Economic decision-making compared with an equivalent motor task

Abstract

There is considerable evidence that human economic decisionmaking deviates from the predictions of expected utility theory (EUT) and that human performance conforms to EUT in many perceptual and motor decision tasks. It is possible that these results reflect a real difference in decision-making in the 2 domains but it is also possible that the observed discrepancy simply reflects typical differences in experimental design. We developed a motor task that is mathematically equivalent to choosing between lotteries and used it to compare how the same subject chose between classical economic lotteries and the same lotteries presented in equivalent motor form. In experiment 1, we found that subjects are more risk seeking in deciding between motor lotteries. In experiment 2, we used cumulative prospect theory to model choice and separately estimated the probability weighting functions and the value functions for each subject carrying out each task. We found no patterned differences in how subjects represented outcome value in the motor and the classical tasks. However, the probability weighting functions for motor and classical tasks were markedly and significantly different. Those for the classical task showed a typical tendency to overweight small probabilities and underweight large probabilities, and those for the motor task showed the opposite pattern of probability distortion. This outcome also accounts for the increased risk-seeking observed in the motor tasks of experiment 1. We conclude that the same subject distorts probability, but not value, differently in making identical decisions in motor and classical form.expected utility theory ͉ independence axiom ͉ movement planning ͉ prospect theory ͉ decision from experience

Different Physical Intuitions Exist Between Tasks, Not Domains

“…These findings mirror a broader pattern of results in the psychological literature: people's behavior differs between tasks that require interaction with the environment and those that require verbal responses (Chen et al 2014;Glaser et al 2012;Wu et al 2009). Some behavior, especially in lower level perceptual and motor domains, is near optimal given the information and processing constraints associated with a particular task (Griffiths and Tenenbaum 2006;Stocker and Simoncelli 2006;Trommershäuser et al 2006;Wolpert et al 1995) while other behavior, especially in higher level cognition, is subject to gross biases and errors (McCloskey et al 1980;Tversky and Kahneman 1983).…”

Causal Model Comparison Shows That Human Representation Learning Is Not Bayesian

“…These findings stand in contrast to multiple demonstrations of Bayes-optimality (Doya 2007) in perceptual decision-making (Gold and Shadlen 2002;Knill and Pouget 2004), motor control (Trommershäuser et al 2003(Trommershäuser et al , 2005, multimodal integration (Körding and Wolpert 2004), reasoning (Oaksford and Chater 1994), and even setting metalearning parameters for reinforcement learning (Behrens et al 2007;Yu 2007). However, whereas Bayesian inference may be computationally feasible (and indeed, simple) in scenarios that can be reduced to a several-alternative forced-choice decision (Gold and Shadlen 2002) or a choice between lotteries (Wu et al 2009), representation learning in natural environments places much heavier computational demands on the learning system. In particular, the dimensionality of the environment is essentially unbounded (given that dimensions such as previous actions and events can be, and frequently are, relevant for task performance), and whereas feedback is often available for one's actions, the environment does not provide any supervision regarding one's representations.…”

Perceptuo-motor, cognitive, and description-based decision-making seem equally good

“…The present study made use of the idea that classical decision tasks can be altered to require participants to use their own internal estimates of probabilities (19,20) and was inspired by experiment 2 in ref. 20.…”