Frequent winners explain apparent skewness preferences in experience-based decisions.

Do people's attitudes toward the (a)symmetry of an outcome distribution affect their choices? Financial investors seek return distributions with frequent small returns but few large ones, consistent with leading models of choice in economics and finance that assume right-skewed preferences. In contrast, many experiments in which decision-makers learn about choice options through experience find the opposite choice tendency, in favor of left-skewed options. To reconcile these seemingly contradicting findings, the present work investigates the effect of skewness on choices in experience-based decisions. Across seven studies, we show that apparent preferences for left-skewed outcome distributions are a consequence of those distributions having a higher value in most direct outcome comparisons, a "frequent-winner effect." By manipulating which option is the frequent winner, we show that choice tendencies for frequent winners can be obtained even with identical outcome distributions. Moreover, systematic choice tendencies in favor of right- or left-skewed options can be obtained by manipulating which option is experienced as the frequent winner. We also find evidence for an intrinsic preference for right-skewed outcome distributions. The frequent-winner phenomenon is robust to variations in outcome distributions and experimental paradigms. These findings are confirmed by computational analyses in which a reinforcement-learning model capturing frequent winning and intrinsic skewness preferences provides the best account of the data. Our work reconciles conflicting findings of aggregated behavior in financial markets and experiments and highlights the need for theories of decision-making sensitive to joint outcome distributions of the available options.

Absolute and relative stability of loss aversion across contexts.

Individuals' decisions under risk tend to be in line with the notion that "losses loom larger than gains." This loss aversion in decision making is commonly understood as a stable individual preference that is manifested across different contexts. The presumed stability and generality, which underlies the prominence of loss aversion in the literature at large, has been recently questioned by studies reporting how loss aversion can disappear, and even reverse, as a function of the choice context. The present study investigated whether loss aversion reflects a trait-like attitude of avoiding losses or rather individuals' adaptability to different contexts. We report three experiments investigating the within-subject context sensitivity of loss aversion in a two-alternative forced-choice task. Our results show that the choice context can shift people's loss aversion, though somewhat inconsistently. Moreover, individual estimates of loss aversion are shown to have a considerable degree of stability. Altogether, these results indicate that even though the absolute value of loss aversion can be affected by external factors such as the choice context, estimates of people's loss aversion still capture the relative dispositions toward gains and losses across individuals. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The repulsion effect in preferential choice and its relation to perceptual choice.

People rely on the choice context to guide their decisions, violating fundamental principles of rational choice theory and exhibiting phenomena called context effects. Recent research has uncovered that dominance relationships can both increase or decrease the choice share of the dominating option, marking the two ends of an attraction-repulsion continuum. However, empirical links between the two opposing effects are scarce and theoretical accounts are missing altogether. The present study (N = 55) used eye tracking alongside a within-subject design that contrasts a perceptual task and a preferential-choice analog in order to bridge this gap and uncover the underlying information-search processes. Although individuals differed in their perceptual and preferential choices, they generally engaged in alternative-wise comparisons and a repulsion effect was present in both conditions that became weaker the more predominant the attribute-wise comparisons were. Altogether, our study corroborates the notion that repulsion effects are a robust and general phenomenon that theoretical accounts need to take seriously.

The elusiveness of context effects in decision making.

Contextual features influence human and non-human decision making, giving rise to preference reversals. Decades of research have documented the species and situations in which these effects are observed. More recently, however, researchers have focused on boundary conditions, that is, settings in which established effects disappear or reverse. This work is scattered across academic disciplines and some results appear to contradict each other. We synthesize recent findings and resolve apparent contradictions by considering them in terms of three core categories of decision context: spatial arrangement, attribute concreteness, and deliberation time. We suggest that these categories could be understood using theories of choice representation, which specify how context shapes the information over which deliberation processes operate.

The simultaneous recognition of multiple words: A process analysis.

In everyday life, recognition decisions often have to be made for multiple objects simultaneously. In contrast, research on recognition memory has predominantly relied on single-item recognition paradigms. We present a first systematic investigation into the cognitive processes that differ between single-word and paired-word tests of recognition memory. In a single-word test, participants categorize previously presented words and new words as having been studied before (old) or not (new). In a paired-word test, however, the test words are randomly paired, and participants provide joint old-new categorizations of both words for each pair. Across two experiments (N = 170), we found better memory performance for words tested singly rather than in pairs and, more importantly, dependencies between the two single-word decisions implied by the paired-word test. We extended two popular model classes of single-item recognition to paired-word recognition, a discrete-state model and a continuous model. Both models attribute performance differences between single-word and paired-word recognition to differences in memory-evidence strength. Discrete-state models account for the dependencies in paired-word decisions in terms of dependencies in guessing. In contrast, continuous models map the dependencies on mnemonic (Experiment 1 & 2) as well as on decisional processes (Experiment 2). However, in both experiments, model comparison favored the discrete-state model, indicating that memory decisions for word pairs seem to be mediated by discrete states. Our work suggests that individuals tackle multiple-item recognition fundamentally differently from single-item recognition, and it provides both a behavioral and model-based paradigm for studying multiple-item recognition.

Response time models separate single- and dual-process accounts of memory-based decisions.

Human decisions often deviate from economic rationality and are influenced by cognitive biases. One such bias is the memory bias according to which people prefer choice options they have a better memory of-even when the options' utilities are comparatively low. Although this phenomenon is well supported empirically, its cognitive foundation remains elusive. Here we test two conceivable computational accounts of the memory bias against each other. On the one hand, a single-process account explains the memory bias by assuming a single biased evidence-accumulation process in favor of remembered options. On the contrary, a dual-process account posits that some decisions are driven by a purely memory-driven process and others by a utility-maximizing one. We show that both accounts are indistinguishable based on choices alone as they make similar predictions with respect to the memory bias. However, they make qualitatively different predictions about response times. We tested the qualitative and quantitative predictions of both accounts on behavioral data from a memory-based decision-making task. Our results show that a single-process account provides a better account of the data, both qualitatively and quantitatively. In addition to deepening our understanding of memory-based decision-making, our study provides an example of how to rigorously compare single- versus dual-process models using empirical data and hierarchical Bayesian parameter estimation methods.

A reinforcement learning diffusion decision model for value-based decisions.

Psychological models of value-based decision-making describe how subjective values are formed and mapped to single choices. Recently, additional efforts have been made to describe the temporal dynamics of these processes by adopting sequential sampling models from the perceptual decision-making tradition, such as the diffusion decision model (DDM). These models, when applied to value-based decision-making, allow mapping of subjective values not only to choices but also to response times. However, very few attempts have been made to adapt these models to situations in which decisions are followed by rewards, thereby producing learning effects. In this study, we propose a new combined reinforcement learning diffusion decision model (RLDDM) and test it on a learning task in which pairs of options differ with respect to both value difference and overall value. We found that participants became more accurate and faster with learning, responded faster and more accurately when options had more dissimilar values, and decided faster when confronted with more attractive (i.e., overall more valuable) pairs of options. We demonstrate that the suggested RLDDM can accommodate these effects and does so better than previously proposed models. To gain a better understanding of the model dynamics, we also compare it to standard DDMs and reinforcement learning models. Our work is a step forward towards bridging the gap between two traditions of decision-making research.

How similarity between choice options affects decisions from experience: The accentuation-of-differences model.

Traditional theories of decision making require that humans evaluate choice options independently of each other. The independence principle underlying this notion states that the relative choice probability of two options should be independent of the choice set. Previous research demonstrated systematic violations of this principle in decisions from description (context effects), leading to the development of various models explaining them. Yet, the cognitive processes underlying multi-alternative decisions from experience remain unclear. Furthermore, it is not known whether context effects also occur in such decisions, and existing learning models do not predict them. In three experiments, the similarity effect, compromise effect, and attraction effect were explored in a 3-armed bandit task with full feedback. Participants' behavior systematically violated the independence principle, although mostly not in line with past context-effect patterns in decisions from description. The observed similarity effect and the reversals of the compromise and the attraction effects can be explained by a similarity mechanism, according to which options with similar outcomes appear less attractive. We propose the accentuation-of-differences model that relies on this mechanism. We further validated the model in a fourth experiment in which we demonstrated a new violation of independence, the accentuation effect. Across all experiments, the model outperformed traditional reinforcement-learning models in describing the observed findings. Finally, the model's generalizability was confirmed using the Iowa gambling task. In summary, the present work is the first to demonstrate systematic violations of the independence principle in various decisions-from-experience designs and to offer a model to explain the observed phenomena. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Value-based attentional capture affects multi-alternative decision making.

Humans and other animals often violate economic principles when choosing between multiple alternatives, but the underlying neurocognitive mechanisms remain elusive. A robust finding is that adding a third option can alter the relative preference for the original alternatives, but studies disagree on whether the third option's value decreases or increases accuracy. To shed light on this controversy, we used and extended the paradigm of one study reporting a positive effect. However, our four experiments with 147 human participants and a reanalysis of the original data revealed that the positive effect is neither replicable nor reproducible. In contrast, our behavioral and eye-tracking results are best explained by assuming that the third option's value captures attention and thereby impedes accuracy. We propose a computational model that accounts for the complex interplay of value, attention, and choice. Our theory explains how choice sets and environments influence the neurocognitive processes of multi-alternative decision making.

When the Good Looks Bad: An Experimental Exploration of the Repulsion Effect.

When people are choosing among different options, context seems to play a vital role. For instance, adding a third option can increase the probability of choosing a similar dominating option. This attraction effect is one of the most widely studied phenomena in decision-making research. Its prevalence, however, has been challenged recently by the tainting hypothesis, according to which the inferior option contaminates the attribute space in which it is located, leading to a repulsion effect. In an attempt to test the tainting hypothesis and explore the conditions under which dominated options make dominating options look bad, we conducted four preregistered perceptual decision-making studies with a total of 301 participants. We identified two factors influencing individuals' behavior: stimulus display and stimulus design. Our results contribute to a growing body of literature showing how presentation format influences behavior in preferential and perceptual decision-making tasks.

The relative merit of empirical priors in non-identifiable and sloppy models: Applications to models of learning and decision-making : Empirical priors.

Formal modeling approaches to cognition provide a principled characterization of observed responses in terms of a set of postulated processes, specifically in terms of parameters that modulate the latter. These model-based characterizations are useful to the extent that there is a clear, one-to-one mapping between parameters and model expectations (identifiability) and that parameters can be recovered from reasonably sized data using a typical experimental design (recoverability). These properties are sometimes not met for certain combinations of model classes and data. One suggestion to improve parameter identifiability and recoverability involves the use of "empirical priors", which constrain parameters according to a previously observed distribution of values. We assessed the efficacy of this proposal using a combination of real and artificial data. Our results showed that a point-estimate variant of the empirical-prior method could not improve parameter recovery systematically. We identified the source of poor parameter recovery in the low information content of the data. As a follow-up step, we developed a fully Bayesian variant of the empirical-prior method and assessed its performance. We find that even such a method that takes the covariance structure of the parameter distributions into account cannot reliably improve parameter recovery. We conclude that researchers should invest additional efforts in improving the informativeness of their experimental designs, as many of the problems associated to impoverished designs cannot be alleviated by modern statistical methods alone.