Attribute latencies causally shape intertemporal decisions.

Intertemporal choices - decisions that play out over time - pervade our life. Thus, how people make intertemporal choices is a fundamental question. Here, we investigate the role of attribute latency (the time between when people start to process different attributes) in shaping intertemporal preferences using five experiments with choices between smaller-sooner and larger-later rewards. In the first experiment, we identify attribute latencies using mouse-trajectories and find that they predict individual differences in choices, response times, and changes across time constraints. In the other four experiments we test the causal link from attribute latencies to choice, staggering the display of the attributes. This changes attribute latencies and intertemporal preferences. Displaying the amount information first makes people more patient, while displaying time information first does the opposite. These findings highlight the importance of intra-choice dynamics in shaping intertemporal choices and suggest that manipulating attribute latency may be a useful technique for nudging.

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.

A dynamic computational model of gaze and choice in multi-attribute decisions.

When making decisions, how people allocate their attention influences their choices. One empirical finding is that people are more likely to choose the option that they have looked at more. This relation has been formalized with the attentional drift-diffusion model (aDDM; Krajbich et al., 2010). However, options often have multiple attributes, and attention is also thought to govern the relative weighting of those attributes (Roe et al., 2001). Little is known about how these two distinct features of the choice process interact; we still lack a model (and tests of that model) that incorporate both option- and attribute-wise attention. Here, we propose a multi-attribute attentional drift-diffusion model (maaDDM) to account for attentional discount factors on both options and attributes. We then use five eye-tracking datasets (two-alternative, two-attribute preferential tasks) from different choice domains to test the model. We find very stable option-level and attribute-level attentional discount factors across datasets, though nonfixated options are consistently discounted more than nonfixated attributes. Additionally, we find that people generally discount the nonfixated attribute of the nonfixated option in a multiplicative way, and so that feature is consistently discounted the most. Finally, we also find that gaze allocation reflects attribute weights, with more gaze to higher-weighted attributes. In summary, our work uncovers an intricate interplay between attribute weights, gaze processes, and preferential choice. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Predictions and choices for others: Some insights into how and why they differ.

Being able to learn another person's preferences and choose on their behalf are important skills. However, people often do not choose what the other would choose for themselves. Over two incentive-compatible studies, we identify how and why people choose differently for others than the others would choose for themselves. Participants observed choices made by another person and then (a) predicted what this person would choose or (b) chose for them in new decisions, while we tracked their mouse movements. Participants learned noisy human preferences as easily as they learned noiseless algorithms. Moreover, participants' predictions of what others would choose were in line with the others' actual choices roughly 80% of the time, regardless of whether they were paid for predicting consistently with the others' actual choices. Thus, neither difficulty in learning noisy preferences nor motivation appear to be major factors in how people choose for others. However, participants were much less consistent with their recipients' preferences when choosing for them. Surrogates incorporated their own preferences and tried to maximize expected value. Mouse-tracking results imply that the recipient's preferences affect the surrogate's decision later in the choice process when choosing (vs. predicting). (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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.

Decomposing preferences into predispositions and evaluations.

When making decisions, people can be influenced by the context (or framing) of the decisions in addition to the features of the choice options. It has recently been argued that people can use context to develop predispositions toward certain categories (or types) of options. This research has shown that predispositions increase the efficiency of the choice process by reducing the need for in-depth evaluation of the features but that they also bias choice. Here, we experimentally studied the dynamics of predispositions and their link to evaluations. In our first experiment, using real choices between healthy and unhealthy foods, we found that predispositions arise whenever one category is made to appear generally better than the other, regardless of the specific features of the options in a given decision. We found that predispositions toward healthy and unhealthy foods can be altered but that people's favorable evaluations of healthy foods persist. In our second experiment, we induced changes in both predispositions and evaluations. We again found that predispositions evolve in response to subjects' choice biases while evaluations do not. These changes occur over very short periods of time, highlighting the malleability of people's predispositions. Our findings provide a framework for understanding the factors that affect preferences and for attributing them to context-dependent predispositions or decision-level evaluations. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

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.

The influence of visual attention on memory-based preferential choice.

Many decisions rely on past experiences. Recent research indicates that people's choices are biased towards choosing better-remembered options, even if these options are comparatively unattractive (i.e., a memory bias). In the current study, we used eye tracking to compare the influence of visual attention on preferential choice between memory-based and non-memory-based decisions. Participants completed the remember-and-decide task. In this task, they first learned associations between screen locations and snack items. Then, they made binary choices between snack items. These snacks were either hidden and required recall (memory-based decisions), or they were visible (non-memory-based decisions). Remarkably, choices were more strongly influenced by attention in memory-based compared to non-memory-based decisions. However, visual attention did not mediate the memory bias on preferential choices. Finally, we adopt and expand a recently proposed computational model to provide a comprehensive description of the role of attention in memory-based decisions. In sum, the present work elucidates how visual attention interacts with episodic memory and preference formation in memory-based decisions.

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.

In our everyday lives, we often have to choose between many different options. When deciding what to order off a menu, for example, or what type of soda to buy in the supermarket, we have a range of possibilities to consider. So how do we decide what to go for? Researchers believe we make such choices by assigning a subjective value to each of the available options. But we can do this in several different ways. We could look at every option in turn, and then choose the best one once we have considered them all. This is a so-called 'rational' decision-making approach. But we could also consider each of the options one at a time and stop as soon as we find one that is good enough. This strategy is known as 'satisficing'. In both approaches, we use our eyes to gather information about the items available. Most scientists have assumed that merely looking at an item ­ such as a particular brand of soda ­ does not affect how we feel about that item. But studies in which animals or people choose between much smaller sets of objects ­ usually up to four ­ suggest otherwise. The results from these studies indicate that looking at an item makes that item more attractive to the observer, thereby increasing its subjective value. Thomas et al. now show that gaze also plays an active role in the decision-making process when people are spoilt for choice. Healthy volunteers looked at pictures of up to 36 snack foods on a screen and were asked to select the one they would most like to eat. The researchers then recorded the volunteers' choices and response times, and used eye-tracking technology to follow the direction of their gaze. They then tested which of the various decision-making strategies could best account for all the behaviour. The results showed that the volunteers' behaviour was best explained by computer models that assumed that looking at an item increases its subjective value. Moreover, the results confirmed that we do not examine all items and then choose the best one. But neither do we use a purely satisficing approach: the volunteers chose the last item they had looked at less than half the time. Instead, we make decisions by comparing individual items against one another, going back and forth between them. The longer we look at an item, the more attractive it becomes, and the more likely we are to choose it.

Using dynamic monitoring of choices to predict and understand risk preferences.

Navigating conflict is integral to decision-making, serving a central role both in the subjective experience of choice as well as contemporary theories of how we choose. However, the lack of a sensitive, accessible, and interpretable metric of conflict has led researchers to focus on choice itself rather than how individuals arrive at that choice. Using mouse-tracking-continuously sampling computer mouse location as participants decide-we demonstrate the theoretical and practical uses of dynamic assessments of choice from decision onset through conclusion. Specifically, we use mouse tracking to index conflict, quantified by the relative directness to the chosen option, in a domain for which conflict is integral: decisions involving risk. In deciding whether to accept risk, decision makers must integrate gains, losses, status quos, and outcome probabilities, a process that inevitably involves conflict. Across three preregistered studies, we tracked participants' motor movements while they decided whether to accept or reject gambles. Our results show that 1) mouse-tracking metrics of conflict sensitively detect differences in the subjective value of risky versus certain options; 2) these metrics of conflict strongly predict participants' risk preferences (loss aversion and decreasing marginal utility), even on a single-trial level; 3) these mouse-tracking metrics outperform participants' reaction times in predicting risk preferences; and 4) manipulating risk preferences via a broad versus narrow bracketing manipulation influences conflict as indexed by mouse tracking. Together, these results highlight the importance of measuring conflict during risky choice and demonstrate the usefulness of mouse tracking as a tool to do so.

Salient nutrition labels shift peoples' attention to healthy foods and exert more influence on their choices.

Nutrition labels are the most commonly used tools to promote healthy choices. Research has shown that color-coded traffic light (TL) labels are more effective than purely numerical Guideline Daily Amount (GDA) labels at promoting healthy eating. While these effects of TL labels on food choice are hypothesized to rely on attention, how this occurs remains unknown. Based on previous eye-tracking research we hypothesized that TL labels compared to GDA labels will attract more attention, will induce shifts in attention allocation to healthy food items, and will increase the influence of attention to the labels on food choice. To test our hypotheses, we conducted an eye-tracking experiment where participants chose between healthy and unhealthy food items accompanied either by TL or GDA labels. We found that TL labels biased choices towards healthier items because their presence caused participants to allocate more attention to healthy items and less to unhealthy items. Moreover, our data indicated that TL labels were more likely to be looked at, and had a larger effect on choice, despite attracting less dwell time. These results reveal that TL labels increase healthy food choice, relative to GDA labels, by shifting attention and the effects of attention on choice.

Mouse tracking reveals structure knowledge in the absence of model-based choice.

Converging evidence has demonstrated that humans exhibit two distinct strategies when learning in complex environments. One is model-free learning, i.e., simple reinforcement of rewarded actions, and the other is model-based learning, which considers the structure of the environment. Recent work has argued that people exhibit little model-based behavior unless it leads to higher rewards. Here we use mouse tracking to study model-based learning in stochastic and deterministic (pattern-based) environments of varying difficulty. In both tasks participants' mouse movements reveal that they learned the structures of their environments, despite the fact that standard behavior-based estimates suggested no such learning in the stochastic task. Thus, we argue that mouse tracking can reveal whether subjects have structure knowledge, which is necessary but not sufficient for model-based choice.

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.

Acetaminophen influences social and economic trust.

Acetaminophen has long been assumed to selectively alleviate physical pain, but recent research has started to reveal its broader psychological effects. Building on this work, we find suggestive evidence that acetaminophen affects the basic social process of trust across a national survey and five lab experiments. In a national community sample (MIDUS II), acetaminophen usage was negatively associated with neighborhood trust and feelings of social integration. In a series of lab experiments (N = 767), acetaminophen reduced the influence of self-generated expectations on investments in a trust game. When we manipulated trust game investor expectations, acetaminophen increased investments regardless of expectations. These results provide the first demonstration that an over-the-counter drug can impact trust-related behavior. Overall, the findings paint a complex picture of how situational factors may influence drug effects.

The spillover effects of attentional learning on value-based choice.

What role does attention play in decision-making? Prior research has demonstrated a link between visual attention and value-based choice, but the direction of causality is still unclear. Here we aimed to demonstrate that attention has a causal influence on choice. We tested whether spatially biasing attention in a visual search task would produce choice biases in a later choice task. We ran four experiments where the search target was more likely to appear on one "rich" side of the screen. In the subsequent choice tasks, participants were more likely to choose items appearing on the rich side and the average choice bias depended on how well participants learned the regularity in the search task. Additionally, eye-tracking data revealed a first-fixation bias toward the rich side, which in turn influenced choices. Taken together, these results provide novel support for a causal effect of attention on choice.

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.

Accounting for attention in sequential sampling models of decision making.

When making decisions, people tend to shift their attention back and forth between stimuli, choosing options that they look at more overall and immediately before their responses. These relationships, and others, are well-described by sequential sampling models that assume that evidence for a given alternative is collected over time in proportion to its subjective value, amplified by attention. Furthermore, findings from a number of studies support a causal effect of attention on choice. This research is mostly focused on two-alternative forced choice, though some work has confirmed these relationships in multi-attribute and multi-alternative choice. Finally, we discuss recent interest in understanding what drives attention during the choice process, with findings suggesting that attention is drawn to noisier and more salient stimuli in two-alternative choice, as well as higher-value options in multi-alternative choice.