A map of decoy influence in human multialternative choice

Origins of noise in both improving and degrading decision making

Noise is a fundamental problem for information processing in neural systems. In decision-making, noise is assumed to have a primary role in errors and stochastic choice behavior. However, little is known about how noise arising from different sources contributes to value coding and choice behaviors, especially when it interacts with neural computation. Here we examine how noise arising early versus late in the choice process differentially impacts context-dependent choice behavior. We found in model simulations that early and late noise predict opposing context effects: under early noise, contextual information enhances choice accuracy; while under late noise, context degrades choice accuracy. Furthermore, we verified these opposing predictions in experimental human choice behavior. Manipulating early and late noise - by inducing uncertainty in option values and controlling time pressure - produced dissociable positive and negative context effects. These findings reconcile controversial experimental findings in the literature reporting either context-driven impairments or improvements in choice performance, suggesting a unified mechanism for context-dependent choice. More broadly, these findings highlight how different sources of noise can interact with neural computations to differentially modulate behavior.

Significance

The current study addresses the role of noise origin in decision-making, reconciling controversies around how decision-making is impacted by context. We demonstrate that different types of noise - either arising early during evaluation or late during option comparison - leads to distinct results: with early noise, context enhances choice accuracy, while with late noise, context impairs it. Understanding these dynamics offers potential strategies for improving decision-making in noisy environments and refining existing neural computation models. Overall, our findings advance our understanding of how neural systems handle noise in essential cognitive tasks, suggest a beneficial role for contextual modulation under certain conditions, and highlight the profound implications of noise structure in decision-making.

Matching provides efficient decisions

How humans and animals distribute their behavior across choice options has been of key interest to economics, psychology, ecology, and related fields. Neoclassical and behavioral economics have provided prescriptions for how decision-makers can maximize their reward or utility, but these formalisms are used by decision-makers rarely. Instead, individuals allocate their behavior in proportion to the worth of their options, a phenomenon captured by the generalized matching law. Why biological decision-makers adopt this strategy has been unclear. To provide insight into this issue, this article evaluates the performance of matching across a broad spectrum of decision situations, using simulations. Matching is found to attain a high or near-optimal gain, and the strategy achieves this level of performance following a single evaluation of the decision options. Thus, matching provides highly efficient decisions across a wide range of choice environments. This result offers a quantitative explanation for the broad adoption of matching by biological decision-makers.

Matching provides efficient decisions

How humans and animals distribute their behavior across choice options has been of key interest to economics, psychology, ecology, and related fields. Neoclassical and behavioral economics have provided prescriptions for how decision-makers can maximize their reward or utility, but these formalisms are used by decision-makers rarely. Instead, individuals allocate their behavior in proportion to the worth of their options, a phenomenon captured by the generalized matching law. Why biological decision-makers adopt this strategy has been unclear. To provide insight into this issue, this article evaluates the performance of matching across a broad spectrum of decision situations, using simulations. Matching is found to attain a high or near-optimal gain, and the strategy achieves this level of performance following a single evaluation of the decision options. Thus, matching provides highly efficient decisions across a wide range of choice environments. This result offers a quantitative explanation for the broad adoption of matching by biological decision-makers.

The effect of preference learning on context effects in multi-alternative, multi-attribute choice

Within the domain of preferential choice, it has long been thought that context effects, such as the attraction and compromise effects, arise due to the constructive nature of preferences and thus should not emerge when preferences are stable. We examined this hypothesis with a series of experiments where participants had the opportunity to experience selected alternatives and develop more enduring preferences. In our tasks, the options are presented in a description-based format so that participants need only learn their preferences for various options rather than the objective values of those options. Our results suggest that context effects can still emerge when stable preferences form through experience. This suggests that multi-alternative, multi-attribute decisions are likely influenced by relative evaluations, even when participants have the opportunity to experience options and learn their preferences. We hypothesize what was learned from experience in our tasks is the weights for various attributes. Through model simulations, we show that the observed choice patterns are well captured by a model with unequal attribute weights. A secondary finding is that the direction of observed context effects is opposite to standard effects and appears to be quite robust. Model simulations show that reserved effects can arise through various processes including representational noise and sensitivity to advantages and disadvantages when comparing options.

Intraparietal stimulation disrupts negative distractor effects in human multi-alternative decision-making

There has been debate about whether addition of an irrelevant distractor option to an otherwise binary decision influences which of the two choices is taken. We show that disparate views on this question are reconciled if distractors exert two opposing but not mutually exclusive effects. Each effect predominates in a different part of decision space: (1) a positive distractor effect predicts high-value distractors improve decision-making; (2) a negative distractor effect, of the type associated with divisive normalisation models, entails decreased accuracy with increased distractor values. Here, we demonstrate both distractor effects coexist in human decision making but in different parts of a decision space defined by the choice values. We show disruption of the medial intraparietal area (MIP) by transcranial magnetic stimulation (TMS) increases positive distractor effects at the expense of negative distractor effects. Furthermore, individuals with larger MIP volumes are also less susceptible to the disruption induced by TMS. These findings also demonstrate a causal link between MIP and the impact of distractors on decision-making via divisive normalisation.

Testing models of context-dependent outcome encoding in reinforcement learning

Previous studies of reinforcement learning (RL) have established that choice outcomes are encoded in a context-dependent fashion. Several computational models have been proposed to explain context-dependent encoding, including reference point centering and range adaptation models. The former assumes that outcomes are centered around a running estimate of the average reward in each choice context, while the latter assumes that outcomes are compared to the minimum reward and then scaled by an estimate of the range of outcomes in each choice context. However, there are other computational mechanisms that can explain context dependence in RL. In the present study, a frequency encoding model is introduced that assumes outcomes are evaluated based on their proportional rank within a sample of recently experienced outcomes from the local context. A range-frequency model is also considered that combines the range adaptation and frequency encoding mechanisms. We conducted two fully incentivized behavioral experiments using choice tasks for which the candidate models make divergent predictions. The results were most consistent with models that incorporate frequency or rank-based encoding. The findings from these experiments deepen our understanding of the underlying computational processes mediating context-dependent outcome encoding in human RL.

Context-effect bias in capuchin monkeys (Sapajus spp.): exploring decoy influences in a value-based food choice task

Decision making is known to be liable to several context effects. In particular, adding a seemingly irrelevant alternative (decoy) to a set of options can modify preferences: typically, by increasing choices towards whatever option clearly dominates the decoy (attraction effect), but occasionally also decreasing its appeal and generating a shift in the opposite direction (repulsion effect). Both types of decoy effects violate rational choice theory axioms and suggest dynamic processes of preference-formation, in which the value of each alternative is not determined a priori, but it is instead constructed by comparing options during the decision process. These effects are well documented, both in humans and in other species: e.g., amoebas, ants, honeybees, frogs, birds, cats, dogs. However, evidence of decoy effects in non-human primates remains surprisingly mixed. This study investigates decoy effects in capuchin monkeys (Sapajus spp.), manipulating time pressure across different conditions, to test whether such effects require time-consuming comparative processes among available alternatives. Whereas the time-dependent nature of decoy effects is a robust finding in the human literature, this is its first investigation in non-human animals. Our results show that capuchins exhibit an attraction effect with decoys targeting their preferred food, and that this effect disappears under time pressure; moreover, we observe preliminary evidence of a repulsion effect when decoys target instead the less-preferred food, possibly due to the larger distance between decoy and target in the attribute space. Taken together, these results provide valuable insight on the evolutionary roots of comparative decision making.

Context-dependent choice and evaluation in real-world consumer behavior

A body of work spanning neuroscience, economics, and psychology indicates that decision-making is context-dependent, which means that the value of an option depends not only on the option in question, but also on the other options in the choice set-or the 'context'. While context effects have been observed primarily in small-scale laboratory studies with tightly constrained, artificially constructed choice sets, it remains to be determined whether these context effects take hold in real-world choice problems, where choice sets are large and decisions driven by rich histories of direct experience. Here, we investigate whether valuations are context-dependent in real-world choice by analyzing a massive restaurant rating dataset as well as two independent replication datasets which provide complementary operationalizations of restaurant choice. We find that users make fewer ratings-maximizing choices in choice sets with higher-rated options-a hallmark of context-dependent choice-and that post-choice restaurant ratings also varied systematically with the ratings of unchosen restaurants. Furthermore, in a follow-up laboratory experiment using hypothetical choice sets matched to the real-world data, we find further support for the idea that subjective valuations of restaurants are scaled in accordance with the choice context, providing corroborating evidence for a general mechanistic-level account of these effects. Taken together, our results provide a potent demonstration of context-dependent choice in real-world choice settings, manifesting both in decisions and subjective valuation of options.

How do (perceptual) distracters distract?

When a target stimulus occurs in the presence of distracters, decisions are less accurate. But how exactly do distracters affect choices? Here, we explored this question using measurement of human behaviour, psychophysical reverse correlation and computational modelling. We contrasted two models: one in which targets and distracters had independent influence on choices (independent model) and one in which distracters modulated choices in a way that depended on their similarity to the target (interaction model). Across three experiments, participants were asked to make fine orientation judgments about the tilt of a target grating presented adjacent to an irrelevant distracter. We found strong evidence for the interaction model, in that decisions were more sensitive when target and distracter were consistent relative to when they were inconsistent. This consistency bias occurred in the frame of reference of the decision, that is, it operated on decision values rather than on sensory signals, and surprisingly, it was independent of spatial attention. A normalization framework, where target features are normalized by the expectation and variability of the local context, successfully captures the observed pattern of results.

In the real world, visual scenes usually contain many objects. As a consequence, we often have to make perceptual judgments about a specific ‘target’ stimulus in the presence of irrelevant ‘distracter’ stimuli. For instance, when hanging a picture frame, we want to discern whether it is hanging straight, ignoring the surrounding, potentially tilted frames. Laboratory experiments have shown that the presence of distracter stimuli (i.e. other picture frames) makes this type of perceptual judgment less accurate. However, the specific effect distracters have on judgments is controversial. Here, we conducted a series of experiments to compare two alternative theories of distracter influence: one in which distracters compete with targets to determine choices (independent model) and one in which distracters wield a more indirect influence on choices (interaction model). We found evidence for the latter account. Our results suggest distracters affect perceptual decisions by adjusting how sensitive decisions are to the target stimulus.

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.

Theories of Context Effects in Multialternative, Multiattribute Choice

Over the past several decades, researchers in psychology, neuroscience, marketing, and economics have been keen to understand context effects in multialternative, multiattribute decision making. These effects occur when choices among existing alternatives are altered by the addition of a new alternative to the choice set. The effects violate classic decision theories and have led to the development of computational and mathematical models that explain how underlying cognitive and neural mechanisms give rise to the effects. This article reviews dynamic models of these effects, comparing mechanisms across models. Most models of context effects incorporate an attention mechanism, which suggests that attention plays an important role in multialternative, multiattribute decision making. I conclude by discussing recent empirical studies of attention and context effects and hypothesize that changes in attention could be responsible for recently observed reversals in context effects.

Medial and orbital frontal cortex in decision-making and flexible behavior

The medial frontal cortex and adjacent orbitofrontal cortex have been the focus of investigations of decision-making, behavioral flexibility, and social behavior. We review studies conducted in humans, macaques, and rodents and argue that several regions with different functional roles can be identified in the dorsal anterior cingulate cortex, perigenual anterior cingulate cortex, anterior medial frontal cortex, ventromedial prefrontal cortex, and medial and lateral parts of the orbitofrontal cortex. There is increasing evidence that the manner in which these areas represent the value of the environment and specific choices is different from subcortical brain regions and more complex than previously thought. Although activity in some regions reflects distributions of reward and opportunities across the environment, in other cases, activity reflects the structural relationships between features of the environment that animals can use to infer what decision to take even if they have not encountered identical opportunities in the past.

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.

Decision neuroscience and neuroeconomics: Recent progress and ongoing challenges

In the past decade, decision neuroscience and neuroeconomics have developed many new insights in the study of decision making. This review provides an overarching update on how the field has advanced in this time period. Although our initial review a decade ago outlined several theoretical, conceptual, methodological, empirical, and practical challenges, there has only been limited progress in resolving these challenges. We summarize significant trends in decision neuroscience through the lens of the challenges outlined for the field and review examples where the field has had significant, direct, and applicable impacts across economics and psychology. First, we review progress on topics including reward learning, explore-exploit decisions, risk and ambiguity, intertemporal choice, and valuation. Next, we assess the impacts of emotion, social rewards, and social context on decision making. Then, we follow up with how individual differences impact choices and new exciting developments in the prediction and neuroforecasting of future decisions. Finally, we consider how trends in decision-neuroscience research reflect progress toward resolving past challenges, discuss new and exciting applications of recent research, and identify new challenges for the field. This article is categorized under: Psychology > Reasoning and Decision Making Psychology > Emotion and Motivation.

Normative Principles for Decision-Making in Natural Environments

The decisions we make are shaped by a lifetime of learning. Past experience guides the way that we encode information in neural systems for perception and valuation, and determines the information we retrieve when making decisions. Distinct literatures have discussed how lifelong learning and local context shape decisions made about sensory signals, propositional information, or economic prospects. Here, we build bridges between these literatures, arguing for common principles of adaptive rationality in perception, cognition, and economic choice. We discuss how a single common framework, based on normative principles of efficient coding and Bayesian inference, can help us understand a myriad of human decision biases, including sensory illusions, adaptive aftereffects, choice history biases, central tendency effects, anchoring effects, contrast effects, framing effects, congruency effects, reference-dependent valuation, nonlinear utility functions, and discretization heuristics. We describe a simple computational framework for explaining these phenomena. Expected final online publication date for the Annual Review of Psychology, Volume 73 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

A rational model of people's inferences about others' preferences based on response times

There's a difference between someone instantaneously saying "Yes!" when you ask them on a date compared to "…yes." Psychologists and economists have long studied how people can infer preferences from others' choices. However, these models have tended to focus on what people choose and not how long it takes them to make a choice. We present a rational model for inferring preferences from response times, using a drift diffusion model to characterize how preferences influence response time, and Bayesian inference to invert this relationship. We test our model's predictions for three experimental questions. Matching model predictions, participants inferred that a decision-maker preferred a chosen item more if the decision-maker spent less time deliberating (Experiment 1), participants predicted a decision-maker's choice in a novel comparison based on inferring the decision-maker's relative preferences from previous response times and choices (Experiment 2), and participants could incorporate information about a decision-maker's mental state of cautious or careless (Experiments 3, 4A, and 4B).

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.

Two sides of the same coin: Beneficial and detrimental consequences of range adaptation in human reinforcement learning

Evidence suggests that economic values are rescaled as a function of the range of the available options. Although locally adaptive, range adaptation has been shown to lead to suboptimal choices, particularly notable in reinforcement learning (RL) situations when options are extrapolated from their original context to a new one. Range adaptation can be seen as the result of an adaptive coding process aiming at increasing the signal-to-noise ratio. However, this hypothesis leads to a counterintuitive prediction: Decreasing task difficulty should increase range adaptation and, consequently, extrapolation errors. Here, we tested the paradoxical relation between range adaptation and performance in a large sample of participants performing variants of an RL task, where we manipulated task difficulty. Results confirmed that range adaptation induces systematic extrapolation errors and is stronger when decreasing task difficulty. Last, we propose a range-adapting model and show that it is able to parsimoniously capture all the behavioral results.