Winning at Rock-Paper-Scissors Reveals How Context Trumps Body Language

Every social encounter is, at its core, a prediction problem. When a stranger reaches across a dinner table for a bottle, the grasping movement itself may look almost identical whether the person intends to pour water into their own glass, fill a neighbor’s, or simply move the bottle out of reach of a playful child. Yet human observers routinely resolve such ambiguity in fractions of a second. The secret, researchers have long argued, lies in fine-grained movement details: grip height, wrist orientation, velocity, trajectory and timing all encode the actor’s true intention, and experienced observers can learn to read these kinematic signatures with impressive accuracy. A new study published in the journal iScience, however, adds a crucial twist to this picture. In fast-paced competitive interactions, the authors report, the body’s signals are only half the story, and the winners are those who know when to stop trusting them.

Johannes Keck, Kazuyuki Sato and Rouwen Cañal-Bruland of Friedrich-Schiller-University Jena set out to close a persistent gap in the science of action understanding. Most previous experiments examined movement cues and contextual inference in isolation, often asking passive observers to make explicit judgments about pre-recorded actions. Real social life is nothing like that. Interacting partners respond to each other continuously, in real time, while the information available to them shifts from moment to moment. To capture this dynamism, the team turned to an unlikely but elegant experimental model: the children’s game rock-paper-scissors. As a zero-sum game with a cyclic, non-transitive payoff structure—rock beats scissors, scissors beats paper, paper beats rock—it combines genuine competition, multiple action alternatives and a rich history of play, all of which unfold within roughly a second and a half per game.

Ninety participants, paired into forty-five dyads, played hundreds of iterative games of rock-paper-scissors while wearing full-body motion capture suits studded with fifty-seven reflective markers. A sixteen-camera near-infrared system recorded their movements at 240 hertz, allowing the researchers to reconstruct the characteristic up-and-down wrist swings in exquisite detail. Each game was segmented into three phases corresponding to the first, second and final swing of the wrist, and eleven movement features—ranging from peak velocity and acceleration to trajectory skewness, positional variability and a measure of velocity unpredictability called velocity entropy—were extracted for every phase. After preprocessing, thirty-eight dyads with complete, reliably reconstructed trajectories entered the final analysis.

The first question was whether intention information is actually embedded in these naturally generated movements. To find out, the researchers trained a multinomial logistic classifier to map the phase-specific kinematic features onto the action each player ultimately performed. The classifier decoded the intended gesture with an accuracy of 47.6 percent, significantly above the 33.3 percent chance level. Crucially, this encoding accuracy did not differ between winning and losing trials, nor across the three movement phases. In other words, every player’s wrist leaked information about their upcoming choice, whether that choice would ultimately triumph or fail. The information was there for the taking, in every game, at every stage of the unfolding movement.

The surprise came when the researchers asked whether opponents actually exploited this information. Because explicit perceptual reports would have disrupted continuous play, the team derived an indirect, choice-based measure they call likely kinematic readout: if a player chose rock, for example, the analysis inferred that the player expected the opponent to throw scissors, and then tested whether the opponent’s kinematics on that trial actually supported that inference. Overall, likely readout exceeded chance, reaching 39.2 percent. But the outcome-dependent pattern was striking. On losing trials, readout was significantly above chance in every phase and rose steadily as the game progressed, peaking at 38.6 percent in the final swing. On winning trials, it never exceeded chance at all and actually declined to 31.1 percent by the end. Players who consistently followed the movement-derived signal across all three phases lost significantly more often than those who did not.

At first glance this seems paradoxical: why would reading the opponent’s body better make you lose? The authors argue that in fast-paced competitive settings with multiple possible outcomes, kinematic cues are noisier and more ambiguous than in the controlled, cooperative tasks used in most prior studies. Opponents may also deliberately disguise their intentions, converting kinematic information into kinematic disinformation, as research on deceptive movements in sports has repeatedly shown. Under such conditions, generating several competing kinematic predictions can reduce reliability despite high perceptual sensitivity, and observers may do better to complement—or override—movement evidence with contextual cues drawn from the game’s structure and history.

To test this idea formally, the researchers modeled each player’s contextual inference using four competing computational models drawn from two conceptual families. Action-based models predicted choices from sequential regularities in previous actions: a first-order Markov model tracking transition probabilities between consecutive gestures, and the cognitive hierarchy assessment model, or CHASE, which formalizes recursive strategic reasoning across levels of sophistication. Outcome-based models adapted predictions according to wins and losses: a win-stay/lose-shift model and a reinforcement-learning model implementing a Rescorla-Wagner update rule with softmax action selection. These model-derived posteriors were then fed, together with the kinematic measures, into hierarchical mixed-effects logistic regressions predicting single-trial winning.

The results were unambiguous. Reinforcement learning was the strongest positive predictor of winning: trials in which the model assigned high posterior probability to the player’s chosen action were significantly more likely to end in victory, with an odds ratio of 1.37. Win-stay/lose-shift contributed a smaller positive effect, whereas reliance on first-order Markov sequential regularities actually predicted reduced winning, suggesting that players who exploit simple action patterns become more exploitable themselves. On the kinematic side, richer intention information encoded in one’s own movement was modestly beneficial, but stronger behavioral alignment with the likely kinematic readout significantly reduced the odds of winning. Both information sources contributed unique predictive value, but context explained substantially more trial-to-trial variation in success than movement did.

The most compelling evidence came from conflict trials—decisive games in which the contextual model and the likely kinematic readout recommended different actions. Here, success can be attributed to one source or the other, and context won decisively. Following the reinforcement-learning recommendation more than doubled the odds of winning relative to following the kinematic signal, with an odds ratio of 2.33. Descriptively, participants won 60.6 percent of disagreement trials when they followed the reinforcement-learning recommendation, compared with only 42.7 percent when they followed the kinematic readout—a 17.9 percentage point advantage that significantly exceeded the benefits of the Markov, win-stay/lose-shift and CHASE models. An interaction analysis reinforced the picture: the behavioral influence of kinematic readout became increasingly detrimental as reinforcement-learning evidence accumulated, while the contribution of kinematic encoding itself remained additive across contextual states.

Taken together, the findings suggest that successful action understanding in dynamic social interaction is not a matter of maximizing sensitivity to bodily signals but of adaptively arbitrating between information sources. Movement kinematics consistently carried intention-related information, yet its behavioral value depended on the contextual state in which it was interpreted. The study also refines earlier neural work suggesting that tracking recent outcomes can impair performance in rock-paper-scissors: rather than contextual information being generally maladaptive, different forms of context have markedly different consequences, with reward-based learning outperforming simple sequential dependencies. The authors caution that their readout measure is an indirect behavioral proxy, and they propose that immersive virtual reality with systematically manipulated avatar kinematics could validate it more directly. For now, the message is clear: in the heat of real-time competition, the body talks, but the smartest listeners weigh what it says against everything else they know.

Subject of Research: Kinematic and contextual information integration in real-time competitive social interaction

Article Title: Context shapes action understanding in social interactions

Article References: Keck, J., Sato, K., & Cañal-Bruland, R. (2026). Context shapes action understanding in social interactions. iScience, 29(11), Article 117796. https://doi.org/10.1016/j.isci.2026.117796

Image Credits: AI Generated

DOI: Not provided

Keywords: action understanding, kinematics, social interaction, rock-paper-scissors, reinforcement learning, motion capture, action prediction, deception, computational modeling, decision making, contextual inference, iScience