We run an eye-tracking experiment to investigate whether players change their gaze patterns and choices after they experience alternative models of choice in one-shot games. In phase 1 and 3, participants play 2 × 2 matrix games with a human counterpart; in phase 2, they apply specific decision rules while playing with a computer with known behavior. We classify participants in types based on their gaze patterns in phase 1 and explore attentional shifts in phase 3, after players were exposed to the alternative decision rules. Results show that less sophisticated players, who focus mainly on their own payoffs, change their gaze patterns towards the evaluation of others’ incentives in phase 3. This attentional shift predicts an increase in equilibrium responses in relevant classes of games. Conversely, cooperative players do not change their visual analysis. Our results shed new light on theories of bounded rationality and on theories of social preferences.

We examine the ability of eye movement data to help understand the determinants of decision-making over risky prospects. We start with structural models of choice under risk, and use that structure to inform what we identify from the use of process data in addition to choice data. We find that information on eye movements does significantly affect the extent and nature of probability weighting behavior. Our structural model allows us to show the pathway of the effect, rather than simply identifying a reduced form effect. This insight should be of importance for the normative design of choice mechanisms for risky products. We also show that decision-response duration is no substitute for the richer information provided by eye-tracking.

It has been observed that, in a variety of tasks, losses have a larger impact on behavior than do gains of equal size. This phenomenon is referred to as loss aversion. It is thought that the negativity bias in behavior is reflected in and potentially causally related to a negativity bias in emotional arousal. We examine skin conductance responses—a psychophysiological marker for emotional arousal—during the anticipation of gains and losses. In contrast to most previous research, gains and losses were separated from each other and symmetric in magnitude. We found that skin conductance responses during the anticipation phase increased with the magnitude of both gains and losses. Contrary to the predictions of the loss aversion hypothesis, the anticipation of a loss did not elicit stronger reactions than the anticipation of a gain of equal size.

We conduct a laboratory experiment to investigate the impact of deliberation time on behavior under risk and uncertainty. Towards this end we ask our participant to make quick, intuitive evaluations of a number of lotteries and report resulting certainty equivalents. Yet, we invite them to modify these initial decisions, whenever they find, after (additional) deliberation, that they do not precisely represent their preference. Both certainty equivalents are incentivized (a double-response method). The choice of evaluated lotteries allows us to semi-parametrically estimate the value function and the probability weighting function within the paradigm of the cumulative prospect theory. The main finding is that deliberation raises the probability weighting function (reduces pessimism), especially in the case of lotteries involving unknown probabilities.

I show how using response times as a proxy for effort can address a long-standing issue of how to separate the effect of cognitive ability on performance from the effect of motivation. My method is based on a dynamic stochastic model of optimal effort choice in which ability and motivation are the structural parameters. I show how to estimate these parameters from the data on outcomes and response times in a cognitive task. In a laboratory experiment, I find that performance on a digit-symbol test is a noisy and biased measure of cognitive ability. Ranking subjects by their performance leads to an incorrect ranking by their ability in a substantial number of cases. These results suggest that interpreting performance on a cognitive task as ability may be misleading.

The high rewards people desire are often unlikely. Here, we investigated whether decision-makers exploit such ecological correlations between risks and rewards to simplify their information processing. In a learning phase, participants were exposed to options in which risks and rewards were negatively correlated, positively correlated, or uncorrelated. In a subsequent risky choice task, where the emphasis was on making either a ‘fast’ or the ‘best’ possible choice, participants’ eye movements were tracked. The changes in the number, distribution, and direction of eye fixations in ‘fast’ trials did not differ between the risk–reward conditions. In ‘best’ trials, however, participants in the negatively correlated condition lowered their evidence threshold, responded faster, and deviated from expected value maximization more than in the other risk–reward conditions. The results underscore how conclusions about people’s cognitive processing in risky choice can depend on risk–reward structures, an often neglected environmental property.

When making decisions, people tend to look back and forth between the alternatives until they eventually make a choice. Eye-tracking research has established that these shifts in attention are strongly linked to choice outcomes. A predominant framework for understanding the dynamics of the choice process, and thus the effects of attention, is sequential sampling of information. However, existing methods for estimating the attention parameters in these models are computationally costly and overly flexible, and yield estimates with unknown precision and bias. Here we propose an estimation method that relies on a link between sequential sampling models and random utility models (RUM). This method uses familiar econometric tools (i.e., logistic regression) and yields estimates that appear to be unbiased and relatively precise compared to existing methods, in a small fraction of the computation time. The RUM thus appears to be a useful tool for estimating the effects of attention on choice.

Consumers typically overstate their intentions to purchase products, compared to actual rates of purchases, a pattern called “hypothetical bias”. In laboratory choice experiments, we measure participants’ visual attention using mousetracking or eye-tracking, while they make hypothetical as well as real purchase decisions. We find that participants spent more time looking both at price and product image prior to making a real “buy” decision than making a real “don’t buy” decision. We demonstrate that including such information about visual attention improves prediction of real buy decisions. This improvement is evident, although small in magnitude, using mousetracking data, but is not evident using eye-tracking data.

Protocol analysis, in the form of concurrent verbal ‘thinking aloud’ reports, is a method of collecting and analyzing data about cognitive processes. This approach can help economists in evaluating competing theories of behavior and in categorizing heterogeneity of thinking patterns. As a proof of concept, I tested this method in the context of a guessing game. I found that concurrent think aloud protocols can inform us about individual’s thought processes without affecting decisions. The method allowed me to identify game theoretic thinking and heterogeneous approaches to unravelling the guessing game. The think aloud protocol is inexpensive and scalable, and it is a useful tool for identifying empirical regularities regarding decision processes.

$\mu$Cap is a software package (citeware) for economic experiments enabling experimenters to analyze emotional states of subjects using z-Tree and FaceReader™. $\mu$Cap is able to create videos of subjects on client computers based on stimuli shown on screen and restrict recording material to relevant time frames. Another feature of $\mu$Cap is the creation of time stamps in csv format at prespecified screens (or at prespecified points in time) during the experiment, measured on the client computer. The software makes it possible to import these markers into FaceReader™ easily. $\mu$Cap is the first program that significantly simplifies the process of connecting z-Tree and FaceReader™ with the additional benefit of extremely high precision. This paper describes the usage, underlying principles as well as advantages and limitations of $\mu$Cap. Furthermore, we give a brief outlook of how $\mu$Cap can be beneficial in other contexts.