Adults with autism overestimate the volatility of the sensory environment

May combine two claims.

This pattern resembles earlier electrophysiological findings of reduced mismatch negativity and smaller P300, and BOLD findings of reduced repetition suppression, but now in pupillometry.

“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.”

• cites [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). sourceNot checked — no full text of the source
• 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). sourceDisputed

  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.

  Experimental — accuracy not yet measured
• 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). sourcePartly 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)

  Experimental — accuracy not yet measured
• cites [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). sourceNot checked — no full text of the source

Classified as probably not a substantial claim.

Bayesian accounts of perception in ASD propose that underweighting prior expectations relative to sensory input produces the condition's perceptual atypicalities.

“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”

• cites [1] Kenny, L. et al. Which terms should be used to describe autism? Perspectives from the UK autism community. Autism 20, 442–462 (2016). sourceNot checked — no full text of the source
• 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). sourceSupported
  Experimental — accuracy not yet measured
• cites [3] Van de Cruys, S. et al. Precise minds in uncertain worlds: predictive coding in autism. Psychol. Rev. 121, 649–675 (2014). sourceSupported
  Experimental — accuracy not yet measured
• cites [4] Lawson, R.P., Rees, G. & Friston, K.J. An aberrant precision account of autism. Front. Hum. Neurosci. 8, 302 (2014). sourceSupported
  Experimental — accuracy not yet measured
• cites [5] Friston, K.J., Lawson, R. & Frith, C.D. On hyperpriors and hypopriors: comment on Pellicano and Burr. Trends Cogn. Sci. 17, 1 (2013). sourceNot checked — no full text of the source
• 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). sourceSupported
• cites [7] Palmer, C.J., Lawson, R.P. & Hohwy, J. Bayesian approaches to autism: towards volatility, action, and behavior. Psychol. Bull. 143, 521–542 (2017). sourceNot checked — no full text of the source

Classified as probably not a substantial claim.

Computational studies of reward and punishment decision-making show that people adjust how quickly they learn action-outcome contingencies when environmental volatility changes.

“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”

• cites [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). sourceNot checked — no full text of the source
• 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). sourceSupported
• 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). sourcePartly 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)

  Experimental — accuracy not yet measured

Classified as probably not a substantial claim.

Recent neurocomputational accounts propose that aberrant volatility signalling could cause pathological neural gain, matching autism's cognitive and perceptual profile.

“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.”

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

  Experimental — accuracy not yet measured
• 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). sourcePartly 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)

  Experimental — accuracy not yet measured

Classified as probably not a substantial claim. May combine two claims.

Noradrenaline is thought to signal contextual change and to enhance bottom-up thalamocortical transmission of sensory information.

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

• cites [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). sourceNot checked — no full text of the source
• cites [18] Kobayashi, M. et al. Selective suppression of horizontal propagation in rat visual cortex by norepinephrine. Eur. J. Neurosci. 12, 264–272 (2000). sourceNot checked — no full text of the source
• 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). sourceCould 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)

  Experimental — accuracy not yet measured

Classified as probably not a substantial claim. May combine two claims.

In volatile environments, people should weight recent sensory outcomes more heavily when forming expectations; in stable environments, they should weight the distant past more heavily.

“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.”

• cites [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). sourceNot checked — no full text of the source
• 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). sourceSupported

May combine two claims.

Aberrant phasic NA may change the signal-to-noise ratio of cortical responses, broaden sensory tuning functions, and improve discrimination behaviour.

“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.”

• cites [4] Lawson, R.P., Rees, G. & Friston, K.J. An aberrant precision account of autism. Front. Hum. Neurosci. 8, 302 (2014). sourceSupported
• 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). sourceSupported
• cites [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). sourceNot checked — no full text of the source
• 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). sourcePartly 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)

  Experimental — accuracy not yet measured
• 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). sourcePartly 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)

  Experimental — accuracy not yet measured

• AssumptionThe authors state this as an assumption: Pupil size is an index of noradrenaline release from the locus coeruleus.

Classified as probably not a substantial claim.

Conservative readings of non-hierarchical Bayesian models of ASD and of predictive processing models that posit uniformly high, inflexible sensory precision entail this explanation.

“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.”

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

Classified as probably not a substantial claim.

A recent study reported no group differences in how flexibly autistic children adjusted their learning rate during a reward task fitted with a delta learning rule.

“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.”

• 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). sourceSupported

Probably not a substantial claim: may not pass S3 (load-bearing).

Atypical social prediction error processing in the gyral surface of the anterior cingulate cortex has recently been demonstrated in autism.

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

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

Classified as probably not a substantial claim.

The aberrant precision account of ASD proposes that difficulties in high-level volatility learning, together with ratified neuromodulatory changes, underlie the challenges experienced by autistic people.

“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.”

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

  Experimental — accuracy not yet measured
• cites [5] Friston, K.J., Lawson, R. & Frith, C.D. On hyperpriors and hypopriors: comment on Pellicano and Burr. Trends Cogn. Sci. 17, 1 (2013). sourceNot checked — no full text of the source
• 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). sourcePartly 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)

  Experimental — accuracy not yet measured

Probably not a substantial claim: may not pass S3 (load-bearing).

That study also reported no group differences in basic task behaviour.

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

• 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). sourcePartly 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)

  Experimental — accuracy not yet measured

May combine two claims.

The elevated pupil response to volatility surprise in adults with ASD aligns with evidence that noradrenaline antagonism selectively disrupts volatility learning.

“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).”

• cites [13] Marshall, L. et al. Pharmacological fingerprints of contextual uncertainty. PLoS Biol. 14, e1002575 (2016). sourcePartly 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)

  Experimental — accuracy not yet measured

Probably not a substantial claim: may not pass S2 (checkable).

The separate but complementary findings from the ground-truth and computational analyses support the value of computational methods for understanding neurodevelopmental and psychiatric conditions and informing clinical practice.

“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.”

• 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). sourceSupported
  Experimental — accuracy not yet measured
• 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). sourcePartly 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)

  Experimental — accuracy not yet measured
• 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). sourceSupported
  Experimental — accuracy not yet measured

May combine two claims.

Only the expectedness-by-group interaction was significant; the noise-by-group, noise-by-expectedness, and three-way interactions were not.

“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).”

May combine two claims.

Follow-up t-tests showed that ASD adults updated α2 less than NT adults but updated α3 more than NT adults.

“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).”

May combine two claims.

Further analyses found that neither the volatility learning rate (α3) nor the probability learning rate (α2) was encoded in pupil response in either group.

“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).”

May combine two claims.

Analysis of average learning rates found no α-type main effect, no group main effect, and no group-by-α-type interaction, suggesting both groups learned the task equally well.

“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.”

May combine two claims.

In a linear regression predicting UE-E RT from group, mean RT, and mean errors, the model was significant and only group predicted the difference; mean RT and mean errors did not.

“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.”

May combine two claims.

The UE-minus-E percentage-error index of surprise was above zero only in NT participants and was smaller in ASD than NT.

“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).”

May combine two claims.

In both groups, more sensory noise slowed responses, but only the ASD group showed reduced modulation of reaction time by learned expectations.

“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.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Control analyses found no group differences in response time or accuracy between face and house stimuli.

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

May combine two claims.

Control analyses found no group differences in fixation compliance across conditions or in the association between pupil size and trial-wise RT.

“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).”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

A third regression in a smaller sample that added ASQ sensory sensitivity was not significant, and communication scores approached significance.

“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).”

May combine two claims.

A multiple regression across all trials and time points found a sustained positive association between pupil size and precision-weighted prediction errors (ε3) in ASD participants, significantly different from the NT group and from zero.

“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.”

Probably not a substantial claim: may not pass S3 (load-bearing).

The groups did not differ in tonic pupil size.

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

May combine two claims.

Analysis of the change in α2 and α3 from stable to volatile periods showed a trend toward a group main effect, a significant α-type main effect, and a significant α-type-by-group interaction.

“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).”

May combine two claims.

Two linear regressions predicting Δα2 and Δα3 were each significant.

“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).”

May combine two claims.

Outcome uncertainty, phasic volatility, third-level tonic volatility, and baseline log RT each contributed significantly to predicting group status.

“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).”

May combine two claims.

A second regression that also included baseline RT narrowly missed overall significance, and communication scores were again the only significant predictor.

“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).”

May combine two claims.

A binary logistic regression using all eight model parameters to predict group status was significant, with overall prediction success of 81.6% (76% ASD, 88% NT) and cross-validated success of 68%.

“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%.”

May combine two claims.

A multiple regression predicting UE-E RT from ADOS-2 communication, social reciprocal interaction, and IQ was significant; only communication score predicted the measure, while IQ and social reciprocal interaction did not.

“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.”

May combine two claims.

For error rates, expectedness had a significant main effect and the group-by-expectedness interaction was significant: NT participants made more errors on unexpected than expected trials, while ASD participants did not.

“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).”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

For error rates, the noise main effect and the noise-by-group, noise-by-expectedness, and three-way interactions were not significant.

“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).”

May combine two claims.

The UE-minus-E reaction-time difference, an index of surprise, was above zero in both groups but smaller in ASD than NT.

“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).”

Classified as probably not a substantial claim.

The high AQ group had significantly higher AQ scores than the low AQ group.

“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).”

May combine two claims.

A hierarchical Bayesian model indicates that in ASD, over-learning about volatility when the environment changes reduces learning about probabilistically aberrant events, which may make those events less surprising.

“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.”

Classified as probably not a substantial claim.

NA is thought to change cortical gain in response to surprise, particularly salient events signalling that global context has changed.

“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.”

• 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 matched

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

• cites [25] Yu, A.J. Change is in the eye of the beholder. Nat. Neurosci. 15, 933–935 (2012). sourceNot checked — no full text of the source

May combine two claims.

The surprise measure depends on adequate learning of the ground truth, and computational analysis indicates ASD and NT participants differ in how they estimate volatility and adjust learning rates when the environment changes.

“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”

Probably not a substantial claim: may not pass S3 (load-bearing).

The same analysis on log-transformed reaction times gave unchanged results.

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

May combine two claims.

This replication increases confidence in the clinical finding and supports generalising the result to the broader autism spectrum in the wider population.

“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.”

Classified as probably not a substantial claim.

When uncertainty about one's beliefs is high, top-down prior expectations should be suppressed relative to new bottom-up sensory evidence to promote learning about the current context.

“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.”

• 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 matched

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

May combine two claims.

This is consistent with the findings that ASD participants tend toward smaller Δα2, show less behavioural surprise, and have larger metavolatility estimates.

“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).”

May combine two claims.

ω3 captures metavolatility, and higher values in ASD imply a belief that instability itself is unstable.

“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).”

May combine two claims.

This suggests that ASD participants distinguished less between unexpected and expected outcomes, provided they had learned the ground truth adequately.

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

May combine two claims.

These findings suggest that ASD involves difficulties representing and responding to environmental change, specifically a greater tendency to expect the unexpected.

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

Classified as probably not a substantial claim.

Whether an unexpected outcome is disregarded or taken seriously depends on how precise one's beliefs are about environmental change.

“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).”

Probably not a substantial claim: may not pass S3 (load-bearing).

The same analysis on log-transformed error rates gave very similar results.

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

May combine two claims.

If the NA system signals more high-level surprise in ASD, this may imply atypical cortical gain during sensory processing, making one disproportionately receptive to sensory inputs.

“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.”

• AssumptionThe authors state this as an assumption: Pupil size is an index of noradrenaline release from the locus coeruleus.

Probably not a substantial claim: may not pass S2 (checkable).

The study provides new understanding of the behavioural, algorithmic, and physiological processes involved in learning about and responding to environmental change in ASD.

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

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Neither data collection nor analysis was blinded to experimental conditions.

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

Probably not a substantial claim: may not pass S3 (load-bearing).

A further 57 NT participants took part in a replication of the main behavioural finding.

“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.”

• cites [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). sourceNot checked — no full text of the source
• cites [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). sourceNot checked — no full text of the source

May combine two claims.

This study's findings suggest that autistic adults tend to overestimate environmental volatility, which comes at the cost of forming stable expectations that produce adaptive surprise.

“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.”

Probably not a substantial claim: may not pass S3 (load-bearing).

For a between-subjects ANOVA with three levels and a group factor, a total of 48 participants was required for at least 90% power to detect an F-test effect size of 0.25.

“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.”

Probably not a substantial claim: may not pass S3 (load-bearing).

A qualified administrator used the Autism Diagnostic Observation Schedule, second edition, to measure symptom severity in the ASD participants.

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

• cites [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). sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing).

The target sample size was doubled to about 28 participants per group.

“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.”

• cites [61] Ioannidis, J.P. Why most discovered true associations are inflated. Epidemiology 19, 640–648 (2008). sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing).

The groups did not differ significantly in the mean number of eye-tracking trials included.

“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).”

May combine two claims.

The task kept the marginal probabilities of faces and houses equal at all times, so the prior probability of a face or house was always 50% before the tone; thus, expectations about the visual stimulus could only depend on the preceding tone.

“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.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Noradrenaline, dopamine, and acetylcholine are all plausible contributors to the neurobiological mechanisms behind responses to environmental change in ASD.

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

• 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 matched

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

Probably not a substantial claim: may not pass S3 (load-bearing).

Post-hoc power analysis showed that the 11 ASD participants in the pupil analysis gave 86% power to detect the observed mean positive β of 0.72.

“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).”

Probably not a substantial claim: may not pass S3 (load-bearing).

After exclusions, the analysed sample included 24 ASD participants and 25 NT participants.

“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).”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Eye-tracking data were available for only 14 NT and 11 ASD participants, because calibration failed for everyone wearing glasses and the eye tracker had a fatal technical failure.

“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.”

• AssumptionRests on an assumption: The available eye-tracking data from 14 NT and 11 ASD participants are representative of the full samples.

Probably not a substantial claim: may not pass S3 (load-bearing).

Full-scale IQ did not differ between the ASD and NT groups.

“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)”

• cites [53] Wechsler, D. & Hsiao-pin, C. WASI-II: Wechsler Abbreviated Scale of Intelligence (Pearson, 2011). sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Subjects and conditions were not assigned using randomisation.

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

Classified as probably not a substantial claim.

An independent clinician had previously diagnosed the ASD participants using DSM-IV or ICD-10 criteria.

“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].”

• 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).Not matched

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

Probably not a substantial claim: may not pass S3 (load-bearing).

An a priori calculation indicated that at least 14 participants were needed for 95% power to detect a similar effect in the NT group.

“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.”

Classified as probably not a substantial claim.

From a Bayesian standpoint, the simplest explanation for persistent overweighting of all sensory inputs relative to prior expectations would be a generally larger outcome learning rate α, reflecting chronic and inflexible overweighting of recent over past sensory history.

“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.”

Probably not a substantial claim: may not pass S3 (load-bearing).

The groups did not differ significantly in overall missing data percentage.

“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).”

Classified as probably not a substantial claim.

To protect the validity of the Bayesian modelling, participants were excluded if they had over 20% overall errors or mean reaction times more than two standard deviations from their group mean.

“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.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Age did not differ between the ASD and NT groups.

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