The spillover effects of attentional learning on value-based choice

Value-modulated attentional capture is augmented by win-related sensory cues

Attentional prioritisation of stimuli in the environment plays an important role in overt choice. Previous research shows that prioritisation is influenced by the magnitude of paired rewards, in that stimuli signalling high-value rewards are more likely to capture attention than stimuli signalling low-value rewards; and this attentional bias has been proposed to play a role in addictive and compulsive behaviours. A separate line of research has shown that win-related sensory cues can bias overt choices. However, the role that these cues play in attentional selection is yet to be investigated. Participants in this study completed a visual search task in which they responded to a target shape in order to earn reward. The colour of a distractor signalled the magnitude of reward and type of feedback on each trial. Participants were slower to respond to the target when the distractor signalled high reward compared to when the distractor signalled low reward, suggesting that the high-reward distractors had increased attentional priority. Critically, the magnitude of this reward-related attentional bias was further increased for a high-reward distractor with post-trial feedback accompanied by win-related sensory cues. Participants also demonstrated an overt choice preference for the distractor that was associated with win-related sensory cues. These findings demonstrate that stimuli paired with win-related sensory cues are prioritised by the attention system over stimuli with equivalent physical salience and learned value. This attentional prioritisation may have downstream implications for overt choices, especially in gambling contexts where win-related sensory cues are common.

Psychological value theory: A computational cognitive model of charitable giving

Charitable giving involves a complex economic and social decision because the giver expends resources for goods or services they will never receive. Although psychologists have identified numerous factors that influence charitable giving, there currently exists no unifying computational model of charitable choice. Here, we submit one such model, based within the strictures of Psychological Value Theory (PVT). In four experiments, we assess whether charitable giving is driven by the perceived Psychological Value of the recipient. Across all four experiments, we simultaneously predict response choice and response time with high accuracy. In a fifth experiment, we show that PVT predicts charitable giving more accurately than an account based on competence and warmth. PVT accurately predicts which charity a respondent will choose to donate to and separately, whether a respondent will choose to donate at all. PVT models the cognitive processes underlying charitable donations and it provides a computational framework for integrating known influences on charitable giving. For example, we show that in-group preference influences charitable giving by changing the Psychological Values of the options, rather than by bringing about a response bias toward the in-group.

Monkeys exhibit human-like gaze biases in economic decisions

In economic decision-making individuals choose between items based on their perceived value. For both humans and nonhuman primates, these decisions are often carried out while shifting gaze between the available options. Recent studies in humans suggest that these shifts in gaze actively influence choice, manifesting as a bias in favor of the items that are viewed first, viewed last, or viewed for the overall longest duration in a given trial. This suggests a mechanism that links gaze behavior to the neural computations underlying value-based choices. In order to identify this mechanism, it is first necessary to develop and validate a suitable animal model of this behavior. To this end, we have created a novel value-based choice task for macaque monkeys that captures the essential features of the human paradigms in which gaze biases have been observed. Using this task, we identified gaze biases in the monkeys that were both qualitatively and quantitatively similar to those in humans. In addition, the monkeys' gaze biases were well-explained using a sequential sampling model framework previously used to describe gaze biases in humans-the first time this framework has been used to assess value-based decision mechanisms in nonhuman primates. Together, these findings suggest a common mechanism that can explain gaze-related choice biases across species, and open the way for mechanistic studies to identify the neural origins of this behavior.

Real-world object categories and scene contexts conjointly structure statistical learning for the guidance of visual search

We examined how object categories and scene contexts act in conjunction to structure the acquisition and use of statistical regularities to guide visual search. In an exposure session, participants viewed five object exemplars in each of two colors in each of 42 real-world categories. Objects were presented individually against scene context backgrounds. Exemplars within a category were presented with different contexts as a function of color (e.g., the five red staplers were presented with a classroom scene, and the five blue staplers with an office scene). Participants then completed a visual search task, in which they searched for novel exemplars matching a category label cue among arrays of eight objects superimposed over a scene background. In the context-match condition, the color of the target exemplar was consistent with the color associated with that combination of category and scene context from the exposure phase (e.g., a red stapler in a classroom scene). In the context-mismatch condition, the color of the target was not consistent with that association (e.g., a red stapler in an office scene). In two experiments, search response time was reliably lower in the context-match than in the context-mismatch condition, demonstrating that the learning of category-specific color regularities was itself structured by scene context. The results indicate that categorical templates retrieved from long-term memory are biased toward the properties of recent exemplars and that this learning is organized in a scene-specific manner.

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.

Eye-tracking research on sensory and consumer science: A review, pitfalls and future directions

Visual processing is a core cognitive element of sensory and consumer science. Consumers visually attend to food types, packaging, label design, advertisements, supermarket shelves, food menus, and other visible information. During the past decade, sensory and consumer science have used eye tracking to elucidate visual processing by consumers. This review paper summarizes earlier findings in terms of bottom-up (i.e., stimulus-driven) processing such as visual salience, size, and top-down (i.e., goal-driven) processing such as goals, task instructions, task complexity, and emotions. Downstream effects of gaze on choice are also reviewed. Pitfalls and future directions of eye-tracking research on sensory and consumer science are also discussed.

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.

The postdictive effect of choice reflects the modulation of attention on choice

Our conscious perception of the world is not an instantaneous, moment-by-moment construction. Rather, our perception of an event is influenced, over time, by information gained after the event; this is known as a postdictive effect. A recent study reported that this postdictive effect could occur even in choice. The present study sought to test whether the striking postdictive effect of choice reflects the modulation of attention on choice, by directly and systematically manipulating attention in two experiments. Specifically, Experiment 1 revealed that the robust postdictive effect of choice was almost completely eliminated when attentional bias was removed. More important, Experiment 2 demonstrated that the postdictive effect of choice could be modulated by directly manipulating participants’ attention with a spatial cue, in particular, when the cue appeared at short time delays. These results suggest that choice could be considerably postdictively influenced by attention and this effect was most pronounced within a short time window wherein decision making was most likely in progress. The current study not only enables clarification of the mechanism of the newly discovered postdictive effect of choice, but also extends evidence of the modulation of attention on decision making.

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.

Statistical learning of anomalous regions in complex faux X-ray images does not transfer between detection and discrimination

The visual environment contains predictable information - "statistical regularities" - that can be used to aid perception and attentional allocation. Here we investigate the role of statistical learning in facilitating search tasks that resemble medical-image perception. Using faux X-ray images, we employed two tasks that mimicked two problems in medical-image perception: detecting a target signal that is poorly segmented from the background; and discriminating a candidate anomaly from benign signals. In the first, participants searched a heavily camouflaged target embedded in cloud-like noise. In the second, the noise opacity was reduced, but the target appeared among visually similar distractors. We tested the hypothesis that learning may be task-specific. To this end, we introduced statistical regularities by presenting the target disproportionately more frequently in one region of the space. This manipulation successfully induced incidental learning of the target's location probability, producing faster search when the target appeared in the high-probability region. The learned attentional preference persisted through a testing phase in which the target's location was random. Supporting the task-specificity hypothesis, when the task changed between training and testing, the learned priority did not transfer. Eye tracking showed fewer, but longer, fixations in the detection than in the discrimination task. The observation of task-specificity of statistical learning has implications for theories of spatial attention and sheds light on the design of effective training tasks.