Adults with autism overestimate the volatility of the sensory environment

In the paper

“This pattern mirrors previous findings in the domains of electrophysiology (reduced mismatch negativity in ASD/smaller P300 36,37 and BOLD imaging (reduced fMRI repetition suppression in ASD 21,23) but now in the novel domain of pupillometry.”

What the sources say

• ?Not checked — no full text of the sourcecites [36] Courchesne, E., Kilman, B.A., Galambos, R. & Lincoln, A.J. Autism: processing of novel auditory information assessed by event-related brain potentials. Electroencephalogr. Clin. Neurophysiol. 59, 238–248 (1984). source
• Disputed

  The citation is attached to the electrophysiological finding of a smaller P300 in ASD. In Results §3.2 (page unavailable), the paper reports no significant group effect for P300; its finding that the ASD group did not differentiate expected from unexpected conditions is not a finding of a smaller P300 than the TD group. The mismatch-negativity and BOLD findings are attached to other citations. (LLM, conf=0.85)

  Checked by a reasoning model — may be wrong.

  cites [37] Jeste, S.S. et al. Electrophysiological evidence of heterogeneity in visual statistical learning in young children with ASD. Dev. Sci. 18, 90–105 (2015). sourceExperimental — accuracy not yet measured
• Partly supported

  The Discussion (page unavailable) reports diminished fMRI repetition suppression with increasing autistic traits in neurotypical participants. It does not establish reduced repetition suppression in people diagnosed with ASD, as the citing sentence states. The electrophysiology findings are attached to other citations and are not attributed to this paper. (LLM, conf=0.88)

  cites [21] Ewbank, M.P. et al. Repetition suppression in ventral visual cortex is diminished as a function of increasing autistic traits. Cereb. Cortex 25, 3381–3393 (2015). sourceExperimental — accuracy not yet measured
• ?Not checked — no full text of the sourcecites [23] Kleinhans, N.M. et al. Reduced neural habituation in the amygdala and social impairments in autism spectrum disorders. Am. J. Psychiatry 166, 467–475 (2009). source

In the paper

“Bayesian theories of perception in ASD1 propose that reduced weighting of prior expectations, relative to sensory inputs, leads to the perceptual atypicalities associated with the condition2–7”

What the sources say

• ?Not checked — no full text of the sourcecites [1] Kenny, L. et al. Which terms should be used to describe autism? Perspectives from the UK autism community. Autism 20, 442–462 (2016). source
• Supported

  In “Autistic perception within a Bayesian framework,” the authors propose that attenuated priors, including how priors combine with sensory information, underlie atypical autistic perception. In “Hypo-priors may explain many of the non-social symptoms of autism,” they say hypo-priors increase reliance on incoming sensory signals and could explain sensory atypicalities. Page numbers were not provided. (LLM, conf=0.94)

  cites [2] Pellicano, E. & Burr, D. When the world becomes 'too real': a Bayesian explanation of autistic perception. Trends Cogn. Sci. 16, 504–510 (2012). sourceExperimental — accuracy not yet measured
• Supported

  In “Other Information Processing Accounts of ASD,” the paper describes a Bayesian account in which less influential priors leave perception closer to sensory input and help explain atypical responses to visual illusions. “Local versus global perception” likewise discusses reduced influence of contextual or prior information in ASD. The paper distinguishes its own proposed mechanism from weaker priors, but the claim attributes the general proposal to Bayesian accounts, not specifically to the authors’ model. (LLM, conf=0.90)

  cites [3] Van de Cruys, S. et al. Precise minds in uncertain worlds: predictive coding in autism. Psychol. Rev. 121, 649–675 (2014). sourceExperimental — accuracy not yet measured
• Supported

  The Related accounts of autism section says that reduced precision of priors relative to sensory evidence makes perception more driven by sensory input and less sensitive to context. The Conclusions attribute abnormalities in autistic perception to this imbalance, while allowing that increased sensory precision can produce the same effect. (LLM, conf=0.92)

  cites [4] Lawson, R.P., Rees, G. & Friston, K.J. An aberrant precision account of autism. Front. Hum. Neurosci. 8, 302 (2014). sourceExperimental — accuracy not yet measured
• ?Not checked — no full text of the sourcecites [5] Friston, K.J., Lawson, R. & Frith, C.D. On hyperpriors and hypopriors: comment on Pellicano and Burr. Trends Cogn. Sci. 17, 1 (2013). source
• Supported

  In “A more precise look at context in autism” (page not provided), the authors describe a proposed account in which sensory precision is too high relative to the precision of prior beliefs. They also state that aberrant precision-weighting can result in context-insensitive perception, supporting the paraphrase as a description of the theory rather than an established cause. (LLM, conf=0.90)

  cites [6] Lawson, R.P., Friston, K.J. & Rees, G. A more precise look at context in autism. Proc. Natl. Acad. Sci. USA 112, E5226 (2015). source
• ?Not checked — no full text of the sourcecites [7] Palmer, C.J., Lawson, R.P. & Hohwy, J. Bayesian approaches to autism: towards volatility, action, and behavior. Psychol. Bull. 143, 521–542 (2017). source

In the paper

“While computational studies of decision-making about rewards and punishments show that participants adapt their rate of learning about action-outcome contingencies in response to changes in environmental volatility 8–10”

What the sources say

• ?Not checked — no full text of the sourcecites [8] Behrens, T.E., Woolrich, M.W., Walton, M.E. & Rushworth, M.F. Learning the value of information in an uncertain world. Nat. Neurosci. 10, 1214–1221 (2007). source
• Supported

  The Results section (‘Effect of Environmental Volatility on Learning Rate’) reports higher learning rates in volatile than stable blocks of the aversive task and says the study replicated prior reward findings. This supports the claim for both punishment and reward decision-making; page numbers are not supplied in our copy. (LLM, conf=0.94)

  cites [9] Browning, M., Behrens, T.E., Jocham, G., O'Reilly, J.X. & Bishop, S.J. Anxious individuals have difficulty learning the causal statistics of aversive environments. Nat. Neurosci. 18, 590–596 (2015). source
• Partly supported

  The paper’s Abstract (page unavailable) says the optimal learning rate depends on volatility: faster in volatile conditions and slower in stable ones. It also reports that participants used model-tracked reward information in their choices. The study concerns reward outcomes and social advice, however, not punishment decision-making, so it does not support that part of the cited sentence. (LLM, conf=0.85)

  cites [10] Behrens, T.E.J., Hunt, L.T., Woolrich, M.W. & Rushworth, M.F.S. Associative learning of social value. Nature 456, 245–249 (2008). sourceExperimental — accuracy not yet measured

In the paper

“Recent neurocomputational accounts of autism have proposed that aberrant signalling of volatility could result in pathological neural gain, consistent with the cognitive and perceptual profile of autism such as enhanced perceptual functioning, sensory overload and context insensitivity4–6,20.”

What the sources say

• Supported

  In “Representing and responding to uncertainty” (page unavailable), the paper explains that estimating environmental volatility determines how sensory evidence is weighted against prior beliefs and discusses a possible failure to contextualize sensory processing in autism. “The neuromodulatory basis of precision” identifies neural gain as the mechanism for that weighting, while “Sensory attenuation” links excessive sensory influence to enhanced local perception, sensory overload, and reduced sensitivity to context. The claim is a fair, hedged synthesis of the proposed account, not a reported experimental finding. (LLM, conf=0.84)

  cites [4] Lawson, R.P., Rees, G. & Friston, K.J. An aberrant precision account of autism. Front. Hum. Neurosci. 8, 302 (2014). sourceExperimental — accuracy not yet measured
• ?Not checked — no full text of the sourcecites [5] Friston, K.J., Lawson, R. & Frith, C.D. On hyperpriors and hypopriors: comment on Pellicano and Burr. Trends Cogn. Sci. 17, 1 (2013). source
• Partly supported

  In the main text (page unavailable), the paper links environmental volatility to the weighting of sensory evidence, links precision to neural gain, and proposes that aberrant precision-weighting in autism could produce context-insensitive perception. It does not specifically propose aberrant volatility signalling as the cause of pathological neural gain, or discuss sensory overload and enhanced perceptual functioning as outcomes of that mechanism. (LLM, conf=0.82)

  cites [6] Lawson, R.P., Friston, K.J. & Rees, G. A more precise look at context in autism. Proc. Natl. Acad. Sci. USA 112, E5226 (2015). sourceExperimental — accuracy not yet measured
• Partly supported

  The Discussion (page unavailable in our copy) describes a proposed imbalance in the precision, or neuromodulatory gain, assigned to sensory input in autism. The Abstract links altered adaptation to reduced sensitivity to sensory context. These passages support a gain-and-context account, but the paper does not specifically propose aberrant volatility signalling as its cause. (LLM, conf=0.83)

  cites [20] Lawson, R.P., Aylward, J., White, S. & Rees, G. A striking reduction of simple loudness adaptation in autism. Sci. Rep. 5, 16157 (2015). sourceExperimental — accuracy not yet measured

In the paper

“Noradrenaline (NA), in particular, is thought to signal contextual change, leading to enhanced bottom-up, thalamocortical transmission of sensory information17–19.”

What the sources say

• ?Not checked — no full text of the sourcecites [17] Hasselmo, M.E. & McGaughy, J. High acetylcholine levels set circuit dynamics for attention and encoding and low acetylcholine levels set dynamics for consolidation. Prog. Brain Res. 145, 207–231 (2004). source
• ?Not checked — no full text of the sourcecites [18] Kobayashi, M. et al. Selective suppression of horizontal propagation in rat visual cortex by norepinephrine. Eur. J. Neurosci. 12, 264–272 (2000). source
• ?Could not verify

  The Discussion (page unavailable) speculates that norepinephrine may facilitate synaptic plasticity and says its role in thalamocortical connectivity remains to be tested. Neither that passage nor the additional searches for contextual change and sensory transmission say that norepinephrine signals contextual change or enhances bottom-up thalamocortical transmission. (LLM, conf=0.76)

  cites [19] Shepard, K.N., Liles, L.C., Weinshenker, D. & Liu, R.C. Norepinephrine is necessary for experience-dependent plasticity in the developing mouse auditory cortex. J. Neurosci. 35, 2432–2437 (2015). sourceExperimental — accuracy not yet measured

In the paper

“When the environment is volatile people should give more weight to recent sensory outcomes in building expectations about what they will see next (e.g. adopt a high α), in contrast they should give information from the distant past more weight when the environment is stable (e.g. adopt a low α) 8,9.”

What the sources say

• ?Not checked — no full text of the sourcecites [8] Behrens, T.E., Woolrich, M.W., Walton, M.E. & Rushworth, M.F. Learning the value of information in an uncertain world. Nat. Neurosci. 10, 1214–1221 (2007). source
• Supported

  The Abstract says that recent events should guide choices in volatile environments, whereas outcomes from many previous actions should be averaged when surprising outcomes reflect noise. The Results section, “Effect of Environmental Volatility on Learning Rate” (page not supplied), defines high α as giving recent outcomes more influence and reports higher learning rates in volatile than stable blocks. (LLM, conf=0.93)

  cites [9] Browning, M., Behrens, T.E., Jocham, G., O'Reilly, J.X. & Bishop, S.J. Anxious individuals have difficulty learning the causal statistics of aversive environments. Nat. Neurosci. 18, 590–596 (2015). source

In the paper

“Aberrant phasic NA (c.f precision on prediction errors4,6) may alter the signal-to-noise ratio of cortical responses 41,42, broaden the tuning functions of sensory responses and, subsequently improve discrimination behaviour43.”

What the sources say

• Supported

  This source is cited for the parenthetical link to precision on prediction errors, not for the later claims about signal-to-noise ratio, sensory tuning, or discrimination, which have separate citations. The Introduction links prediction-error precision to neuromodulatory gain control; the Monoamines section discusses norepinephrine in perceptual uncertainty, and the Summary notes that tonic versus phasic neuromodulator availability can shape perception and learning. No page numbers are available in our copy. (LLM, conf=0.86)

  cites [4] Lawson, R.P., Rees, G. & Friston, K.J. An aberrant precision account of autism. Front. Hum. Neurosci. 8, 302 (2014). source
• Supported

  The citation to this paper is attached to the parenthetical comparison with precision on prediction errors, not to the later claims about signal-to-noise ratio, sensory tuning, or discrimination, which have separate citations. In “A more precise look at context in autism” (page unavailable), the paper links norepinephrine to the weighting of sensory evidence and locates precision-weighting in the gain of cells encoding prediction errors. (LLM, conf=0.86)

  cites [6] Lawson, R.P., Friston, K.J. & Rees, G. A more precise look at context in autism. Proc. Natl. Acad. Sci. USA 112, E5226 (2015). source
• ?Not checked — no full text of the sourcecites [41] Hasselmo, M.E., Linster, C., Patil, M., Ma, D. & Cekic, M. Noradrenergic suppression of synaptic transmission may influence cortical signal-to-noise ratio. J. Neurophysiol. 77, 3326–3339 (1997). source
• Partly supported

  The discussion section reports that noradrenergic activation increases signal-to-noise ratios in VPM thalamocortical cells, including for cortical inputs. It does not establish a change in the signal-to-noise ratio of responses recorded in cortex. The tuning and discrimination clauses appear to be attached to a different citation and are not judged here. (LLM, conf=0.84)

  cites [42] Hirata, A., Aguilar, J. & Castro-Alamancos, M.A. Noradrenergic activation amplifies bottom-up and top-down signal-to-noise ratios in sensory thalamus. J. Neurosci. 26, 4426–4436 (2006). sourceExperimental — accuracy not yet measured
• Partly supported

  The Results section (‘Locus coeruleus pairing improves auditory perception’; page not available) reports that auditory-cortical tuning initially broadened and discrimination later improved above baseline. But it says performance initially worsened during the broadening phase and improved after tuning width recovered; it does not show that broadening itself improved discrimination. The source studies locus coeruleus pairing, not aberrant phasic noradrenaline. (LLM, conf=0.86)

  cites [43] Martins, A.R.O. & Froemke, R.C. Coordinated forms of noradrenergic plasticity in the locus coeruleus and primary auditory cortex. Nat. Neurosci. 18, 1483–1492 (2015). sourceExperimental — accuracy not yet measured

In the paper

“Such an explanation is implied by conservative interpretations of non-hierarchical Bayesian accounts of ASD 2 and predictive processing accounts that emphasise “uniform” inflexibly high precision in sensory processing 3.”

What the sources say

• Supported

  The section “Autistic perception within a Bayesian framework” proposes a Bayesian account of ASD in which broader, attenuated priors leave perception less constrained by prior knowledge. This supports the account attributed to this source; the separate claim about uniformly high sensory precision is attached to citation 3, not this paper. (LLM, conf=0.88)

  cites [2] Pellicano, E. & Burr, D. When the world becomes 'too real': a Bayesian explanation of autistic perception. Trends Cogn. Sci. 16, 504–510 (2012). source
• Supported

  The “Predictive Coding in ASD” section describes the paper’s model as assigning high, inflexible precision to prediction errors in autism. It says low-level sensory prediction errors are assigned precision that is too high and independent of context, supporting the part of the citing sentence attributed to this source. (LLM, conf=0.91)

  cites [3] Van de Cruys, S. et al. Precise minds in uncertain worlds: predictive coding in autism. Psychol. Rev. 121, 649–675 (2014). source

In the paper

“Motivated by these claims, a relatively recent study found no differences in learning rate malleability in autistic children during a reward learning task modelled with a delta learning rule 45.”

What the sources say

• Supported

  The Results report that learning rates rose in the volatile condition, with no significant group-by-condition interaction: autistic children adjusted their rates to a similar extent as typically developing children and adults. Materials and methods states that learning rates were estimated in the reward task using a delta-learning rule. (LLM, conf=0.94)

  cites [45] Manning, C., Kilner, J., Neil, L., Karaminis, T. & Pellicano, E. Children on the autism spectrum update their behaviour in response to a volatile environment. Dev. Sci. http://dx.doi.org/10.1111/desc.12435 (2016). source

In the paper

“Atypical social prediction error processing in the gyral surface of the ACC(g) has recently been shown in autism44”

What the sources say

• Supported

  The Results section, “Social prediction error signals in the gyral surface of the anterior cingulate cortex,” reports a group difference in ACCg responses to social prediction errors. Typically developing participants showed an ACCg response when another person received an unexpected reward, whereas the ASD group did not show that distinction. (LLM, conf=0.94)

  cites [44] Balsters, J.H. et al. Disrupted prediction errors index social deficits in autism spectrum disorder. Brain 140, 235–246 (2017). source

In the paper

“Under the aberrant precision account of ASD it is problems with high-level learning about volatility, and the ratified neuromodulatory changes, that is hypothesised to underlie the difficulties faced by people on the spectrum 4–6.”

What the sources say

• Partly supported

  The ‘Related accounts of autism’ section links aberrant precision to perceptual learning, hierarchical modeling of volatility, and possible compensatory neuromodulatory changes at higher levels (page unavailable). But it describes those learning and neuromodulatory changes as secondary consequences of an imbalance in precision, not as the primary causes of autistic difficulties; the Conclusions identify that imbalance as the proposed explanation. (LLM, conf=0.82)

  cites [4] Lawson, R.P., Rees, G. & Friston, K.J. An aberrant precision account of autism. Front. Hum. Neurosci. 8, 302 (2014). sourceExperimental — accuracy not yet measured
• ?Not checked — no full text of the sourcecites [5] Friston, K.J., Lawson, R. & Frith, C.D. On hyperpriors and hypopriors: comment on Pellicano and Burr. Trends Cogn. Sci. 17, 1 (2013). source
• Partly supported

  In “A more precise look at context in autism” (page not specified), the paper links adaptive perception and action to estimating environmental volatility and says neuromodulators putatively track it. It also proposes that aberrant precision-weighting could produce context-insensitive perception and action, but does not specifically say that impaired high-level volatility learning and neuromodulatory changes together underlie autistic people's difficulties; it calls the mechanisms empirical questions. (LLM, conf=0.83)

  cites [6] Lawson, R.P., Friston, K.J. & Rees, G. A more precise look at context in autism. Proc. Natl. Acad. Sci. USA 112, E5226 (2015). sourceExperimental — accuracy not yet measured

In the paper

“Notably, however, there were no group differences in simple behaviour reported on this task.”

What the sources say

• Partly supported

  The Results report no significant differences between autistic and typically developing children in how well the behavioural models fit their choices, and no group difference in learning rates. But they also report one group difference: more children than adults were fit by the win-stay lose-shift model. The unqualified claim of no group differences therefore overstates the findings (Results; page unavailable in our copy). (LLM, conf=0.82)

  cites [45] Manning, C., Kilner, J., Neil, L., Karaminis, T. & Pellicano, E. Children on the autism spectrum update their behaviour in response to a volatile environment. Dev. Sci. http://dx.doi.org/10.1111/desc.12435 (2016). sourceExperimental — accuracy not yet measured

In the paper

“A recent pharmacological study employing the HGF, indicates that noradrenaline antagonism selectively impairs volatility learning13, which accords with the raised pupillometric response to surprise about volatility reported in the adults with ASD here (Figure 5b).”

What the sources say

• Partly supported

  The Results section ('The Influence of NA and ACh in Perceptual Uncertainty Computations'; page unavailable) reports that noradrenaline antagonism changed volatility updating without changing the tonic learning rate about probabilistic contexts. But it reports faster, more variable volatility updating—not a demonstrated impairment—and notes that the effect did not survive one correction for multiple comparisons. The cited paper does not report the ASD pupillometry finding; the citing authors present that comparison as their own. (LLM, conf=0.85)

  cites [13] Marshall, L. et al. Pharmacological fingerprints of contextual uncertainty. PLoS Biol. 14, e1002575 (2016). sourceExperimental — accuracy not yet measured

In the paper

“The distinct but complementary results provided by the ground truth and computational levels of analysis in our study underwrite the utility of computational approaches in better understanding neuro-developmental and psychiatric conditions with the aim of influencing clinical practice 47–49.”

What the sources say

• Supported

  Haker, Schneebeli, and Stephan’s “Can Bayesian Theories of Autism Spectrum Disorder Help Improve Clinical Practice?” supports the claim’s central point: a cited-paper snippet (score 0.498) proposes computational models as clinically applicable assays for psychiatric disorders, and another (score 0.451) describes using modeling to improve ASD diagnosis and care. The paper does not, however, report its own separate ground-truth and computational analyses; that part of the wording overstates what the source establishes. The prior weak-match verdict is not evidence of a contrary argument. (LLM, conf=0.82)

  cites [47] Haker, H., Schneebeli, M. & Stephan, K.E. Can Bayesian theories of autism spectrum disorder help improve clinical practice? Front. Psychiatry 7, 107 (2016). sourceExperimental — accuracy not yet measured
• Partly supported

  The paper’s opening and concluding discussions (page numbers not provided) argue that computational psychiatry links levels of analysis, helps explain mental illness, and offers a path toward clinical use. It does not specifically address neurodevelopmental conditions. The ground-truth and computational results are identified as findings of the citing authors’ own study, not findings attributed to this source. (LLM, conf=0.84)

  cites [48] Corlett, P.R. & Fletcher, P.C. Computational psychiatry: a Rosetta Stone linking the brain to mental illness. Lancet Psychiatry 1, 399–402 (2014). sourceExperimental — accuracy not yet measured
• Supported

  Teufel and Fletcher’s paper supports the claim’s general point: a paper-scoped snippet (score 0.54) says computational models may deepen understanding of psychiatric and neurological disorders and aid diagnosis and treatment. It also describes models as a way to synthesize disparate evidence. The paper does not appear to report the specific ‘ground-truth and computational analyses’ mentioned in the citing sentence, so the citation supports the broader interpretation rather than those particular findings. The prior disputed verdict is not warranted; the cited text makes no contrary argument. (LLM, conf=0.81)

  cites [49] Teufel, C. & Fletcher, P.C. The promises and pitfalls of applying computational models to neurological and psychiatric disorders. Brain 139, 2600–2608 (2016). sourceExperimental — accuracy not yet measured

In the paper

“Crucially, only the expectedness*group interaction was significant in this analysis (F(2,94)=4.47, P=0.014; Figure 2a). The noise*group (F(2,94)=0.06, P=0.94), noise*expectedness (F(4,188)=0.47, P=0.76) and expectedness*noise*group interactions were not significant (F(4,188)=1.31, P=0.28).”

In the paper

“Follow up independent-samples t-tests revealed that the ASD group did not update α2 as much as NT adults (t(47)=-2.37, P=0.02) whereas they updated α3 more than NT adults(t(47)=3.16, P=0.03; Figure 4c).”

In the paper

“Additional analyses revealed that the volatility learning rate (α3), and the probability learning rate (α2) are not encoded in the pupil response in either group (Figure S9).”

In the paper

“This analysis revealed no main effect of α type (F(1,47)=2.61, P=0.11), no main effect of group (F(1,47)=2.01, P=0.16, and no group* α type interaction (F(1,47)=2.54, P=0.12), suggesting that, in general, both groups were able to learn the this task equally well.”

In the paper

“This model was significant overall (F(3,48)=5.58, P=0.002) and the only significant predictor of UE-E RT difference was group (t=-2.87, P=0.008). Mean RT (t=-1.08. P=0.28) and mean errors (t=1.06, P=0.29) were not significant predictors.”

In the paper

“Subtracting % errors to UE from those to E outcomes provides a low-level index of surprise which is only significantly greater than zero in the NT group (ASD, t(23)=1.11, P=0.28; NT, t(24)=3.65, P=0.001) and attenuated in the ASD group relative to the NT group (t(33.4)=2.83, P=0.007; Figure 2d).”

In the paper

“This suggests that for both groups increasing sensory noise results in slower RT (Figure S1) but adults with ASD only show reduced modulation of RT as a function of learned expectations.”

In the paper

“Control analyses indicated that there were no group differences in response time or accuracy across the face and house stimuli (Figure S5).”

In the paper

“Finally, control analyses revealed that there were no group differences in fixation compliance across conditions (Figure S11) or the relationship between pupil size and simple behaviour such as trial-wise RT (Figure S12).”

In the paper

“A third regression model, in a reduced sample size (see Online Methods), additionally included sensory sensitivity scores (ASQ) as a predictor of UE-E RT. This model was not significant (F(4,21)=1.28, P=0.32) and the only predictor approaching significance was, again, communication scores (t=-1.89, P=0.076).”

In the paper

“Applying multiple regression across every trial and every time point in the pupil time trace, we found a sustained positive relationship between pupil size and precision-weighted PE’s (ε3) in the ASD participants (Figure 5b), which significantly differed from the NT group and zero.”

In the paper

“This demonstrated no group differences in tonic pupil size in this sample (t(23)=.36, P=0.72).”

In the paper

“This analysis revealed a trend towards a main effect of group (F(1,47)=0.26, P=0.061), a significant main effect of α type (F(1,47)=6.07, P=0.017) and crucially an α type* group interaction (F(1,47)=9.80), P=0.003).”

In the paper

“In each case the model was significant (Δα2 : F(2,48)=68.94, P<0.001, R2 =0.75; Δα3 : F(2,48)=102.53, P<0.001, R2 =0.82).”

In the paper

“The Wald statistic demonstrated that outcome uncertainty (β2, P=0.043), phasic volatility (β4, P=0.006) tonic volatility at the 3rd level (ω3, P=0.024) and baseline log RT (β0, P=0.007) made a significant contribution to prediction (Figure 4b).”

In the paper

“A second regression model that also contained baseline RT as a predictor narrowly missed overall significance (F(6,23)=2.41, P=0.07), and communication scores were once again the only significant predictor (t=-2.81, P=0.012).”

In the paper

“A binary logistic regression model predicting group status (ASD=1, NT=0), with all eight model parameters as predictors was significant (X2= 26.83, P=.001) and prediction success overall was 81.6% (76% ASD, 88% NT), with cross-validated prediction success of 68%.”

In the paper

“This model was significant (F(3,23)=3.28, P=0.04) and communication score was the only significant predictor (t=-2.57, P=0.018; Figure 3). IQ (t=1.45, P=0.16) and social reciprocal interaction scores (t=0.95, P=0.35) were not significant predictors.”

In the paper

“There was a significant main effect of expectedness (F(1.5,70.5)=11.71, P<0.001) and a significant group*expectedness interaction (F(2,94)=6.34, P=0.003) indicating that NT group made more errors on unexpected, relative to expected trials, whereas the ASD group did not (Figure 2c).”

In the paper

“The main effect of noise was not significant in this analysis (F(1.7,78.8)=0.08, P=0.92) and neither was the noise*group (F(2,94)=0.29, P=0.75), noise*expectedness (F(4,188)=0.76, P=0.55) and expectedness*noise*group interactions (F(4,188)=1.28, P=0.28).”

In the paper

“Subtracting RTs to UE from those to E outcomes provides a low-level index of “surprise” which is significantly greater than zero in both groups (ASD, t(23)=4.66, P<0.001; NT, t(24)=7.25, P<0.001) but attenuated in the ASD group relative to the NT group (t(47)=3.51, P=0.001; Figure 2b).”

In the paper

“AQ score was significantly higher in the in the high AQ group (mean=27, SD=6.4, range=18-45), relative to the low AQ group (mean=11.5, SD=3.4, range=5-17; t (55) = 11.28, P<0.001).”

In the paper

“A hierarchical Bayesian model of learning suggests that in ASD a tendency to over-learn about volatility in the face of environmental change drives a corresponding reduction in learning about probabilistically aberrant events – putatively rendering them less surprising.”

In the paper

“NA is believed to change cortical gain in response to surprise, specifically; salient events indicating that global context has changed cf. “unexpected uncertainty” 15,25.”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [15] Yu, A. & Dayan, P. Expected and unexpected uncertainty: ACh and NE in the neocortex. Adv. Neural Inf. Process. Syst. 15, 173–180 (2003).
• ?Not checked — no full text of the sourcecites [25] Yu, A.J. Change is in the eye of the beholder. Nat. Neurosci. 15, 933–935 (2012). source

In the paper

“However, this notion of surprise is conditioned upon adequate learning of the ground truth, and our computational analysis indicates that ASD and NT participants show a dissociation in how they estimate volatility and adapt their learning rates in response to the changeability of the environment”

In the paper

“Results were unchanged when the identical analysis was carried out on log reaction times (Table S1).”

In the paper

“Not only does this bolster confidence in our clinical finding but additionally supports generalisation of this result to the broader autism spectrum in the wider population.”

In the paper

“In a state where uncertainty about one’s beliefs is high (e.g. in volatile conditions), top-down prior expectations should be suppressed, relative to new bottom-up sensory evidence, in order to promote new learning about the current environmental context15.”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [15] Yu, A. & Dayan, P. Expected and unexpected uncertainty: ACh and NE in the neocortex. Adv. Neural Inf. Process. Syst. 15, 173–180 (2003).

In the paper

“This fits with our finding that the ASD participants, who tend towards a smaller Δα2 (Figure 4c), show reduced behavioural ‘surprise’ (Figure 2b) and also larger ‘metavolitility’ estimates (Figure 4b).”

In the paper

“ω3 can be understood as capturing ‘metavolatility’ (i.e., the tonic volatility of the phasic volatility, with higher values in the ASD group implying a belief in a world where instability itself is instable (Figure S8).”

In the paper

“This suggests less distinction between UE and E outcomes in ASD, though this is conditioned upon adequate learning of the ground truth.”

In the paper

“In general, these findings point towards problems representing and responding to environmental change in ASD, specifically, an increased tendency to expect the unexpected.”

In the paper

“Knowing whether to disregard an unexpected outcome or take it seriously (i.e. whether to adopt a high or low learning rate about cue-outcome probabilities) depends on the precision of your beliefs about environmental change (i.e. whether you adopt a high or low learning rate about volatility).”

In the paper

“Results were very similar when the identical analysis was carried out on log error rates (Table S1).”

In the paper

“If the NA system is signalling more high-level surprise in ASD then this may imply atypical cortical gain during sensory processing, resulting in a state where one is disproportionately receptive to sensory inputs.”

In the paper

“This study offers novel insight into the behavioural, algorithmic and physiological mechanisms that underlie learning about, and responses to, environmental change in ASD.”

In the paper

“Data collection and analysis were not performed blind to the conditions of the experiment.”

In the paper

“An additional 57 NTs were studied as part of a replication of our key behavioural result (25 male, 32 female; mean age: 27.1, age range: 19–50) and additionally completed the Autism Spectrum Quotient (AQ) questionnaire; a 50-item self-report measure of autistic traits 32,55.”

What the sources say

• ?Not checked — no full text of the sourcecites [32] Baron-Cohen, S., Wheelwright, S., Skinner, R., Martin, J. & Clubley, E. The autism-spectrum quotient (AQ): evidence from Asperger syndrome/high-functioning autism, males and females, scientists and mathematicians. J. Autism Dev. Disord. 31, 5–17 (2001). source
• ?Not checked — no full text of the sourcecites [55] Woodbury-Smith, M.R., Robinson, J., Wheelwright, S. & Baron-Cohen, S. Screening adults for Asperger Syndrome using the AQ: a preliminary study of its diagnostic validity in clinical practice. J. Autism Dev. Disord. 35, 331–335 (2005). source

In the paper

“The results of this study imply that adults with autism show a tendency to over-estimate the volatility of the sensory environment, at the expense of learning to build stable expectations that lead to adaptive surprise.”

• Also stated in: § Conclusion · hedged

  “In other words, adults with autism may be mildly surprised by the pineapple and the socks.”

In the paper

“This indicated that a total sample size of 48 participants would be necessary to have at least 90% power to detect an F-test effect size of 0.25.”

In the paper

“The Autism Diagnostic Observation Schedule (2nd edition) 54 assessment was completed by a qualified administrator to assess symptom severity in the ASD participants.”

What the sources say

• ?Not checked — no full text of the sourcecites [54] Lord, C. et al. The autism diagnostic observation schedule-generic: a standard measure of social and communication deficits associated with the spectrum of autism. J. Autism Dev. Disord. 30, 205–223 (2000). source

In the paper

“Given that initial effect sizes are often inflated 61 and that we sought power to detect a difference between two groups, we doubled this estimate and aimed to test ~28 participants in each group with some attrition expected.”

What the sources say

• ?Not checked — no full text of the sourcecites [61] Ioannidis, J.P. Why most discovered true associations are inflated. Epidemiology 19, 640–648 (2008). source

In the paper

“There was no significant difference in the mean number of included trials between the groups (mean good trials ASD=298; NT=261; t(23)=0.803, P=0.43).”

In the paper

“To ensure that participants’ responses were not biased by learned expectations about the relative frequencies of the visual stimuli, the task was designed such that the marginal probabilities of faces and houses were identical at any point in time (Figure 1) and each block contained equal numbers of randomly intermixed high and low tone trials.”

In the paper

“All three of these neuromodulators are likely candidates in the neurobiological mechanisms underlying responses to environmental change in ASD.”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [15] Yu, A. & Dayan, P. Expected and unexpected uncertainty: ACh and NE in the neocortex. Adv. Neural Inf. Process. Syst. 15, 173–180 (2003).

In the paper

“Post-hoc power calculations indicate that with 11 ASD participants included in the actual analysis, we had 86% power to detect the mean positive β (slope=0.72) that we observed in these participants (α = 0.05; 2-tailed).”

In the paper

“This left 24 participants in the ASD group (18 males; mean age: 35.5, age range: 20-61) and 25 in the NT group (16 males; mean age: 36, age range: 19-62).”

In the paper

“Calibration of the eye tracker was unsuccessful in all participants wearing glasses and the eye tracker suffered a fatal technical failure before testing was completed, therefore eye tracking data are only available for 14 NT’s and 11 ASD’s.”

In the paper

“The Wechsler Adult Intelligence Scale (WAIS 3rd edition UK) had previously been administered to assess IQ 53 and participants were matched on full-scale scores (ASD mean: 117; range: 80-142; NT mean: 120, range: 99-145; t(47)=-0.93, P=0.36)”

What the sources say

• ?Not checked — no full text of the sourcecites [53] Wechsler, D. & Hsiao-pin, C. WASI-II: Wechsler Abbreviated Scale of Intelligence (Pearson, 2011). source

In the paper

“No randomisation was used to assign subjects or conditions.”

In the paper

“ASD participants had previously been diagnosed by an independent clinician, according to the DSM-IV51 or ICD-10 criteria52 [19 Asperger Syndrome, 3 Autistic Disorder, 1 High Functioning Autism, 1 Atypical Autism].”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [51] American Psychiatric Association. Diagnostic and Statistical Manual–Text Revision (DSM-IV-TRim, 2000) (American Psychiatric Association, 2000).
• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [52] World Health Organization. International classification of diseases (ICD-10) (World Health Organization, 2012).

In the paper

“This analysis indicated that we would need a minimum of 14 participants to achieve 95% power to detect a similar (α = 0.05; 2-tailed) effect in the NT group.”

In the paper

“From a Bayesian perspective, the simplest way in which persistent overweighting of all sensory inputs (relative to prior expectations) might occur would be a generally larger outcome α – reflecting chronic and inflexible overweighting of recent, relative to past, sensory history.”

In the paper

“For the analysis of RT’s, too fast and too slow (<100 or >1000ms) responses were excluded and, including missing responses, there was no significant difference between the groups in the overall percentage of missing data (1.9% ASD, 2.3% NT, t(47)=0.45, p=0.65).”

In the paper

“Following data examination, participants with more than 20% overall errors or mean reaction times (RT’s) > 2 standard deviations from their respective group mean RT were excluded from subsequent analysis to ensure the validity of the Bayesian modelling.”

In the paper

“The ASD and NT groups were matched on age (t(47)=0.54, P=0.87).”