Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We show that the acuity of the Approximate Number System (ANS), a cognitive system that allows humans and many animal species to evaluate quantities without using exact calculations, is a strong predictor of subjects’ earnings in experimental markets. We measure ANS acuity with a bounded number line estimation (NLE) task and find that subjects who perform better on the NLE task, obtain higher earnings in a continuous double auction experimental market. We underline two channels through which high ANS acuity subjects achieve better performance: they are rewarded for offering liquidity and are faster at exploiting trading opportunities. We also show that, in a given market, the distribution of NLE scores influences mispricing. Our results are unchanged when we control for differences in trading intensity, risk aversion, background education or demographic characteristics.