Percent framing attenuates the magnitude effect in a preference-matching task of intertemporal choice.

Research in intertemporal decisions shows that people value future gains less than equivalent but immediate gains by a factor known as the discount rate (i.e., people want a premium for waiting to receive a reward). A robust phenomenon in intertemporal decisions is the finding that the discount rate is larger for small gains than for large gains, termed the magnitude effect. However, the psychological underpinnings of this effect are not yet fully understood. One explanation proposes that intertemporal choices are driven by comparisons of features of the present and future choice options (e.g., information on rewards). According to this explanation, the hypothesis is that the magnitude effect is stronger when the absolute difference between present and future rewards is emphasized, compared to when their relative difference is emphasized. However, this hypothesis has only been tested using one task (the two-choice paradigm) and only for gains (i.e., not losses). It's therefore unclear whether the findings that support the hypothesis can be generalized to different methodological paradigms (e.g., preference matching) and to the domain of losses. To address this question, we conducted experiments using the preference-matching method whereby the premium amounts that people could ask for were framed in terms of either currencies (emphasizing absolute differences) or percentages (emphasizing relative differences). We thus tested the robustness of the evidence in support of the hypothesis that percent framing, relative to currency framing, attenuates the magnitude effect in the domain of gains (Studies 1, 2, and 3) and in the domain of losses (Study 1, 3, and 4). The data were heavily skewed and the assumption of equal variances was violated. Therefore, in place of parametric statistical tests, we calculated and interpreted parametric and nonparametric standardized and unstandardized effect size estimates and their confidence intervals. Overall, the results support the hypothesis.

Priming exploration across domains: does search in a spatial environment influence search in a cognitive environment?

Is there a general tendency to explore that connects search behaviour across different domains? Although the experimental evidence collected so far suggests an affirmative answer, this fundamental question about human behaviour remains open. A feasible way to test the domain-generality hypothesis is that of testing the so-called priming hypothesis: priming explorative behaviour in one domain should subsequently influence explorative behaviour in another domain. However, only a limited number of studies have experimentally tested this priming hypothesis, and the evidence is mixed. We tested the priming hypothesis in a registered report. We manipulated explorative behaviour in a spatial search task by randomly allocating people to search environments with resources that were either clustered together or dispersedly distributed. We hypothesized that, in a subsequent anagram task, participants who searched in clustered spatial environments would search for words in a more clustered way than participants who searched in the dispersed spatial environments. The pre-registered hypothesis was not supported. An equivalence test showed that the difference between conditions was smaller than the smallest effect size of interest (d = 0.36). Out of several exploratory analyses, we found only one inferential result in favour of priming. We discuss implications of these findings for the theory and propose future tests of the hypothesis.

Hierarchical decision-making produces persistent differences in learning performance.

Human organizations are commonly characterized by a hierarchical chain of command that facilitates division of labor and integration of effort. Higher-level employees set the strategic frame that constrains lower-level employees who carry out the detailed operations serving to implement the strategy. Typically, strategy and operational decisions are carried out by different individuals that act over different timescales and rely on different kinds of information. We hypothesize that when such decision processes are hierarchically distributed among different individuals, they produce highly heterogeneous and strongly path-dependent joint learning dynamics. To investigate this, we design laboratory experiments of human dyads facing repeated joint tasks, in which one individual is assigned the role of carrying out strategy decisions and the other operational ones. The experimental behavior generates a puzzling bimodal performance distribution-some pairs learn, some fail to learn after a few periods. We also develop a computational model that mirrors the experimental settings and predicts the heterogeneity of performance by human dyads. Comparison of experimental and simulation data suggests that self-reinforcing dynamics arising from initial choices are sufficient to explain the performance heterogeneity observed experimentally.

Neural network models of learning and categorization in multigame experiments.

Previous research has shown that regret-driven neural networks predict behavior in repeated completely mixed games remarkably well, substantially equating the performance of the most accurate established models of learning. This result prompts the question of what is the added value of modeling learning through neural networks. We submit that this modeling approach allows for models that are able to distinguish among and respond differently to different payoff structures. Moreover, the process of categorization of a game is implicitly carried out by these models, thus without the need of any external explicit theory of similarity between games. To validate our claims, we designed and ran two multigame experiments in which subjects faced, in random sequence, different instances of two completely mixed 2 × 2 games. Then, we tested on our experimental data two regret-driven neural network models, and compared their performance with that of other established models of learning and Nash equilibrium.

Predicting human interactive learning by regret-driven neural networks.

Much of human learning in a social context has an interactive nature: What an individual learns is affected by what other individuals are learning at the same time. Games represent a widely accepted paradigm for representing interactive decision-making. We explored the potential value of neural networks for modeling and predicting human interactive learning in repeated games. We found that even very simple learning networks, driven by regret-based feedback, accurately predict observed human behavior in different experiments on 21 games with unique equilibria in mixed strategies. Introducing regret in the feedback dramatically improved the performance of the neural network. We show that regret-based models provide better predictions of learning than established economic models.