The multiattribute linear ballistic accumulator model of context effects in multialternative choice

Understanding responses of people with ASD in diverse reasoning tasks: A formal study

Recent studies have shown that in some reasoning tasks people with Autism Spectrum Disorder perform better than typically developing people. This paper compares four such tasks, namely a syllogistic task, two decision-making tasks, and a task from the heuristics and biases literature, the aim being to identify common structure as well as differences. In the terminology of David Marr's three levels of cognitive systems, the tasks show commonalities on the computational level in terms of the effect of contextual stimuli, though an in-depth analysis of such contexts provides certain distinguishing features in the algorithmic level. We also make some general remarks on our approach, so as to set the stage for further studies in the area which could provide a better understanding of the reasoning process of ASD individuals.

Building Human-Like Artificial Agents: A General Cognitive Algorithm for Emulating Human Decision-Making in Dynamic Environments

One of the early goals of artificial intelligence (AI) was to create algorithms that exhibited behavior indistinguishable from human behavior (i.e., human-like behavior). Today, AI has diverged, often aiming to excel in tasks inspired by human capabilities and outperform humans, rather than replicating human cogntion and action. In this paper, I explore the overarching question of whether computational algorithms have achieved this initial goal of AI. I focus on dynamic decision-making, approaching the question from the perspective of computational cognitive science. I present a general cognitive algorithm that intends to emulate human decision-making in dynamic environments, as defined in instance-based learning theory (IBLT). I use the cognitive steps proposed in IBLT to organize and discuss current evidence that supports some of the human-likeness of the decision-making mechanisms. I also highlight the significant gaps in research that are required to improve current models and to create higher fidelity in computational algorithms to represent human decision processes. I conclude with concrete steps toward advancing the construction of algorithms that exhibit human-like behavior with the ultimate goal of supporting human dynamic decision-making.

Multiattribute Decision-making in Macaques Relies on Direct Attribute Comparisons

In value-based decisions, there are frequently multiple attributes, such as cost, quality, or quantity, that contribute to the overall goodness of an option. Because one option may not be better in all attributes at once, the decision process should include a means of weighing relevant attributes. Most decision-making models solve this problem by computing an integrated value, or utility, for each option from a weighted combination of attributes. However, behavioral anomalies in decision-making, such as context effects, indicate that other attribute-specific computations might be taking place. Here, we tested whether rhesus macaques show evidence of attribute-specific processing in a value-based decision-making task. Monkeys made a series of decisions involving choice options comprising a sweetness and probability attribute. Each attribute was represented by a separate bar with one of two mappings between bar size and the magnitude of the attribute (i.e., bigger = better or bigger = worse). We found that translating across different mappings produced selective impairments in decision-making. Choices were less accurate and preferences were more variable when like attributes differed in mapping, suggesting that preventing monkeys from easily making direct attribute comparisons resulted in less accurate choice behavior. This was not the case when mappings of unalike attributes within the same option were different. Likewise, gaze patterns favored transitions between like attributes over transitions between unalike attributes of the same option, so that like attributes were sampled sequentially to support within-attribute comparisons. Together, these data demonstrate that value-based decisions rely, at least in part, on directly comparing like attributes of multiattribute options.

An investigation into the influence of context effects on crowd exit selection under gender difference in indoor evacuation

Introduction

Exit selection is crucial in indoor emergency evacuation. Domestic and foreign scholars have found that exit choice behavior is influenced by three factors: environmental factors, social interactions, and individual internal factors. Previous studies have shown that in addition to a single environmental factor affecting exit decisions, the influence of other available exit options in the context can ultimately lead to a reversal of exit decisions -The context effect. However, the impact of context effects on exit decisions in emergency situations has not been thoroughly explored. Therefore, this article identifies three basic independent variables: context effects, crowd flows, and gender differences, to study the exit decisions of different gender groups facing different crowd flows, as well as how context effects affect existing exit decisions.

Methods

In this paper, we used virtual reality technology to construct an indoor fire scene and designed a total of 15 virtual experiments with different crowd distribution or context effects. 131 participants were divided into two groups, male and female, and their exit decisions were observed under different crowd flows and contextual effects.

Results

The research results show that: 1) Both men and women have an innate preference to avoid crowded exits, and the proportion of following crowd evacuation significantly decreases when there are crowded crowds in the scene; 2) The exit decisions of female participants are more influenced by the crowd, while men tend to be more influenced by context effects when evacuating independently; 3) The context effects on exit decisions in emergency situations is statistically significant, and this performance is more significant in the male population. Further analysis reveals that similarity effects have a more significant impact on exit decisions than attraction effects.

Discussions

These findings provide deeper insights into the exit choice behavior of the population and may contribute to the design of safe exits in indoor buildings. In addition, this article emphasizes the importance of context effects and provides a foundation for future research.

Available and unavailable decoys in capuchin monkeys (Sapajus spp.) decision-making

Decision-making has been observed to be systematically affected by decoys, i.e., options that should be irrelevant, either because unavailable or because manifestly inferior to other alternatives, and yet shift preferences towards their target. Decoy effects have been extensively studied both in humans and in several other species; however, evidence in non-human primates remains scant and inconclusive. To address this gap, this study investigates how choices in capuchin monkeys (Sapajus spp.) are affected by different types of decoys: asymmetrically dominated decoys, i.e., available and unavailable options that are inferior to only one of the other alternatives, and phantom decoys, i.e., unavailable options that are superior to another available alternative. After controlling for the subjective strength of initial preferences and the distance of each decoy from its target in attribute space, results demonstrate a systematic shift in capuchins' preference towards the target of both asymmetrically dominated decoys (whether they are available or not) and phantom decoys, regardless of what options is being targeted by such decoys. This provides the most comprehensive evidence to date of decoy effects in non-human primates, with important theoretical and methodological implications for future comparative studies on context effects in decision-making.

Are there jumps in evidence accumulation, and what, if anything, do they reflect psychologically? An analysis of Lévy Flights models of decision-making

According to existing theories of simple decision-making, decisions are initiated by continuously sampling and accumulating perceptual evidence until a threshold value has been reached. Many models, such as the diffusion decision model, assume a noisy accumulation process, described mathematically as a stochastic Wiener process with Gaussian distributed noise. Recently, an alternative account of decision-making has been proposed in the Lévy Flights (LF) model, in which accumulation noise is characterized by a heavy-tailed power-law distribution, controlled by a parameter, [Formula: see text]. The LF model produces sudden large "jumps" in evidence accumulation that are not produced by the standard Wiener diffusion model, which some have argued provide better fits to data. It remains unclear, however, whether jumps in evidence accumulation have any real psychological meaning. Here, we investigate the conjecture by Voss et al. (Psychonomic Bulletin & Review, 26(3), 813-832, 2019) that jumps might reflect sudden shifts in the source of evidence people rely on to make decisions. We reason that if jumps are psychologically real, we should observe systematic reductions in jumps as people become more practiced with a task (i.e., as people converge on a stable decision strategy with experience). We fitted five versions of the LF model to behavioral data from a study by Evans and Brown (Psychonomic Bulletin & Review, 24(2), 597-606, 2017), using a five-layer deep inference neural network for parameter estimation. The analysis revealed systematic reductions in jumps as a function of practice, such that the LF model more closely approximated the standard Wiener model over time. This trend could not be attributed to other sources of parameter variability, speaking against the possibility of trade-offs with other model parameters. Our analysis suggests that jumps in the LF model might be capturing strategy instability exhibited by relatively inexperienced observers early on in task performance. We conclude that further investigation of a potential psychological interpretation of jumps in evidence accumulation is warranted.

The competition dynamics of approach and avoidance motivations following interpersonal transgression

Two behavioral motivations coexist in transgressors following an interpersonal transgression-approaching and compensating the victim and avoiding the victim. Little is known about how these motivations arise, compete, and drive transgressors' decisions. The present study adopted a social interaction task to manipulate participants' (i.e., the transgressor) responsibility for another's (i.e., the victim) monetary loss and measure the participants' tradeoff between compensating the victim and avoiding face-to-face interactions with the victim. Following each transgression, participants used a computer mouse to choose between two options differing in the amount of compensation to the victim and the probability of face-to-face contact with the victim. Results showed that as participants' responsibility increased, 1) the decision weights on contact avoidance relative to compensation increased, and 2) the onset of the contact-avoidance attribute was expedited and that of the compensation attribute was delayed. These results demonstrate how competing social motivations following transgression evolve and determine social decision-making and shed light on how social-affective state modulates the dynamics of decision-making in general.

Parallel processing of value-related information during multi-attribute decisions

When choosing between options with multiple attributes, do we integrate all attributes into a unified measure for comparison, or does the comparison also occur at the level of each attribute, involving parallel processes that can dynamically influence each other? What happens when independent sensory features all carry information about the same decision factor, such as reward value? To investigate these questions, we asked human participants to perform a two-alternative forced choice reaching task in which the reward value of a target was indicated by two visual attributes-its brightness ("bottom-up," BU feature) and its orientation ("top-down," TD feature). If decisions always occur after the integration of both features, there should be no difference in the reaction time (RT) regardless of the attribute combinations that drove the choice. Counter to that prediction, RT distributions depended on the attribute combinations of given targets and the choices made by participants. RTs were shortest when both attributes were congruent or when the choice was based on the bottom-up feature, and longer when the attributes were in conflict (favoring opposite options). In conflict trials, nearly two-thirds of participants made faster decisions when choosing the option favored by the bottom-up feature than when choosing the top-down-favored option. We also observed mid-reach changes-of-mind in a subset of conflict trials, mostly changing from the bottom-up to the top-down-favored target. These data suggest that multi-attribute value-based decisions are better explained by a distributed process including competition among different features than by a competition based on a single, integrated estimate of value.NEW & NOTEWORTHY We show that during value-based decisions, humans do not always use all reward-related information to make their choice, but instead can "jump the gun" using partial information. In particular, when different sources of information were in conflict, early decisions were mostly based on fast bottom-up information, and sometimes followed by corrective changes-of-mind based on slower top-down information. This supports parallel decision processes among different information sources, as opposed to a single integrated "common currency."

A query theory account of the attraction effect

We provide novel support for Query Theory, a reason-based decision framework, extending it to multialternative choices and applying it to the classic phenomenon known as the attraction effect. In Experiment 1 (N = 261), we generalised the two key metrics used in Query Theory from binary to multialternative choices and found that reasons supporting the target option were generated earlier and in greater quantity than those supporting the competitor, as predicted by the theory. In Experiment 2 (N = 703), we investigated the causal relationships between reasoning and choices by exogenously manipulating the order in which participants generated their reasons. As predicted, the size of the attraction effect was a function of this query order manipulation. We also introduced a bidirectional reason coding protocol to measure the valence of reasons, which confirmed support for Query Theory. We suggest the Query Theory framework can be useful for studying the high-level deliberation processes behind multialternative choices.

Social incentives are stronger predictors of drinking decisions than alcohol incentives in young adults: The role of alcohol use disorder

This study investigated the influence of social incentives, alcohol incentives, and responsibility disincentives on decisions to attend and drink at party events in young adult college students (n = 82; 55 women, 27 men) where 36 (20 women; 16 men) had an Alcohol Use Disorder (AUD) and 46 (35 women; 11 men) were control participants without an AUD. In this within-subjects design, participants were presented with a series of hypothetical drinking event scenarios that varied in terms of social incentives (knowing many vs. few people), alcohol incentives (more vs. less alcohol available), and next-day responsibility disincentives (high vs. moderate vs. low). Participants were asked whether they would attend the event and how many drinks they would consume. Social incentives significantly predicted both decisions to attend party events and decisions about how much to drink for all participants. Participants were more likely to decide to attend and drink more at high social incentive party events (where they knew more people). However, while low social incentives generally discouraged attendance decisions, AUD participants were more likely than controls to decide to attend party events in low social incentive contexts. Alcohol incentives did not affect attendance decisions. However, alcohol incentives did increase drinking amount decisions for AUD participants. Finally, while disincentives decreased attendance and drinking amount decisions in general, AUD participants were less deterred by responsibility disincentives than controls. The results highlight the important influence of social rewards on drinking-related decisions and suggest individual differences in how incentives and disincentives affect drinking decisions in persons with an AUD.

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.

When alternative hypotheses shape your beliefs: Context effects in probability judgments

When people are asked to estimate the probability of an event occurring, they sometimes make different subjective probability judgments for different descriptions of the same event. This implies the evidence or support recruited to make these judgments is based on the descriptions of the events (hypotheses) instead of the events themselves, as captured by Tversky and Koehler's (1994) support theory. Support theory, however, assumes each hypothesis elicits a fixed level of support (support invariance). Here, across three studies, we tested this support invariance assumption by asking participants to estimate the probability that an event would occur given a set of relevant statistics. We show that the support recruited about a target hypothesis can depend on the other hypotheses under consideration. Results reveal that for a pair of competing hypotheses, one hypothesis (the target hypothesis) appears more competitive relative to the other when a dud-a hypothesis dominated by the target hypothesis-is present. We also find that the target hypothesis can appear less competitive relative to the other when a resembler-a hypothesis that is similar to the target hypothesis-is present. These context effects invalidate the support invariance assumption in support theory and suggest that a similar process that drives preference construction may also underlie belief construction.

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.

Gaze-dependent evidence accumulation predicts multi-alternative risky choice behaviour

Choices are influenced by gaze allocation during deliberation, so that fixating an alternative longer leads to increased probability of choosing it. Gaze-dependent evidence accumulation provides a parsimonious account of choices, response times and gaze-behaviour in many simple decision scenarios. Here, we test whether this framework can also predict more complex context-dependent patterns of choice in a three-alternative risky choice task, where choices and eye movements were subject to attraction and compromise effects. Choices were best described by a gaze-dependent evidence accumulation model, where subjective values of alternatives are discounted while not fixated. Finally, we performed a systematic search over a large model space, allowing us to evaluate the relative contribution of different forms of gaze-dependence and additional mechanisms previously not considered by gaze-dependent accumulation models. Gaze-dependence remained the most important mechanism, but participants with strong attraction effects employed an additional similarity-dependent inhibition mechanism found in other models of multi-alternative multi-attribute choice.

The Effect of Ingroup vs. Outgroup Members' Behavior on Charity Preference: A Drift-Diffusion Model Approach

Providing potential donors with information about the behavior of others (i.e., social information) is an increasingly used strategy to nudge prosocial decision-making. In the present study, we investigated the effect of ingroup vs. outgroup information on participants' charity preferences by applying a Drift Diffusion Model (DDM) approach. In a joint evaluation scenario, we manipulated different levels of ingroup/outgroup preference ratios for two charities within subjects. Every subject was presented with three stimulus types (i.e., high, medium, and low ingroup ratio) randomized in 294 trials divided into six blocks. We expected that for stimuli with a high ingroup/outgroup ratio, participants should more often and faster decide for the ingroup's most favored charity. We expected that the speed of evidence accumulation will be higher the larger the ingroup/outgroup ratio. Additionally, we investigated whether variations in model parameters can explain individual differences in participants' behaviors. Our results showed that people generally followed ingroup members' preferences when deciding for a charity. However, on finding an unexpected pattern in our results, we conducted post-hoc analyses which revealed two different behavioral strategies used by participants. Based on participants' decisions, we classified them into “equality driven” individuals who preferred stimuli with the least difference between ingroup and outgroup percentages or “ingroup driven” individuals who favored stimuli with the highest ingroup/outgroup ratio. Results are discussed in line with relevant literature, and implications for practitioners are given.

Context effects on choice under cognitive load

The context-dependent nature of choice is well illustrated by decoy effects, in which adding an alternative to a choice set can change the preference relations among the other alternatives. The current within-subjects study tested whether manipulating cognitive load affects the magnitude of attraction and compromise decoy effects. Participants (n = 96) made simulated online grocery shopping choices from three options described by price and quality for each grocery item they encountered. On half the 96 trials, they had to memorize a telephone number prior to encountering the choice set, after which they recalled the number. The choice task was rated significantly more difficult under load, providing some face validity for the load manipulation. Across decoy types, context effects were large and unaffected by the load manipulation. Bayesian analysis provided substantial evidence in favor of this null effect, with the study powered at better than .95 to detect a moderate effect. Across individuals, the magnitude of decoy effects was positively correlated with perception of the greater difficulty of the task under load, with this relationship fully mediated by increases in response times. These results are consistent with the idea that compromise and attraction decoy effects can operate relatively automatically and require minimal effortful processing.

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.

Ambivalence in decision making: An eye tracking study

An intuition of ambivalence in cognition is particularly strong for complex decisions, for which the merits and demerits of different options are roughly equal but hard to compare. We examined information search in an experimental paradigm which tasked participants with an ambivalent question, while monitoring attentional dynamics concerning the information relevant to each option in different Areas of Interest (AOIs). We developed two dynamical models for describing eye tracking curves, for each response separately. The models incorporated a drift mechanism towards the various options, as in standard drift diffusion theory. In addition, they included a mechanism for intrinsic oscillation, which competed with the drift process and undermined eventual stabilization of the dynamics. The two models varied in the range of drift processes postulated. Higher support was observed for the simpler model, which only included drifts from an uncertainty state to either of two certainty states. In addition, model parameters could be weakly related to the eventual decision, complementing our knowledge of the way eye tracking structure relates to decision (notably the gaze cascade effect).

High-value decisions are fast and accurate, inconsistent with diminishing value sensitivity

It is a widely held belief that people's choices are less sensitive to changes in value as value increases. For example, the subjective difference between $11 and $12 is believed to be smaller than between $1 and $2. This idea is consistent with applications of the Weber-Fechner Law and divisive normalization to value-based choice and with psychological interpretations of diminishing marginal utility. According to random utility theory in economics, smaller subjective differences predict less accurate choices. Meanwhile, in the context of sequential sampling models in psychology, smaller subjective differences also predict longer response times. Based on these models, we would predict decisions between high-value options to be slower and less accurate. In contrast, some have argued on normative grounds that choices between high-value options should be made with less caution, leading to faster and less accurate choices. Here, we model the dynamics of the choice process across three different choice domains, accounting for both discriminability and response caution. Contrary to predictions, we mostly observe faster and more accurate decisions (i.e., higher drift rates) between high-value options. We also observe that when participants are alerted about incoming high-value decisions, they exert more caution and not less. We rule out several explanations for these results, using tasks with both subjective and objective values. These results cast doubt on the notion that increasing value reduces discriminability.

A causal role for the right frontal eye fields in value comparison

Recent studies have suggested close functional links between overt visual attention and decision making. This suggests that the corresponding mechanisms may interface in brain regions known to be crucial for guiding visual attention - such as the frontal eye field (FEF). Here, we combined brain stimulation, eye tracking, and computational approaches to explore this possibility. We show that inhibitory transcranial magnetic stimulation (TMS) over the right FEF has a causal impact on decision making, reducing the effect of gaze dwell time on choice while also increasing reaction times. We computationally characterize this putative mechanism by using the attentional drift diffusion model (aDDM), which reveals that FEF inhibition reduces the relative discounting of the non-fixated option in the comparison process. Our findings establish an important causal role of the right FEF in choice, elucidate the underlying mechanism, and provide support for one of the key causal hypotheses associated with the aDDM.

Establishing the laws of preferential choice behavior

Mathematical and computational decision models are powerful tools for studying choice behavior, and hundreds of distinct decision models have been proposed over the long interdisciplinary history of decision making research. The existence of so many models has led to theoretical fragmentation and redundancy, obscuring key insights into choice behavior, and preventing consensus about the essential properties of preferential choice. We provide a synthesis of formal models of risky, multiattribute, and intertemporal choice, three important domains in decision making. We identify recurring insights discovered by scholars of different generations and different disciplines across these three domains, and use these insights to classify over 150 existing models as involving various combinations of eight key mathematical and computational properties. These properties capture the main avenues of theoretical development in decision making research and can be used to understand the similarities and differences between decision models, aiding both theoretical analyses and empirical tests.

Decoy Effect on Consumers' Purchase Behaviors in Relation to Meat Products: Comparison of Pork and Chicken

Few studies have analyzed the behaviors of consumers in relation to their purchase of meat products produced with animal welfare in consideration under different decoy scenarios; thus, it is difficult to accurately understand consumer behaviors and to reduce the bias in the conclusions of this study regarding consumption preferences in relation to meat products that had been produced with animal welfare in consideration. With the frequent occurrence of cases connected with the quality and safety of meat in China, the welfare conditions of livestock and poultry urgently need to be improved. We used 810 consumers in Wuxi City, Jiangsu Province, China as our study sample, chose pigs and chickens, i.e., the two common species of livestock and poultry, as study cases, and set four types of decoy scenarios based on breeding time, breeding model, diet cleanliness label, and price attributes, to explore the purchasing options of consumers for meat produced with high levels of animal welfare, under different decoy conditions. A decoy effect was observed in a bounded rational consumption situation in relation to the purchasing behaviors of both chicken and pork. In a situation of chicken purchase, the decoy effect of the breeding model was the strongest, followed by that of price, diet cleanliness label, and breeding time. In the case of pork purchase, the decoy effect of the diet cleanliness label was the strongest, followed by price, breeding model, and breeding time. In a comprehensive comparison between the two types of consumption experiments, price decoy constantly played a significant role, while the decoy effect of breeding time was the weakest. Accordingly, we proposed that in addition to strengthening the knowledge of people in the welfare of livestock and poultry, designing a breeding model decoy or price decoy in the process of chicken sales and designing a diet cleanliness label decoy or price decoy in the process of pork sales can guide the demand of consumers for meat produced with high levels of animal welfare. The welfare of livestock and poultry should thus be improved.

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.

Healthful choices depend on the latency and rate of information accumulation

The drift diffusion model provides a parsimonious explanation of decisions across neurobiological, psychological and behavioural levels of analysis. Although most drift diffusion model implementations assume that only a single value guides decisions, choices often involve multiple attributes that could make separable contributions to choice. Here we fit incentive-compatible dietary choices to a multi-attribute, time-dependent drift diffusion model, in which taste and health could differentially influence the evidence accumulation process. We find that these attributes shaped both the relative value signal and the latency of evidence accumulation in a manner consistent with participants' idiosyncratic preferences. Moreover, by using a dietary prime, we showed how a healthy choice intervention alters multi-attribute, time-dependent drift diffusion model parameters that in turn predict prime-dependent choices. Our results reveal that different decision attributes make separable contributions to the strength and timing of evidence accumulation, providing new insights into the construction of interventions to alter the processes of choice.

Untangling Decision Routes in Moral Dilemmas: The Refugee Dilemma

It is sometimes considered that there are 2 routes to moral choice, deontological and utilitarian (with debate as to whether each of these routes uniquely reflect emotional vs. analytic components). Yet in moral judgments we are often faced with a third route to choice, based on rules imposed by an outside agency (most typically the legal framework of a country). Whether we agree with these rules or not, we can choose to guide moral choice in this way. Moreover, depending on the situation, we can sometimes choose to prioritize such external rules or not. Sometimes external rules coincide with either deontological or utilitarian biases. But what happens when they do not? Using a novel paradigm for moral judgment, the refugee dilemma, we provide evidence for such a route to moral choice based on external rules. We show in 3 experiments that participants with high scores on a cognitive reflection test or under cognitive load were more likely to adopt utilitarian or rule-based responses, as opposed to emotional ones. We also found that rule-based respondents reported the highest average psychological distance. These findings extend the predominant approach of dual process models. Supplemental materials are available here: https://www.press.uillinois.edu/journals/ajp/media/untangling_decision_routes_in_moral_dilemmas/index.html

"Is it Reasonable to Study Decision-Making Quantitatively?"

Scientists studying decision-making often provide a set of choices, each specified with values or distributions of values, and probabilities or distributions of probabilities. For example, "Would you prefer $100 with probability 1.0 or $1 with probability .9 and $1,000 with probability 0.1?" Other decision research examines choices made in the absence of most quantitative information; for example, "Would you prefer a Ford now or a Porsche a year from now?," "Which food would you prefer," but models the findings with precise quantitative assumptions. Yet other research does neither; for example, modeling verbally stated choices with verbally stated heuristics. This article asks about the relevance of the first two research approaches for much of the decision-making made in life. The use of quantitative research and modeling is unsurprising, given that this approach underlies most of science. In life, values and probabilities are almost always partly or wholly vague and qualitative rather than quantitative. For example, when deciding which house to buy, there are relevant features such as size, color, neighborhood schools, construction materials, attractiveness, and many more, but the decision-maker finds it difficult and of little use to assign these precise values or weights. Nonetheless, humans have evolved to make decisions in such vaguely specified settings. I provide an example showing how a very high degree of uncertainty can defeat the application of quantitative decision-making, but such a demonstration is not critical if quantitative research and modeling produce a good understanding of and a good approximation to decision-making in the natural environment. This perspective addresses these issues.

Influence of divided attention on the attraction effect in multialternative choice

The attraction effect in multialternative decision making reflects the context-dependent violation of rational choice axioms. This study examined the effect of concurrent divided attention in three-alternative visual choice tasks. The concurrent divided attention task is considered to consume the mental resources available for the choice task. There were three conditions: (a) the task-relevant condition, in which the auditory task should consume resources across multiple levels; (b) the task-irrelevant condition, in which the auditory stimuli should consume perceptual resources; (c) and the control condition, in which the resources should not be consumed by auditory stimuli. Thirty-three participants solved 24 hypothetical purchase problems with three alternatives that differed in terms of two attributes. The results indicated that the choice proportion of the target was significantly higher in the task-relevant condition than in the task-irrelevant and control conditions, thereby suggesting that a reduction in cognitive (and/or response) resources is critical for the attraction effect.

Temporal oscillations in preference strength provide evidence for an open system model of constructed preference

The decision process is often conceptualized as a constructive process in which a decision maker accumulates information to form preferences about the choice options and ultimately make a response. Here we examine how these constructive processes unfold by tracking dynamic changes in preference strength. Across two experiments, we observed that mean preference strength systematically oscillated over time and found that eliciting a choice early in time strongly affected the pattern of preference oscillation later in time. Preferences following choices oscillated between being stronger than those without prior choice and being weaker than those without choice. To account for these phenomena, we develop an open system dynamic model which merges the dynamics of Markov random walk processes with those of quantum walk processes. This model incorporates two sources of uncertainty: epistemic uncertainty about what preference state a decision maker has at a particular point in time; and ontic uncertainty about what decision or judgment will be observed when a person has some preference state. Representing these two sources of uncertainty allows the model to account for the oscillations in preference as well as the effect of choice on preference formation.

Uncovering the computational mechanisms underlying many-alternative choice

How do we choose when confronted with many alternatives? There is surprisingly little decision modelling work with large choice sets, despite their prevalence in everyday life. Even further, there is an apparent disconnect between research in small choice sets, supporting a process of gaze-driven evidence accumulation, and research in larger choice sets, arguing for models of optimal choice, satisficing, and hybrids of the two. Here, we bridge this divide by developing and comparing different versions of these models in a many-alternative value-based choice experiment with 9, 16, 25, or 36 alternatives. We find that human choices are best explained by models incorporating an active effect of gaze on subjective value. A gaze-driven, probabilistic version of satisficing generally provides slightly better fits to choices and response times, while the gaze-driven evidence accumulation and comparison model provides the best overall account of the data when also considering the empirical relation between gaze allocation and choice.

Fixation patterns in simple choice reflect optimal information sampling

Simple choices (e.g., eating an apple vs. an orange) are made by integrating noisy evidence that is sampled over time and influenced by visual attention; as a result, fluctuations in visual attention can affect choices. But what determines what is fixated and when? To address this question, we model the decision process for simple choice as an information sampling problem, and approximate the optimal sampling policy. We find that it is optimal to sample from options whose value estimates are both high and uncertain. Furthermore, the optimal policy provides a reasonable account of fixations and choices in binary and trinary simple choice, as well as the differences between the two cases. Overall, the results show that the fixation process during simple choice is influenced dynamically by the value estimates computed during the decision process, in a manner consistent with optimal information sampling.

Attraction to similar options: The Gestalt law of proximity is related to the attraction effect

Previous studies have suggested that there are common mechanisms between perceptual and value-based processes. For instance, both perceptual and value-based choices are highly influenced by the context in which the choices are made. However, the mechanisms which allow context to influence our choice process as well as the extent of the similarity between the perceptual and preferential processes are still unclear. In this study, we examine a within-subject relation between the attraction effect, which is a well-known effect of context on preferential choice, and the Gestalt law of proximity. Then, we aim to use this link to better understand the mechanisms underlying the attraction effect. We conducted one study followed by an additional pre-registered replication study, where subjects performed a Gestalt-psychophysical task and a decoy task. Comparing the behavioral sensitivity of each subject in both tasks, we found that the more susceptible a subject is to the proximity law, the more she displayed the attraction effect. These results demonstrate a within-subject relation between a perceptual phenomenon (proximity law) and a value-based bias (attraction effect) which further strengthens the notion of common rules between perceptual and value-based processing. Moreover, this suggests that the mechanism underlying the attraction effect is related to grouping by proximity with attention as a mediator.

How do insects choose flowers? A review of multi-attribute flower choice and decoy effects in flower-visiting insects

Understanding why animals (including humans) choose one thing over another is one of the key questions underlying the fields of behavioural ecology, behavioural economics and psychology. Most traditional studies of food choice in animals focus on simple, single-attribute decision tasks. However, animals in the wild are often faced with multi-attribute choice tasks where options in the choice set vary across multiple dimensions. Multi-attribute decision-making is particularly relevant for flower-visiting insects faced with deciding between flowers that may differ in reward attributes such as sugar concentration, nectar volume and pollen composition as well as non-rewarding attributes such as colour, symmetry and odour. How do flower-visiting insects deal with complex multi-attribute decision tasks? Here we review and synthesise research on the decision strategies used by flower-visiting insects when making multi-attribute decisions. In particular, we review how different types of foraging frameworks (classic optimal foraging theory, nutritional ecology, heuristics) conceptualise multi-attribute choice and we discuss how phenomena such as innate preferences, flower constancy and context dependence influence our understanding of flower choice. We find that multi-attribute decision-making is a complex process that can be influenced by innate preferences, flower constancy, the composition of the choice set and economic reward value. We argue that to understand and predict flower choice in flower-visiting insects, we need to move beyond simplified choice sets towards a view of multi-attribute choice which integrates the role of non-rewarding attributes and which includes flower constancy, innate preferences and context dependence. We further caution that behavioural experiments need to consider the possibility of context dependence in the design and interpretation of preference experiments. We conclude with a discussion of outstanding questions for future research. We also present a conceptual framework that incorporates the multiple dimensions of choice behaviour.

A map of decoy influence in human multialternative choice

Imagine you are deciding between two goods: A is simple but inexpensive, B is luxurious but more costly. Introducing a less advantageous option (e.g., lower quality than A, same price) should not alter your choice between A and B. However, this principle is often violated; three classic biases known as “decoy effects” have been identified, each describing a stereotyped choice pattern in the presence of irrelevant information. Through behavioral testing in human participants and computer simulations, we show that these decoy effects are special cases of a wider principle, whereby stimulus value information is encoded in a relative, rather than an absolute, format. This work clarifies the origin of three behavioral phenomena that are widely studied in psychology and economics.

Human decisions can be biased by irrelevant information. For example, choices between two preferred alternatives can be swayed by a third option that is inferior or unavailable. Previous work has identified three classic biases, known as the attraction, similarity, and compromise effects, which arise during choices between economic alternatives defined by two attributes. However, the reliability, interrelationship, and computational origin of these three biases have been controversial. Here, a large cohort of human participants made incentive-compatible choices among assets that varied in price and quality. Instead of focusing on the three classic effects, we sampled decoy stimuli exhaustively across bidimensional multiattribute space and constructed a full map of decoy influence on choices between two otherwise preferred target items. Our analysis reveals that the decoy influence map is highly structured even beyond the three classic biases. We identify a very simple model that can fully reproduce the decoy influence map and capture its variability in individual participants. This model reveals that the three decoy effects are not distinct phenomena but are all special cases of a more general principle, by which attribute values are repulsed away from the context provided by rival options. The model helps us understand why the biases are typically correlated across participants and allows us to validate a prediction about their interrelationship. This work helps to clarify the origin of three of the most widely studied biases in human decision-making.

Deviations of rational choice: an integrative explanation of the endowment and several context effects

People's choices are often found to be inconsistent with the assumptions of rational choice theory. Over time, several probabilistic models have been proposed that account for such deviations from rationality. However, these models have become increasingly complex and are often limited to particular choice phenomena. Here we introduce a network approach that explains a broad set of choice phenomena. We demonstrate that this approach can be used to compare different choice theories and integrates several choice mechanisms from established models. A basic setup implements bounded rationality, loss aversion, and inhibition in a natural fashion, which allows us to predict the occurrence of well-known choice phenomena, such as the endowment effect and the similarity, attraction, compromise, and phantom context effects. Our results show that this network approach provides a simple representation of complex choice behaviour, and can be used to gain a better understanding of how the many choice phenomena and key theoretical principles from different types of decision-making are connected.

Attraction comes from many sources: Attentional and comparative processes in decoy effects

The attraction effect emerges when adding a seemingly irrelevant option (decoy) to a binary choice shifts preference towards a target option. This suggests that choice behaviour is dynamic, i.e., choice values are developed during deliberation, rather than manifesting some pre-existing preference set. Whereas several models of multialternative and multiattribute decision making consider dynamic choice processes as crucial to explain the attraction effect, empirically investigating the exact nature of such processes requires complementing choice output with other data. In this study, we focused on asymmetrically dominated decoys (i.e., decoys that are clearly dominated only by the target option) to examine the attentional and comparative processes responsible for the attraction effect. Through an eye-tracker paradigm, we showed that the decoy option can affect subjects’ preferences in two different and not mutually exclusive ways: by focusing the attention on the salient option and the dominance attribute, and by increasing comparisons with the choice dominant pattern. Although conceptually and procedurally distinct, both pathways for decoy effects produce an increase in preferences for the target option, in line with attentional and dynamic models of decision making. Eye-tracking data provide further details to the verification of such models, by highlighting the context-dependent nature of attention and the development of similarity-driven competitive decisional processes.

Psychological mechanisms of loss aversion: A drift-diffusion decomposition

Decision makers often reject mixed gambles offering equal probabilities of a larger gain and a smaller loss. This important phenomenon, referred to as loss aversion, is typically explained by prospect theory, which proposes that decision makers give losses higher utility weights than gains. In this paper we consider alternative psychological mechanisms capable of explaining loss aversion, such as a fixed utility bias favoring rejection, as well as a bias favoring rejection prior to gamble valuation. We use a drift diffusion model of decision making to conceptually distinguish, formally define, and empirically measure these mechanisms. In two preregistered experiments, we show that the pre-valuation bias provides a very large contribution to model fits, predicts key response time patterns, reflects prior expectations regarding gamble desirability, and can be manipulated independently of the valuation process. Our results indicate that loss aversion is the result of multiple different psychological mechanisms, and that the pre-valuation bias is a fundamental determinant of this well-known behavioral tendency. These results have important implications for how we model behavior in risky choice tasks, and how we interpret its relationship with various psychological, clinical, and neurobiological variables.

A parallel accumulator model accounts for decision randomness when deciding on risky prospects with different expected value

In decision-making situations individuals rarely have complete information available to select the best option and often show decisional randomness, i.e. given the same amount of knowledge individuals choose different options at different times. Dysfunctional processes resulting in altered decisional randomness can be considered a target process for psychiatric disorders, yet these processes remain poorly understood. Advances in computational modeling of decision-making offer a potential explanation for decisional randomness by positing that decisions are implemented in the brain through accumulation of noisy evidence, causing a generally less preferred option to be chosen at times by chance. One such model, the linear ballistic accumulator (LBA), assumes that individuals accumulate information for each option independently over time and that the first option to reach a threshold will be selected. To investigate the mechanisms of decisional randomness, we applied the LBA to a decision-making task in which risk and expected value (EV) were explicitly signaled prior to making a choice, and estimated separate drift rates for each of the four task stimuli (representing high and low EV and high and low risk). We then used the fitted LBA parameters to predict subject response rates on held-out trials for each of the 6 possible stimulus pairs. We found that choices predicted by LBA were correlated with actual choices across subjects for all stimulus pairs. Taken together, these findings suggest that sequential sampling models can account for decisional randomness on an explicit probabilistic task, which may have implications for understanding decision-making in healthy individuals and in psychiatric populations.

The power of last fixation: Biasing simple choices by gaze-contingent manipulation

Among the established findings in eye movement during decision-making, decision-makers are likely to choose the last fixated option, and this phenomenon has proven robust. However, the causal link between last fixation and choices requires further examination. In Study 1 (N = 40), a gaze-contingent manipulation paradigm was developed by controlling the timing of decision prompts to manipulate the last fixation. The results showed that participants' value-based choices were biased toward the last fixated option. However, the manipulation in Study 1 may disturb their decision process, leading to an unnatural decision environment. In Study 2 (N = 40), the gaze-contingent paradigm was further developed to manipulate the last fixation by directing an additional fixation on the target option after the participants' decision prompts. The results showed that participants' choices were also biased in the uninterrupted decisions. Our findings suggest a causal link between last fixation and value-based choices.

Framing context effects with reference points

Research on reference points highlights how alternatives outside the choice set can alter the perceived value of available alternatives, arguably framing the choice scenario. The present work utilizes reference points to study the effects of framing in preferential choice, using the similarity and attraction context effects as performance measures. We specifically test the predictions of Multialternative Decision by Sampling (MDbS; Noguchi & Stewart, 2018), a recent preferential choice model that can account for both reference points and context effects. In Experiment 1, consistent with predictions by MDbS, we find a standard similarity effect when no reference point is given that increases when both dimensions are framed negatively and decreases when both dimensions are framed positively. Contrary to predictions by MDbS, when the two dimensions are framed as tradeoffs, participants prefer whichever alternative performs best in the negatively framed dimension. Performance of MDbS was improved by the addition of a frame-based global attention allocation mechanism. Experiment 2 extends these results to a "by-dimension" presentation format in an attempt to bring participant behavior in line with MDbS assumptions. The empirical and modeling results replicated those of Experiment 1. Experiment 3 used the attraction effect to test the effects of framing when the best-performing alternative on each dimension was identical across target conditions, therefore reducing the potential effects of a global attention allocation mechanism. The effects of framing were indeed greatly reduced, and the performance of MDbS was markedly improved. The results extend framing to the context effects literature, provide new benchmarks for models and theories of context effects, and point to the need for a global attention mechanism.

From high- to one-dimensional dynamics of decision making: testing simplifications in attractor models

Computational models introduce simplifications that need to be understood and validated. For attractor models of decision making, the main simplification is the high-level representation of different sub-processes of the complex decision system in one dynamic description of the overall process dynamics. This simplification implies that the overall process dynamics of the decision system are independent from specific values handled in different sub-processes. Here, we test the validity of this simplification empirically by investigating choice perseveration in a nonverbal, value-based decision task. Specifically, we tested whether choice perseveration occurred irrespectively of the attribute dimension as suggested by a simulation of the computational model. We find evidence supporting the validity of the simplification. We conclude that the simplification might capture mechanistic aspects of decision-making processes, and that the summation of the overall process dynamics of decision systems into one single variable is a valid approach in computational modeling. Supplement materials such as empirical data, analysis scripts, and the computational model are publicly available at the Open Science Framework (osf.io/7fb5q).

Similarity, Attraction, and Compromise Effects: Original Findings, Recent Empirical Observations, and Computational Cognitive Process Models

Preference reversals—a decision maker prefers A over B in one situation but B over A in another—demonstrate that human behavior violates invariance assumptions of (utility-based) rational choice theories. In the field of multi-alternative multi-attribute decision-making research, 3 preference reversals received special attention: similarity, attraction, and compromise effects. The 3 so-called context effects are changes in (relative) choice probabilities for 2 choice alternatives after a third “decoy” option is added to the set. Despite their simplicity, the effects demonstrate that choice probabilities in multi-alternative decision making are contingent on the local context, that is, on the choice set under consideration. Because of their simplicity, on the other hand, similarity, attraction, and compromise effects have been successfully examined in numerous studies to date, and they have become of increasing interest for differentiating between computational cognitive process models of multi-alternative multi-attribute decision making. However, the stimulus arrangement for producing the effects seems to vary between studies, which becomes challenging when model accounts are compared. The purpose of this review is to present various paradigms in a coherent way and describe various model accounts based on a common structure.

Combinations of low-level and high-level neural processes account for distinct patterns of context-dependent choice

Context effects have been explained by either low-level neural adjustments or high-level cognitive processes but not their combination. It is currently unclear how these processes interact to shape individuals’ responses to context. Here, we used a large cohort of human subjects in experiments involving choice between two or three gambles in order to study the dependence of context effects on neural adaptation and individuals’ risk attitudes. Our experiments did not provide any evidence that neural adaptation on long timescales (~100 trials) contributes to context effects. Using post-hoc analyses we identified two groups of subjects with distinct patterns of responses to decoys, both of which depended on individuals’ risk aversion. Subjects in the first group exhibited strong, consistent decoy effects and became more risk averse due to decoy presentation. In contrast, subjects in the second group did not show consistent decoy effects and became more risk seeking. The degree of change in risk aversion due to decoy presentation was positively correlated with the original degrees of risk aversion. To explain these results and reveal underlying neural mechanisms, we developed new models incorporating both low- and high-level processes and used these models to fit individuals’ choice behavior. We found that observed distinct patterns of decoy effects can be explained by a combination of adjustments in neural representations and competitive weighting of reward attributes, both of which depend on risk aversion but in opposite directions. Altogether, our results demonstrate how a combination of low- and high-level processes shapes choice behavior in more naturalistic settings, modulates overall risk preference, and explains distinct behavioral phenotypes.

A large body of experimental work has illustrated that the introduction of a new, and often irrelevant, option can influence preference among the existing options, a phenomenon referred to as context or decoy effects. For example, introducing a new option that is worse than one of the two existing options in all its attributes but better than the alternative option in some attributes (and thus should not ever be selected) can increase the preference for the former option. Context effects have been explained by high-level cognitive processes—such as comparisons and competitions between attributes—or low-level adjustments of neural representations. However, it is unclear how these processes interact to shape individuals’ responses to context. Here, we show that both high-level cognitive processes and low-level neural adjustments shift risk preference during choice between multiple risky options but in opposite directions. Moreover, we demonstrate that combinations of these processes can account for distinct patterns of context effects in human subjects.