Pith. sign in

REVIEW 4 major objections 5 minor 28 references

An entropic explanation of insistence on sameness in autism

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper argues that insistence on sameness in autism is an optimal response to cognitive limits: with memory restricted to raw, tangible stimuli, the only way to reduce surprise and uncertainty is to keep the environment identical to…

desk verdict A clear, honest framework whose central explanation is an unstated assumption; worth reading as a hypothesis, not as a derivation. read the letter →

arxiv 2608.04616 v1 pith:GPMPHWQT submitted 2026-08-05 cs.LG q-bio.NCstat.ML

Przemys{l}aw Śliwiński This is my paper · ORCID
classification cs.LGq-bio.NCstat.ML
keywords insistenceonsamenessautismentropydistanceuncertaintyminimizationconditionalmutualinformationtherapeuticguidelinesdefinition
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper proposes that insistence on sameness in autism is not a primary symptom but a rational strategy for minimizing surprise and uncertainty when learning is severely restricted. It models the world as random stimulus sequences $R$ and memory as $M$, measuring the gap between them with the entropy metric $D_H(R,M)=H(R|M)+H(M|R)$, where the two terms quantify surprise and uncertainty. Because the framework assumes memory stores only raw, tangible stimulus sequences and no abstract or semantic meanings, the individual cannot reduce the gap by learning; the only available move is to constrain $R$ to the already-known $M$. If true, this reframes rigid, repetitive behavior as an adaptive response and supports a new definition of autism as an impairment restricted to discrimination, memorization, and prediction of tangible properties. This matters because it turns therapy into a constrained optimization problem: maximize the mutual information between environment and memory while keeping surprise and anxiety below thresholds.

What carries the argument

The machinery is the entropy metric $D_H(R,M)=H(R|M)+H(M|R)$ together with a stimuli processing loop and Assumptions A1–A4. The metric's two conditional entropies measure surprise during perception and uncertainty during prediction; the loop updates memory by nearest-neighbor classification $m_n=\rho(r_n)$; and Assumption A3 together with Remark 2.1 make memory a nearest-neighbor store of raw sequences with no semantic content. The identity $D_H(R,M)=H(R,M)-I(R;M)$ connects minimization of the metric to maximization of mutual information, which becomes the objective in the therapy-as-optimization formulation.

What would settle it

A concrete observation that would settle it: demonstrate that an individual with autism reliably uses abstract or semantic information—for example, transferring learning across contexts through symbolic labels, understanding delayed reward, or generalizing categories—and the premise that only tangible raw sequences are available fails. Alternatively, run the proposed Turing-test-like validation: build a digital twin governed by nearest-neighbor memory and no semantics; if its simulated behavior does not exhibit insistence on sameness under entropy minimization, the analogy fails.

Watch

Extended reading notes

Core claim

The central discovery is the identification of insistence on sameness with the constrained minimization of $D_H(R,M)=H(R|M)+H(M|R)$: to lower the entropy distance between environment and memory, an agent can learn about $R$ and store that knowledge in $M$, or it can restrict $R$ to the already-known $M$. For a non-verbal low-functioning individual with autism, whose memory is assumed to be a nearest-neighbor store of raw stimulus sequences without access to abstract or semantic properties, the learning route is effectively closed, so insistence on sameness—pedantry, routine, ritual, rigidity, vigilance, and aversion to new stimuli—is the remaining way to keep surprise and uncertainty below thresholds $T_{so}$ and $T_a$. Consequently, the paper proposes Definition 4.1: autism is an impairment in which cognitive functions are restricted to discrimination, memorization, and prediction of tangible properties of the environment.

Load-bearing premise

The load-bearing premise is that an autistic person's memory stores raw stimulus sequences by nearest-neighbor matching and has no access to abstract or semantic properties, so constraining the environment to match memory is the only way to reduce surprise and uncertainty.

Editorial extensions

If this is right

  • Insistence on sameness—pedantry, routine, ritual, rigidity, vigilance, and aversion to new stimuli—is recast as the optimal available strategy for keeping surprise and uncertainty below thresholds $T_{so}$ and $T_a$ when learning is restricted.
  • Learning therapy becomes an optimization problem: find environments $R$ that maximize mutual information $I(R;M)$ subject to $H(R|M=m_{n-1}) < T_{so}$ and $H(M|R=r_n) < T_a$.
  • Therapeutic guidelines follow: use only tangible objects, assume pictures and words are unrelated to what they represent unless taught, treat memorized sequences as wholes, keep branches in sequences minimal, and use distinguishing artifacts to reduce choice uncertainty.
  • Self-stimulation activities may be low-entropy known stimuli that keep surprise and uncertainty inside the comfort zone during sensory deprivation.
  • A new definition follows: autism is an impairment in which cognitive functions are restricted to discrimination, memorization, and prediction of tangible properties of the environment.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If this account is correct, insistence on sameness should intensify when novelty or cognitive load increases; graded-exposure studies in familiar settings could test that prediction directly.
  • The proposed avatar-based validation risks circularity if the avatar's behavior is generated by the same assumptions being validated; a stronger test would have the avatar predict behaviors not explicitly programmed, such as a preference for branches located early in a sequence.
  • The framework suggests that interventions teaching abstract concepts such as delayed reward or social roles will not transfer for low-functioning individuals unless anchored to tangible sequences—an empirical claim that could be compared against existing intervention outcomes.
  • If some autistic individuals demonstrate abstract transfer despite restricted memory, the theory would need to relax Remark 2.1 rather than abandon the entropy-minimization core.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes an information-theoretic framework to explain insistence on sameness in autism. It defines an entropy distance D_H(R,M)=H(R|M)+H(M|R) between an environmental random variable R and a memory variable M, interprets the two conditional entropies as surprise and uncertainty, and argues that an individual can minimize D_H either by learning about R or by constraining R to the already-known M. Under assumptions A1-A4 and Remark 2.1, which restrict the individual to nearest-neighbor memorization of tangible properties without abstract semantics, the paper concludes that insistence on sameness is a manifestation of the second strategy. It then derives therapeutic guidelines G1-G5, formulates learning therapy as an optimization problem (Problem 3.1), proposes a Turing test-like validation (V2), and offers a new definition of autism (Definition 4.1). The framework is explicitly presented as a first step requiring validation.

Significance. The mathematical identities in Section 2.1 are correct, and the paper is transparent about its assumptions and limitations. If the framework were supported by evidence, it could provide a formal basis for therapy design and a new definition of autism. However, the central claim, that insistence on sameness is a consequence of constraining R to memory, is an analogy rather than a derivation, and the paper provides no empirical data. The assumptions in A2-A3 and Remark 2.1 are strong empirical claims that are stated rather than justified, and the proposed validation V2 cannot test these assumptions independently. These issues undermine the explanatory contribution as it stands.

major comments (4)
  1. [Sec. 2.1, Sec. 3.1, Remark 2.2, Problem 3.1] The central dichotomy in Section 2.1, that D_H can be minimized either by learning about R or by constraining R to M, is a property of the metric, but the conclusion in Section 3.1 that insistence on sameness is the latter strategy requires an independent reason why the learning route is unavailable. Assumptions A2-A3 and Remark 2.2 describe a memory capable of storing new sequences and retrieving them, so nothing in the model rules out learning the raw distribution of R. Problem 3.1 treats M as fixed and optimizes over R only, which presupposes the conclusion rather than deriving it. The paper needs an explicit assumption about a cost or bound on memory updates, or an empirical argument, to support the claimed asymmetry.
  2. [Remark 2.1, Definition 4.1] The assertion that individuals with autism have no access to abstract semantics and are restricted to discrimination, memorization, and prediction of tangible properties is a strong empirical claim. It is introduced as an assumption in Remark 2.1 and then restated as Definition 4.1, but no evidence or independent argument is provided. The nearest-neighbor memory model in A3 is itself a modeling choice, so the definition is circular with the assumptions and cannot serve as independent support for the framework. This is load-bearing because Definition 4.1 is presented as a conclusion of the paper.
  3. [Sec. 3.1, B1-B5] The behaviors B1-B5 are translated into the model's vocabulary using surprise and uncertainty, which are the same quantities that define the metric. For example, B3 says that forcing deterministic scenarios reduces surprise and uncertainty, which follows directly from Eq. (4), but it does not explain why the individual adopts this strategy rather than some other. The explanations in B1-B5 are to a large extent restatements of the model's definitions in clinical terms, and their explanatory value is limited without independent evidence linking the abstract information-theoretic quantities to the observed behaviors.
  4. [Sec. 4.1, V2] The proposed Turing test-like validation method cannot test the core assumptions A2-A3 and Remark 2.1, because the avatar's behavior is generated by the framework itself. Observing that the simulated behavior resembles clinical descriptions of insistence on sameness only shows internal consistency of the model, not that the assumptions hold for real individuals with autism. Thus the paper's claim that the framework can be validated without experiments involving individuals with autism is not supported. A validation that does not confront the model with independent data cannot break the circularity between the assumptions and the conclusions.
minor comments (5)
  1. [Sec. 2.5, Eqs. (2)-(3)] The notation in Eqs. (2) and (3) is confusing: the conditioning event in D_H(R,M|M=m_{n-1}) and D_H(R,M|M=r_n) does not match the terms inside, and the claim that H(M=m_{n-1}|R)=0 in Eq. (2) is not obvious as written. Please clarify the conditioning notation and the derivation of these zero terms.
  2. [Sec. 3.1, B5] In B5, 'version to learning' appears to be a typo for 'aversion to learning.'
  3. [Sec. 2.3, A3] Assumption A3 refers to a nearest-neighbor algorithm but does not specify the distance function. Since the framework's predictions may depend on this choice, please state whether the results are invariant to the distance metric or specify what class of distance functions is assumed.
  4. [Sec. 3.2, G2] The phrasing 'should a priori be assumed as unrelated' is awkward; 'should be assumed a priori to be unrelated' would be clearer.
  5. [Conclusion] In the Conclusion, 'the insistent on sameness' should be 'the insistence on sameness.'

Circularity Check

2 steps flagged · score 6.0 of 10

The central explanation reduces to the definition of the entropy metric and to assumptions that are later reissued as Definition 4.1.

  1. self definitional [Sec. 2.1, Eq. (1); Sec. 3.1; Abstract]
    "It is first inferred that to minimize the metric an individual can learn about R (and store that knowledge in M) or can restrict R to the already known M. Then, it is concluded that insistence on sameness is a manifestation of the latter."

    With Eq. (1), D_H(R,M)=H(R|M)+H(M|R), choosing R=M gives D_H=0 by the definition of conditional entropy: if the two variables are equal, each determines the other. The abstract and Sec. 3.1 identify insistence on sameness with 'restrict R to the already known M' (making R resemble M). Therefore the claim that the behavior reduces surprise/uncertainty is a restatement of the metric's definition, not an independent derivation. The metric itself also allows the symmetric route 'learn about R (store in M)', and nothing in Eq. (1) says which route an individual takes; the paper assigns the constrain route to autism only by the extra, stated assumption that semantics are unavailable (Remark 2.1). Hence the central 'result' that sameness minimizes D_H is true by construction.

  2. renaming known result [Sec. 4.2, Definition 4.1]
    "Therefore, a distinctive character of insistence on sameness should rather be attributed to cognitive limitations of a person with autism. Thus, if correct, the claim corroborates the following definition of the disorder; cf. (Mottron and Bzdok, 2020), Assumptions A2-A3 and Remark 2.1: Definition 4.1 Autism is an impairment in which cognitive functions are restricted to discrimination, memorization and prediction of tangible properties of the environment."

    Definition 4.1 is a direct paraphrase of Assumptions A2-A3 (memory stores/retrieves sequences; items are nearest-neighbor classifications) and Remark 2.1 (semantics/abstract relations unavailable), which are the paper's input assumptions about autistic cognition. The paper then says the framework 'corroborates' this definition. Because the definition is lifted from the assumptions, the corroboration is circular: the framework's conclusion is its premise restated. This redefinition is also what supports the choice of the 'constrain R' branch in Sec. 3.1, so the self-definitional step above is load-bearing.

full rationale

The main explanatory inference is circular in two connected places. First, the metric (1) makes R=M the zero of the distance, and the paper's B1-B5 describe insistence on sameness exactly as forcing R to resemble M; therefore the 'prediction' that sameness minimizes surprise/uncertainty is true by definition of conditional entropy. Second, Definition 4.1 reissues the framework's own Assumptions A2-A3 and Remark 2.1 as the 'new definition of autism' and uses it to select the constrain-R route, so the empirical conclusion is not independent of the premises. The score is 6 rather than 8-10 because the information-theoretic metric itself is an external construction and some parts (the optimization formulation of therapy, Problem 3.1, and the threshold-anxiety hypotheses) are not entailed by the definition of the metric alone; these could in principle be tested. The paper's own caveat that validation is deferred ('if positively validated') is acknowledged, but the deferral does not repair the definitional circularity in the central explanatory claim. The failure to formally rule out the learning route is an unsupported assumption rather than a circular equation, and no load-bearing self-citation was found; Friston/Lawson citations are external and used as inspiration, not as the sole support.

Assumptions & free parameters 3 free parameters · 7 assumptions · 0 invented entities

The framework relies on standard information theory and on the clinical and behavioral assumptions A1-A4 plus the minimization objective. The minimization objective and the restriction to tangible properties are ad hoc to the paper and carry the weight of the explanation. Thresholds Tso and Ta are free, unestimated parameters. No new physical entities are introduced.

free parameters (3)
  • Sensory overload threshold Tso
    Introduced in Assumption A4 as a random variable, not estimated from data. It is a free component of the model that determines when surprise causes overload.
  • Anxiety threshold Ta
    Introduced in Assumption A4 as a random variable, not estimated from data. It determines when uncertainty causes anxiety.
  • Nearest-neighbor distance function
    Assumption A3 states that memory items are assigned by a nearest neighbor algorithm, but the distance function and the space of stimuli are left unspecified, which is a free modeling choice.
assumptions (7)
  • standard math Standard information-theoretic identities hold for H, DH, and mutual information (MacKay, 2003).
    Used in Sec. 2.1 and Problem 3.1 to relate DH to mutual information.
  • domain assumption A1: Stimuli form sequences and the mixture is denoted R.
    Defines the environment as random variables in Sec. 2.3.
  • domain assumption A2: Memory stores and retrieves sequences.
    Defines memory M in Sec. 2.3.
  • domain assumption A3: Memory items are results of nearest-neighbor classification.
    Limits discrimination abilities, Sec. 2.3.
  • domain assumption A4: Thresholds Tso and Ta are random variables.
    Allows individual-specific thresholds, Sec. 2.3.
  • ad hoc to paper The individual's goal is to minimize DH(R,M).
    This is the central behavioral premise, introduced as an 'attempt to reduce surprise and uncertainty' in the abstract and used to derive the conclusion. It is not empirically established.
  • ad hoc to paper Individuals with autism are restricted to tangible properties, with no abstract semantics.
    Remark 2.1 and Definition 4.1 assume this restriction; it is the basis for G1-G2 and the final definition.

how reviews work

0 comments
Cite this review

Pith. "Pith review of An entropic explanation of insistence on sameness in autism." pith.science (2026). https://pith.science/paper/GPMPHWQT

@misc{pith2026260804616,
  author       = {Pith},
  title        = {Pith review of: An entropic explanation of insistence on sameness in autism},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GPMPHWQT}},
  note         = {Machine review of arXiv:2608.04616}
}
abstract

An information theory-based framework is proposed in attempt to explain insistence on sameness in autism as an instance of a general behavior pattern in which an individual tries to reduce surprise and uncertainty. It offers a new definition of autism as an impairment in which cognitive functions are restricted to discrimination, memorization and prediction of tangible properties of the environment. An analogy between insistence on sameness and constrained minimization of the entropy metric is observed and examined for a set of assumptions that describe cognitive limitations of a person with autism. The metric is given by the formula $D_H(R, M) = H(R|M) + H(M|R)$, where $R$ represents sequences of random stimuli, $M$ is a memory that stores and retrieves them, and where $H(.|.)$ denotes their conditional entropies interpreted as surprise and uncertainty, respectively. It is first inferred that to minimize the metric an individual can learn about $R$ (and store that knowledge in $M$) or can restrict $R$ to the already known $M$. Then, it is concluded that insistence on sameness is a manifestation of the latter. Moreover, it is shown that the proposed framework: (1) Helps to quantify the concepts of surprise, uncertainty, sensory overload and deprivation, anxiety, comfort zone, disappointment, disorientation, pedantry, rigidness, observance or aberrant precision. (2) Leads to a list of guidelines for learning therapies and daily care routines, and allows them to be defined as optimization algorithms and implemented as programs for robotic live-in caregivers. (3) Can be validated with the help of a Turing test-like approach that requires no experiments involving individuals with autism. The framework-if positively validated-will provide formal foundations and design guidelines for therapies aimed at improving self-reliance of individuals with autism in basic activities of daily living.

Figures

Figures reproduced from arXiv: 2608.04616 by the authors.

Figure 1
Figure 1. Stimuli processing loop. A3: Each memory item, mn = ρ(rn), is a result of a classification of a single stimulus, rn, by a nearest neighbor algorithm. A4: Both sensory overload and anxiety thresholds, Tso, Ta > 0, are random variables. 2.4 Comments on assumptions Each stimulus, rn, can be interpreted as a ”snapshot” of the environment and mn = ρ (rn) as its counterpart perceived by the senses and located in memory M … view at source ↗
Figure 2
Figure 2. An instance of a graph composed of memorized stimuli sequences, mn = ρ(rn), n = 0, 1, . . . with a set Mn+1 = {m′ n+1, m′′ n+1} consisting of a pair of alternative events. while the level of uncertainty in the latter, given the incoming stimulus, rn, is DH (R, M|M = rn) = H (R = rn|M) | {z } =0 + H (M|R = rn), (3) so that during perception we need to consider only the first term, H (R|M = mn−1), and only the other, … view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

28 extracted references · 21 canonical work pages

  1. [3]

    URL https://doi.org/10.1038/s41562-024-01939-z . J. S. Betty P. V. Ho and M. Carter. Cognitive–behavioural approach for children with autism spectrum disorder: A literature review. Journal of Intellectual & Developmental Disability , 40(2):213–229,

    work page doi:10.1038/s41562-024-01939-z

  2. [7]

    PMID: 25248666

    URL https://doi.org/10.1177/1362361314552198. PMID: 25248666. D. Dillion, N. Tandon, Y. Gu, and K. Gray. Can AI language models replace human participants? Trends in Cognitive Sciences, 27(7):597–600,

    work page doi:10.1177/1362361314552198

  3. [10]

    URL https://doi.org/10.1109/TCDS.2021.3093040

    1109/TCDS.2021.3093040. URL https://doi.org/10.1109/TCDS.2021.3093040. L. D. Wiggins, D. L. Robins, R. Bakeman, and L. B. Adamson. Brief report: sensory abnormalities as distinguishing symptoms of autism spectrum disorders in young children. Journal of Autism and Developmental Disorders , 39 (7):1087–1091,

    arXiv 2021

  4. [12]

    URL https://doi.org/10.1038/s41593-023-01399-0 . I. Kamp-Becker. Autism spectrum disorder in ICD-11—a critical reflection of its possible impact on clinical practice and research. Molecular Psychiatry , 29:633–638,

    work page doi:10.1038/s41593-023-01399-0

  5. [13]

    URL https://doi.org/10.1038/s41380-023-02354-y . E. J. Kim and J. J. Kim. Neurocognitive effects of stress: a metaparadigm perspective. Molecular Psychiatry , 28: 2750 – 2763,

    work page doi:10.1038/s41380-023-02354-y

  6. [14]

    URL https://doi.org/10.1038/s41380-023-01986-4 . M.-C. Lai. Mental health challenges faced by autistic people. Nature Human Behaviour , 7:1620–1637,

    doi:10.1038/s41380-023-01986-4

  7. [15]

    URL https://doi.org/10.1038/s41562-023-01718-2 . R. P. Lawson, G. Rees, and K. J. Friston. An aberrant precision account of autism. Frontiers in Human Neuroscience, 8,

    work page doi:10.1038/s41562-023-01718-2

  8. [17]

    URL https://doi.org/ 10.1038/s41380-024-02407-w . D. J. MacKay. Information Theory, Inference and Learning Algorithms . Cambridge University Press,

    work page doi:10.1038/s41380-024-02407-w

Show all 28 references
  1. [21]

    org/10.1098/rsfs.2022.0029

    URL https://doi. org/10.1098/rsfs.2022.0029. A. K. Sauer, J. Stanton, S. Hans, and A. Grabrucker. Autism spectrum disorders: etiology and pathology. Exon Publications, pages 1–15,

    2022

  2. [22]

    2021.etiology

    URL https://doi.org/10.36255/exonpublications.autismspectrumdisorders. 2021.etiology. T. Shiraishi, Y. Katayama, M. Nishiyama, H. Shoji, T. Miyakawa, T. Mizoo, et al. The complex etiology of autism spectrum disorder due to missense mutations of CHD8. Molecular Psychiatry , 29:...

    2021 doi

  3. [23]

    URL https: //doi.org/10.1038/s41380-024-02491-y . A. Singer, A. Lutz, J. Escher, and A. Halladay. A full semantic toolbox is essential for autism research and practice to thrive. Autism Research, 16(3):497–501,

    doi

  4. [24]

    URL https://doi.org/10.1002/aur.2876. M. Uljarević, A. L. Richdale, D. W. Evans, R. Y. Cai, and S. R. Leekam. Interrelationship between insistence on sameness, effortful control and anxiety in adolescents and young adults with autism spectrum disorder (ASD). Molecular Autism, 8:36,

    doi

  5. [25]

    URL https://doi.org/10.1186/s13229-017-0158-4 . F. R. Volkmar. Encyclopedia of Autism Spectrum Disorders . Springer,

    doi

  6. [26]

    URL https://doi.org/10.1038/s41593-023-01444-y . Z. Wang, J. Liu, W. Zhang, W. Nie, and H. Liu. Diagnosis and intervention for children with autism spectrum disorder: A survey. IEEE Transactions on Cognitive and Developmental Systems , 14(3):819–832,

    doi

  7. [1993]

    URL https://doi.org/10.1109/18.243461

    doi: 10.1109/18.243461. URL https://doi.org/10.1109/18.243461. K. Friston. The free-energy principle: a unified brain theory? Nature Reviews Neuroscience , 11(2):127–138,

    doi

  8. [2009]

    URL https://doi.org/10.1007/s10803-009-0711-x . 11

    doi

  9. [2010]

    URL https://doi.org/10.1038/nrn2787. B. Galitsky. Computational Autism . Springer,

    doi

  10. [2013]

    URL https://doi.org/10.1176/appi.books.9780890425596. E. Assary, O. A. Oginni, G. Morneau-Vaillancourt, G. Krebs, A. J. Peel, E. Palaiologou, et al. Genetics of en- vironmental sensitivity and its association with variations in emotional problems, autistic traits, and wellbein...

    doi

  11. [2014]

    URL https://doi.org/10.3389/fnhum.2014.00302

    ISSN 1662-5161. URL https://doi.org/10.3389/fnhum.2014.00302. T.-J. Lyu, J. Ma, X.-Y. Zhang, G.-G. Xie, C. Liu, J. Du, et al. Deficiency of FRMD5 results in neurodevelopmental dysfunction and autistic-like behavior in mice. Molecular Psychiatry, 29:1253–1264,

    2014

  12. [2015]

    org/10.3109/13668250.2015.1023181

    URL https://doi. org/10.3109/13668250.2015.1023181. R. Chapman and W. Veit. The essence of autism: fact or artefact? Molecular Psychiatry , 26(5):1440–1441,

    2015

  13. [2016]

    URL https://doi.org/10.1007/978-3-319-39972-0 . A. Jourdon, F. Wu, J. Mariani, D. Capauto, S. Norton, L. Tomasini, et al. Modeling idiopathic autism in fore- brain organoids reveals an imbalance of excitatory cortical neuron subtypes during early neurogenesis. Nature Neuroscie...

    doi

  14. [2017]

    URL https://doi.org/10.1037/bul0000097. S. Perochon, J. M. Di Martino, K. L. H. Carpenter, S. Compton, N. Davis, B. Eichner, et al. Early detection of autism using digital behavioral phenotyping. Nature Medicine , 29:2489–2497,

    doi

  15. [2020]

    URL https://doi.org/10.1038/s41380-020-0748-y . A. C. Nobre. Opening questions in visual working memory. Journal of Cognitive Neuroscience , 35(1):49–59,

    doi

  16. [2021]

    URL https://doi.org/10.1038/s41380-020-00959-1 . S. Da Silva, M. Fiebig, and R. Matsushita. Opportunity costs, cognitive biases, and autism. Journal of Mind and Medical Sciences, 12(1),

    doi

  17. [2022]

    URL https://doi.org/10.1162/jocn_a_01920. J.-P. Noel, E. Balzani, L. Acerbi, J. Benson, D. Angelaki, et al. A common computational and neural anomaly across mouse models of autism. Nature Neuroscience , 28:1519–1532,

    doi

  18. [2023]

    URL https://doi.org/10.1016/j.tics.2023.04.008

    ISSN 1364-6613. URL https://doi.org/10.1016/j.tics.2023.04.008. L. Ekroot and T. M. Cover. The entropy of Markov trajectories. IEEE Transactions on Information Theory , 39(4): 1418–1421,

    2023 doi

  19. [2024]

    URL https://doi.org/10.1038/s41380-024-02508-6 . A. Bäck. Aristotle’s Theory of Abstraction , volume

    doi

  20. [2025]

    URL https://doi.org/10.3390/jmms12010011

    ISSN 2392-7674. URL https://doi.org/10.3390/jmms12010011. G. Davis and K. Plaisted-Grant. Low endogenous neural noise in autism. Autism, 19(3):351–362,

    doi

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.