Xiaozhi Taro Yang

People tend to choose the options they look at more. Prior work explains this effect by assuming that visual attention increases preference for the attended option. However, it remains unclear why looking at an option increases its preference and when this relationship is weakened. Using an experimental paradigm that combines eye-tracking and a think-aloud protocol, we provide evidence that visual attention shapes preference by activating attributes associated with the attended option. Critically, the content of these activated attributes depends on contextual factors such as stimulus desirability and behavioral goals. In standard choice settings, where the goal is to choose the preferred option, visual attention primarily activates positive attributes. In contrast, when these contextual factors are incongruent, activated attributes become less positive, reducing the influence of visual attention on choice. To account for these patterns, we propose a computational model of gaze-driven attribute activation. Our model subsumes existing theories as special cases, and uniquely predicts which attributes are brought to mind by gaze and how they shape downstream decisions. Together, our work offers a mechanistic explanation of a fundamental driver of choice as well as its boundary conditions.

People tend to choose the options they look at more. Prior work explains this effect by assuming that visual attention increases preference for the attended option. However, it remains unclear why looking at an option increases its preference and when this relationship isweakened. Using an experimental paradigm that combines eye-tracking and a think-aloud protocol, we provide evidence that visual attention shapes preference by activating attributes associated with the attended option. Critically, the content of these activated attributesdepends on contextual factors such as stimulus desirability and behavioral goals. In standard choice settings, where the goal is to choose the preferred option, visual attention primarily activates positive attributes. In contrast, when these contextual factors are incongruent, activated attributes become less positive, reducing the influence of visual attention on choice. To account for these patterns, we propose a new computational model of gaze-driven attribute activation. Our model subsumes existing theories as special cases, and uniquely predicts which attributes are brought to mind by gaze and how they shape downstream decisions. Together, our work offers a mechanistic explanation of a fundamental driver of choice as well as its boundary conditions.

Many real-world decisions require selecting from large displays of options that vary along multiple attributes, as when choosing a product from a store shelf. Here, we examine how decision-makers navigate such displays and how search strategies vary as the number of options increases. In a large-scale mobile eye-tracking study (n > 200), participants shopped in a controlled mock grocery store and chose among varying set sizes of products across categories. Our descriptive analysis showed that participants used perceptually accessible attributes, particularly brand, to guide search. This attribute-based search increased in larger choice sets and was strongest in categories where brand was most visually distinct, and among individuals who rated brand as more important. Using an efficient search model based on Bayesian updating, we show that relying on accessible attributes is consistent with an adaptive strategy. Together, these results provide novel insight into how attribute-guided search supports naturalistic multi-option decision making.

Autonomous AI agents, now embedded in everyday browsers, can complete online surveys and experiments with the click of a button, threatening the integrity of behavioral research that depends on online samples. Existing defenses, such as CAPTCHAs and attention checks, are easily bypassed. Webcam eye tracking offers a different defense: proof by presence. Unlike behavioral signals that AI agents increasingly mimic, gaze relies on physiological signals that are not feasible to counterfeit. We introduce a compendium of open-source tools that integrate webcam eye tracking into the platforms responsible for most online behavioral research: Qualtrics for surveys; jsPsych and PsychoPy for cognitive experiments; and oTree for experimental economics. We also provide guidance for researchers to screen out AI agents without penalizing human participants.

Many decisions arise from a dynamic process of information accumulation and comparison. Thus, to fully understand decision making, we must decompose the choice process into its parts. Here, we review foundational work and recent advances in the use of dynamic - e.g., drift-diffusion (DDMs) - models to study economic/value-based decision-making. We first introduce the DDM, which uses choice and response times (RT) to distinguish between the effects of choice options vs. choice context. We then review modifications to the DDM for multi-attribute settings, where attributes are considered at different points in time, showing how these models can be related to traditional dual-process theories. Beyond choices and RT, DDMs also account for the fact that overt visual attention amplifies option- and attribute-level information, resulting in links between gaze and choice. While most DDMs are applied to binary choice, a growing literature extends these models to scenarios with many options or a continuum of responses (e.g. Likert ratings). We also consider work in adjacent domains, such as social applications, where people may infer information from observing each other’s decision processes, and in cognitive neuroscience, which has helped us understand how this dynamic decision process plays out in the brain. We conclude by highlighting emerging trends and future directions, including reinforcement learning, natural language processing, and applications in field settings.

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).

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.

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 use 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 widely used JavaScript library among 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.