An attentional drift diffusion model over binary-attribute choice

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.

Humans can infer social preferences from decision speed alone

Humans are known to be capable of inferring hidden preferences and beliefs of their conspecifics when observing their decisions. While observational learning based on choices has been explored extensively, the question of how response times (RT) impact our learning of others' social preferences has received little attention. Yet, while observing choices alone can inform us about the direction of preference, they reveal little about the strength of this preference. In contrast, RT provides a continuous measure of strength of preference with faster responses indicating stronger preferences and slower responses signaling hesitation or uncertainty. Here, we outline a preregistered orthogonal design to investigate the involvement of both choices and RT in learning and inferring other's social preferences. Participants observed other people's behavior in a social preferences task (Dictator Game), seeing either their choices, RT, both, or no information. By coupling behavioral analyses with computational modeling, we show that RT is predictive of social preferences and that observers were able to infer those preferences even when receiving only RT information. Based on these findings, we propose a novel observational reinforcement learning model that closely matches participants' inferences in all relevant conditions. In contrast to previous literature suggesting that, from a Bayesian perspective, people should be able to learn equally well from choices and RT, we show that observers' behavior substantially deviates from this prediction. Our study elucidates a hitherto unknown sophistication in human observational learning but also identifies important limitations to this ability.

Attention biases the process of risky decision-making: Evidence from eye-tracking

Attention determines what kind of option information is processed during risky choices owing to the limitation of visual attention. This paper reviews research on the relationship between higher-complexity risky decision-making and attention as illustrated by eye-tracking to explain the process of risky decision-making by the effect of attention. We demonstrate this process from three stages: the pre-phase guidance of options on attention, the process of attention being biased, and the impact of attention on final risk preference. We conclude that exogenous information can capture attention directly to salient options, thereby altering evidence accumulation. In particular, for multi-attribute risky decision-making, attentional advantages increase the weight of specific attributes, thus biasing risk preference in different directions. We highlight the significance of understanding how people use available information to weigh risks from an information-processing perspective via process data.

Peripheral Visual Information Halves Attentional Choice Biases

A growing body of research has shown that simple choices involve the construction and comparison of values at the time of decision. These processes are modulated by attention in a way that leaves decision makers susceptible to attentional biases. Here, we studied the role of peripheral visual information on the choice process and on attentional choice biases. We used an eye-tracking experiment in which participants (N = 50 adults) made binary choices between food items that were displayed in marked screen "shelves" in two conditions: (a) where both items were displayed, and (b) where items were displayed only when participants fixated within their shelves. We found that removing the nonfixated option approximately doubled the size of the attentional biases. The results show that peripheral visual information is crucial in facilitating good decisions and suggest that individuals might be influenceable by settings in which only one item is shown at a time, such as e-commerce.

Measuring utility with diffusion models

The drift diffusion model (DDM) is a prominent account of how people make decisions. Many of these decisions involve comparing two alternatives based on differences of perceived stimulus magnitudes, such as economic values. Here, we propose a consistent estimator for the parameters of a DDM in such cases. This estimator allows us to derive decision thresholds, drift rates, and subjective percepts (i.e., utilities in economic choice) directly from the experimental data. This eliminates the need to measure these values separately or to assume specific functional forms for them. Our method also allows one to predict drift rates for comparisons that did not occur in the dataset. We apply the method to two datasets, one comparing probabilities of earning a fixed reward and one comparing objects of variable reward value. Our analysis indicates that both datasets conform well to the DDM. We find that utilities are linear in probability and slightly convex in reward.

Attribute attention and option attention in risky choice

Probability weighting is one of the most powerful theoretical constructs in descriptive models of risky choice and constitutes a central component of cumulative prospect theory (CPT). Probability weighting has been shown to be related to two facets of attention allocation: one analysis showed that differences in the shape of CPT's probability-weighting function are linked to differences in how attention is allocated across attributes (i.e., probabilities vs. outcomes); another analysis (that used a different measure of attention) showed a link between probability weighting and differences in how attention is allocated across options. However, the relationship between these two links is unclear. We investigate to what extent attribute attention and option attention independently contribute to probability weighting. Reanalyzing data from a process-tracing study, we first demonstrate links between probability weighting and both attribute attention and option attention within the same data set and the same measure of attention. We then find that attribute attention and option attention are at best weakly related and have independent and distinct effects on probability weighting. Moreover, deviations from linear weighting mainly emerged when attribute attention or option attention were imbalanced. Our analyses enrich the understanding of the cognitive underpinnings of preferences and illustrate that similar probability-weighting patterns can be associated with very different attentional policies. This complicates an unambiguous psychological interpretation of psycho-economic functions. Our findings indicate that cognitive process models of decision making should aim to concurrently account for the effects of different facets of attention allocation on preference. In addition, we argue that the origins of biases in attribute attention and option attention need to be better understood.

Complex economic decisions from simple neurocognitive processes: the role of interactive attention

Neurocognitive theories of value-based choice propose that people additively accumulate choice attributes when making decisions. These theories cannot explain the emergence of complex multiplicative preferences such as those assumed by prospect theory and other economic models. We investigate an interactive attention mechanism, according to which attention to attributes (like payoffs) depends on other attributes (like probabilities) attended to previously. We formalize this mechanism using a Markov attention model combined with an accumulator decision process, and test our model on eye-tracking and mouse-tracking data in risky choice. Our tests show that interactive attention is necessary to make good choices, that most participants display interactive attention and that allowing for interactive attention in accumulation-based decision models improves their predictions. By equipping established decision models with sophisticated attentional dynamics, we extend these models to describe complex economic choice, and in the process, we unify two prominent theoretical approaches to studying value-based decision making.

Preferences for seeking effort or reward information bias the willingness to work

Research suggests that the temporal order in which people receive information about costs and benefits whilst making decisions can influence their choices. But, do people have a preference for seeking information about costs or benefits when making effort-based decisions, and does this impact motivation? Here, participants made choices about whether to exert different levels of physical effort to obtain different magnitudes of reward, or rest for low reward. Prior to each effort-based choice, they also had to decide which information they wanted to see first: how much physical effort would be required, or how large the reward would be. We found no overall preference for seeking reward or effort information first, but motivation did change when people saw reward or effort information first. Seeking effort information first, both someone's average tendency to do so and their choice to see effort first on a given trial, was associated with reductions in the willingness to exert higher effort. Moreover, the tendency to prefer effort information first was associated with reduced vigorous exercise and higher levels of fatigue in everyday life. These findings highlight that preferences for seeking effort information may be a bias that reduces people's willingness to exert effort in the lab and in everyday life.

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.

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.

Devaluation of NoGo stimuli is both robust and fragile

Consistently not responding to stimuli during go/no-go training leads to lower evaluations of these NoGo stimuli. How this NoGo-devaluation-effect can be explained has remained unclear. Here, we ran three experiments to test the hypothesis that people form stimulus-stop-associations during the training, which predict the strength of the devaluation-effect. In Experiment 1, we tried to simultaneously measure the stimulus-stop-associations and NoGo-devaluation, but we failed to find these effects. In Experiment 2, we measured NoGo-devaluation with established procedures from previous work, and stimulus-stop-associations with a novel separate task. Results revealed a clear NoGo-devaluation-effect, which remained visible across multiple rating blocks. Interestingly, this devaluation-effect disappeared when stimulus-stop-associations were measured before stimulus evaluations, and there was no evidence supporting the formation of the stimulus-stop-associations. In Experiment 3, we found evidence for the acquisition of stimulus-stop-associations using an established task from previous work, but this time we found no subsequent NoGo-devaluation-effect. The present research suggests that the NoGo-devaluation-effect and stimulus-stop-associations can be found with standard established procedures, but that these effects are very sensitive to alterations of the experimental protocol. Furthermore, we failed to find evidence for both effects within the same experimental protocol, which has important theoretical and applied implications.

Peer presence increases the prosocial behavior of adolescents by speeding the evaluation of outcomes for others

Peer presence can elicit maladaptive adolescent decision-making, potentially by increasing sensitivity to the rewards one receives. It remains unknown whether peer presence also increases adolescents' sensitivity to others' outcomes, which could have an adaptive effect in contexts allowing pro-social behaviors. Here, we combine social utility modeling and real-time decision process modeling to characterize how peer presence alters adolescents' processing of self and other outcomes. We found that adolescents behaved selfishly when privately allocating monetary rewards for themselves and a peer in an incentive-compatible task. In peer presence, however, adolescents became more altruistic. Real-time decision process estimates collected using computer mouse tracking showed that altruistic behavior was associated with relatively earlier influence of peer-outcomes relative to self-outcomes, and that peer presence sped the influence of peer-outcomes without altering the time at which self-outcomes began to influence the decision process. Our results indicate a mechanism through which peer presence prompts greater prosocial behavior by altering how adolescents process prosocial outcomes.

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.

Webcam-based online eye-tracking for behavioral research

Experiments are increasingly moving online. This poses a major challenge for researchers who rely on in-lab techniques such as eye-tracking. Researchers in computer science have developed web-based eye-tracking applications (WebGazer; Papoutsaki et al., 2016) but they have yet to see them used in behavioral research. This is likely due to the extensive calibration and validation procedure, inconsistent temporal resolution (Semmelmann & Weigelt, 2018), and the challenge of integrating it into experimental software. Here, we incorporate WebGazer into a JavaScript library widely used by behavioral researchers (jsPsych) and adjust the procedure and code to reduce calibration/validation and improve the temporal resolution (from 100–1000 ms to 20–30 ms). We test this procedure with a decision-making study on Amazon MTurk, replicating previous in-lab findings on the relationship between gaze and choice, with little degradation in spatial or temporal resolution. This provides evidence that online web-based eye-tracking is feasible in behavioral research.

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.

Mental representations distinguish value-based decisions from perceptual decisions

In our daily lives, we make a wide variety of decisions. One major distinction that has been made is between perceptual decisions and value-based (economic) decisions. We argue that this distinction is ill-defined, because these decisions vary on multiple dimensions. We present an alternative way to categorize decisions, based on two dimensions: subjective versus objective criteria, and evaluation of a stimulus versus a representation. We experimentally study the decision-making process (with eye-tracking) in each of the four resulting categories, using the same stimulus set of food images. Using a combination of individual-level and group-level modeling, we find surprisingly consistent patterns of behavior across the categories. However, we find stronger similarities between the subjective and objective categories, and stronger differences between the stimulus and representation categories.

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.

Neural correlates of visual attention during risky decision evidence integration

Value-based decision-making is presumed to involve a dynamic integration process that supports assessing the potential outcomes of different choice options. Decision frameworks assume the value of a decision rests on both the desirability and risk surrounding an outcome. Previous work has highlighted neural representations of risk in the human brain, and their relation to decision choice. Key neural regions including the insula and anterior cingulate cortex (ACC) have been implicated in encoding the effects of risk on decision outcomes, including approach and avoidance. Yet, it remains unknown whether these regions are involved in the dynamic integration processes that precede and drive choice, and their relationship with ongoing attention. Here, we used concurrent fMRI and eye-tracking to discern neural activation related to visual attention preceding choice between sure-thing (i.e. safe) and risky gamble options. We found activation in both dorsal ACC (dACC) and posterior insula (PI) scaled in opposite directions with the difference in attention to risky rewards relative to risky losses. PI activation also differentiated foveations on both risky options (rewards and losses) relative to a sure-thing option. These findings point to ACC involvement in ongoing evaluation of risky but higher value options. The role of PI in risky outcomes points to a more general evaluative role in the decision-making that compares both safe and risky outcomes, irrespective of potential for gains or losses.

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.

Does Attention Increase the Value of Choice Alternatives?

A growing recognition of the role of attention in decision-making has been driven by both the technology of eye tracking and the development of models that explicitly incorporate attention. One result of this convergence is the arresting claim that attention, by itself, can increase the perceived value of a decision alternative. In this review, we cover the origins of that claim, its empirical foundation, and the reasoning that supports it. The conclusion is that, to date, there is not sufficient evidence to support the claim. Alternative explanations for the extant evidentiary base are discussed, as is the balance between the bottom-up influence of empirical evidence and the top-down commitment to a conceptual framework.

A nudge in the right direction: the role of food choice architecture in changing populations' diets

Populations' diets typically fall short of recommendations. The implication of this on ill health and quality of life is well established, as are the subsequent health care costs. An area of growing interest within public health nutrition is food choice architecture; how a food choice is framed and its influence on subsequent food selection. In particular, there is an appeal to manipulating the choice architecture in order to nudge individuals' food choice. This review outlines the current understanding of food choice architecture, theoretical background to nudging and the evidence on the effectiveness of nudge strategies, as well as their design and implementation. Interventions emphasising the role of nudge strategies have investigated changes to the accessibility, availability and presentation of food and the use of prompts. Empirical studies have been conducted in laboratories, online and in real-world food settings, and with different populations. Evidence on the effectiveness of nudge strategies in shifting food choice is encouraging. Underpinning mechanisms, not yet fully explicated, are proposed to relate to salience, social norms and the principle of least effort. Emerging evidence points to areas for development including the effectiveness of choice architecture interventions with different and diverse populations, and the combined effect of multiple nudges. This, alongside further examination of theoretical mechanisms and guidance to engage and inspire across the breadth of food provision, is critical. In this way, the potential of choice architecture to effect meaningful change in populations' diets will be realised.

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.

Perceptual salience influences food choices independently of health and taste preferences

Background

Making decisions about food is a critical part of everyday life and a principal concern for a number of public health issues. Yet, the mechanisms involved in how people decide what to eat are not yet fully understood. Here, we examined the role of visual attention in healthy eating intentions and choices. We conducted two-alternative forced choice tests of competing food stimuli that paired healthy and unhealthy foods that varied in taste preference. We manipulated their perceptual salience such that, in some cases, one food item was more perceptually salient than the other. In addition, we manipulated the cognitive load and time pressure to test the generalizability of the salience effect.

Results

Manipulating salience had a powerful effect on choice in all situations; even when an unhealthy but tastier food was presented as an alternative, healthy food options were selected more often when they were perceptually salient. Moreover, in a second experiment, food choices on one trial impacted food choices on subsequent trials; when a participant chose the healthy option, they were more likely to choose a healthy option again on the next trial. Furthermore, robust effects of salience on food choice were observed across situations of high cognitive load and time pressure.

Conclusions

These results have implications both for understanding the mechanisms of food-related decision-making and for implementing interventions that might make it easier for people to make healthy eating choices.

The effect of spatial location of calorie information on choice, consumption and eye movements

We manipulated the presence and spatial location of calorie labels on menus while tracking eye movements. A novel "lab-in-the-field" experimental design allowed eye movements to be recorded while participants chose lunch from a menu, unaware that their choice was part of a study. Participants exposed to calorie information ordered 93 fewer calories (11%) relative to a control group who saw no calorie labels. The difference in number of calories consumed was greater still. The impact was strongest when calorie information was displayed just to the right of the price, in an equivalent font. The effects were mediated by knowledge of the amount of calories in the meal, implying that calorie posting led to more informed decision-making. There was no impact on enjoyment of the meal. The eye-tracking data suggested that the spatial arrangement altered individuals' search strategies while viewing the menu. This research suggests that the spatial location of calories on menus may be an important consideration when designing calorie posting legislation and policy.

GLAMbox: A Python toolbox for investigating the association between gaze allocation and decision behaviour

Recent empirical findings have indicated that gaze allocation plays a crucial role in simple decision behaviour. Many of these findings point towards an influence of gaze allocation onto the speed of evidence accumulation in an accumulation-to-bound decision process (resulting in generally higher choice probabilities for items that have been looked at longer). Further, researchers have shown that the strength of the association between gaze and choice behaviour is highly variable between individuals, encouraging future work to study this association on the individual level. However, few decision models exist that enable a straightforward characterization of the gaze-choice association at the individual level, due to the high cost of developing and implementing them. The model space is particularly scarce for choice sets with more than two choice alternatives. Here, we present GLAMbox, a Python-based toolbox that is built upon PyMC3 and allows the easy application of the gaze-weighted linear accumulator model (GLAM) to experimental choice data. The GLAM assumes gaze-dependent evidence accumulation in a linear stochastic race that extends to decision scenarios with many choice alternatives. GLAMbox enables Bayesian parameter estimation of the GLAM for individual, pooled or hierarchical models, provides an easy-to-use interface to predict choice behaviour and visualize choice data, and benefits from all of PyMC3's Bayesian statistical modeling functionality. Further documentation, resources and the toolbox itself are available at https://glambox.readthedocs.io.

The formation of preference in risky choice

A key question in decision-making is how people integrate amounts and probabilities to form preferences between risky alternatives. Here we rely on the general principle of integration-to-boundary to develop several biologically plausible process models of risky-choice, which account for both choices and response-times. These models allowed us to contrast two influential competing theories: i) within-alternative evaluations, based on multiplicative interaction between amounts and probabilities, ii) within-attribute comparisons across alternatives. To constrain the preference formation process, we monitored eye-fixations during decisions between pairs of simple lotteries, designed to systematically span the decision-space. The behavioral results indicate that the participants' eye-scanning patterns were associated with risk-preferences and expected-value maximization. Crucially, model comparisons showed that within-alternative process models decisively outperformed within-attribute ones, in accounting for choices and response-times. These findings elucidate the psychological processes underlying preference formation when making risky-choices, and suggest that compensatory, within-alternative integration is an adaptive mechanism employed in human decision-making.

Affect and Decision Making: Insights and Predictions from Computational Models

In recent years interest in integrating the affective and decision sciences has skyrocketed. Immense progress has been made, but the complexities of each field, which can multiply when combined, present a significant obstacle. A carefully defined framework for integration is needed. The shift towards computational modeling in decision science provides a powerful basis and a path forward, but one whose synergistic potential will only be fully realized by drawing on the theoretical richness of the affective sciences. Reviewing research using a popular computational model of choice (the drift diffusion model), we discuss how mapping concepts to parameters reduces conceptual ambiguity and reveals novel hypotheses.

Gaze bias differences capture individual choice behaviour

How do we make simple choices such as deciding between an apple and an orange? Recent empirical evidence suggests that choice behaviour and gaze allocation are closely linked at the group level, whereby items looked at longer during the decision-making process are more likely to be chosen. However, it is unclear how variable this gaze bias effect is between individuals. Here we investigate this question across four different simple choice experiments and using a computational model that can be easily applied to individuals. We show that an association between gaze and choice is present for most individuals, but differs considerably in strength. Generally, individuals with a strong association between gaze and choice behaviour are worse at choosing the best item from a choice set compared with individuals with a weak association. Accounting for individuals' variability in gaze bias in the model can explain and accurately predict individual differences in choice behaviour.

Amount and time exert independent influences on intertemporal choice

Intertemporal choices involve trade-offs between the value of rewards and the delay before those rewards are experienced. Canonical intertemporal choice models such as hyperbolic discounting assume that reward amount and time until delivery are integrated within each option prior to comparison1,2. An alternative view posits that intertemporal choice reflects attribute-wise processes in which amount and time attributes are compared separately3-6. Here, we use multi-attribute drift diffusion modelling (DDM) to show that attribute-wise comparison represents the choice process better than option-wise comparison for intertemporal choice in a young adult population. We find that, while accumulation rates for amount and time information are uncorrelated, the difference between those rates predicts individual differences in patience. Moreover, patient individuals incorporate amount earlier than time into the decision process. Using eye tracking, we link these modelling results to attention, showing that patience results from a rapid, attribute-wise process that prioritizes amount over time information. Thus, we find converging evidence that distinct evaluation processes for amount and time determine intertemporal financial choices. Because intertemporal decisions in the lab have been linked to failures of patience ranging from insufficient saving to addiction7-13, understanding individual differences in the choice process is important for developing more effective interventions.

Gaze Amplifies Value in Decision Making

When making decisions, people tend to choose the option they have looked at more. An unanswered question is how attention influences the choice process: whether it amplifies the subjective value of the looked-at option or instead adds a constant, value-independent bias. To address this, we examined choice data from six eye-tracking studies ( Ns = 39, 44, 44, 36, 20, and 45, respectively) to characterize the interaction between value and gaze in the choice process. We found that the summed values of the options influenced response times in every data set and the gaze-choice correlation in most data sets, in line with an amplifying role of attention in the choice process. Our results suggest that this amplifying effect is more pronounced in tasks using large sets of familiar stimuli, compared with tasks using small sets of learned stimuli.