autismo e ultimatum

Dilemmas and bargains: Autism, theory-of-mind, cooperation and fairness. Elisabeth Hill1* and David Sally2 1 2 Institute of Cognitive Neuroscience, University College London, UK. Cornell University, USA. * Corresponding author. Institute of Cognitive Neuroscience University College London 17 Queen Square London WC1N 3AR. UK. Tel: +44 (0)20 7679 1177 Fax: +44 (0)20 7813 2835 Email: e.hill@ucl.ac.uk KEYWORDS: autism, bargaining, cooperation, dilemmas, theory-of-mind ABSTRACT Mentalising is assumed to be involved in decision-making that is necessary to social interaction. We investigated the relationship between mentalising and two types of strategic games - those involving the choice to cooperate with another for joint gain or compete for own gain and those involving bargaining and division of a surplus - in children and adults with and without autistic spectrum disorders. The results suggest that strategic responses in the first type of game, the wellknown prisoner’s dilemma, are associated with mentalising ability. In contrast, generosity in bargaining tasks did not depend upon mentalising skills, but initial strategically unequal offers did. These two essential social games appeared to be differentially compensated for in highfunctioning individuals with autistic spectrum disorders. 2 INTRODUCTION As crawling gives way to toddling and then striding, a child may move more steadily through the physical world. So too, improvements in her ability to mentalise—that is, attribute, understand and manipulate mental states such as beliefs, feelings, thoughts, intentions and deceptions—allow her to navigate away from the home harbour and into the swift currents and crosswinds of the broader social world. For individuals with autistic spectrum disorders there exists a fundamental difficulty in mentalising and social life is a series of strong headwinds, uncertain tacks, buffeting waves and treacherous eddies. Specifically, individuals with autism fail to understand not only that others have minds, but also that other minds have different thoughts, and that behaviour is determined by mental states. Thus, individuals with autistic spectrum disorders are considered to lack a ‘theory-of-mind’ (Baron-Cohen, Leslie & Frith, 1985, Baron-Cohen, Tager-Flusberg & Cohen, 1993; 2000). While difficulties with theory-of-mind are widespread in individuals with autism, certain of these individuals appear able to acquire some degree of theory-of-mind understanding. Accumulating evidence suggests that this apparent understanding arises out of a compensatory, rule-based ability that can reproduce the sympathetic insights of more direct and natural mindreading. In a meta-analysis, Happé (1995) reported that children with autism required a higher level of verbal ability, as measured by the British Picture Vocabulary Scale, in order to pass simple theory-of-mind tasks (the Sally-Anne and Smarties false belief tasks), than did normally developing three- and four-year-olds or children with learning disabilities but without autism. It may be that the high verbal ability of these individuals allowed them to ‘hack out’ a solution to the tasks (Happé, 1995). Consequently, depending on a situation’s degree of social complexity, the responses of such individuals with autism may be adequate and yet odd, or simply inapt and inept. 3 The limitations of a postulated compensatory mechanism have been demonstrated in a number of recent experiments. Bowler (1992) reported that adults with Asperger syndrome – considered to represent high-functioning autism – were able to pass tests of false belief, although they did so using a slow, cumbersome route, as evidenced by the explanations that they gave in justification of their answers, which lacked higher-order, mentalising bases. Castelli, Frith, Happé and Frith (2002) asked participants to provide verbal interpretations of animations of two moving triangles. Each animation was scripted to show random, goal-directed or mentalising movements. Compared to their normal peers, individuals with high-functioning autism or Asperger syndrome made fewer and less accurate interpretations only of the animations that evoked mentalising, for example when two triangles bounced up and down together in glee. A much more natural interaction was viewed by participants in Klin, Jones, Schultz, Volkmar and Cohen’s (2002) study. While their eye movements were tracked, these participants watched dramatic scenes from a famous Hollywood movie. Normal individuals focused mostly on the eyes of the actors; individuals with autism fixated mainly on the mouths. In contrast, when the scene showed a character reaching for a gun, the eyes of the autistic individuals moved directly to the object while those of the matched controls lingered on the actor’s face, presumably to gather clues about his intentions. Thus, there is striking evidence that individuals with high-functioning autism read minds differently from their normal peers. While their performance on standard laboratory tasks of theory-of-mind (generally false belief) can be good, they are severely developmentally delayed in acquiring such ability, produce unusual explanations of their theory-of-mind understanding, perform poorly on advanced tests of theory-of-mind and do not show the same spontaneous reactions to naturalistic task demands as their normal peers. All the studies above placed the participants in the role of an observer of another person or of a social interaction. Clearly, individuals with autism experience difficulties with mentalising in this setting, but little has been documented empirically about the implication of this difficulty 4 in their daily interactions with others. What happens when the person with autism is not spectator but participant, not outside but inside the interaction? The aim of the current study was to pursue this question by investigating the performance of individuals with autistic spectrum disorders using a series of well-known tasks drawn from the field of 'game theory'. Since its formalization by von Neumann and Morgenstern (1944), game theory has developed into one of the most important theoretical and empirical tools in the social and biological sciences. It is at one and the same time a theory of tic-tac-toe, poker and draughts, and of predator-prey confrontations, arms races and industrial conspiracies, a continuum resting on an established, adaptable definition of “game.” A game is simply a rule-bound, multiple-agent, interdependent decision-making problem (Gibbons, 1992). Formally, a game is fully described by a set of players, which may include Nature or Chance, the strategies available to each player (i.e., sequences of moves allowed within the rules), and payoffs to the players that arise from the particular combination of selected strategies. Games can be analysed to determine their equilibria, sets of players’ strategies that are stable in the face of hypothetical or real changes in tactics or the game’s rules. Formal equilibria serve as a predictive benchmark in a game: players with adequate levels of mutual knowledge, self-interest and coherent decision-making should arrive at an equilibrium. For example, tic-tac-toe (noughts and crosses) has two players, a set of weakly dominant strategies involving an initial mark in the centre box, the payoffs representative of pure conflict—either the players tie, or one wins and the other loses—, and equilibria entailing only drawn games. Tic-tac-toe, as any ten-year-old can testify, is a fairly trivial game, in part because the optimal strategy is so easily learned and is largely impervious to the behaviour or characteristics of one’s opponent. However, when the game becomes more complex and its strategy space grows, optimal strategies are harder to calculate, errors are more readily made, and signals are more easily sent. In these games, the ability to mentalise may help a player anticipate where the 5 other’s memory, information, and calculation are limited and what strategy she is likely to employ. A well-drawn mental model may also distinguish when the counterpart’s surprising move is a mistake or a bluff or a trap. The participants in the complex games of poker and combat testify to the value of mentalising. A high-stakes card player, whose winnings depend on his abilities to deceive his competitors and to dispel others’ bluffs, claims (with a bit of grandiosity), “A man’s character is stripped bare at the poker table. If the other players read him better than he does, he has only himself to blame. Unless he is both able and prepared to see himself as others do, flaws and all, he will be a loser in cards, as in life” (Holden, 1990). Similarly, leaders on the battlefield rely on mental models of their counterparts to construct strategies and decipher tactical movements (see, for example, the memoirs of Rommel (1953) and Grant (1999[1885]). Noughts and crosses, poker, and combat are zero-sum games, in which a gain for one side represents an equal loss for the other. Quite naturally, there exists a category of nonzero-sum games in which the stakes are not fixed and players’ actions may raise and lower the total available payoffs. Because they may, together or independently, create and destroy common value in these settings, players have more shared interests in nonzero-sum games than, for example, in poker. Nonzero-sum games can be further divided into coordination games in which the interests of the participants are identical, and mixed-motive games in which their interests are only partially aligned. It is principally through mixed-motive games that researchers have analyzed social and strategic interactions among humans, among other primates, birds and other vertebrates, insects, plants, genes, corporations, countries, political parties, classes, voters, etc. In the laboratory, three mixed-motive games in particular, the prisoner’s dilemma, the ultimatum game, and the dictator game (each of which we will describe in detail below), have been employed to study varying levels of cooperation and competition, concern for fairness, self-interest and altruism among 6 experimental participants. Thousands of studies have been conducted of these three critical games, and participants have usually manifested a cooperativeness greater than that predicted by the models of strict, rational self-interest historically prominent in both biology (natural selection at the individual level) and economics (homo economicus). It is not necessary to mentalise in order to play a mixed-motive game or cooperate within such a game. For example, viruses competing to infect and reproduce in the same set of host cells are “playing” (insensibly and non-mentally) a prisoner’s dilemma game in which an inability to cooperate lowers the fitness of each phage (Turner & Chao, 1999). Furthermore, biologists have discovered relatively sophisticated and accommodating strategies adopted by animals without the cognitive ability to form mental models—rotating responsibilities for approaching possible predators among stickleback fish (Milinski, 1987), reciprocal food sharing by vampire bats (Wilkinson, 1984), and the coordinated capture of grasshoppers and moths by certain species of spiders (Pasquet & Krafft, 1992). Evolutionary game theorists assume that the tactics of these creatures are encoded in their genes, that con-specifics are paired with a certain frequency in strategic interactions, and that fitness and offspring accrue to the genes and the individuals with the more robust strategy. So, vampire bats share food reciprocally because repeated interactions among colony-mates make that strategy more “profitable” than one based on always taking food and never giving. These bats are not analysing, rationalising, mentalising, or improvising: they are simply following an established evolutionary rule in a familiar situation. Nevertheless, among humans mixed-motive games do seem to require that players signal and interpret intentions and develop some theory of the other’s mind (Schelling, 1960). In the parlance of economics, then, mentalising and rule-following are substitutes in the same way that butter and margarine, aluminium and tin, work and the national lottery, cars and public transportation, and wisdom and information are. Few substitutes are perfect for all applications. A chef may be indifferent about which spread is applied to her morning toast, but would refuse to 7 use oleo in even the simplest pastry, as butter makes a distinctly better crust. Similarly, the key research question we raise is, how easily do mentalising and rule-following substitute for each other in mixed-motive games? Does a well-functioning theory-of-mind promote cooperation, generosity, and fairness, or does it foster treachery and selfishness? Are there natural rules that mimic the effects of forming a mental model of the counterpart? In the current study these questions were addressed through a unique, cross-disciplinary approach—testing the basic games of the social and biological sciences on adults and children with autistic spectrum disorders to focus on whether: (i) mentalising is necessary for basic strategic rationality, (ii) mentalising promotes cooperation in the prisoner’s dilemma, (iii) mentalising allows similarities and differences among related games to be more accurately perceived, and (iv) rule-following yields the same results as mentalising in bargaining games when the problem is dividing a given endowment either unilaterally or collaboratively. Experiment One – prisoner’s dilemma Without question, the prisoner’s dilemma (PD) is the most thoroughly studied game in the social and biological sciences because it captures the essence of a frequent social quandary, namely, that what is good for the group may differ from what is good for each individual. This game is central to such social phenomena as foraging, over-harvesting of resources, inadequate investment in public goods, free-riding, retaliation, sacrifice, altruism, etc. The PD involves two players acting simultaneously, two moves characterized as “defection” and “cooperation,” and payoffs such that the total return to mutual cooperation is greater than that to mutual defection, while an individual’s personal payoff is always maximized by defection. A generic PD is shown in Figure 1, with the payoffs to combinations of moves contained in the appropriate cell of the matrix. If the other side cooperates, a player is enticed by the temptation to defect 8 (a-c); if the other side defects, cooperation is penalized by the sucker’s loss (b-d). In both instances, defection guarantees a larger payoff, and so, the only equilibrium is (defection, defection). [Figure 1 about here] Despite the gloomy equilibrium of mutual defection and the predicted triumph of narrow self-interest over altruism, humans and other creatures often “solve” the PD and achieve mutual cooperation. Theorists have distinguished two principal means which aid mutual cooperation: kinship and reciprocity. It makes evolutionary sense for close kin to sacrifice on each other’s behalf in order that their shared genes may prosper, and so brothers and sisters, rather than third cousins, will cooperate in the PD (Hamilton, 1964). Kin recognition depends on cognitive mechanisms able to read and react to perceptible clues of relatedness (Krebs, 1987), and vestiges of these prehistoric mechanisms translate into our modern predilection for people who are proximate, similar and familiar (Sally, 2000). Hence, in laboratory experiments, cooperation is more frequently seen when participants are near each other, can see each other, have information that they share tastes and opinions, like each other, and have interacted previously (Sally, 1995; 2000). Finally, if the PD is repeated, a population of reciprocal altruists whose strategy is based on doing this round what the opponent did the previous round, can multiply and survive by reacting harshly to full-time defectors and nicely to full-time cooperators and to each other. Axelrod (1984) conducted a famous computerized tournament of repeated PDs in which the winner was this so-called tit-for-tat strategy. Tit-for-tat (TFT) is a simple rule that requires absolutely no mentalising. One unilaterally cooperates in the initial round and then observes what move the counterpart actually makes so that it can be reciprocated in the next round. Intentions play no role here. Moreover, this type of player plays TFT with his brother and with a stranger, with a fellow club member and with an enemy, with a counterpart who promises to cooperate and with one who vows to defect. Such a 9 rule-follower is relatively unaffected by changes in the social context of the game and is more likely to miss other changes in a game that occur below the surface. By contrast, kinship-based altruism is more closely linked to mentalising, both because the sharing of mental states is a sign of similarity and relation, and because the presence of those who are close, similar, and familiar is more likely to trigger an individual’s theory-of-mind (Sally 2001). Hence, the identity of the opponent makes a significant difference in the likelihood that a player chooses to cooperate. If the opponent’s identity is changed from human to machine, even though the pre-programmed strategy is unaltered, experimental participants are far more likely to cooperate with the former rather than the latter: 58% versus 41% in Abric and Kahan (1972), and 59% versus 31% in Kiesler, Sproull and Waters (1996). These two results suggest that the more “human” a counterpart is to a player, the more likely that player is to cooperate in the PD. The degree of “humanness” seen in the opponent is a function not only of the opponent’s identity but also of the identity and cognitive abilities of the perceiver. Chief among these abilities is mentalising. Insofar as theory-of-mind develops throughout childhood (Astington, 1994), one would expect, then, that older children would cooperate more than younger ones. Indeed, Fan (2000) found that nine- and eleven-year-old children cooperated more frequently in a ten round repeated PD than did seven-year-olds. This study was less concerned with accounting for the choices of children than with the effects of moral suasion and instruction on the promotion of cooperation in the PD (see also Matsumoto, Haan, Yabrove, Theodorou & Cooke Carney, 1986). The only other study of young children (aged 3-10 years) and the PD that we found showed that older children were more able and willing to pay attention to their opponent's interests than younger children, although this was only true if doing so would help improve their own outcome (Perner, 1979). Other developmental theories and experiments have shown that the frequency of prosocial behaviour increases throughout childhood (Eisenberg & Fabes, 1998). In the PD, cooperation, as opposed to defection, is clearly the prosocial move. 10 The experiments adopted in the current study allowed a comparison of patterns of choices among younger participants and adults playing the same games. Specifically, the performance of normally developing adults and children (aged 6-, 8- and 10 years) as well as high-functioning children and adults with autistic spectrum disorders was investigated on three versions of a PD game. In the PD games the nature of the opponent against whom a participant played was manipulated - each participant played the game against a human and a computer opponent. Furthermore a third manipulation of the PD was included in which participants played against a human opponent where the instructions of the game encouraged participants to cooperate, rather than compete with their opponent (encouraged cooperation PD). Accordingly, based on the current understanding of theory-of-mind within developmental psychology, a number of hypotheses about the decisions of our participant groups were generated. (It is important to note that these very same hypotheses arise from the theories and empirical results most frequently cited within behavioural economics.) With respect to the normal sample a greater degree of cooperation in the PD among the older participants was expected. Given the difficulties of individuals with autistic spectrum disorders in the area of mentalising, and the suggestion that mentalising is involved in a participant's choice on the PD, it was predicted that individuals with autistic spectrum disorders would cooperate less with their opponent than their normal counterparts when playing against the human opponent. Since individuals with autism have been shown to have difficulty only representing the “mind” of a human and not a machine (Leekam & Perner, 1991; Leslie & Thaiss, 1992), a smaller difference in the cooperation rates between the normal and autistic individuals when playing the PD against a computer opponent was expected. With respect to the encouraged cooperation game, the specific instructions negate the need to predict the intentions of the counterpart, and so, individuals with and without autism should manifest similar, elevated rates of cooperation. 11 METHOD Participants. A total of 99 children and adults took part in the study, comprising individuals with and without autism and falling into the following groups: normally developing 6-, 8- and 10year-olds, normal adults, children with autism, adults with autism. Participants were tested individually by an experimenter and - for the experimental tasks – a confederate either in their school, college or at the Institute of Cognitive Neuroscience, UK. All were native English speakers. Participant details are shown in Table 1. The mean chronological age of the adults with autism versus those without did not differ; this was true as well for the 10-year-olds versus the children with autism. All participants with autism had been diagnosed formally with either autism or Asperger syndrome prior to the study. In addition, a checklist was completed by the experimenter and the confederate for all adult participants and approximately one third of the child participants. This checklist was based on observation and related to the key characteristics of the disorder and was used as an aid to confirm an individual’s diagnosis and therefore the results will not be reported here. Ethics approval for the study was granted by the National Autistic Society (UK) and by University College London (UK). Parental consent was required for child participation in the study and the informed consent of all participants (adults and children) was sought. General ability. General ability levels were assessed using the British Picture Vocabulary Scale (BPVS-II, Dunn, Dunn, Whetton & Burley, 1997) for the children and the Wechsler Adult Intelligence Scale (WAIS-III-R; Wechsler, 1998) for the adults. Mean ability levels fell within the normal range in all groups (see Table 1). [Table 1 about here] 12 Theory-of-mind. First-order false belief understanding was assessed in all participants using the Sally-Anne task (Baron-Cohen et al., 1985; Wimmer & Perner, 1983). Second-order false belief understanding was assessed using the Coat story (Bowler, 1992) for the adult participants and the Birthday Puppy story (Sullivan, Zaitchik & Tager-Flusberg, 1994) for the children. Wider aspects of theory-of-mind were assessed in the adults using Happé’s (1994) eight mentalising stories. These were scored for accuracy by two independent raters who resolved disagreements by discussion. The total time taken to read and respond to each story was also recorded. Theory-ofmind performance is shown in Table 2. As expected, children with autism were impaired in both first- and second-order false belief tests in comparison to their normally developing peers [first-order false belief, χ2 (1) = 18.62, p < .001; second-order false belief, χ2 (1) = 9.87, p < .01]. The adults with autism were impaired in second-order false belief understanding [χ2 (1) = 20.0, p < .001] as well as on the wider aspects of mentalising as assessed by the Happé stories [accuracy, t (28) = -4.35, p < .001; time taken when accurate response given, t (19) = 2.88, p < .01]. The lack of a deficit in firstorder false belief understanding in the adults with autism in comparison to the normal adults [χ2 (1) = 2.14, p > .1] is not unexpected given the age and high-functioning status of the adults with autism in our sample. Compensatory learning in these individuals allowed them to pass the most simple false belief task, a consistent finding in the published literature (Bowler, 1992; Happé, 1995). Of greater significance is the large number of these adults who failed the second-order false belief task, as well as their poor performance (both in accuracy and slowed responding) on the advanced mentalising task. While the performance of the children with autism appeared superior to that of the adults with autism on the second-order false belief, this was likely to be a consequence of the differing content of the two stories used to assess this ability in the two age groups (Bowler, 1992). 13 [Table 2 about here] Game theory tasks. The baseline and experimental game theory tasks were presented on a laptop computer, easily visible to both the participant and the confederate who sat side-by-side (the participant to the right). Participant responses were recorded on-line for later analysis. The instructions for each task were presented on the computer, although these were verbalised by the experimenter to ensure that participants (especially children) understood each task. In all tasks, players were told that they must try to win as many points as possible and that the total points won on all games would be exchanged at the end of the testing session for stickers (children) or chocolates (adults). The greater the number of points won, the greater the reward at the end of the test session. Baseline tasks. Two baseline tasks were administered in the order described below, before the experimental tasks, thereby ensuring that the principle of the experimental tasks and the response methods were familiar to participants. Show me a colour. A blue and yellow square were shown on the computer screen. Each player (participant, confederate) independently chose either colour having been told that if they both chose blue they would both win three points, if they both chose yellow they would both win one point, and if they each chose different colours they would both win one point. This information was also presented on the tabletop in front of the players throughout the game, thus ensuring that players had no need to memorise the points allocation. A partition divided the keyboard down the middle, with each player responding by pressing one of two keys on one side of the partition. In this way neither player was able to see their counterpart's response. After players had made their choices by pressing the appropriate key on the keyboard, the computer presented the choices made and the number of points won. The confederate always made the rational choice – blue – in 14 this game. Only those participants who responded rationally on this task would be allowed to continue with the full test battery. Coin flip. The confederate was not a player in this game, rather the participant ‘played’ against a coin. A circle and a triangle were shown on the computer screen. The participant chose either shape by pointing and clicking a mouse, following which the coin was flipped. Points were then awarded according to the combination of shape chosen and the fall of the coin such that if the participant had chosen triangle and the coin landed on heads they were awarded three points, or tails one point. If the participant had chosen circle and the coin landed on heads they were awarded four points, or tails two points. The equilibrium choice for the participant in this game against chance is circle, as the payoff is larger in both instances. The intention here was to have participants play a somewhat “mechanical,” one-sided version of the prisoner’s dilemma. The Prisoner’s dilemma. Three versions of a prisoner’s dilemma task were completed by participants, with the response mode being the same (i.e., keyboard) as that in the ‘show me a colour’ baseline task. Sixteen trials (or rounds) were completed in each version of the task although participants were not told explicitly that this would be the case. This allowed comparison of participants' strategy choice on the first round of the game as well as over all sixteen rounds of each game. Human opponent. A circle and triangle were shown on the computer screen. Each player (participant, confederate) independently chose either shape having been told how the points would be awarded (see Table 3). This information was outlined to the players verbally along the lines described in the 'show me a colour' baseline task and was also presented on the tabletop in front of the players throughout the game. This ensured that players had no need to memorise the points allocation. After players had made their choice by pressing the appropriate key on the keyboard, the computer presented the choices made and the number of points won. The 15 confederate followed a tit-for-tat (TFT) strategy, always making the cooperative choice – triangle – on the first round and then copying the participant’s strategy on the previous round for rounds 2-16. The participant was not told that there would be more than one round of the game. Computer opponent. This version of the task was designed to investigate whether there was a difference in spontaneous strategy used when playing against a human or computer opponent. The task was identical to that described above with the computer replacing the human opponent. The computer was programmed to respond using the same strategy as the human opponent (cooperation on the first round followed by TFT), although this information was not give to participants. When playing the human and computer opponent PD games, defection (i.e., competition) is the equilibrium choice. Empirically, a wealth of studies and broad reviews of the literature (Dawes, 1980; Sally, 1995) suggest an expected cooperation rate of approximately 20% in the first round and then approximately 40% overall as the repeated nature of the tasks is implicitly recognized. In both games a greater degree of competition than cooperation would be expected, especially when the opponent is a computer. Encouraged cooperation. This task was identical to the prisoner’s dilemma with human opponent except that the points won on each round of the game were combined for the two players and divided equally at the end of the game. As before, the participant and confederate made their choice independently by pressing the appropriate key on the keyboard, the computer then presented the choices made and in this game the total points won by the two participants combined were also shown. The confederate continued to follow a TFT strategy, always making the cooperative choice of triangle on the first round of the game and then copying the participant's strategy on the previous round for rounds 2-16. When both players chose triangle, six points were won collectively; circle, four points were won collectively; and in cases where each player chose a different shape, five points were won collectively. Note that with this payoff structure, cooperation becomes the dominant move: irrespective of what the other chooses, a 16 player gains more points by cooperating. However, this dominance is somewhat obscured by the visible payoff matrix and the confederate’s continued retaliatory response to the participant’s choice of circle, and hence, the game is akin to a bluff—it is a PD on the surface and “show me a colour” or “coin flip” in reality. Mentalising players should shift to a purely cooperative strategy, but rule-following players may perseverate and not react to the different payoff calculation. [Table 3 about here] The order of play against the human and computer opponents was counterbalanced across the participants, with the encouraged cooperation task being completed last in all cases. Following completion of all three versions of the task, a semi-structured interview was conducted to elicit information about a participant’s strategy in each game, how players distinguished between the human and computer opponents and whether they had identified their opponent’s strategy. A detailed analysis of the content of the semi-structured interviews is presented in Hill, Sally and Frith (in preparation). RESULTS & DISCUSSION Baseline tasks. Show me a colour. The rational choice in this game was blue. A small number of participants, coming from all groups, selected yellow (9.1% of participants with autism, 19.7% of normal participants), and they were asked why they had made this choice (always saying yellow was their favourite colour). They were then asked which colour choice they would make if thinking only of the points to be won. All participants responded blue to this question. They were therefore considered to have responded rationally and were all included in the full test battery. Coin flip. The equilibrium choice in this game was circle. Surprisingly, there was a significant group difference in the choice of shape made on this task, as 24.2% of participants with autism made the dominated choice of triangle, compared to only 9.1% of the normal 17 participants [F (1, 98) = 4.25, p < .05]. Choosing the triangle is clearly a mistake in this game and must be driven by a rather deep-seated confusion in the face of the uncertainty of the unflipped coin. (Note that through the verbalized instructions the experimenter had insured that the participant had a working understanding of the payoff matrix.) While this was an unexpected finding it could concur with one other set of findings in the literature. Imaging studies have revealed that the quandary of guessing and predicting in an uncertain situation triggers neural activity in the prefrontal cortex (Elliott, Rees & Dolan, 1999; Paulus et al., 2001). Moreover, damage in this region leads to disadvantageous and erroneous decisions (Bechara, Damasio, Tranel & Damasio, 1997). Imaging studies of the brains of those with autism have shown abnormalities in the prefrontal cortex (Abell et al., 1999; Stone, 2000), and these malformations may underlie a similar pattern of overt knowledge of the correct strategy (circle, in this instance) but flawed choice (triangle). Much future work is needed to examine the relationship between autism and uncertain choice and to determine if the significant difference reported here is anomalous. Whether this confusion in the face of uncertainty is related to either cooperative or generous behaviour in the current study will be considered below. The Prisoner's Dilemma. Given the lack of PD studies comparing the strategy of normal children to that of adults, the data for the normally developing groups (adult, 6-, 8- and 10-year-olds) will be presented first, followed by comparisons of the responses of the normal participants and those with autism. The mean number of cooperative responses across the 16 rounds of each PD task and the percentage of each group cooperating on the first round of each task is shown for the normal adults, 6-, 8- and 10-year-olds in Figures 2a and 2b respectively. A repeated measures ANOVA with one between factor (participant group) and one within factor (PD game type) was applied to 18 the data for cooperative responses shown in Figure 2a. For performance across the 16 rounds of each game, there was a significant effect of game type [F (1,61) = 81.25, p < .001], indicating reliably higher levels of cooperation in the encouraged cooperation version of the task in comparison to both the human- and computer-opponent versions. This finding indicates that the premise of each task manipulation was supported. There was no significant difference between the level of cooperation seen across the groups [F (3,61) = 1.12, p > .1]. As indicated in Figure 2a, there was a significant interaction between group and game [F (3,61) = 4.7, p < .01]. This interaction shows that the performance of the normal adult group corresponded to the predicted behaviour far more distinctively than that of the child groups, thereby indicating a developmental progression towards levels of cooperation being distinguished according to the social situation. Thus the task manipulations to encourage cooperation were less effective in this sample of 6- to 10-year-olds than in adults, and the majority of the children failed to adjust adequately their strategy to account for the dominance of cooperation in the third game. [Figure 2 about here] Fan (2000) found, in his sample of Taiwanese schoolchildren, that nine- and eleven-yearolds cooperated more frequently in a ten-round PD than did seven-year-olds. In the tests reported here (Figure 2a), the ten-year-olds cooperated more with the human opponent than did the sixand eight-year-olds, although the difference between the normal children was not statistically significant. The same overall cooperation rate could disguise a number of very different patterns. For example, cooperation could start off very high initially and then decay, or distrust could be prevalent at first with mutual cooperation emerging as TFT strategies are identified and synchronized. Moreover, given that the participants were never told explicitly about the number of rounds, cooperation in the first round was less likely to be motivated by reputation or reciprocity. The first round decisions of participants in each task were examined to determine if 19 age was correlated with a distinct opening move in a given game. In the case of the 6- and 8year-old children, there were no significant differences in first moves between any pairing of the PD tasks, significantly more 10-year-olds cooperated in the encouraged cooperation task than when playing against the computer opponent [Z = -2.0, p < .05], but not when playing against the human opponent. The current study, therefore, supports Fan’s (2000) conclusion that young children are more likely to compete as a default strategy irrespective of task manipulations and that with age, levels of competition can be reduced. Of interest will be whether there is a gradual transition from competition to cooperation (when it is appropriate) with age or whether there is a sudden switch from a default strategy of competition to one that is moulded around the demands of the situation. The data reported here are suggestive of the former view. If theory-of-mind promoted only cooperation in these games, one would expect adults to be even more accommodating than children. If, however, strategic defection can result from mindreading, and if such perfidy is strongly countered by the overtly prosocial norms of childhood and less opposed by the more pragmatic norms of adulthood, then adults may be more competitive than children. The tests reported above revealed that the adults were not uniformly more or less cooperative than the children. When facing a human counterpart in a competitive situation, all ages were equally competitive [t (63) = .21, p > .1]. However, this was not the case when playing the computer opponent and in the encouraged cooperation game. Adults competed significantly more than the children (6-10 years combined) in the former game [t (63) = 2.4, p < .02] and cooperated significantly more in the latter game [t (63) = -4.67, p < .001]. This pattern among the average rates of cooperation is based on significant differences in the underlying distributions: out of fifteen adults, six did not cooperate in a single round with either the human or computer counterpart, while among the fifty normal children, only five were uniformly uncooperative. We analysed this difference by categorising individual participants as either ‘reliably competitive’ (0-1 trials of cooperation), ‘reliably cooperative’ (15-16 trials of 20 cooperation) or ‘variable’ (2-14 trials of cooperation) on each Prisoner's dilemma game. The results of this allocation can be seen in Figure 3, in terms of the percentage of each group who fell into each category. Group performance (adults versus all children together) was compared using a series of chi-square tests for each game separately. This analysis revealed significant differences between the degree of cooperation between the normal adults and normal children in all three versions of the Prisoner’s dilemma game. This difference was particularly striking on the encouraged cooperation game [human opponent, χ2 (2) = 6.84, p < .05; computer opponent, χ2 (2) = 7.49, p < .05; encouraged cooperation, χ2 (2) = 24.46, p < .001]. These differences reflect the fact that the normal adults were reliably competitive on the human and computer opponent games and reliably cooperative on the encouraged cooperation game. In contrast, children were predominantly variable in their strategy on each game. [Figure 3 about here] Finally, while there was no statistically significant difference among the normal participants in terms of the number of people cooperating on the first round of either the human and computer opponent games of the PD [χ2 (3) = 4.29, p > .1 and χ2 (3) = 5.61, p > .1 respectively], there was a significant difference on the encouraged cooperation PD game [χ2 (3) = 11.08, p < .01]. This difference arose because significantly more of the adults cooperated than each of the child groups (6-, 8-, and 10-years), who did not differ from each other on this measure. Adults initiated cooperation significantly more frequently in the encouraged cooperation task than in both the human and computer opponent versions [Z = -3.21, p < .001 and Z = -3.21, p < .001, respectively]. None of the children adjusted fully initially or in later rounds to the ersatz PD—they did not recognize that defection was dominated by cooperation. By comparing the choice made on the last round of the second PD game played (either against the human or computer opponent) with the choice made on the first round of the 21 encouraged cooperation version of the PD (rounds 32 and 33 respectively), the extent to which individuals recognised the switch in the rational choice to be made according to the new task demands was considered. The data were investigated for each group separately using a series of Wilcoxon tests to compare the level of cooperation on each pairing of the PD games (see Table 4). There was no significant difference between the choice of shape made in rounds 32 and 33 when the 6-, 8- and 10-year-olds were considered separately [6 years, Z = -1.63, p > .1; 8 years, Z = -1.73, p > .1; 10 years, Z = -1.0, p > .1], although when combined the children showed a significant difference between the choice of shape in the two rounds [Z = -2.5, p < .01]. By contrast, there was a large and significant difference between the choice of shape made in rounds 32 versus 33 in the adults [Z = -3.16, p < .002]. [Table 4 about here] Having established the existence of a developmental pathway to patterns of cooperation and competition, the autism samples were then added to the analysis and comparisons made between the two adult and child groups (normal age groups collapsed) separately for the degree of cooperation across all 16 rounds of each PD game as well as on the first round of each game only (see Figure 2). For performance across the 16 rounds of each game, there was a significant effect of PD game in the comparisons of both the adult and child groups [Adult, F (1,28) = 47.2, p < .001; Child, F (1,66) = 31.24, p < .001], indicating significantly higher levels of cooperation in the encouraged cooperation version of the game in comparison to both the human and computer opponent versions irrespective of participant group. This finding indicates that the premise of each task manipulation was supported not only in the control, but also in the autistic sample. There was no significant difference between the level of cooperation seen across the groups [adults, F (1,28) = .26, p > .1; children, F (1,66) = .09, p > .1], nor a significant interaction between group and game [adults, F (1,28) = 2.9, p > .1; children, F (1,66) = 1.56, p > .1]. 22 Investigating first round choice for the individuals with autism was achieved through the use of a series of Wilcoxon tests comparing each combination of the Prisoner’s dilemma games for the adults and children with autism separately. In the case of the adults with autism, performance reflected the pattern seen in the 10-year-olds in the current sample, with significantly higher numbers of participants cooperating on the first round of the encouraged cooperation version of the task in comparison to the computer opponent version only [Z = -2.11, p < .05]. The level of cooperation between the human and computer opponent and human opponent and encouraged cooperation games did not differ [human versus computer opponent, Z = -1.41 p > .1; human opponent versus encouraged cooperation, Z = -1.67, p > .1]. There were no differences in the degree of cooperation elicited by the children with autism in any pairing of games, as seen in the profile of the 6- and 8-year-olds in the current sample. These findings suggest that some degree of appropriate cooperation is seen in adults with autism but is not yet present in our sample of children with autism, whose chronological age was similar to that of the 10-year-old normally developing children. Thus at the very least the children with autism must be described as showing a delay – or immaturity – in their development of appropriate levels of cooperation. Were there distinctions in the profiles of play over all sixteen rounds of the game? These data are displayed in Figure 3. Unlike normal adults, many more of whom were reliably competitive against the human and computer opponents than were the normal children, the profiles of the adults with autism were less distinguishable from those of the children with autism across all three games. Group performance (autism adults versus normal adults) was investigated using a series of chi-square tests for each game separately. This analysis revealed significant differences in the degree of cooperation between the two adult groups in the human opponent [χ2 (1) = 6.38, p < .05] and encouraged cooperation [χ2 (1) = 6.93, p < .05] versions of the PD game, 23 but not when playing the computer opponent [χ2 (1) = 1.26, p > .1]. The difference between the groups was of a smaller magnitude on the encouraged cooperation game than seen in the normal sample, who also differed on the computer opponent version of the PD game. What of the performance of the individuals with autism when required to switch from the likely choice of defection (circle) to that of cooperation (triangle) between the last round of the second PD game (human, computer opponent) and the first round of the encouraged cooperation PD? Of interest was whether the individuals with autistic spectrum disorders would be able to make this switch easily and entirely, as did the normal adults, or fitfully and partially, as did the normally developing children. According to our prediction that individuals with autism would cooperate less than their normal peers, we would expect that individuals with autistic spectrum disorders would be less able to switch from a competitive to a cooperative strategy between the last round of the competitive versions of the PD and the first round of the encouraged cooperation version. We investigated this through the use of a series of Wilcoxon tests to compare the level of cooperation on the two critical rounds of the relevant PD games (see Table 4). For the children with autism, there was no significant difference between the choice of shape in the two rounds under consideration [Z = .0, p > .1]. For the adults with autistic spectrum disorders, the choice approached significance [Z = -1.9, p = .058]. It is conceivable that this difficulty in switching strategies and responding to the new payoff matrix is related to a general tendency in autism toward perseveration. Comparison of cross-group performance, and specifically whether the individuals with autism (at least the adults) cooperated significantly less when playing against the human opponent than their normal peers was considered. There was no significant difference between the number of cooperative responses across the sixteen rounds of the prisoner's dilemma played against the human opponent in the two adult groups [F (2,49) = 1.94, p > .1]. Thus, contrary to predictions, the behavioural data suggest that the adults with autism did not cooperate less than 24 their normal peers. In fact, they showed a tendency to cooperate more when playing both against the human and the computer opponent, although the difference between the two adult participant groups was less in the computer opponent version. While this was not a significant difference, it accords well with previous studies suggesting that individuals with autism have less difficulty representing the "minds" of machines such as cameras than they do the minds of other human beings (Leekham & Perner, 1991; Leslie & Thaiss, 1992). The percentage of each group cooperating on each round of each Prisoner's dilemma game is shown in Figure 4. A repeated measures ANOVA with one between factor (group) and two within factors (Prisoner’s dilemma game; round) was applied to the data for the number of cooperative moves, first for the normal groups (adults versus all children together). There were significant effects of group [F (1,62) = 4.08, p < .05], game [F (1,62) = 99.64, p < .001], and significant interactions of group by game [F (1,62) = 16.19, p < .001] and game by round [F (1,62) = 4.58, p < .05]. The first three significant effects have been described previously. The game by round interaction reflected a more similar profile of cooperation in the encouraged cooperation and computer opponent games than in the human opponent game. In the latter game, normal children competed progressively more over the course of the sixteen rounds while normal adults sustained a more even rate of cooperation. [Figure 4 about here] The trial-by-trial decisions of the individuals with autism were compared to those of their normally developing peers for the adult and child groups separately. For the children, there was a significant effect of game [F (1,65) = 30.01, p < .001], described previously. The normally developing children appeared to distinguish more between playing the human opponent than the computer opponent or encouraged cooperation games, highlighted by the less cooperative strategy that they adopted. This performance profile was not evident in the children with autism. There was a significant interaction between game and round [F (1,65) = 11.5, p < .001]. Once 25 again, this interaction reflected a more similar profile of cooperation in the encouraged cooperation and computer opponent games than in the human opponent game. For the adults, there was a significant effect of game only [F (1,28) = 54.15, p < .001], described previously and indicating that there were no significant differences between the adults with autism and the normal adults in terms of cooperative choices across the 16 rounds of each Prisoner’s dilemma game. Figure 4 shows, however, that the adults with autism were consistently less cooperative on the encouraged cooperation game and generally less competitive when playing the other two games, particularly against the human opponent. Thus they were less influenced by the task demands than their normal peers and this neutrality may reflect the application of a particular, pre-set decision rule or pattern across all the games. Furthermore, the responses of the adults with autism in the semi-structured interview reflected the special status of the human opponent PD, indicating that where the adults with autism appeared to produce similar patterns of cooperation as the normal adults, this arose out of their knowledge that they needed to predict the workings of their opponent’s mind, were generally unable to do this spontaneously and thus needed to rely on rule-based methods. This was indicated by many of the adults with autism, for example, "I've gotta put a mental state in Sarah [the confederate], think what she was thinking" (a 32-year-old woman with autism). This was the nature of the response for many of the adults with autism for the PD tasks, the Happé stories as well as in their daily lives. The normal adults did not make such statements during their interviews. Taken together these results show that although there was no significant difference between the degree of cooperation elicited in the individuals with autism and controls when all rounds of each game were taken together, more subtle differences were evident, specifically in the degree of cooperation between the last round of the human or computer version of the game (whichever was played second) and the first round of the encouraged cooperation PD, where a lack of 26 switching between a competitive to a cooperative strategy was evident in the individuals with autism, especially the children. With respect to the human and computer opponents, older children cooperated initially more than did younger children and normal adults, while neither group of children nor adults with autism fully reproduced the level and pattern of competition manifested by normal adults. We can now turn to an explicit analysis of the role of mentalising in fostering or retarding cooperation in these games. In order to establish whether there was a relationship between mentalising ability and choice of strategy when playing each version of the prisoner's dilemma, a comparison was made between performance on the second-order false belief test and the degree of cooperation evidenced in each game for the child participant groups. All members of the normal adult group passed this task, and thus such a comparison was not warranted between the adult groups. An alternative comparison based on performance on the Happé stories was used to investigate any relationship between mentalising and degree of cooperation in the adult groups, and is reported below. To ascertain whether a relationship between mentalising ability and strategy choice existed in the children, a repeated measures ANOVA with two between factors (group; secondorder false belief performance) and one within factor (PD game) was applied to the data for number of cooperative moves (children with autism versus all normally developing children together). There was a significant effect of game [F (1,62) = 19.72, p < .001], as described previously. The effect of false belief approached significance [F (1,62) = 3.72, p = .058], suggesting that second-order false belief passers had a tendency to be more cooperative than second-order false belief failers, irrespective of whether or not an individual was diagnosed with autism. A child who failed this false belief test presumably also had a tough time understanding what the counterpart’s beliefs about the child’s own intentions were in the PD. Generally, a player wants to be cooperative only if she forsakes narrow self-interest and if she can anticipate 27 that the other will cooperate (Kiyonari, Tanida & Yamagishi, 2000). The target of this latter anticipation is, of course, a similarly conditional, cooperative expectation, and hence, the importance of a theory-of-mind. None of the normal children failed the first-order false belief task, while a third of the children with autism did so. To determine the relationship between this fundamental mentalising capacity and cooperation, the performance of the children with autism was analysed with a one between factor (first-order false belief performance) and one within factor (PD game) repeated measures ANOVA. There was a significant effect of the game [F (1,16) = 2.13, p < .05], as described previously. Passing the false belief task significantly decreased cooperation across the games, mean number of cooperative responses, 4.72 and 7.27 for false belief passers and failers, respectively [F (1,16) = 4.46, p < .05]. There was no interaction between game and task performance [F (2,16) = 2.01, p > .1]. This result is the opposite of the one reported for second order false belief: an acutely malfunctioning theory-of-mind enhanced cooperation. The children who could not decipher the first-order (Sally-Anne) false belief task cooperated, on average, in approximately half the trials of each of the PD game versions. One of the ways a player could generate a cooperation rate of 50% is to simply choose randomly on each round without any regard for the decisions of the counterpart. If this form of decision-making was employed, the player would be equally likely to choose the circle or the triangle in a round regardless of which shape the opponent chose on the previous round, in other words, random reciprocation. (The tit-for-tat strategy, by contrast, assures that circle follows the other’s circle and triangle, triangle with certainty.) For each of the children with autism, the conditional response rates to the opponent’s cooperating or defecting in the previous round were calculated. The hypotheses that these conditional response rates were equal to 50% were tested for those children who had passed or failed the Sally-Anne task. For the children with first-order false belief troubles the hypothesis of random reciprocation could not be 28 rejected in three of four instances (computer opponent: cooperation after cooperation t (5) = .0, p > .1, defection after defection t (5) = 1.42, p > .1; human opponent: cooperation after cooperation t (5) = .44, p > .1, defection after defection t (5) = 4.07, p < .01). In this last case, defection was reciprocated a little more than 70% of the time. By contrast, those who passed the first-order false belief test did not respond randomly in any setting (computer opponent: cooperation after cooperation t (12) = -3.19, p < .01, defection after defection t (12) = 4.83, p < .001; human opponent: cooperation after cooperation t (12) = -3.71, p < .005, defection after defection t (12) = 6.23, p < .001). This sub-group of test passers was more likely to reciprocate defection and to exploit cooperation. The evidence, then, strongly suggests that a first-order theory-of-mind or a rule-based, effective substitute is necessary to reciprocate in a strategic fashion in the prisoner’s dilemma. To investigate the relationship between mentalising ability and cooperation in social dilemmas in the adult groups, each adult participant was allocated a categorical score for their performance on the Happé stories. Each story had been scored between 0 and 2. A participant's mean accuracy score was converted into a categorical score of 0, 1 or 2. The data for the adult groups were then analysed using a repeated measures ANOVA with two between factors (group; theory-of-mind categorical score) and one within factor (PD game) on the number of cooperative moves. There was a significant effect of game [F (1,26) = 30.72, p < .001], described previously, and a significant interaction between game and theory-of-mind category [F (2,26) = 3.85, p < .05]. This interaction indicated that those participants with superior theory-of-mind performance adhered to the expected performance profile of more overall competitive play in the human and computer opponent versions of the game and more overall cooperative play in the encouraged cooperation version of the game, irrespective of whether or not an individual was diagnosed with autism. The greater adherence to the expected task performance - as inherent in the rules of each 29 game - suggests that mentalising ability is involved in conditionally responding to the counterpart. One possible explanation of this result is that it is driven simply by a disparate recognition of the changed rules in the initial round of the enforced cooperation game and does not involve an ongoing divergence in strategy. However, the theory-of-mind category score did not correlate with the likelihood of switching from a competitive response on the 32nd round of the game (either playing the human or computer opponent) to a cooperative response on the first round of the encouraged cooperation PD in either adult group [normal adults, r2 (13) = -.12, p > .1; autism adults, r2 (13) = -.33, p > .1]. Thus, mentalising did not appear to be involved in responding to changes in the setting and rules of these PD games, but rather to the maintenance of a consistent strategy across sixteen rounds. This flexibility across games and consistency across rounds of the same game may be attributed to 'real' mentalising skill in the normal adult group, and to a compensatory mechanism in the adults with autism. The small number of the latter individuals who performed relatively successfully on the Happé stories reported doing so in a rule-based way ("It's politeness, that's what my mother taught me. I've never really understood why", a 46-year-old woman with Asperger syndrome; "I should say the opposite of what I think", a 32-year-old woman with autism), and many of the adults with autism reported approaching the PD tasks similarly ("The problem I've got with autism … other people who don't have it, they have slightly different strategies", a 24-year-old man with Asperger syndrome). Taken together, these comments suggest that the individuals with autism may be performing rationally by drawing on their logical reasoning skills. Overall this speculation is supported not only by participant self-report, but also by their slowed responses to the Happé questions. Did players distinguish between the human and computer opponent? The inclusion of the human versus computer opponent version of the Prisoner's dilemma task was a manipulation 30 designed to allow investigation of this issue, in particular relating to a section of the semistructured interview where questions were asked concerning any difference in the feeling or playing of the participants in these two games. On the whole the normal adults felt a difference in these two games and ascribed this difference to the nature of their opponent, using mental state terms and/or a sense of empathy to describe this: "There's no way to judge the computer's actions but I could obviously do so with Sarah [the confederate]." (a 30-year-old normally developing man); "I felt she could be intuitive to what I was doing whereas I don't perceive a computer as being intuitive. Its just mechanical." (a 33-year-old normally developing man). While a proportion of the adults with autism described feeling different in these two tasks, and identified the locus of this difference as lying in the nature of the opponent, responses included fewer mental state terms, no empathic sense and had a sense of learned difference rather than the intuitive human sense seen in the normal adults: "It was more complicated to play with Sarah … Because it [sic] was a person." (a 37-year-old man with Asperger syndrome). "Playing the machine was easier because the machine is predictable … and I felt more comfortable ... It’s easier to read a machine or anticipate a machine … it’s far easier. A person is unpredictable … a machine is easier because you're not up against emotion. It’s safer playing a machine. I don't fear playing a machine but I fear the woman." (a 41-year-old man with Asperger syndrome). A final example that particularly highlights the contrast between the two groups is illustrated by the opposing sense of prediction seen in the following representative comments of an adult with and one without autism: "I actually felt there was something about the way the computer was programmed that I might eventually be able to work out where as I didn't feel that way about Sarah." (a 46-year-old woman with Asperger syndrome); "I felt I could predict Sarah's responses more than I could predict the computer's" (a 21-year-old normally developing woman). Although asked to identify the strategy of their opponent, very few players in either group felt that they could do so concretely (see Hill, Sally & Frith, in preparation). 31 Given the many comparable results of the individuals with and without autism in these PD tasks, there was no prominent quantitative manifestation of internal discomfort with the human opponent. Overall the decisions of the individuals with autism, whether derived through the generation of a relevant rule or the application of a weakened theory-of-mind, largely reproduced those of the matched normal participants. It may be that the discomfort of the autistic individuals matched the distrust of the normal participants, who played the real PDs in a very competitive manner. There was one sub-group, however, who might be more prone to react to the perceived “unpredictability” of the human opponent or take comfort in the assumed “dependability” of the computer opponent, namely, those individuals who failed the coin flip task. These participants made a mistake in the face of the uncertain coin, and their decisions might be similarly affected by an opponent with equal or greater levels of capriciousness. Much evidence suggests that children in general reflect the thoughts voiced by our participants with autism in that they are more reluctant than adults to predict stability in the behaviour of other people (Miller & Aloise, 1989), but are no more likely to find machines or physical objects befuddling (Kalish, 2002). Accordingly, the relationship between levels of cooperation with the human and the computer opponents and the coin flip decision was investigated. A two between factor ANOVA (diagnostic group - normal, autism; shape choice on coin flip task - circle, triangle) was applied to the number of cooperative responses when playing the human and computer opponents separately. The result of interest in these analyses is that of any influence of coin flip choice on cooperation in the PD. There was no significant difference in the cooperation rates of coin flip “passers” and “failers” when playing the human opponent [F (1,94) = 1.58, p > .1]. A significant difference emerged on this measure when comparing performance on the computer opponent PD [F (1,94) = 7.03, p < .01]. In this case those making the nondominant choice on the coin flip were 32 more likely to cooperate on the PD (mean cooperation on PD computer opponent for nondominant versus dominant coin flip choice, 6.21 versus 3.61 respectively). One potential hypothesis concerning these coin flip results is that these individuals have an overwhelming preference for triangles over circles. The fact that coin flip “failers” did not pick triangles (thereby cooperating) more frequently in the human opponent game eliminates this hypothesis. Rather, it seems that the predictability of the computer made these participants feel more secure and hence, much more willing to cooperate. Summary The performance of normally developing children was found to be less strategic than that of the normal adults in the current study, supporting the picture of a developmental trajectory in levels of spontaneous cooperation beginning with competition and ending with competition or cooperation, as appropriate to task demands in adulthood. It is clear that this pathway is not fully matured by the age of ten years. In relation to autism, differences existed in comparison to the normal groups, particularly in terms of less strategic responses to the first round of each game and across the individual rounds. In terms of mentalising and its influence on levels of cooperation, mentalising ability aided strategic behaviour irrespective of the presence or absence of autism. The skills used by those individuals with autism who appeared to have mentalising abilities, and which could be considered to reflect quasi-mentalising ability, may have allowed such individuals to 'get-by' in the Prisoner's dilemma task by using strong reasoning skill. These individuals appreciated that the task required an understanding of another's mind, they had clear insight into their difficulties in this regard and that they must draw on other resources to work out what was expected of them. If this is the case, more subtle experimental manipulations of the setup used in the current study should elicit the oft-reported autism difficulties in this domain, and would be particularly striking in real-life situations. 33 In the Prisoner’s dilemma, the issues of cooperation, generosity, and retribution are interwoven with uncertainty about the counterpart’s intentions and actions and with a multiplicity of rationales for a given action in each round. In a second study, an alternative form of strategic interaction that may clarify these—bargaining—was investigated. Experiment Two – Bargaining Nature, inaccurate accounting, gravity, an overstocked shelf, chance, a misplaced wallet, an academic researcher, or some other dea ex machina might endow an individual not only with a prize or a lump of value but also with a companion and with a decision about division. In the dictator game, the choice is how much of the prize to grant to the other party who is bound to accept the grant. In the ultimatum game, the choice is how much of the value to offer to the other party who then may accept the offer or refuse it. Acceptance achieves the suggested division; refusal results in the whole prize being withdrawn and both parties receiving nothing. For example, an experimenter might confer upon a participant ten candies (Murnighan & Saxon, 1998), ten marks or some other unit of currency (Güth & Tietz, 1990), or ten points or tokens (Harbaugh, Krause & Liday, 2000). This endowed person would, as a dictator, decide how many of the ten units, if any, to give to another person, and as the proposer in the ultimatum game, how many to offer the other party knowing that a rebuff would wipe out the grant entirely. The ultimatum game represents, among other social situations, the possibility in a negotiation of one bargainer making a final offer to the counterpart and walking away from the table leaving the other to sign the deal or not. Experience and introspection tell us that in this setting such a dramatic proposal has a good chance of failing. However, in orthodox economics, such an ultimatum should work: the equilibrium, corresponding to the prediction based on rational self-interest, is that the responder should accept any offer greater than zero, and therefore, the proposer should offer the smallest possible positive amount. In fact, this 34 equilibrium is rarely realized in any of the numerous assays that have been conducted in laboratories and field sites around the world during the last twenty years. The regular findings, rather, are the following: (i) the modal offer is between 40% and 50% of the whole prize, (ii) tiny offers are almost always rejected, and (iii) a majority of responders will refuse offers below a third of the total (see Güth & Tietz, 1990; Camerer & Thaler, 1995; Oosterbeek, Sloof & van de Kuilen, 2001 for reviews). The literature has focused on understanding why offers are so robust, and why responders are so rancorous. One possible explanation for the generosity of the proposers is that they have a certain taste for fairness and a preference for sharing some part, or even half, of their dowry. Forsythe, Horowitz, Savin & Sefton (1994) asked participants to play an ultimatum game and a dictator game to test this hypothesis, since fairness considerations would dictate that proposers give the same amount in both games. However, these authors found that the proportion of equal split offers declined from 75% in a $10 ultimatum game to 21% in a $10 dictator game. The mean offer in a standard 10 unit dictator game is between 20% and 25% (Rigdon, 2002), and in the ultimatum game, 40% and 45%. Roughly, then, half of the typical proposer’s generosity is driven by a taste for fairness and half by strategic considerations of the possible spite of the responder. Subsequent dictator experiments have shown that the taste for fairness can be heightened or slaked by the context of the game. The degree of selfishness among dictators is raised by allowing them complete anonymity, even from the experimenter (Hoffman, McCabe & Smith, 1996) and by placing them in a business setting of buying and selling (Hoffman, McCabe, Shacat & Smith, 1994); the frequency of altruistic grants is raised if the recipient is a charity (Eckel & Grossman, 1996) and if more personal characteristics of the recipient are identified (Bohnet & Frey, 1999; Charness & Gneezy, 2000). Subsequent ultimatum experiments have shown that the strategic and fairness concerns of both parties move in predictable directions: when the proposed 35 split of $10 is generated by a roulette wheel as opposed to another person, the mean minimal offer acceptable to responders is much lower—$1.20 rather than $2.91 (Blount, 1995); when a written note saying “I know you’d like more, but that’s the way it goes” is attached to a small offer, a rebuff from the responders is more likely (Kravitz & Gunto, 1992); when the endowment is worth more to one side than the other (e.g., 50¢ versus 10¢ per chip) and the other side is ignorant of this fact, advantaged proposers are more likely to suggest an even split of the counting units rather than total value, and advantaged responders are more likely to reject fair splits of the underlying units to induce a more equitable split of surplus value (Croson, 1996; Kagel, Kim & Moser, 1996). The variants just described could be reflective of mentalising ability or social rulefollowing. For instance, a person might reject a small offer because she imagines that the proposer thinks she is unworthy, gullible, or dim-witted, or because she recognizes this game as a sharing situation which demands that greedy people be punished. Similarly, a munificent dictator might utilize her theory-of-mind to anticipate the disappointment of an unfunded recipient, or she might recognize the applicability of a sharing norm. A norm may substitute for mentalising: as the other driver in a narrow lane approaches, you need not read his eyes, thoughts, or intentions, you need only remember the locale and move to the left in the UK and to the right in the US. Mentalising may become necessary only if the interaction does not proceed as expected: when the other driver fails to move to the proper side, then you need to notice the direction of his gaze, the tenseness of his hands, and the expression on his face. The evidence on the relative utilization of mentalising versus norm-following in the standard, anonymous ultimatum game is mixed. On the one hand, Henrich et al. (2001) found that much of the variance in mean offers among fifteen small-scale societies (ranging from 26% among the Machiguenga in Peru to 58% among the Lamelara in Indonesia) could be explained by two factors—the importance of cooperation in the society’s economic production and the reliance 36 on market exchange in daily life. These factors would seem to develop and mould norm formation rather than mentalising abilities. The authors themselves suggest that their participants applied the analogous (and varying) norms found in their societies: [W]hen faced with a novel situation (the experiment), they looked for analogues in their daily experience, asking “What familiar situation is this game like?” and then acted in a way appropriate for the analogous situation. For instance, the hyper-fair…offers (greater than 50 percent) and the frequent rejections of these offers among the Au and Gnau reflect the culture of gift-giving…, accepting gifts, even unsolicited ones, commits one to reciprocate at some future time to be determined by the giver (Henrich et al., 2001, p. 76). A second experiment that both documented the under-utilization of mentalising and its potential impact is that of Hoffman, McCabe and Smith (2000). Here, in a $10 ultimatum game with a business context, an additional line was added to the instructions encouraging the proposer (i.e., seller) to strategize and read the mind of the opponent: “Before making your choice, consider what choice you expect the buyer to make. Also consider what you think the buyer expects you to choose.” This encouragement caused the mean offer to rise to $4.17 from $3.71 in a control condition where no explicit mindreading prompt was given, and this increase suggests that proposers in the control condition were solving the game without fully employing their theory-of-mind. On the other hand, studies in which an asymmetry in information between the offerer and responder is strategically exploited support the relative prominence of mentalising. Respondents in one experiment received a set sequence of twelve offers for either $1 or $2 out of a total surplus of $20 (Pillutla & Murnighan, 1996). There was a complicated information structure overlaid on the set of games: (i) during the first half of the sequence, none of the responders knew that the total prize was $20, making it difficult to deem an offer unfair; (ii) half of the responders knew that the (fictitious) offerers knew that the low offers were unfair and therefore could more easily attribute greedy intentions to them. (Note that this second manipulation depends upon a theory-if-mind both to understand the different contents of the other’s mind and to respond 37 emotionally.) After making each decision, participants reported how they reacted to the offer and how they felt. These verbal responses were coded for the degrees of unfairness and anger expressed. In relation to the current study there were two important findings: first, the manipulation relying on theory-of-mind was successful—participants who knew both that an offer was low and that the other knew it was low were far angrier. Second, this anger resulted in a greater frequency of rejection, and was more predictive of the likelihood of rejection than was the expressed degree of unfairness alone. As any overtaxed parent can testify, children reject various ultimatums all day long and readily employ multiple notions of “fairness.” There is a vast literature on prosocial behaviour among children, some of it emphasizing social rules and some, perspective taking. Numerous donation studies (similar to the dictator game) have found that children are more generous when they have seen a model being generous (e.g., Harris, 1970; Wilson, Piazza & Nagle, 1990). Within social learning theory (Bandura, 1986), a model affects the observer by directly representing the presence or application of a rule rather than by triggering a mediating internal process, suggesting that mentalising is less important in giving. On the other hand, a child’s abilities to take the perspective of another person visually, emotionally, and cognitively are positively related to prosocial behaviour in most studies (Underwood & Moore, 1982), and in one specific study, two factors relying on a child’s theory-of-mind, affective reasoning and sympathy, caused a large increase in donations to a needy person (Knight, Johnson, Carlo & Eisenberg, 1994). Both sources of prosociality should become stronger as children grow up, and indeed, a meta-analysis by Eisenberg and Fabes (1998) found that sharing and donating became more prevalent from preschool through adolescence. Two specific ultimatum studies, however, showed a less clear developmental trend. Harbaugh, Krause and Liday (2000) found that fourth and fifth graders made larger grants as dictators than did second graders or ninth graders. While these authors found that ultimatum 38 proposals on average increased with grade level, Murnighan and Saxon (1998) found a nonmonotonic pattern with kindergarteners offering more candies than third graders, who, in a game with a dollar at stake, offered fewer cents than did sixth graders, who were more generous than ninth graders. Finally, younger children in both studies were more likely to accept small offers. This mixture of results demonstrates that the offering and responding behaviour of children may be affected by the specific details of game presentation, and may reflect a general inconsistency in inference about social interaction. Kalish (2002) has shown that while children and adults will equally predict consistency in repeated physical events such as pumice floating in water, children will much more often predict that a person would behave differently in the future than he or she did in the past, for instance, preferring Bert tomorrow despite preferring Ernie today. If the reaction of the other party is inconsistent or unreliable, then it makes sense to accept whatever the current offer is, and to not be too strategic in formulating an offer. By investigating the giving and receiving behaviour of children and adults with and without autism, the importance of mentalising for both generosity and consistency can be determined. METHOD The participants were the same as those included in the first experiment (social dilemmas), with the addition of one eight-year-old. Materials and Methods. The testing set-up remained the same as that reported for the first experiment with participants being assessed in a quiet room, sitting at a laptop to the right of the confederate. Responses were recorded on-line for later analysis. Task instructions were presented on the computer and the experimenter verbalised them to ensure that participants understood each task. Players were told that they must try to win as many points as possible and that the total points won on the games would be exchanged at the end of the testing session for stickers 39 (children) or chocolates (adults). The greater the number of points won, the greater the reward at the end of the test session. Two versions of a bargaining task - dictator game and ultimatum game - were completed by participants, with the dictator game always played first Dictator game. The dictator (participant) was given ten points and asked how much s/he wanted to give to the opponent, knowing that s/he would keep the remainder. Eleven cards were presented on the computer screen, outlining all possible permutations by which the points could be split, ranging from the dictator keeping all ten points for her/himself to giving all ten points to the opponent. The dictator made his or her choice and indicated this by selecting the relevant card on the computer screen. The choice that the dictator had made and the points allocated to both players were displayed. This process was repeated 16 times throughout the course of the game, with the participant acting as the dictator for rounds 1-4 and 9-12, and the confederate taking the part of the dictator for the remaining rounds. The participant was unaware that there would be more than one round of the dictator game and that the confederate would also take a turn as the dictator. In the latter case the confederate allocated approximately the same amount of points to the participant as the participant had to her. Ultimatum game. This game was essentially the same as the dictator game but the opponent had the choice to accept or reject the offer made to them by the proposer in each round of the game. The game started as the dictator game. Once the proposer had made an offer, the opponent indicated whether s/he accepted or rejected that offer. If the opponent accepted the proposer's offer, the points were divided as proposed (exactly as in the dictator game). If the opponent rejected the proposer's offer, neither player received any points. The choices made by each player, as well as the points won were displayed after each round of the game. The set-up of the ultimatum game was identical to that of the dictator game, with the participant acting as the proposer and the confederate as the opponent for rounds 1-4 and 9-12, and with the roles reversed for the remaining rounds. The participant was not told explicitly that there would be more than 40 one round of the game and that the confederate would also take a turn as the proposer. In the latter case the confederate allocated approximately the same amount of points to the participant as the participant had to her and always rejected offers of less than an equal split (i.e. four or fewer points being allocated to the confederate). RESULTS & DISCUSSION Once again, there is a lack of studies which track the strategy of normal children and adults on our version of the dictator and ultimatum games. This comparison will be presented first, followed by the performance of the individuals with autism. Comparisons of the full dataset will then be reported. Offers made. The offers made by the participant to the confederate were expected to be lower in the dictator game than in the ultimatum game. The mean points offered to the confederate by the participant across the first four rounds of each game, and on the first round only, are shown for each group in Figures 5a and 5b respectively. A repeated measures ANOVA with one between factor (group) and one within factor (game) was applied to the data for mean points offered. For performance across the first four rounds of each game, there was a significant effect of group [F (3,62) = 3.17, p < .05]. A series of Tukey tests revealed this difference to arise from higher offers being made by the six- in comparison to the eight-year-old children [p < .05]. There was a significant effect of game [F (1,62) = 77.87, p < .001], reflecting higher offers being made to the confederate in the ultimatum game, and a significant interaction between group and game [F (3,62) = 3.5, p < .05]. The interaction reflected a greater difference between the offers made across the two games by the adult group. Thus, while both adults and children responded in the predicted manner, the adults did so more strikingly, making a greater distinction between the size of their offers in the 41 two games, with a greater number of points being kept for self in the dictator game versus the ultimatum game. This pattern of performance was also seen in the analysis of the mean offers made to the confederate on the first round of each game, with the exception of the difference between the groups [group, F (3,62) = .62, p > .1; game, F (1,62) = 59.03, p < .001; group by game, F (3,62) = 3.07, p < .05]. These results correspond to the equivocal results of previous ultimatum studies in that there was no clear trend in offer amounts across age groups. More work is needed to explain this finding within the findings of the prosocial literature which shows that sharing and donating become more prevalent as children grow up (Eisenberg & Fabes, 1989). A tentative explanation for the differences may be that this task evoked exchange norms which are already fairly firmly implanted by the age of six. [Figure 5 about here] Having established the pattern of performance across the two bargaining tasks used in the current study, the autism samples were added to the analysis and comparisons made between the two adult and child groups separately for the mean number of points offered by the participants to the confederate across the first four rounds of each game as well as on the first round only (see Figure 5). In all cases (adults; children; four rounds; first round) there was a significant effect of game [adults, F (1,28) = 34.19, p < .001; children, F (1,67) = 27.75, p < .001], with more points being offered to the confederate in the ultimatum game, but no effect of group and a mildly significant interaction between group and game [adults: group, F (1,28) = .02, p > .1; group by game, F (1,28) = 3.27, p > .05; children: group, F (1,67) = .7, p > .1; group by game, F (1,67) = 2.95, p > .05. On average, it appears that the individuals with autism approached the bargaining tasks in a way that was comparable to their normally developing peers, suggesting that some individuals with autism may have an intact mechanism that deals with fairness. There is, also, some indication that individuals with autism did not react as vigorously to the strategic dimensions of the ultimatum game. 42 More evidence for the relative effects of autism on fairness and tactical giving appear when looking at the underlying distributions of offers. In particular, Figure 6 displays the dispersal of first round ultimatum offers by adults and children with and without autism. Visually, there appears to be a strong divergence. The powerful Epps and Singleton (1986) test was used to determine if these two samples of discrete data were likely to be from identical populations. This CF statistic, based on the empirical characteristic function, is asymptotically distributed as chi-square with four degrees of freedom and can be corrected for small samples. For the first round ultimatum of autistic and normal adults displayed in Figure 6a, the null hypothesis of similar distributions is strongly rejected [CF = 11.83, p < .02]. One interpretation of this result is that adults with autism applied one of two rules: split the amount fairly and squarely, or take everything that you can. A far greater proportion of the normal adults tried to strategically shade their offers by taking a point or two extra for themselves. [Figure 6 about here] We can contrast the distributions of first round ultimatum offers of children with and without autistic spectrum disorder in Figure 6b. Here, again, there was evidence of distinct patterns of first offers [CF = 9.96, p < .05]. There were no significant differences in offer distributions between the adults and the children who shared the same condition [normal, CF = 6.59, p > .1; autism, CF = 2.54, p > .1]. Moreover, when comparing first round dictator offers there were no significant differences in the underlying distributions across any groups. Since the initial dictator grants were the same, it can be concluded that core generosity did not vary by age or with autism. However, once an element of strategic anticipation was added by empowering the responder in the ultimatum game, those with autism seemed to employ one of two salient rules: cut the total in half, or keep it all. In contrast, the mentalising abilities of the normal participants were utilized to generate mildly unequal, slightly shaded offers. 43 The direct effects of mentalising on first round offers among the children can be seen in Figure 7. The striking visual difference in the distribution of offers is strongly confirmed by an Epps-Singleton test (CF = 9.64, p < .05). Here, the majority of those who failed the second-order false belief test offered the responder nothing or only one point, while the majority of those who passed the test offered an even split. (These distributions of initial offers have means whose distinctiveness approaches significance [F (1,63) = 3.73, p = .058].) A theory-of-mind that was effective due to intuition or synthetic construction was very helpful to the child making a reasonable offer in this game. [Figure 7 about here] The extent of learning across rounds of the ultimatum game was investigated, in light of the apparently different strategies of the individuals with and without autism. The confederate consistently rejected offers of four points or fewer, so participants had the chance to learn, adjust and converge on the optimal offer of an even split. A comparison of the groups on a round by round basis was made on the dictator and ultimatum games for all eight rounds (see Figure 8). A repeated measures ANOVA with one between factor (group) and two within factors (bargaining game, round) was applied to the data for the mean offer made by each group on all eight rounds of each game. For the comparison of the normal adults and children, there was a significant effect of game [F (1,64) = 118.66, p < .001], and a significant interaction between group and game [F (1,64) = 7.93, p < .01]. These significant effects have been described previously. When comparing the mean offers of adults with and without autism, there was a significant effect of game only [F (1,28) = 38.19, p < .001]. Unlike the first round, however, this similarity in the means across the groups for rounds two through four and nine through twelve is also reflected in comparable underlying distributions of individual offers (all CFs < 7.0, all p’s > .1). Moreover, mentalising, as measured by performance on the Happé stories, was insignificant in an ANOVA of the average offer over multiple rounds. Once autistic adults had a single experience with the 44 ultimatum game, their offers were largely indistinguishable from those of the control group. Hence, the combination of a scaffolded theory-of-mind, choice of a relevant norm and one reaction from another person allowed the adults with autism to mirror the behavior of their normal counterparts. With respect to the average offers of the children there was a significant effect of game [F (1,67) = 62.38, p < .001], described previously and a significant interaction between group, bargaining game and round [F (1,67) = 4.21, p < .01]. This interaction highlighted divergence between the amounts offered to the confederate in the two games on round one in the normally developing children only. Most of the children seem to learn the game as quickly as the adults: by the second round the offers of autistic and normal children as a group are dispersed in statistically similar ways. This concurrence is confirmed by the insignificance of second-order theory-ofmind in an ANOVA of average offer over all eight rounds. The one group that remains distinct are the six-year olds: even after eleven rounds of the game, in their final turns as proposers, their offers are scattered over all the possibilities and are distributed differently from those of the eight- and ten-year olds [CF = 11.62, p < .02] and those of the autistic children [CF = 8.29, p < .1]. [Figure 8 about here] Offers rejected. The mean points rejected over the eight rounds of the ultimatum game by each participant group are shown in Figure 9. A one factor ANOVA was used to compare the mean points rejected by each participant group. There was a significant difference between offers rejected by the normal groups [F (1,65) = 5.09, p < .05], reflecting a developmental trend from childhood to adulthood, with lower offers being acceptable to the children [mean (SD) points rejected: adults, 3.23 (2.71); children, 2.34 (1.22)]. No significant differences existed between acceptable offers to either 45 adults with or without autism [F (1,29) = .44, p > .1], children with or without autism [F (1,68) = .08, > .1], or autistic adults or children [F (1,32) = .62, p > .1],. Thus in terms of offers made by the confederate that were deemed acceptable by the participant, a developmental progression emerged: Both those with and without autism were tolerant of another's gain, accepting offers of less than 50% for themselves. There was, however, a limit in all groups with adults rejecting offers to self of less than 32% and children being more tolerant, rejecting offers of less than 23% of the share for self. This conclusion is in accord with the most robust finding in the literature on children and bargaining: children, younger children in particular, are less likely to reject smaller offers. [Figure 9 about here] Lastly, mentalising appeared to play no role in the rejection of offers. Performance on the relevant theory-of-mind task did not explain any of the variance in average offer rejected by normal adults and children or by autistic adults and children. Hence, for our participants the ability to decipher the confederate’s intentions and envisage her disdain did not affect the likelihood of a small offer being rejected. It is as though the offence generated internally within the recipient of an undersized offer is sufficiently motivating. Summary. The youngest normally developing children (aged 6 years) were found to be more prone to sharing than the 8-year-olds in this sample, and the normally developing children as a whole more tolerant of their opponent's gain (as measured by the offers rejected by participants). Normal children demonstrated more pure generosity in the dictator game and less strategic munificence in the ultimatum game than did adults. There were hints of a similar pattern among our autistic participants. Lastly, for each group with the exception of the 6-year-olds, repeating the ultimatum game caused differences to dissipate. The older children learned and adapted so 46 that by the second round their offers were indistinct from those of the adults; meanwhile, even by the twelfth round, the youngest children were still somewhat at a loss. Although the evidence is not overwhelming, there is a suggestion of a developmental trajectory in levels of bargaining. Autistic children and adults were distinguished from their peers in the initial round. The adults with autism spectrum disorder were more likely to choose either an even split or a tender of zero. Autistic children showed a similar predilection for proposals of nil or five in contrast to normal children who were most likely to just divide the prize in half. An ineffective theory-ofmind was apt to result in an initial offer of no more than one point, while deciphering the secondorder false belief story correctly tended to lead to an equitable offer. Autistic adults proposed a little less than four points on average, and normal adults a little more, but this resemblance masks bimodal roots of the former and the deliberately strategic nature of the latter. The development of theory-of-mind skills may help the child first to recognize and act upon relevant norms of behaviour such as fairness, and later, to stretch those norms and improvise away from them when the situation calls for it. A single experience as proposer was sufficient to counteract any deficits in mentalising in the succeeding rounds of the this ultimatum game. Without question, the static nature of the payoffs and counterpart in this bargaining game is essential to the observed decay in the effects of mentalising. A more dynamic and realistic negotiation would probably continue to demand the application of an active theory-of-mind. In contrast, rejection of offers did not vary with the efficacy of the participant’s mentalising capability. The ability to sense another’s unfairness or contempt was not necessary to reject offers of a third or less. So, the generation of an initial appropriate offer depends on theoryof-mind, but the denial of an inapt or inequitable offer does not. 47 GENERAL DISCUSSION In the experiments reported here the approach to two-way, reciprocal social interaction was investigated using three mixed-motive games. The study had two broad aims, first to track the development of decision-making on these games, and second to investigate the relationship between mentalising and strategy choice. Normally developing children did not, on the whole, perform in the same way as the normal adults in situations involving a social dilemma (the prisoner's dilemma). This was the case both for the games that encouraged competition between players and for the game that encouraged cooperation. While normal adults showed a clear pattern of competition and cooperation according to the task requirements, normally developing children aged 6-10 years mixed both behaviours with their opponent in all situations, irrespective of whether such an approach was overtly beneficial to them. There was some evidence of a developmental trend as 10-year-olds were more likely to cooperate in the initial round of the game playing against a human opponent and pursued a level of cooperation that was more responsive to the identity of the counterpart and the rules of the game than did the younger children. Normal children as a group were more cooperative with the computer opponent than were normal adults, and they became relatively more competitive with their human opponent over the course of the sixteen rounds. In contrast, many differences in the bargaining games were resolved after one or two repetitions. As with the prisoner’s dilemma, adults were more sensitive to the strategic possibilities offered by the structure of the games, in this case, the dictator versus the ultimatum game. Adults, on average, changed their offers more in the latter game, an acknowledgement of the responder’s potential veto power. There were no clear age trends with respect to the average generosity of the offers of the young participants, but the very youngest were significantly more likely to accept a share of the endowment that was less than 30%. In addition, many of these 648 year-olds seemed to remain baffled by the ultimatum game after repeated plays, as their offers in the twelfth round were scattered relative to the adults and other children. This excess dispersion may be in keeping with the finding that younger children are less likely to predict consistency in the actions of other people, in this case, the responder (Kalish, 2002). The adult players with autism appeared, on the surface, to perform similarly to their normal adult counterparts on both kinds of game. However, strategy differences were identified on both the prisoner's dilemma and ultimatum game, although on the former to a greater extent than the latter. Specifically the adults with autism showed less extreme behavioural choices of competition and cooperation on the one hand (prisoner's dilemma) and different distributions of opening offers on the other (ultimatum game). Contra our hypotheses, autistic adults were not less (more) likely to cooperate with the human (computer) opponent, and they failed to adjust fully for the dominant choice in the encouraged cooperation version. Moreover, by the second round of the ultimatum game the discrepancy in offer distributions had disappeared. The overall pattern was similar when comparing the two groups of children. The autistic children did not adapt in any noticeable way to the encouraged cooperation PD, a strategic failure that may reflect the perseverative continuation of earlier choice patterns. Normal and autistic children gave the same unilateral gifts in the dictator game. Finally, with the just noted exception of the 6-yearolds, the large difference in initial ultimatum offer distributions among the children was erased by the second round. We also investigated mentalising performance directly. A child’s ability to pass the second-order false belief test, arising from either a functional theory-of-mind or rational, rulebased deduction, was found to be positively related to the likelihood of cooperation in the three PD games and to perfectly fair ultimatum offers rather than very small proposals. Those autistic children who failed the simplest theory-of-mind test cooperated more than did those who passed the test, but this difference seemed to result largely from the much more random responses of the 49 test failers to the moves of the counterpart. Adults with more effective mentalising abilities employed them both to compete against the human and computer opponents and to cooperate in the encouraged cooperation game. However, with this group, performance in the mentalising test (interpretation of Happé stories) did not explain the variance in individual ultimatum offers. All told there are numerous differences in the decisions and underlying strategies of the participant groups. Roughly speaking, the development of theory-of-mind does seem to be correlated with increasing generosity through childhood and then more strategic behaviour in adulthood. However, when compared to the dramatic distinctions anticipated by our hypotheses or the markedly varying introspections of the adults on strategy and choices, these findings are weaker than expected. Arguably, the rather more surprising findings are those of the initial similarity in performance profile, in particular in terms of the mean number of cooperative moves across all rounds of the PD games and the offers made/rejected in the bargaining games. Such similarity does not mirror the behavioural differences observed in autistic and nonautistic individuals in similar real-life situations. How could this similarity on the experimental tasks be explained in light of the differences in strategy-choice adopted by all players, the striking mentalising deficits evidenced by the autistic participants and their relevant difficulties in daily life? A relationship between strong mentalising ability and increased social cooperation in mixed-motive games has intuitive, and some experimental support. On the basis of the findings of the current study it might seem that mentalising is differentially involved in the prisoner's dilemma versus the bargaining games, being more critical in the former and less in the latter, especially when supplemented by direct experience. However, the underlying differences in strategy identified through statistical analysis, and corroborated in introspective reports as well as in the comments of autistic adults about their interactions in daily life suggest, rather, that some form of compensatory mechanism is driving the choices of the autistic individuals. One candidate 50 for compensation would be the substitute of intuitive understanding with a laboured, rule-based approach in autism whereby the 'rules' of social interaction are gradually learned. These rules must then be applied in on-line situations. This would likely be a laborious process. By such an account it would not be surprising that behaviour could appear odd since social interaction does not, in reality, operate on the strict application of rules alone. If a rule-based mechanism was acting as a substitute for intuitive, easy interactions in the mixed-motive games reported here, it is conceivable that the greater group differences highlighted in choices on the prisoner's dilemma suggest that these social games were compensated for differentially in high-functioning individuals with autistic spectrum disorders. This could arise because the 'rules' recruited for bargaining can be more easily learned and/or applied than those for dealing with a dilemma. If this were the case one might predict that an offer of exactly half would be made to the opponent in the ultimatum game, a logical rule which cannot fail to be acceptable. In the first round, this was exactly the profile identified in 60% of the autistic participants but in less than a third of the normal participants. Moreover, more than a quarter of the autistic adults and children wished to keep all ten points for themselves in the initial trial. Perhaps these participants had not developed a rule for fairness, or identified the ultimatum game as a “finders keepers” game, i.e., if you’re given something, it is yours alone. In contrast the 'rules' of the prisoner's dilemma are more opaque and therefore harder to identify and use as successfully. Though relatively simple, a rule such as ‘tit-for-tat’ may not suggest itself as easily in the PD as the fairness rules do in the dictator and ultimatum games and so, the need to mentalise remains prominent throughout the PD. While it is not within the scope of the current study to pursue the issue of neural activation when performing mixed-motive social games, published findings might shed some light on the compensatory mechanism potentially adopted by the autistic individuals. Neuroimaging studies of mentalising in normal individuals have identified a network of brain regions 51 that is consistently active during mentalising over and above the other task demands. This network involves the medial prefrontal cortex (especially anterior paracingulate cortex), the temporal-parietal junction and the temporal poles (Fletcher et al., 1995; Brunet, Sarfate, HardyBayle & Decety, 2000; Castelli, Happé., Frith & Frith, 2000; Gallagher et al., 2000; Vogeley et al., 2001). These areas appear to be activated less in the brains of autistic adults when performing mentalising tasks (Happé et al., 1996; Baron-Cohen et al., 1999; Castelli, Frith, Happé., & Frith, 2002). With respect to mixed-motive games, a recent functional magnetic resonance imaging (fMRI) study in which playing a two-way reciprocal trust game against both a human and computer opponent was contrasted showed increased activation in non-specified areas of the prefrontal cortex in those players who consistently attempted cooperation with a human opponent, than when the same players played against a computer following a fixed, known probabilistic strategy. Those players who did not cooperate consistently did not show this pattern of increased prefrontal cortex activation (McCabe, Houser, Ryan, Smith & Trouard., 2001). This finding may accord with the notion of a social brain network described above, in which the medial prefrontal cortex plays an important role in verbal and non-verbal mentalising tasks, as well as in monitoring your own inner states. In a second fMRI study, Rilling et al. (2002) required normal individuals to play an iterated prisoner's dilemma against both a human and a computer opponent. In this study, mutual cooperation was associated with consistent activation in brain areas that have been linked with reward processing, in particular the nucleus accumbens, caudate nucleus, ventromedial frontal/orbitofrontal cortex and rostral anterior cingulate cortex. Taken together these two studies suggest the potential involvement of mentalising and/or reward/punishment systems in the brain. While the current study can not draw specific comparisons to brain imaging data, it would be of interest to establish whether the brain activations of autistic individuals would reflect those of normal individuals when making 52 behavioural choices that are - in outcome - identical across the two groups. Given the differing levels and patterns of neural activation of this population for mentalising tasks, and the differing strategies adopted by the two groups in the current study, it would seem most likely that the neural activations of autistic and non-autistic individuals would differ. Information as to the nature of the approach taken by the individuals with autism could be revealed by such imaging studies. The tests reported in this paper identified an unexpected profile of similarities as well as differences in the performance of individuals with and without autism. Mentalising per se may not be necessary for basic strategic rationality, but a quasi-mentalising compensatory mechanism may be needed. Better mentalising (or greater success of a compensatory strategy) increased levels of cooperation in some settings and of strategic behaviour in others. Thus on the surface a putative compensatory mechanism in the autistic participants yielded similar behaviour to that seen in the normal individuals. This contrasts with the striking difficulties in social interaction experienced by individuals with autism in social interactions in the real-world. Perhaps it is the on-line aspects of mentalising and mental flexibility which cause the greatest difficulty for highfunctioning autistic individuals in dilemma and bargaining situations in the real-world where far more distractions and far fewer cues to guide behavioural choices exist than do in the almost sterile laboratory where tasks can be seen to be abstract. We are currently investigating this possibility. 53 ACKNOWLEDGEMENTS This research was supported by funding from the UK's Medical Research Council (grant number G9716841). We gratefully acknowledge the willing participation of all individuals in this study. We are indebted to Sarah Griffiths, Zoë Fortune and Sakina Adam-Saib for substantial help with data collection and especially to Professor Uta Frith for invaluable support for, and discussion of the project. 54 REFERENCES Abell, F., Krams, M., Ashburner, J., Passingham, R., Friston, K., Frakowiak, R., Happé, F., Frith, C., & Frith, U. (1999). The neuroanatomy of autism: a voxel-based whole brain analysis of structural scans. Neuroreport, 10, 1647-1651. Abric, J.C., & Kahan, J.P. (1972). The effects of representations and behavior in experimental games. European Journal of Social Psychology, 2, 129-144. Astington, J.W. (1994). The Child's Discovery of the Mind. London: Fontana. Axelrod, R. (1984). The Evolution of Cooperation. 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Cognition, 13, 103-128. 59 FIGURE LEGENDS Figure 1: A generic prisoner's dilemma Figure 2: Mean number of cooperative responses across all 16 rounds of each Prisoner's dilemma game (Figures 2a and 2c) and percentage of each participant group cooperating on the first round of each Prisoner's dilemma game (Figures 2b and 2d) Figure 3: Percentage of each group (6-10 year olds combined) being 'reliably cooperative', 'reliably competitive' and 'variable' when playing the human opponent (Figure 3a), computer opponent (Figure 3b) and encouraged cooperation (Figure 2c) versions of the Prisoner's dilemma game Figure 4: Percentage of each group (6-10 year olds combined) cooperating on each of the sixteen rounds of each Prisoner's dilemma game Figure 5: Mean points offered (max=10) by the participant groups to the confederate across four rounds of the dictator and ultimatum games (Figures 5a and 5c) and on first round only (Figures 5b and 5d). Error bars show standard deviation Figure 6: Dispersal of first round ultimatum offers by adults and children with and without autism (Figures 6a and 6b, respectively) Figure 7: First round ultimatum offers based on second order false belief test result 60 Figure 8: Mean points offered (max=10) by the participant to the confederate for each participant group (6-10 year olds combined) on the eight rounds of the dictator and ultimatum games Figure 9: Mean number of points (max=10) rejected by participants (6-10-year-olds combined). Error bars show standard deviation 61 Table 1. Participant details Normal participants 6 years 8 years 10 years Adult Autism participants Children Adult N. Male (female) CA Mean SD Range (yr.mth) VIQ* Mean SD Range 14 8 (6) 19 10 (9) 18 10 (8) 15 7 (8) 18 16 (2) 15 12 (3) 6.7 .2 6.04-6.11 8.5 .3 8.00-8.11 10.6 .3 10.03-10.11 34.0 12.3 21-62 10.6 3.1 6-15 34.4 11.0 18-49 109.22 10.87 98.4-133.3 103.22 16.35 75.1-136.5 109.3 11.92 89.8-129.6 117.13 11.01 95-130 96.29 33.7 63.2-211.9 92.29 21.21 60-128 * calculated from verbal mental age for children and WAIS for adults 62 Table 2a. Percent of each group passing first- and second-order false belief tasks Normal participants 6 years 8 years 10 years Adult Autism participants Children Adult 1st order 2nd order 100 71.43 100 94.12 100 100 100 100 66.67 55.56 86.67 13.33 Table 2b. Mean (SD) score (max=16) of each adult group on Happé’s mentalising stories (left panels of table) and mean (SD) time (sec) taken to complete only those stories where a mentalising response (i.e., response gaining the maximum score) was given (right panels of table) Normal adults Accuracy: Mean Range 13.8 (1.61) 11-16 8.87 (4.09) 1-15 Autism adults Time (sec):* Mean Range 23.43 (5.75) 15.5-36.24 53.13 (33.82) 22.09-137.12 Normal adults Autism adults * based on 88 and 47 mentalising responses in the normal adult and autism adult groups respectively. 63 Table 3. Points awarded to the participant and confederate in the Prisoner's dilemma games PARTICIPANT Cooperate (triangle) Compete (circle) CONFEDERATE Cooperate 3, 3 1, 4 (triangle) Compete (circle) 4, 1 2, 2 Note. The players did not see the terms cooperate and compete. The terms confederate and participant were replaced with the appropriate names of the two players in each case. 64 Table 4. % of each participant group making the cooperative (triangle) choice on the last round of the human or computer opponent (round 32) and the first round (round 33) of the encouraged cooperation PD Last round against human or computer opponent Normal adults (n=15) 6 years (n=14) 8 years (n=18) 10 years (n=18) 6-10 years combined (n=50) Autism adults (n=15) Autism children (n=18) 26.67 7.14 16.67 38.89 22 33.33 38.89 First round of encouraged cooperation PD 93.33 35.71 50 55.56 48 73.33 38.89 Sig. level < .002 n.s. n.s. n.s. < .01 .058 n.s. 65 FIGURE 1. Player 2 Cooperate Coop. Player 1 Defect a, b d, d c, c Defect b, a a > c > d > b and c+c > a+b 66 FIGURE 2A 16 14 12 10 8 6 4 2 0 Human opponent Computer opponent Encouraged cooperation 100 90 80 70 60 50 40 30 20 10 0 FIGURE 2B Human opponent Computer opponent Encouraged cooperation FIGURE 2C 16 14 12 10 8 6 4 2 0 Human opponent Computer opponent Encouraged cooperation 100 90 80 70 60 50 40 30 20 10 0 FIGURE 2D Human opponent Computer opponent Encouraged cooperation 67 Figure 3a: Human opponent Figure 3b: Computer opponent Figure 3c: Encouraged cooperation Autism adults Normal adults Autism adults Normal adults Autism adults Normal adults Autism children Normal children Autism children Normal children Autism children Normal children reliablycompetitive variable reliablycooperative 68 FIGURE 4. Normal adults 100 90 80 70 60 50 40 30 20 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Round Normal children 100 90 80 70 60 50 40 30 20 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Round Human opponent Computer opponent Encouraged cooperation Autism adults 100 90 80 70 60 50 40 30 20 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Round 100 90 80 70 60 50 40 30 20 10 0 1 2 3 4 5 6 Autism children 7 8 9 10 11 12 13 14 15 16 Round 69 FIGURE 5A 10 8 Mean points offered 6 4 2 0 Dictator game Ultimatum game Mean points offered 10 8 6 4 2 0 FIGURE 5B 6 years 8 years 10 years Adults Dictator game Ultimatum game FIGURE 5C 10 8 Mean points offered 6 4 2 0 Dictator game Ultimatum game Mean points offered FIGURE 5D 10 8 6 4 2 0 Dictator game Ultimatum game Adults Children 70 FIGURE 6A 0.6 0.5 Proportion of Sample 0.4 0.3 Autistic Normal 0.2 0.1 0 0 1 2 3 4 5 6 7 8 9 10 Offer to Counterpart FIGURE 6B 0.6 0.5 Proportion of Sample 0.4 Autistic Normal 0.3 0.2 0.1 0 0 1 2 3 4 5 6 7 8 9 10 Offer to Counterpart 71 FIGURE 7. 0.6 0.5 Proportion of Sample 0.4 Failed Passed 0.3 0.2 0.1 0 0 1 2 3 4 5 6 7 8 9 10 Offer to Counterpart 72 FIGURE 8. Normal adults 10 8 6 4 2 0 1 2 3 4 Round 9 10 11 12 10 8 6 4 2 0 1 2 3 4 Round 9 10 11 12 Autism adults Normal children 10 8 6 4 2 0 1 2 3 4 Round 9 10 11 12 10 8 6 4 2 0 1 2 3 Autism children 4 Round 9 10 11 12 Dictator game Ultimatum game 73 FIGURE 9. 10 8 Mean points rejected 6 4 2 0 Normal adults Autism adults Normal children Autism children 74