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. 2020 Mar 16;16(3):e1007700. doi: 10.1371/journal.pcbi.1007700

Social behavioural adaptation in Autism

Baudouin Forgeot d'Arc 1,2, Marie Devaine 3,4, Jean Daunizeau 3,4,*
Editor: Michael Moutoussis5
PMCID: PMC7108744  PMID: 32176684

Abstract

Autism is still diagnosed on the basis of subjective assessments of elusive notions such as interpersonal contact and social reciprocity. We propose to decompose reciprocal social interactions in their basic computational constituents. Specifically, we test the assumption that autistic individuals disregard information regarding the stakes of social interactions when adapting to others. We compared 24 adult autistic participants to 24 neurotypical (NT) participants engaging in a repeated dyadic competitive game against artificial agents with calibrated reciprocal adaptation capabilities. Critically, participants were framed to believe either that they were competing against somebody else or that they were playing a gambling game. Only the NT participants did alter their adaptation strategy when they held information regarding others' competitive incentives, in which case they outperformed the AS group. Computational analyses of trial-by-trial choice sequences show that the behavioural repertoire of autistic people exhibits subnormal flexibility and mentalizing sophistication, especially when information regarding opponents’ incentives was available. These two computational phenotypes yield 79% diagnosis classification accuracy and explain 62% of the severity of social symptoms in autistic participants. Such computational decomposition of the autistic social phenotype may prove relevant for drawing novel diagnostic boundaries and guiding individualized clinical interventions in autism.

Author summary

Autism or AS is mostly characterized by impairments in a very specific yet intricate skill set, namely: social intelligence. In this work, we focus on "social reciprocity", i.e. the continuous adaptation of one's behaviour that both moulds and appropriately responds to others' behaviour. Our working hypothesis is that social reciprocity deficits in people with AS derive from a basic inability to tune one's adaptation strategy to contextual knowledge about the stakes of social interactions (e.g., others' cooperative or competitive incentives). We ask participants to engage in simple interactive games with AI agents that are endowed with calibrated reciprocal adaptation capabilities. Critically, participants are framed to believe either that they are competing against somebody else (social framing) or that they are playing a gambling game (non-social framing). Only in the social condition do participants know about the (competitive) incentives of their opponents. Computational analyses of action sequences in the games show that, contrary to healthy controls, people with AS do not change their strategy according to whether they hold information regarding their opponents' incentives or not. In addition, these analyses yield 79% diagnosis out-of-sample classification accuracy (AS versus controls) and predict 62% of the severity of social symptoms in people with AS. This demonstrates the feasibility of AI-based quantitative assessments of social cognition and its deficits.

Introduction

Autism spectrum (AS, or ASD in DSM-5- American Psychiatric Association, 2013; Kenny et al., 2016) is a highly heterogeneous condition defined by altered reciprocal social interaction and inflexible patterns of behavior. Despite refinement of diagnostic tools in the last decades, standardized clinical assessments have limited reliability regarding milder forms of autism seen in adults and adolescent: we still lack a solid test for autism [1]. In turn, the clinical identification of autism relies on sociopsychological constructs such as interpersonal contact and reciprocity, which remain elusive and beyond the reach of objective measurement [2,3]. This work evaluates the clinical relevance of a computational decomposition of the latter notion, relying on the quantitative assessment of adaptation strategies in the context of simple dyadic games.

Most recent neurocognitive work on autism, including computational modelling approaches, offers an excellent mechanistic account of general perceptual and/or cognitive deficits [49]. They, however, cannot explain the specific issues autistic people face with social interactions [10]. Instead, the latter are typically viewed as resulting from an underlying impairment in Theory of Mind or ToM [11,12], i.e. the ability to understand others' covert mental states. ToM impairments have been repeatedly evidenced in autistic children using tests of, e.g., false belief understanding [1315], sarcasm/irony detection [16,17] or moral evaluation [18,19]. However, these tests yield quite unreliable results and have poor psychometric properties in older individuals [20], including ceiling effects in adolescents and adults [21,22]. This is why, although theoretically relevant to autism, quantitative tests of ToM has had only limited impact on diagnosis or intervention to date [23].

These mixed results call for a refinement of the "mind blindness" theory of social deficits in autism [24,25]. In line with recent pleas for "second-person"—i.e. interactionist—approaches to social cognition [2629], we propose to reconsider how sociocognitive skills such as ToM may contribute to reciprocity. Reciprocity is a feature of ecological social interactions, the typical intricacy of which overwhelms autistic people. Not only may subtle variations in social signals (e.g., facial expressions, speech prosody, etc …) reflect profoundly different mental states, but the stakes of social exchanges may be dynamic, partially implicit, multiple and even conflicting (e.g., impose a deal and induce sympathy). In this context, we define reciprocity as the continuous adaptation of one's behaviour that both moulds and appropriately responds to others' behaviour [2,30]. Our working assumption is two-fold. First, we reason that reciprocity relies on the ability to tune one's adaptation strategy to contextual knowledge about the stakes of social interactions (e.g., others' cooperative or competitive incentives). In contrast to neurotypic controls [31], autistic people may thus not benefit from information regarding others' incentives when adapting to them. Second, reciprocity may be decomposed into basic (social and non-social) computational components. Arguably, it should improve with the ease with which one switches between different cognitive modes and/or behavioural strategies, which we term flexibility. Recent theoretical [32] and empirical [33] work on the evolution of mentalizing shows that it also critically relies upon ToM sophistication, as proxied by the depth of recursive beliefs (as in "I believe that you believe that I believe …"). ToM sophistication and flexibility thus provide a minimal computational basis for decomposing reciprocity, which should explain the severity of social symptoms in autism.

We test these assumptions using simple repeated dyadic games, whereby participants play against learning machines endowed with artificial ToM of calibrated sophistication (Baker et al., 2011; Devaine et al., 2014b; Yoshida et al., 2008). To win, participants' must learn to anticipate their opponent's next choice and/or try to influence it. Critically, participants are not told about the algorithmic nature of their opponents. Rather, we have them believe either that they were competing against somebody else (social framing) or that they were playing a gambling game (non-social framing). The objective information available to the participants on each trial is the same for both conditions (actions and feedbacks). However, only in the social condition do participants hold information regarding their opponent's competitive incentives. Critical here is the notion that people may engage the game equipped with a behavioural repertoire composed of many adaptation strategies. In appropriate experimental contexts (in particular: dyadic games), these can be disclosed from computational analyses of trial-by-trial choice sequences. One can then measure and compare the computational properties of people's adaptation repertoire, in particular: its ToM-sophistication and its flexibility [31]. In what follows, we refer to these as "computational phenotypes" of social reciprocity. As we will see, they provide a quantitative insight into the specificity of the autistic social phenotype.

Results

We asked 24 adult participants with ASD and 24 control participants to play repeated dyadic games against artificial "mentalizing" opponents, which differ in their ToM sophistication (hereafter: k-ToM agents, see below). In total, each participant played 4x2x2 = 16 games (4 opponent types, 2 framing conditions, 2 repetitions), where each game consisted in 60 successive trials. To succeed, subjects had to anticipate and predict the behaviour of their opponent, who hid himself in one out of two possible locations at each trial (see Fig 1 below).

Fig 1. Experimental protocol.

Fig 1

Left: social framing ("hide-and-seek" game). Right: non-social framing (gambling game). At each trial, participants have 1300 msec to pick one of the two options (social framing: wall or tree, non-social framing: left or right slot machine). Feedback is displayed for 1 sec; and includes the trial outcome (win or loss) and the actual winning option (social framing: character picture, non-social framing: three identical items).

Opponents either followed a predetermined pseudo-random sequence with a 65% bias for one hand (RB), or were designed to deceive the participants from learned anticipations of their behaviour (0-ToM, 1-ToM and 2-ToM). The difference between k-ToM opponents lies in how they learn from the past history of participants’ actions, where k refers to their calibrated ToM sophistication. In brief, 0-ToM does not try to interpret the participants' action sequence in terms of a strategic attempt to win. Rather, it simply assumes that abrupt changes in the participants' behaviour are a priori unlikely. It thus tracks the evolving frequency of participants’ actions, and chooses to hide the reward where it predicts the opponent will not seek. It is an extension of “fictitious play” learning [34], which can exploit participants' tendency to repeat their recent actions. In contrast, 1-ToM is equipped with (limited) artificial mentalizing, i.e. it attributes simple beliefs and desires to participants. More precisely, it assumes that participants’ actions originate from the strategic response of a 0-ToM agent that attempts to predict its own actions. Note that the computational sophistication of artificial mentalizing is not trivial, since 1-ToM has to explicitly represent and update its (recursive) belief about its opponents' beliefs. Practically speaking, 1-ToM learning essentially consists in an on-line estimation of 0-ToM’s parameters (e.g., learning rate and behavioural temperature) given the past history of both players’ actions. This makes 1-ToM a so-called “meta-Bayesian” agent [32,35] that can outwit strategic opponents who do not mentalize when competing in the game (such as 0-ToM). Although 1-ToM is mentalizing, it is not capable of dealing with other mentalizing agents. This is the critical difference between 1-ToM and 2-ToM. At this point, suffices to say that 2-ToM is an artificial mentalizing agent that can learn to predict how other mentalizing agents (such as 1-ToM) will behave.

Critically, participants were not cued about opponent conditions. This implies that they had to adapt their behaviour according to their understanding of the history of past actions and outcomes. In addition, except in the control (RB) condition, there is no possibility to learn the correct answer from simple reinforcement. This is because k-ToM artificial learners exhibit no systematic preference for any particular action. Further details regarding the experimental protocol as well as k-ToM artificial agents can be found in the Methods section below.

We first focus on peoples' ability to alter their adaptation strategy as a function of whether or not they hold information about their opponents’ competitive incentives. Fig 2 below summarizes the performance results, in terms of the net rate of correct answers in each of 4x2 conditions, for both (NT and AS) groups.

Fig 2. Behavioural performance results.

Fig 2

Group average net rate of correct answers (y-axis) against the four opponent types (x-axis) for both framing conditions (blue: social, red: non-social) in both AS (left) and control (right) participants. Note: The net rate of correct answers is defined as (nc-ni)/(nc+ni), where nc and ni are the number of correct and incorrect responses, respectively. Hence, it is null when participants perform at chance level (50% accuracy). In this and all subsequent figures, error bars depict the standard error around the mean.

One can see that the performance patterns are markedly different between NT and AS participants. To begin with, the performance of NT participants qualitatively reproduces previous experiments with healthy human adults [31]. In brief, in the non-social framing condition, NT participants eventually lose against artificial mentalizing agents (1-ToM and 2-ToM) whereas they maintain their earnings in the social framing condition. The AS group however, seems to show no effect of the framing manipulation, i.e. their performance pattern across opponents is the same, irrespective of whether they know about their opponent’s competitive incentives. Interestingly, they seem to lose against artificial mentalizing agents (as NT controls in the non-social framing condition), but they outperform NT controls against non-mentalizing learning agents (0-ToM). We performed a pooled variance ANOVA to assess the statistical significance of these observations. We found a significant three-way interaction between group (AS vs NT), opponent and framing (F[3,690] = 3.6, p = 0.014, R2 = 1.5%), a significant interaction between group and opponent (F[3,690] = 9.5, p<10−4, R2 = 4.0%) and a main effect of opponent (F[3,690] = 33.7, p<10−4, R2 = 12.8%). We then looked more closely at the three-way interaction using post-hoc tests. In the NT group, there was a main effect of opponent (F = 4.5, p = 0.004), no main effect of framing (F = 2.6, p = 0.11) but a significant interaction opponent x framing (F = 3.7, p = 0.011). In the AS group, there was a main effect of opponent (F = 38.7, p<10−4) but no main effect of framing (F = 0.5, p = 0.46) nor interaction (F = 1.3, p = 0.27). In other terms, only NT participants show the opponent x framing interaction. This is because NT participants perform better in the social than in the non-social framing only against artificial mentalizing agents (p<10−4). Now focusing on performances against artificial mentalizing agents, there was a significant interaction between group and framing (p = 0.001). This is because against 1-ToM and 2-ToM, NT participants perform significantly better than AS people against artificial mentalizing agents in the social framing (p<10−4) but not in the non-social framing (p = 0.65). Besides, AS participants perform significantly better than NT participants against 0-ToM (p<10−4), and this effect does not depend upon the game's framing (p = 0.46).

One of the main differences between NT and AS participants is thus that the latter seem to be insensitive to information regarding their opponents’ competitive incentives. This is in fact confirmed by additional analyses showing that (i) performance variations induced by opponent types in different framing conditions are significantly correlated (see section 5 in S1 Text), and (ii) model-free decompositions of their trial-by-trial choice sequences show no effect of framing (see section 6 in S1 Text).

At this point, we asked whether we could classify AS and NT participants based upon their performance patterns in the task. Averaging performances over repetitions yielded a feature space of 8 dimensions (4 opponent types, 2 framings), which was then fed to a classifier based upon logistic regression [36]. Test classification accuracy was evaluated using a simple leave-one-out cross-validation scheme. The classifier achieved 73% of correct out-of-sample classifications, which is statistically better than chance (p = 0.001). This will serve as a reference point for evaluating the added-value of computational phenotypes.

We now ask whether differences in computational phenotypes such as ToM sophistication and flexibility predict social deficits. We considered a set of eight distinct adaptation strategies that constitute peoples' potential behavioural repertoire. Somewhere at the end of the sophistication spectrum lie social adaptation strategies that derive from recursive ToM [31,37,38]. We also considered adaptation strategies that take simpler forms, ranging from mundane heuristics, to trial-and-error learning, to cognitive shortcuts of ToM that simply care about tracking others' overt reaction to one's own actions [39]. Each of these adaptation strategies corresponds to a formal learning/decision model that provides a probabilistic prediction of observed peoples' trial-by-trial choice sequences. We then performed a subject-specific bayesian model comparison of these models. Note that, in contrast to the NT group which shows strong inter-individual variability in terms of behavioural strategies, trial-by-trial choice sequences of most AS players, in both framing conditions, are captured by a single model, namely: "influence learning" (see section 7 in the Supplementary Text). We then evaluated both the flexibility (f^, rate of strategy switching) and the ToM-sophistication (k^, recursive depth of beliefs) of peoples' behavioural repertoire. We refer the interested reader to the Methods section.

We first asked whether control and AS participants would show differences in their repertoire's ToM-sophistication. Fig 3 below shows the repertoire's ToM-sophistication k^ averaged across repetitions, across opponent conditions and across participants, for each group and for both framing conditions.

Fig 3. Model-based analysis of trial-by-trial choice sequences: ToM sophistication scores.

Fig 3

ToM sophistication scores are shown as a function of framing conditions (left: social, right: non-social) for both control (gray) and AS participants (back).

A simple ANOVA shows no evidence for an interaction between group and framing (F[1,46] = 0.6, p = 0.42, R2 = 1.4%), no main effect of framing (F[1,46] = 1.8, p = 0.18, R2 = 3.8%), but a significant group effect (t[46] = 1.9, p = 0.03, R2 = 7.3%). Post-hoc tests show that this group difference is mostly driven by the social framing condition (t[46] = 1.9, p = 0.03, R2 = 7.5%), whereas there is no significant group difference in the non-social condition (t[46] = 1.1, p = 0.13, R2 = 2.7%). In other words, only in the social framing do control participants exhibit higher ToM-sophistication than AS participants.

We then investigated whether control and AS participants show differences in their repertoire's flexibility. Fig 4 below shows the repertoire's flexibility f^, both across framings and across repetitions. The former measures peoples' tendency to change their adaptation strategy in response to information regarding others' incentives. The latter can be thought of as a base rate of strategy switching, across identical situations. Note that, when evaluating flexibility between repetitions separately in both framing conditions, only in the NT group is it significantly increased when participants know about others' incentives (see section 8 in S1 Text). Here again, there is no significant interaction between group and flexibility type (F[1,46] = 0.55, p = 0.46, R2 = 1.2%), but there is a significant main effect of flexibility type (F[1,46] = 5.54, p = 0.02, R2 = 10.7%) and a main effect of group (t[46] = 3.4, p = 0.001, R2 = 20.4). Post-hoc tests show that this group difference in repertoire's flexibility is strong both across framings (t[46] = 3.4, p = 0.001, R2 = 20.7%) and across repetitions (t[46] = 2.8, p = 0.004, R2 = 14.4%). Also, AS participants show no "flexibility gap", i.e. no difference between flexibility across framings and flexibility across conditions (p = 0.26). This contrasts with control participants, who exhibit a significant flexibility gap (p = 0.03).

Fig 4. Model-based analysis of trial-by-trial choice sequences: Repertoire's flexibility.

Fig 4

The repertoire's flexibility is shown across framing conditions (left) and across repetitions (right) for both control (gray) and AS participants (back).

If only, this computational analysis confirms that AS participants exhibit a distinct pattern of social computational phenotypes (when compared with NT controls). But do the latter provide diagnosis-relevant information, above and beyond performance scores in the task? When augmenting the previous classifier with social computational phenotypes, classification accuracy reaches 79% of correct out-of-sample classifications (p<10−4). This matches the diagnosis reliability of trained psychologists, as measured in terms of the inter-rater agreement rate in the use of the standard Autism Diagnosis Observation Schedule [40]. Note that the probability that a (yet unseen) individual will be better classified with than without computational phenotypes is 0.79, and that inter-individual variability in flexibility does not correlate with ToM sophistication (see section 9 in the Supplementary Text). This is important, since it means that all computational phenotypes bring additional, diagnosis-relevant, information.

Finally, we asked whether we could predict, from estimated computational phenotypes, inter-individual variations in symptom severity among AS participants. More precisely, we focused on the 'social' and ‘stereotyped behavior’ subscores of the ADOS scale, which quantify social and non-social deficits, respectively. We found that inter-individual differences in computational phenotypes predict social deficits with high accuracy (F[4,15] = 6.1, p = 0.004, R2 = 62.1%), but do not predict non-social deficits (F[4,15] = 1.5, p = 0.25, R2 = 28.8%). Post-hoc univariate tests actually show that social deficits significantly decrease with ToM sophistication improvement Δk^=k^sock^NS (t[18] = -1.8, p = 0.04, R2 = 15.9%) and with the flexibility gap Δf^=f^framingf^repetitions (t[18] = -2.6, p = 0.009, R2 = 27.5%). This concludes our computational decomposition of social reciprocity and its alteration in AS.

Discussion

Maybe the most striking result of our work is that autistic people are insensitive to the task framing, i.e. they do not adjust their adaptation strategy in response to information about others' incentives. Recall that we demonstrated this in three different ways: (i) AS participants show no difference between performance or ToM-sophistication scores between framing conditions (cf. Fig 2), (ii) model-free decompositions of their trial-by-trial choice sequences show no effect of framing (see section 6 in S1 Text), and (iii) their behavioural repertoire exhibits very low flexibility across framing conditions (cf. Fig 4). Importantly, participants' debriefing showed that the framing manipulation was similarly credible in both groups of subjects (see section 2 in S1 Text). In line with social motivational theories of autism [41], one may argue that, in contrast to control participants, AS participants may not have been interested enough to invest the cognitive effort required for improving their performance in the social framing condition. Such global motivational and/or attentional interpretations are unlikely however, because AS participants actually outperform controls against 0-ToM in the social framing condition. In addition, financial incentive manipulations have no effect on performance in the game, for both AS and NT groups. This is despite the fact that both groups are consistently and equally sensitive to monetary incentives in the context of cognitive control tasks (see section 3 in S1 Text). Taken together, our results support the idea that adults with AS are not unwilling, but rather unable to exploit knowledge about the stakes of social interactions when adapting to others. However, it remains possible that social motivation might account for other aspects of autism, for example in altering the normal scaffolding of social cognition during development.

Of particular interest is the finding that autistics outperform controls in certain conditions of the game. In particular: they win against non-mentalizing learning opponents, irrespective of the task framing. Given that control participants merely achieve null earnings in the same condition, this result is a striking demonstration of the efficiency of autistics' behavioural strategy. Although strengths and peaks of ability have been reported since the first descriptions of autism as core features, they have been largely ignored in the more recent scientific literature, with few exceptions (Ostrolenk, Forgeot d'Arc, Jelenic, Samson, & Mottron, 2017). In fact, a possible explanation for such success is that non-mentalizing agents are somehow more "autistic", i.e. more similar to patients' expectations (see below). This is reminiscent to the so-called "social interaction mismatch" hypothesis, which suggests that autistic persons find it easier to relate to other autistic persons [42,43]. In any case, future studies including measures of everyday functioning might test whether such performance peaks in the task relate to autistic strengths in real-life situations.

One may ask whether the performance pattern we report here may not be due to the fact that AS individuals are typically slower than neurotypic people. This would be because in the task, participants have only 1.3 second to respond, which would potentially be too short for AS individuals to reach the correct decision. The ensuing "behindhand errors" could then confound analyses of performance data. In particular, this would explain performance differences in situations where strategic thinking is (in principle) most needed, i.e. against mentalizing agents in the social framing condition. We find that this is an unlikely confound however, because the pattern of behindhand responses (i.e. responses whose RT reaches the decision time limit) and performance are globally inconsistent with each other (cf. section 4 of the S1 Text). Nevertheless, we acknowledge that dual analyses of concurrent performance/RT data (such as those based upon accumulation-to-bound models that generate both choice and RT data) may indeed provide insights into the neural implementational processes underlying our behavioural results. This may be addressed in future work.

One may also question the nature of the social cognitive processes that the task assesses. Although our original intention was to address the elusive notions of contact and reciprocity, the task itself falls short of a few important features of real-life social interactions. For example, it ignores the diversity of social signals (e.g., verbal/body language, facial expressions) and modulatory factors (e.g., in-group/out-group context, familiarity) that are relevant for establishing contact with others. It also does not involve changes in others' intentions (e.g., competitive/cooperative) and/or attitudes (e.g., friendly/aggressive, dominant/submissive), which would be necessary to assess certain aspects of social reciprocity. Instead, it focuses on peoples' ability to respond and/or influence others' actions in a simplified competitive setting. Clearly, this cannot account for the breadth of social cognitive processes that underlie contact and reciprocity. One might even think that task performance may not load very heavily on social cognitive processes, when compared with other instrumental processes (e.g., working memory or reasoning). This is unlikely, however, given that we have shown, in a very large online population sample, that a very small amount of inter-individual variance in the game's performance can be predicted from cognitive control skills [44]. In any case, we think the simplicity of our task design also has its virtues. This is because it eventually enables us to construct a non-social control condition that is matched with the social condition in terms of goal-relevant information (cf. trial-by-trial feedbacks). This turns out to be critical to discriminate between AS and NT participants.

Now is our approach really useful for clinical purposes? That it can achieve 79% of accuracy in diagnostic prediction is only relevant for comparing this test with other tests of the same kind, or as a proof-of-concept demonstration. In fact, the long-term goal of approaches of this kind is not to reflect the diagnosis per se (which is irrelevant), but rather to guide clinical decisions. Ideally, a useful approach should reflect a pathological mechanism and predict outcome and/or treatment response. Diagnosis is just a proxy for such prediction, and one has to admit that current psychiatric categorical diagnoses are not quite satisfactory in this regard [45]. In turn, evaluating the clinical utility of our approach would require assessing how it relates to genetic variants, brain metrics, specific outcomes or response to intervention. This is beyond the scope of the current work, but we intend to pursue these issues in forthcoming publications. That our approach predicts 62% of inter-individual variance in social symptoms may be more interesting at first sight. This is because explaining variability beyond categorical diagnosis may be relevant for identifying clinical subcategories. But here again, establishing the clinical utility of such findings can only be done on the basis of, e.g. treatment outcome prediction. In addition, such significant but modest explanatory power is a reminder that social symptoms in AS are not solely due to mentalizing deficits. For example, they could be driven by some other issues in social cognition, including, but not limited to, social anxiety [46] or the misperception/misunderstanding of social norms [47,48]. Evaluating the relative contribution of these processes to social symptoms is clearly a promising research avenue for the computational psychiatric approach to AS.

Let us now discuss the main qualitative difference between adaptation strategies in people with and without autism. If anything, the adaptation strategies of NT control participants exhibit strong intra- and inter-individual variability. In contrast, trial-by-trial choice sequences of most AS players, in both framing conditions, are captured by a single model, namely: "influence learning" [39]. From a computational standpoint, this model possesses broad adaptive fitness because it essentially is a generic way of dealing with environments that react to one's actions [33]. In other words, influence learning can be seen as an all-purpose cognitive toolkit that would be expected to perform well in a wide range of contexts, excluding competitive interactions with mentalizing agents (cf. pattern of performances against RB, 0-ToM, 1-ToM and 2-ToM in section 9 in the S1 Text). Note that this explains why AS participants perform better than NT controls against non-mentalizing agents (in both framing conditions), and why they show worse performance against mentalizing agents (in the social framing condition). That they rely on influence learning in both framings also explains their lower flexibility score, as well as the absence of a framing effect on raw performance. Strictly speaking, an agent capable of influence learning is thus not "mind blind", but it cannot adjust its behavioural strategy to the intentions of mentalizing agents. In other words, even if equipped with a sophisticated perceptual apparatus (that would enable the recognition of ecological social signals), an influence learner would show limited social reciprocity. Reliance on this -or similar- adaptation strategy thus provides a computational explanation for an important aspect of the autistic social phenotype, which would not depend upon motivational factors and/or cognizance of the social context.

Obviously, our experimental claim does not go as far as to assert that the behavioural repertoire of autistic people is generally limited to influence learning. Nevertheless, it clearly exhibits subnormal flexibility, which corroborates previous reports of executive dysfunction in autism [5,49,50]. Note that, together with ToM sophistication, our computational measure of flexibility contributes to predict social symptoms and AS diagnosis. It does not, however, relate to the ADOS' index of repetitive behaviours. This may be because repetitive behaviours in autism tend to decrease with age [51] and might not be consistently accessible through direct observation during the administration of the ADOS [3]. Interestingly, only in the NT group is flexibility (between repetitions) significantly higher in the social than in the non-social framing condition (see section 7 in S1 Text). And inter-individual variability in flexibility does not correlate with ToM sophistication (see section 8 in the Supplementary Text). This suggests that impairments in flexibility may contribute to social deficits, independently of mentalizing skills [5254]. This is important, because inter-individual differences in flexibility and ToM sophistication may separately contribute to diversity in the autism spectrum. Thus, these computational phenotypes may serve to draw novel diagnostic boundaries and guide individualized clinical interventions [6].

Methods

Ethics statement

Behavioural assessments were performed in accordance with institutional ethical guidelines, which comply with the guidelines of the declaration of Helsinki. The research protocol was approved by the Ethical Committee of the Hôpital Rivière-des-Prairies, Montréal, where the tests were performed.

Experimental methods

Participants: n = 24 adults with ASD without mental nor language deficiency and n = 24 NT control subjects participated in the study. All subjects were French speakers (Québec), and both groups were matched in terms of gender balance (AS: 21 males, NT: 21 males), age (AS: 25.5 y.o. ± 5.7; NT: 27.9 y.o. ± 8.6) and IQ (AS: 104 ± 17; NT: 106 ± 14). AS participants were assessed with ADOS-G and met DSM-5 criteria for ASD. NT participants went through a semi-structured interview to screen for any psychiatric treatment history, learning disorders, personal or family history (2 degrees) for mood disorder, ASD or schizophrenia. No included participant reported strong depressive symptoms (Beck depression Inventory score<20). All participants gave their informed consent, were fully debriefed at the end of the experiment, and received a financial compensation for their participation.

The behavioural task consists of a computerized game (60 trials each) with two framing conditions. In the social condition, the task was framed as an online competitive game with someone else. In the non-social condition, it was framed as a gambling game. In fact, both games were played against four different learning algorithms with different artificial mentalizing sophistication (ranging from a random sequence with a bias to so-called 2-ToM agents: see below). Note that, on top of framing and opponent factors, we also varied the financial payoff attached to a correct answer in the games. More precisely, the maximal payoff that participants could earn over one game session was either 10$ (high reward condition) or 1 cent (low reward condition). This manipulation, however, did not induce any effect (cf. section 3 of the S1 Text). In what follows, we thus refer to this experimental factor as a repetition of the task conditions. At each trial, subjects had 1300 ms to make a binary choice (the place to hide or the slot machine to try), which was fed to the learning algorithms to compute online predictions of the participant's action at the next trial. In total, each participant performed 2×4×2 = 16 games (2 framings, 4 opponent types, 2 repetitions) in a pseudo-randomized order. We refer the interested reader to the S1 Text for more details regarding the experimental protocol.

Computational modelling of adaptation strategies

In this section, we give a brief overview of the set of candidate learning/decision models, with a particular emphasis on k-ToM models (because these are also used as on-line algorithms during the experimental phase). We will consider repeated dyadic (two-players) games, in which only two actions are available for each player (the participant and his opponent). Hereafter, the action of a given agent (resp., his opponent) is denoted by aself (resp., aop). By convention, actions aop and aself take binary values encoding the first (a = 1) and the second (a = 0) available options. A game is defined in terms of its payoff table, whose entries are the player-specific utility U(aself,aop) of any combination of players' actions at each trial. In particular, competitive social interactions simply reduce to anti-symmetric players’ payoff tables (see Table 1 below).

Table 1. Competitive payoff table (hider's payoff, seeker's payoff).

Participants play the role of the seeker, the opponent is the hider.

Hider
Seeker
a = 1 a = 0
a = 1 1,0 0,1
a = 0 0,1 1,0

According to Bayesian decision theory, agents aim at maximising expected payoff V = E[U(aself,aop)], where the expectation is defined in relation to the agent's uncertain predictions about his opponent's next move. This implies that the form of the decision policy is the same for all agents, irrespective of their ToM sophistication. Here, we consider that choices may exhibit small deviations from the rational decision rule, i.e. we assume agents employ the so-called "softmax" probabilistic policy:

P(aself=1)=11+exp(ΔVβ) (1)

where P(aself = 1) is the probability that the agent chooses the action aself = 1, ΔV is the expected payoff difference (between actions aself = 1 and aself = 0), and β is the so-called behavioural "temperature" (which controls the magnitude of deviations from rationality). The sigmoidal form of Eq 1 simply says that the probability of choosing the action aself = 1 increases with the expected payoff difference ΔV, which is given by:

ΔV=pop(U(1,1)U(0,1))+(1pop)(U(1,0)U(0,0))=2pop1 (2)

where pop is the probability that the opponent will choose the action aop = 1, and the second line derives from inserting the above payoff matrix (Table 1). In brief, Eq 2 simply says that participants are rewarded for correctly guessing where their opponent is hiding.

Let us now summarize the mathematical derivation of k-ToM models, which essentially differ in how they estimate pop from the repeated observation of their opponent's behaviour. We will see that k indexes a specific form of ToM sophistication, namely: the recursive depth of learners’ beliefs (as in "I believe that you believe that I believe …"). Note that k-ToM’s learning rule can be obtained recursively, starting with 0-ToM [32].

By convention, a 0-ToM agent does not attribute mental states to his opponent, but rather tracks his overt behavioural tendency without mentalizing. More precisely, 0-ToM agents simply assume that their opponents choose the action aop = 1 with probability pop = s(xt), where the unknown log-odds xt varies across trials t with a certain volatility σ0 (and s is the sigmoid function). Observing his opponent's choices gives 0-ToM information about the hidden state x, which can be updated trial after trial using Bayes rule, as follows:

μt0μt10+Σt0(atops(μt10))Σt011Σt10+σ0+s(μt10)(1s(μt10)) (3)

where μt0 (resp. Σt0) is the approximate mean (resp. variance) of 0-ToM's posterior distribution p(xt0|a1:top). Inserting p^t+1op=E[s(xt+1)|a1:top] into Eq 1 now yields 0-ToM's decision rule. Here, the effective learning rate is the subjective uncertainty ∑0, which is controlled by the volatility σ0. At the limit σ0→0, Eq 3 converges towards the (stationary) opponent's choice frequency and 0-ToM essentially reproduce "fictitious play" strategies [34].

0-ToM's learning rule is the starting point for a 1-ToM agent, who considers that she is facing a 0-ToM agent. This means that 1-ToM has to predict 0-ToM's next move, given his beliefs and the choices' payoffs. The issue here is that 0-ToM's parameters (volatility σ0 and exploration temperature β) are unknown to 1-ToM and have to be learned, through their non-trivial effect on 0-ToM's choices. At trial t+1, a 1-ToM agent predicts that 0-ToM will chose the action aop = 1 with probability pt+1op,0=sv0(xt0,a1:t), where the hidden states xt0 lumps σ0 and β together and the mapping v0 is derived from inserting 0-ToM's learning rule (Eq 3) into Eqs 1 and 2. Similarly to 0-ToM agents, 1-ToM assumes that the hidden states xt0 vary across trials with a certain volatility σ1, which yields a meta-Bayesian learning rule similar in form to 0-ToM's, but relying on first-order meta-beliefs (i.e. beliefs about beliefs). In brief, 1-ToM eventually learns how her (0-ToM) opponent learns about herself, and acts accordingly (cf. Eqs 1 and 2).

1-ToM agents are well equipped to deal with situations of observational learning. However, when it comes to reciprocal social interactions, one may benefit from considering that others are also using ToM. This calls for learning strategies that rely upon higher-order meta-beliefs. By construction, k-ToM agents (k≥2) consider that their opponent is a κ-ToM agent with a lower ToM sophistication level (i.e.: κ<k). Importantly, the sophistication level κ of k-ToM's opponent has to be learned, in addition to the hidden states xκ that control the opponent's learning and decision making. The difficulty for a k-ToM agent is that she needs to consider different scenarios: each of her opponent's possible sophistication level κ yields a specific probability pt+1op,κ=svκ(xtκ,a1:t) that she will choose action aop = 1. The ensuing meta-Bayesian learning rule entails updating k-ToM's uncertain belief about her opponent's sophistication level κ and hidden states xκ:

λtk,κ[λt1k,κptop,κκ'<kλt1k,κ'ptop,κ']atop[λt1k,κ(1ptop,κ)κ'<kλt1k,κ'(1ptop,κ')]1atopμtk,κμt1k,κ+λtκΣtk,κWt1κ(atopsvκ(μt1k,κ))Σtk,κ[(Σt1k,κ+σk)1+s'vκ(μt1k,κ)λtκWt1κTWt1κ]1 (4)

where λtk,κ is k-ToM's posterior probability that her opponent is κ-ToM, and Wκ is the gradient of vκ with respect to the hidden states xκ. Eq 4 also captures 1-ToM’s learning rule, when setting λt1,01. Note that although the dimensionality of k-ToM's beliefs increases with k, k-ToM models do not differ in terms of the number of their free parameters. More precisely, k-ToM’s learning and decision rules are entirely specified by their prior volatility σk and behavioural temperature β.

Formally speaking, only k-ToM agents with k≥1 are mentalizing about others' covert mental states, i.e. represent and update others’ beliefs. They can do this because they adopt the intentional stance [55], i.e. they assume that pop is driven by their opponent's hidden beliefs and desires. More precisely, they consider that the opponent is himself a Bayesian agent, whose decision policy pop = P(aop = 1) is formally similar to Eq 1. This makes k-ToM meta-Bayesian learners [35] that relies upon recursive belief updating ("I believe that you believe that I believe …"). Critically, the recursion depth k induces distinct ToM sophistication levels, whose differ in terms of how they react to the history of players’ actions in the game.

With the exception of 0-ToM, we so far only described sophisticated learning models that are capable of (artificial) ToM. But clearly 0-ToM is not the only way people may learn in social contexts without mentalizing. We thus consider below other adaptation strategies that may populate peoples’ behavioural repertoire.

First, let us consider a heuristic learning model, whose sophistication somehow lies in between 0-ToM and 1-ToM. In brief, "influence learning" adjusts a 0-ToM-like learning rule to account for how her own actions may influence her opponent’s behaviour [39]:

pt+1op=ptop+η(atopptop)predictionerror+λptop(1ptop)(12atselfβs1(ptop))"influence"adjustmentterm (5)

where η (resp. λ) controls the relative weight of its prediction error (resp. the “influence” adjustment term). Numerical simulations show that, in a competitive game setting, Inf wins over 0-ToM but loses against k-ToM players with k≥1. In other terms, although it is in principle able to adapt to reactive environments, Inf cannot successfully compete with learners endowed with mentalizing [33].

Second, participants may learn by trial and error, eventually reinforcing the actions that led to a reward. Such adaptation strategy is the essence of classical conditioning, which is typically modelled using reinforcement learning or RL [56]. In this perspective, participants would directly learn the value of alternative actions, which bypasses Eq 2. More precisely, an RL agent would update the value of the chosen option in proportion to the reward prediction error, as follows:

{Vt+1i=Vti+α(RtVti)ifactionatself=iwaschosenVt+1i=Vtiotherwise (6)

where Rt=U(atself,atop) is the last reward outcome and α is the (unknown) learning rate. At the time of choice, RL agents simply tend to pick the most valuable option (cf. Eq 1).

Third, an even simpler way of adapting one's behaviour in operant contexts such as this one is to repeat one's last choice if it was successful and alternate otherwise. This can be modeled by the following update in action values:

{Vt+1i=Rtifactionatself=iwaschosenVt+1i=Rtotherwise (7)

This strategy is called win-stay/lose-switch (WS), and is almost identical to the above RL model when the learning rate is α = 1. Despite its simplicity, WS can be shown to have remarkable adaptive properties [57].

Last, the agent may simply act randomly, which can be modeled by fixing the value difference to zero (ΔV = 0). Although embarrassingly simple, this probabilistic policy eventually prevents one's opponent from controlling one's expected earnings. It thus minimizes the risk of being exploited at the cost of providing chance-level expected earnings. It is the so-called "Nash equilibrium" of our "hide and seek" game. Since we augment this model with a potential bias for one of the two alternative options (as all the above learning models), we refer to it as biased Nash or BN.

Empirical estimates of computational phenotypes

Our working hypothesis is that people may not always rely on the same adaptation strategy across different game sessions or conditions. Rather, they select a strategy from among a repertoire, whose flexibility and ToM sophistication define our computational phenotypes. The empirical estimation of these thus consists of three steps. First, we perform a statistical (Bayesian) comparison of learning models [58]. For each subject, we fit trial-by-trial actions sequences a1:60 with each learning model (m∈{BN, WSLS, RL, 0-ToM, Inf, 1-ToM, 2-ToM, 3-ToM}) using a variational Bayesian approach [59,60]. This eventually yields 8x48x4x2x2 = 6144 model evidences p(a1:60|m) (8 models, 48 participants, 4 opponent conditions, 2 framing conditions, 2 repetitions).

Second, we define the repertoire's flexibility f^(1,2) (between conditions 1 and 2) in terms of the posterior probability that a given participant employs different adaptation strategies across two conditions:

f^(1,2)=p(m(1)m(2)|a1:60(1),a1:60(2))=1mp(m(1)=m|a1:60(1))p(m(2)=m|a1:60(2)) (8)

where m(1) (resp. m(1)) is the participants' adaptation strategy in the first (resp. second) condition, p(m(1)=m|a1:60(1)) (resp. p(m(2)=m|a1:60(2))) is the probability that the participant had an adaptation strategy m given his trial-by-trial choice sequence a1:60(1) (resp. a1:60(2)) in condition 1 (resp. condition 2). Note that we measure the repertoire's flexibility f^ both across framings and across repetitions.

Third, we define the repertoire's ToM-sophistication k^ in terms of the expected depth of recursive belief update:

k^=E[k|a1:60]=kkp(k|a1:60) (9)

where p(k|a1:60) = p(m = "kToM"|a1:60) is the posterior probability of model k-ToM given the participant's trial-by-trial choice sequence a1:60. Note that we restrict the summation in Eq 9 to k-ToM models, because the depth k of recursive beliefs is not defined for the other learning models. Note that we measure the repertoire's ToM-sophistication k^ in both framing conditions (social and non-social).

All statistical analyses were performed using the VBA toolbox [36], which contains the above eight learning/decision models as well as the bayesian statistical machinery required for model inversion.

Supporting information

S1 Text. This document provides supplementary information regarding: the experimental protocol (section 1), the credibility of the framing manipulation (section 2), the effect of motivational manipulations on the game's performance (section 3), differences in reaction times (section 4), model-free Volterra decompositions of trial-by-trial choice sequences (section 5), differences in adaptation strategies between AS and NT participants (section 6), the impact of the framing manipulation on the repertoire's flexibility (section 7), the statistical relationships between computational phenotypes (section 8), and their relationship with performance in the game (section 9).

(DOCX)

Acknowledgments

Authors thank Alexandra Duquette and Patricia Jelenic for contributing with data collection, and Pr. Laurent Mottron for enabling the recruitment of ASD patients.

Data Availability

We note that we have already made our entire data analysis code entirely available as part of an open-science collaborative project (see https://mbb-team.github.io/VBA-toolbox/). In addition,our raw data is accessible online at the following address: https://owncloud.icm-institute.org/index.php/s/TsguzHSdgCAQPAL

Funding Statement

BFA acknowledges support from "Fondation Les Petits Trésors de l’Hôpital Rivière des Prairies" and Fonds de Recherche en Santé du Québec (FRQS). MD and JD have nothing to declare. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS Comput Biol. doi: 10.1371/journal.pcbi.1007700.r001

Decision Letter 0

Samuel J Gershman, Michael Moutoussis

9 Sep 2019

Dear Dr Daunizeau,

We would like to invite you to submit a revision of your manuscript to PLOS-CB, as the comments and suggestions of the two reviewers below would add greatly to the value of the paper. If I were to pick a theme which is most important to address, is the one of validity and subsequent diagnostic value of the method advocated here. As far as using this test in a clinical setting is concerned, the analysis that this manuscript present does not improve on the accuracy (hence scientific validity) of clinician diagnosis, and in fact this is impossible if the latter is taken as the measure by which the task-based diagnosis is judged. Of course this is most problematic if such tasks are seen as stand-alone assessments; They may indeed be very useful as screening tools (with the caveat that they then have to be tested against much more straightforward self- and collateral- reported questionnaires). Please read through the automated mail below that corresponds to our decision to ask you to submit a major revision of your manuscript, paying special attention to address all the reviewers' comments point-by-point, and rewriting the relevant sections of the manuscript to match.

                                                                   ~ ~ ~ ~ ~ ~ ~ ~

Thank you very much for submitting your manuscript 'Social behavioural adaptation in Autism' for review by PLOS Computational Biology. Your manuscript has been fully evaluated by the PLOS Computational Biology editorial team and in this case also by independent peer reviewers. The reviewers appreciated the attention to an important problem, but raised some substantial concerns about the manuscript as it currently stands. While your manuscript cannot be accepted in its present form, we are happy to consider a revised version in which the issues raised by the reviewers have been adequately addressed. We cannot, of course, promise publication at that time.

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A link appears below if there are any accompanying review attachments. If you believe any reviews to be missing, please contact ploscompbiol@plos.org immediately:

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Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: In this excellent ms. the authors ambitiously suggest to "decompose reciprocial social interactions into their computational constituents". This goal is pursued by asking 24 adult autistic participants to 24 neurotypical (NT) participants engage in a "repeated dyadic competitive game against artificial agents with calibrated reciprocal adaptation capabilities".

More concretely, the game includes playing against a learning machine that algorithmically adapts to the player's behavior. To win, participants' must learn to anticipate their opponent's next choice and/or try to influence it. Importantly, a cover story was used to introduce a social condition, in which participants thought they were playing against a real person.

I am very impressed by goal of the study and the methodology used to assess cognitive processes that subtend behavioral performance. However, I am less impressed by the choice of the task, because it seems to be yet another example of a task with relatively low ecological validity. I would be more excited, if someone could demonstrate how performance on such a task relates to everyday life interaction difficulties that autistic persons experience. It is nice to show that mentalizing performance is suboptimal on this task, but what if mentalizing is not the key ingredient to successfully navigating the real social environment? Please discuss this particularly in the context that your analysis predict (only) 62% of the severity of social symptoms.

Other comments:

How do the authors explain that NT participants lose in the non-social condition, but retain earnings in the social condition? What is the role of motivation here?

The most interesting finding in my mind is that patients do better than NT in dealing with non-mentalizing agents. Could that be because this behavior is more 'autistic', more similar to patients' expectation? Bolis et al. have recently proposed the 'social interaction mismatch' hypothesis, which suggests that autistic persons find it easier to relate to other autistic persons (or agents). Please discuss.

With regard to the highly important topic of social reciprocity the authors do not discuss revent developments and relevant papers in social neuroscience (Schilbach et al. 2013, BBS; Schilbach 2016, Phil Trans Roy Soc; Redcay & Schilbach 2019, Nat Rev Neurosci) that have addressed this issue both conceptually and empirically.

Having said all of this, I think that the paper could make an important contribution, but should be revised to address the above described comments.

Reviewer #2: In this work, the authors aim to decompose and measure reciprocal social interactions to predict ASD diagnosis. The authors present behavioral results for ASD and TD individuals performing a competitive game against artificial agents with different levels of intelligence and theory of mind. The experimental design included four different AI agents, two framing conditions (social and non-social) and two financial incentives (high vs. low). The authors main finding is that only TD individuals changed their behavior in response to the social framing, showing superior performance of TD compared to ASD when the task was framed to make individuals believe that they were competing against somebody else. Authors used the financial incentives manipulation to suggest that motivational factors did not play a role in the after mentioned framing x group observed effect. Furthermore, the authors modeled several strategies (theory of mind, RL, random choice) to explain individuals' behavior, while also modeling the possibility that participants shifted between different strategies. Modeling result suggest lower flexibility and metalizing tendencies for ASD group. The authors report 79% diagnosis classification accuracy in a leave-on-out logistic regression, and suggest that the method and modelling approach may prove useful for novel diagnostic methods for autism.

First, allow me to thank you for the opportunity to review this paper. I believe the use of sophisticated AI agents to objectively measure human behavioral tendencies is promising and relevant. I would like to also note that the authors expertise in modeling is well observed, and the manuscript seem to include a rich and sophisticated modeling section that, in my opinion, fits well with the journal scope and aims. However, as noted below I have a few major concerns, the main of which regards the claim that these results are valuable diagnostically. I do hope the authors will find these comments helpful, and would be happy to see further versions in the future.

Major comments:

1. My main concern is the statement made by the authors claiming that the results can be taken as support for novel diagnosis metric. I appreciate the LOO CV result, and they are indeed impressive. However, it is my view that this reflects a large, and consistent effect within the current sample, and as such do not add much over the ANOVA results. The current study do not address within group variability or the issue of whether this method can be generalize outside the current sample (keeping in mind that ASD is a highly heterogeneous group). In that sense the CV results should be taken carefully, mostly as resonating the ANOVA results. Furthermore, the authors do not show whether symptoms severity, or specific clinical symptoms can/cannot be predicted using this approach, which might be very important. It might be also useful if the authors can find a way to shed some light on individuals that where miss classified. Did those individuals had stronger framing effect? Is there any reason to suggest they were miss-classified by the clinicians (which might actually mean this approach can add additional value to the current clinical approach)?

Second, the current test provides, like almost all measures in the field, an assessment of some symptoms that should be assumed to be predicted by ASD (not vice versa). I agree that a task-based objective measure could facilitate symptom-based diagnosis, which is currently done only by interviews and self-report. However, I think the authors need to specify how exactly this could be helpful. My understanding is that in the current sample the classification accuracy is not much worse than clinical measures. Given that task based estimates tend to be in general much less reliable compared to self-report measures (e.g., Enkavi et al., 2019), we can only assume that in the long run, it might be very difficult to justify the use of such approach as a clinical measure.

2. ASD individuals might have unpleasant memories from social interactions, more compared to TD individuals. Therefore, is it possible that for the ASD group the social version is less engaging, leading to worse performance compared to TD? I appreciate the fact that the authors manipulated reward magnitude, yet this might be somewhat unrelated since the claim here is that ASD might have been more anxious in the social condition, lowering their performance (What could really help in future studies is a social relevant clinical control group like individuals with social anxiety).

3. The authors use a correlational analysis to support the fact that the ASD group show no framing effect, which is essentially a null result. While the group x framing interaction might be enough here, I am not sure I understand why the correlational analysis in the SI, section 4 helps ith NHT issues regarding the simple effect of framing for the ASD group. Since the whole distribution can be shifted between conditions, the correlations can be high or low regardless of whether a simple framing effect is present within the ASD group. I would suggest that the authors use a Bayesian ANOVA, and report effect-size measures such as partial et-square (or any other) to demonstrate the main result – no framing effect for ASD compared to TD.

4. Is it possible that the k-ToM model fits the ASD better, thus leading to lower flexibility? If this is the case, is it possible that ASD find it harder to play the task due to the fact that the AI agent is more suitable opponent for their strategy? Finally, could this mean that they do not show framing effect for high k opponents, due to the fact that the game is (much) more difficult for them, not leaving much room for improvement?

5. Given the 1.3 response deadline, is it possible that the ASD (which can show prolonged RTs) had to lower their response threshold, and suffered from less optimal speed-accuracy trade-off point?

6. I think the results regarding flexibility are not explained with enough data. Is there any way the authors can outline the fit of the different models, and give the reader some understanding of which models where used by which groups? What is the relationship between flexibility parameter and overall model fit?

Minor comments:

- The author describe former ToM tests as having 'unreliable results and poor psychometric properties' (bottom, pg3). I think it would be great if the authors could specific more exact reliability/validity estimates, and show where possible how the current test differ, if they are to keep the claim that this method is proved to be clinically valuable.

- I understand the need to keep some results out of the main text, but please clearly note the full design in the main text (including the reward manipulation) so the reader will know what to except.

- Fig 1 – I'm not sure I understood why use a net rate rather than the more straight forward accuracy rate?

- The authors report in the first paragraph of the discussion four analyses to support their claims yet three of which are tacked in the SI, which seems to be written with less care. Is there any way to improve the presentation?

- The authors suggest that a completely random strategy is "embarrassingly simple". I would argue that asking a human individual to act in a perfectly random manner is rather difficult, and against some human opponents could be useful to shift to. In that sense, it is interesting to see what role this strategy played in the individuals data.

Typos:

- SI section 4:" whether there are there",

- "Figure A2 below show the correlation between" I think you meant Fig A3

- " (Fig A3, right inset) analyses show a significant correlation between framings for the AS group" I think you meant between repetitions.

- Please carefully proof the SI as it seems to include valuable parts of the study.

**********

Have all data underlying the figures and results presented in the manuscript been provided?

Large-scale datasets should be made available via a public repository as described in the PLOS Computational Biology data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #1: No

Reviewer #2: No

PLoS Comput Biol. doi: 10.1371/journal.pcbi.1007700.r003

Decision Letter 1

Samuel J Gershman, Michael Moutoussis

31 Dec 2019

Dear Dr Daunizeau,

Thank you very much for submitting your manuscript, 'Social behavioural adaptation in Autism', to PLOS Computational Biology. As with all papers submitted to the journal, yours was fully evaluated by the PLOS Computational Biology editorial team, and in this case, by independent peer reviewers. The reviewers appreciated the attention to an important topic but identified an aspect of the manuscript that should still be addressed.

We feel that your article is very near to a standard where it can be accepted for publication in PLOS-CB, and it will not need to be returned to the reviewers.

I would like to comment on the points still raised by Reviewer 2, and ask that you kindly respond in the Discussion to their first point, which regards response times. I think that Reviewer 2's second point, although of some validity, will not be a serious concern for our readership. The term 'clinical utility' may be changed, for example, to 'clinical relevance' at the autor's discertion, but otherwise the Discussion (and slightly less so the Introduction) now make it clear that the paper makes an incremental contribution towards 'bedside' tests for AS, rather than any exaggerated claims. However, I would kindly ask the authors to address the point regarding response times. I feel that Reviewer 2's hypothesis is rather too elaborate, requiring that AS participatns were slower in general, but not so slow as to make more mistakes in the nonsocial condition where they performed better, yet too slow for the social condition. Reviewer 2 does have a point, that high-ToM may be too costly, in terms of response time, for AS participants rather than then being altogether unable to consider it. Therefore, when they attempt such a high-ToM strategy their performance deteriorates and may resemble 'Influence' decision-making. However I feel that RT analysis is outside the scope of this paper and should not delay its publication. I would therefore suggest that the authors address Reviewer 2's suggestion that RT analysis would make conclusions more compelling by discussing it in an additional paragraph on future research directions. Response time analysis - among other methods - may indeed move understanding from the algorithmic level of this paper to one incoroporating neural implementational features (such as accummulator processes that generate response times) and this may be addressed in future work.

We would therefore like to ask you to modify the manuscript according to the review recommendations. Your revisions should address the specific points made by each reviewer and we encourage you to respond to particular issues Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.raised.

In addition, when you are ready to resubmit, please be prepared to provide the following:

(1) A detailed list of your responses to the review comments and the changes you have made in the manuscript. We require a file of this nature before your manuscript is passed back to the editors.

(2) A copy of your manuscript with the changes highlighted (encouraged). We encourage authors, if possible to show clearly where changes have been made to their manuscript e.g. by highlighting text.

(3) A striking still image to accompany your article (optional). If the image is judged to be suitable by the editors, it may be featured on our website and might be chosen as the issue image for that month. These square, high-quality images should be accompanied by a short caption. Please note as well that there should be no copyright restrictions on the use of the image, so that it can be published under the Open-Access license and be subject only to appropriate attribution.

Before you resubmit your manuscript, please consult our Submission Checklist to ensure your manuscript is formatted correctly for PLOS Computational Biology: http://www.ploscompbiol.org/static/checklist.action. Some key points to remember are:

- Figures uploaded separately as TIFF or EPS files (if you wish, your figures may remain in your main manuscript file in addition).

- Supporting Information uploaded as separate files, titled 'Dataset', 'Figure', 'Table', 'Text', 'Protocol', 'Audio', or 'Video'.

- Funding information in the 'Financial Disclosure' box in the online system.

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com  PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org.

We hope to receive your revised manuscript within the next 30 days. If you anticipate any delay in its return, we ask that you let us know the expected resubmission date by email at ploscompbiol@plos.org.

If you have any questions or concerns while you make these revisions, please let us know.

Sincerely,

Michael Moutoussis

Guest Editor

PLOS Computational Biology

Samuel Gershman

Deputy Editor

PLOS Computational Biology

A link appears below if there are any accompanying review attachments. If you believe any reviews to be missing, please contact ploscompbiol@plos.org immediately:

[LINK]

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: The authors have satisfactorily addressed my previous comments in their revision.

Reviewer #2: Dear Authors,

Thank you for your answers - much appreciated. Please find below two followup issues.

Many thanks.

R2.

1. Given the 1.3 response deadline, is it possible that the ASD (which can show prolonged RTs) had to lower their response threshold, and suffered from less optimal speed-accuracy trade-off point?

We think this is unlikely, for two reasons. First, AS and NT participants show no significant difference in IQ, including measures of processing speed. Second, AS participants perform significantly better than controls against non-mentalizing agents, in both social and non-social framing conditions…

Thank you for your reply, but I am not convinced. A response deadline of 1300ms is very strict and could majorly affect decision threshold. ASD are known to show prolonged RTs and increased RTV (mostly a higher rate of very slow RTs, and mostly due to co-occurrence with ADHD symptoms). I would encourage the authors to give this more thought. For example, it might be that with higher mentalizing agents (RB to 2-TOM) all individuals required more time to make a decision. Given that ASD are assumed to be slower in general, the strict RT deadline might render the high TOM conditions much more difficult for them, which can then reduce their ability to show sensitivity to the framing effect. An RT analysis, given this strict response deadline would thus make your mode-free analysis much more compelling. PS pencil-paper test is not enough to indicate no RT differences between the groups as it does not have the required sensitivity.

2. In my previous comments, I suggested that my main concern is the statement made by the authors claiming that the results can be taken as support for novel diagnosis metric. I appreciate the authors changes in the study, but it is my personal view that this paper still creates strong expectations for the reader regarding clinical relevance. For example, the authors note in the first paragraph of the intro that “This work evaluates the clinical utility of a computational decomposition...”, and mention the need for a ‘true test’ for ASD.

**********

Have all data underlying the figures and results presented in the manuscript been provided?

Large-scale datasets should be made available via a public repository as described in the PLOS Computational Biology data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: Yes

Reviewer #2: No: The authors note they intend to make raw data accessible online upon acceptance.

**********

PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

PLoS Comput Biol. doi: 10.1371/journal.pcbi.1007700.r005

Decision Letter 2

Samuel J Gershman, Michael Moutoussis

30 Jan 2020

Dear Dr. Daunizeau,

We are pleased to inform you that your manuscript 'Social behavioural adaptation in Autism' has been provisionally accepted for publication in PLOS Computational Biology.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch within two working days with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

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Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology. 

Best regards,

Michael Moutoussis

Guest Editor

PLOS Computational Biology

Samuel Gershman

Deputy Editor

PLOS Computational Biology

***********************************************************

PLoS Comput Biol. doi: 10.1371/journal.pcbi.1007700.r006

Acceptance letter

Samuel J Gershman, Michael Moutoussis

6 Mar 2020

PCOMPBIOL-D-19-01204R2

Social behavioural adaptation in Autism

Dear Dr Daunizeau,

I am pleased to inform you that your manuscript has been formally accepted for publication in PLOS Computational Biology. Your manuscript is now with our production department and you will be notified of the publication date in due course.

The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any errors. Please note that major changes, or those which affect the scientific understanding of the work, will likely cause delays to the publication date of your manuscript.

Soon after your final files are uploaded, unless you have opted out, the early version of your manuscript will be published online. The date of the early version will be your article's publication date. The final article will be published to the same URL, and all versions of the paper will be accessible to readers.

Thank you again for supporting PLOS Computational Biology and open-access publishing. We are looking forward to publishing your work!

With kind regards,

Bailey Hanna

PLOS Computational Biology | Carlyle House, Carlyle Road, Cambridge CB4 3DN | United Kingdom ploscompbiol@plos.org | Phone +44 (0) 1223-442824 | ploscompbiol.org | @PLOSCompBiol

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Text. This document provides supplementary information regarding: the experimental protocol (section 1), the credibility of the framing manipulation (section 2), the effect of motivational manipulations on the game's performance (section 3), differences in reaction times (section 4), model-free Volterra decompositions of trial-by-trial choice sequences (section 5), differences in adaptation strategies between AS and NT participants (section 6), the impact of the framing manipulation on the repertoire's flexibility (section 7), the statistical relationships between computational phenotypes (section 8), and their relationship with performance in the game (section 9).

    (DOCX)

    Attachment

    Submitted filename: response to reviewers.docx

    Attachment

    Submitted filename: response to reviewers.docx

    Data Availability Statement

    We note that we have already made our entire data analysis code entirely available as part of an open-science collaborative project (see https://mbb-team.github.io/VBA-toolbox/). In addition,our raw data is accessible online at the following address: https://owncloud.icm-institute.org/index.php/s/TsguzHSdgCAQPAL


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