Loss aversion,diminishing sensitivity,and the effect of experience on repeated decisions

Three experiments are presented that explore the assertion that loss aversion and diminishing sensitivity drive the effect of experience on choice behavior. The experiments are focused on repeated choice tasks where decision makers choose repeatedly between alternatives and get feedback after each choice. Experiments 1a and 1b show that behavioral tendencies that were previously interpreted as indications of loss aversion in decisions from experience are better described as products of diminishing sensitivity to absolute payoffs. Experiment 2 highlights a nominal magnitude effect: A decrease in the magnitude of the nominal payoffs eliminates the evidence for diminishing sensitivity. These and related previous results can be captured with a model that assumes reliance on small samples of subjective experiences, and an increase in diminishing sensitivity with payoff variability. Copyright © 2008 John Wiley & Sons, Ltd.     

The presence of aggression cues inverts the relation between digit ratio (2D:4D) and prosocial behaviour in a dictator game

Digit ratio (2D:4D) is a sexually dimorphic trait. Men have relatively shorter index (2D) compared to ring (4D) fingers than women. More masculine ratios are thought to be influenced by higher prenatal testosterone levels. In the present paper, we aim to show the context‐dependency of the relation between 2D:4D and social behaviour. In two studies, we expose participants either to control or to aggression cues. Afterwards, they make a decision in a dictator game. Participants with low 2D:4D showed higher allocation levels (i.e. they were more prosocial) than participants with high 2D:4D in a neutral situation. However, this relationship inverts after exposure to an aggression cue. It turns out that in high 2D:4D people, aggression cues even increase prosocial behaviour. We call for future research which focuses on other plausible interactions between 2D:4D and context cues rather than on linear relations.     

Altruistic behavior as a costly signal of general intelligence

Unconditional altruism is an enduring puzzle for evolutionary approaches to social behavior. In this paper, we argue that costly signaling theory, a well-established framework in biology and economics, may be useful to shed light on the individual differences in human unconditional altruism. Based on costly signaling theory, we propose and show that unconditional altruistic behavior is related to general intelligence. The cost incurred by engaging in unconditional altruism is lower for highly intelligent people than for less intelligent people because they may expect to regain the drained resources. As a result, unconditional altruism can serve as an honest signal of intelligence. Our findings imply that distinguishing altruistic behavior from cooperative behavior in social psychological and economic theories of human behavior might be useful and that costly signaling theory may provide novel insights on various individual difference variables.     

On adaptation, maximization, and reinforcement learning among cognitive strategies

Analysis of binary choice behavior in iterated tasks with immediate feedback reveals robust deviations from maximization that can be described as indications of 3 effects: (a) a payoff variability effect, in which high payoff variability seems to move choice behavior toward random choice; (b) underweighting of rare events, in which alternatives that yield the best payoffs most of the time are attractive even when they are associated with a lower expected return; and (c) loss aversion, in which alternatives that minimize the probability of losses can be more attractive than those that maximize expected payoffs. The results are closer to probability matching than to maximization. Best approximation is provided with a model of reinforcement learning among cognitive strategies (RELACS). This model captures the 3 deviations, the learning curves, and the effect of information on uncertainty avoidance. It outperforms other models in fitting the data and in predicting behavior in other experiments.