Rational After All: Changes in Probability Matching Behaviour Across Time in Humans and Monkeys
- Carmen Saldana, Centre for Language Evolution, The University of Edinburgh, EDINBURGH, United Kingdom
- Nicolas Claidiere, Laboratoire de Psychologie Cognitive, Aix Marseille University, Marseille, France
- Joel Fagot, Laboratoire de Psychologie Cognitive, Aix Marseille University, Marseille, France
- Kenny Smith, Centre for Language Evolution, University of Edinburgh, Edinburgh, United Kingdom
AbstractProbability matching—where subjects given probabilistic in-put respond in a way that is proportional to those input probabilities—has long been thought to be characteristic of primate performance in probability learning tasks in a variety of contexts, from decision making to the learning of linguistic variation in humans. However, such behaviour is puzzling because it is not optimal in a decision theoretic sense; the optimal strategy is to always select the alternative with the highest positive-outcome probability, known as maximising (in decision making) or regularising (in linguistic tasks). While the tendency to probability match seems to depend somewhat on the participants and the task (i.e., infants are less likely to probability match than adults, monkeys probability matchless than humans, and probability matching is less likely in linguistic tasks), existing studies suffer from a range of deficiencies which make it difficult to robustly assess these differences. In this paper we present three experiments which systematically test the development of probability matching behaviour over time in simple decision making tasks, across species (humans and Guinea baboons), task complexity, and task domain (linguistic vs non-linguistic). In Experiments 1and 2 we show that adult humans and Guinea baboons exhibit similar behaviour in a non-linguistic decision-making task and, contrary to the prevailing view, a tendency to maximise (baboons) or significantly over-match (humans) rather than prob-ability match, which strengthens over time and more so with greater task complexity; our non-human sample size (N=20baboons) is unprecedented in the probability-matching literature. Experiment 3 provides evidence against domain-specific probability learning mechanisms, showing that human subjects over-match high positive-outcome probabilities to a similar degree across linguistic and non-linguistic tasks. Our results suggest that previous studies may simply have insufficient trials to show maximising, or be too short to show maximising strategies which unfold over time. We thus provide evidence of shared probability learning mechanisms not only across linguistic and non-linguistic tasks but also across primate species.