A robust Bayesian test for identifying context effects in multiattribute decision-making.

Research on multiattribute decision-making has repeatedly shown that people's preferences for options depend on the set of other options they are presented with, that is, the choice context. As a result, recent years have seen the development of a number of psychological theories explaining context effects. However, much less attention has been given to the statistical analyses of context effects. Traditionally, context effects are measured as a change in preference for a target option across two different choice sets (the so-called relative choice share of the target, or RST). We first show that the frequently used definition of the RST measure has some weaknesses and should be replaced by a more appropriate definition that we provide. We then show through a large-scale simulation that the RST measure as previously defined can lead to biased inferences. As an alternative, we suggest a Bayesian approach to estimating an accurate RST measure that is robust to various circumstances. We applied the two approaches to the data of five published studies (total participants, N = 738), some of which used the biased approach. Additionally, we introduce the absolute choice share of the target (or AST) as the appropriate measure for the attraction effect. Our approach is an example of evaluating and proposing proper statistical tests for axiomatic principles of decision-making. After applying the AST and the robust RST to published studies, we found qualitatively different results in at least one-fourth of the cases. These results highlight the importance of utilizing robust statistical tests as a foundation for the development of new psychological theories.

Disentangling choice value and choice conflict in sequential decisions under risk.

Recent years have witnessed a surge of interest in understanding the neural and cognitive dynamics that drive sequential decision making in general and foraging behavior in particular. Due to the intrinsic properties of most sequential decision-making paradigms, however, previous research in this area has suffered from the difficulty to disentangle properties of the decision related to (a) the value of switching to a new patch versus, which increases monotonically, and (b) the conflict experienced between choosing to stay or leave, which first increases but then decreases after reaching the point of indifference between staying and switching. Here, we show how the same problems arise in studies of sequential decision-making under risk, and how they can be overcome, taking as a specific example recent research on the 'pig' dice game. In each round of the 'pig' dice game, people roll a die and accumulate rewards until they either decide to proceed to the next round or lose all rewards. By combining simulation-based dissections of the task structure with two experiments, we show how an extension of the standard paradigm, together with cognitive modeling of decision-making processes, allows to disentangle properties related to either switch value or choice conflict. Our study elucidates the cognitive mechanisms of sequential decision making and underscores the importance of avoiding potential pitfalls of paradigms that are commonly used in this research area.

A probabilistic atlas of the human ventral tegmental area (VTA) based on 7 Tesla MRI data.

Functional magnetic resonance imaging (fMRI) BOLD signal is commonly localized by using neuroanatomical atlases, which can also serve for region of interest analyses. Yet, the available MRI atlases have serious limitations when it comes to imaging subcortical structures: only 7% of the 455 subcortical nuclei are captured by current atlases. This highlights the general difficulty in mapping smaller nuclei deep in the brain, which can be addressed using ultra-high field 7 Tesla (T) MRI. The ventral tegmental area (VTA) is a subcortical structure that plays a pivotal role in reward processing, learning and memory. Despite the significant interest in this nucleus in cognitive neuroscience, there are currently no available, anatomically precise VTA atlases derived from 7 T MRI data that cover the full region of the VTA. Here, we first provide a protocol for multimodal VTA imaging and delineation. We then provide a data description of a probabilistic VTA atlas based on in vivo 7 T MRI data.

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.

Decomposing the effects of context valence and feedback information on speed and accuracy during reinforcement learning: a meta-analytical approach using diffusion decision modeling.

Reinforcement learning (RL) models describe how humans and animals learn by trial-and-error to select actions that maximize rewards and minimize punishments. Traditional RL models focus exclusively on choices, thereby ignoring the interactions between choice preference and response time (RT), or how these interactions are influenced by contextual factors. However, in the field of perceptual decision-making, such interactions have proven to be important to dissociate between different underlying cognitive processes. Here, we investigated such interactions to shed new light on overlooked differences between learning to seek rewards and learning to avoid losses. We leveraged behavioral data from four RL experiments, which feature manipulations of two factors: outcome valence (gains vs. losses) and feedback information (partial vs. complete feedback). A Bayesian meta-analysis revealed that these contextual factors differently affect RTs and accuracy: While valence only affects RTs, feedback information affects both RTs and accuracy. To dissociate between the latent cognitive processes, we jointly fitted choices and RTs across all experiments with a Bayesian, hierarchical diffusion decision model (DDM). We found that the feedback manipulation affected drift rate, threshold, and non-decision time, suggesting that it was not a mere difficulty effect. Moreover, valence affected non-decision time and threshold, suggesting a motor inhibition in punishing contexts. To better understand the learning dynamics, we finally fitted a combination of RL and DDM (RLDDM). We found that while the threshold was modulated by trial-specific decision conflict, the non-decision time was modulated by the learned context valence. Overall, our results illustrate the benefits of jointly modeling RTs and choice data during RL, to reveal subtle mechanistic differences underlying decisions in different learning contexts.

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).

Striatal activation reflects urgency in perceptual decision making.

Deciding between multiple courses of action often entails an increasing need to do something as time passes - a sense of urgency. This notion of urgency is not incorporated in standard theories of speeded decision making that assume information is accumulated until a critical fixed threshold is reached. Yet, it is hypothesized in novel theoretical models of decision making. In two experiments, we investigated the behavioral and neural evidence for an "urgency signal" in human perceptual decision making. Experiment 1 found that as the duration of the decision making process increased, participants made a choice based on less evidence for the selected option. Experiment 2 replicated this finding, and additionally found that variability in this effect across participants covaried with activation in the striatum. We conclude that individual differences in susceptibility to urgency are reflected by striatal activation. By dynamically updating a response threshold, the striatum is involved in signaling urgency in humans.