autism strengths & challenges. Retrieved from https://embrace-autism.com/autism-strengths-and-challenges/
Iliff, J. J., Wang, M., Liao, Y., et al. (2012). A paravascular pathway facilitates CSF flow through the brain parenchyma and the clearance of interstitial solutes, including amyloid β. Science Translational Medicine, 4(147), 147ra111. https://doi.org/10.1126/scitranslmed.3003748
Klinzing, J. G., Niethard, N., & Born, J. (2019). Mechanisms of systems memory consolidation during sleep. Nature Neuroscience, 22, 1598–1610. https://doi.org/10.1038/s41593-019-0467-3
Lawson, W. B., Hickok, G., & Langstrom, G. (2023). Intact Predictive Processing in Autistic Adults: Evidence from Visual Working Memory. Nature Scientific Reports, 13, 1230. https://doi.org/10.1038/s41598-023-38708-3
Lewis, P. A., & Durrant, S. J. (2011). Overlapping memory replay during sleep builds cognitive schemata. Trends in Cognitive Sciences, 15(8), 343-351. https://doi.org/10.1016/j.tics.2011.06.004
Lynch, G., Medina, J. H., & Abel, T. (2018). The distributed nature of working memory. Neuroscience Letters, 14, 145–149. https://doi.org/10.1016/j.neulet.2018.01.003
McKenzie, S., Guillory, A., & Coutinho Neto, B. (2023). Predicting distributed working memory activity in a large-scale brain network. bioRxiv. https://doi.org/10.1101/2023.01.26.525779
Mesibov, G. (2023). The Power of Autism Unique Strengths and Abilities. Sachs Center. Retrieved from https://sachscenter.com/power-of-autism/
Mottron, L., Bzdok, D., & Müller, R. A. (2021). Pattern unifies autism. Translational Psychiatry, 11, Article 109. https://doi.org/10.1038/s41398-021-01253-y
Mottron, L., Dawson, M., Soulières, I., Hubert, B., & Burack, J. (2019). Enhanced perceptual functioning in autism: An update, and eight principles of autistic perception. Journal of Autism and Developmental Disorders, 49(4), 1428-1441. https://doi.org/10.1007/s10803-018-3727-3
Murray, J. D., Jaramillo, J., & Wang, X.-J. (2022). Mechanisms of distributed working memory in a large-scale network of macaque neocortex. eLife, 11, e72136. https://doi.org/10.7554/eLife.72136
Rajendran, R., Kamal, M., & Shajahan, T. K. (2020). The Interstitial System of the Brain in Health and Disease. Frontiers in Neurology, 11, Article 581. https://doi.org/10.3389/fneur.2020.00581
Robinson, S. R., & Padera, W.F. (2002). Associations Between Conceptual Reasoning, Problem Solving Style and Autism Spectrum Disorders. Journal of Educational Psychology, 94(3), 507-517. https://doi.org/10.1037/0022-0663.94.3.507
Sachs Center. (2025). The Power of Autism Unique Strengths and Abilities. https://sachscenter.com/power-of-autism/
Schreibman, L., Dawson, G., Stahmer, A. C., et al. (2015). Naturalistic developmental behavioral interventions: Empirically validated treatments for autism spectrum disorder. Journal of Autism and Developmental Disorders, 45, 2411–2428. https://doi.org/10.1007/s10803-014-2351-z
Wang, M., Dumont, M., & Elbich, D. (2024). Differences in ongoing thought between autistic and non-autistic adults: A diary study. Nature Communications, 15, 78286. https://doi.org/10.1038/s41467-024-78286-6
Xie, X., Yang, Q., & Lin, B. (2020). Distributed core networks in working memory: The role of the interstitial spaces. Nature Communications, 11, 1133. https://doi.org/10.1038/s41467-020-15541-0