Uncertainty modulates value-driven attentional capture

Manipulating the reliability of target-color information modulates value-driven attentional capture

Previously rewarded stimuli slow response times (RTs) during visual search, despite being physically non-salient and no longer task-relevant or rewarding. Such value-driven attentional capture (VDAC) has been measured in a training-test paradigm. In the training phase, the search target is rendered in one of two colors (one predicting high reward and the other low reward). In this study, we modified this traditional training phase to include pre-cues that signaled reliable or unreliable information about the trial-to-trial color of the training phase search target. Reliable pre-cues indicated the upcoming target color with certainty, whereas unreliable pre-cues indicated the target was equally likely to be one of two distinct colors. Thus reliable and unreliable pre-cues provided certain and uncertain information, respectively, about the magnitude of the upcoming reward. We then tested for VDAC in a traditional test phase. We found that unreliably pre-cued distractors slowed RTs and drew more initial eye movements during search for the test-phase target, relative to reliably pre-cued distractors, thus providing novel evidence for an influence of information reliability on attentional capture. That said, our experimental manipulation also eliminated value-dependency (i.e., slowed RTs when a high-reward-predicting distractor was present relative to a low-reward-predicting distractor) for both kinds of distractors. Taken together, these results suggest that target-color uncertainty, rather than reward magnitude, played a critical role in modulating the allocation of value-driven attention in this study.

Task-irrelevant valence-preferred colors boost visual search for a singleton-shape target

Some studies have suggested that emotion-associated features might influence attentional capture. However, demonstrating valence-dependent distractor interference has proven challenging, possibly due to the neglect of individuals' color-valence preferences in standard, averaged reaction-time (RT) measures. To address this, we investigated valence-driven attentional-capture using an association phase in which emotionally neutral vs. positive-feedback photographs were paired with two alternative target colors, red vs. green. This was followed by a test phase requiring participants to search for a pop-out shape target in the presence or absence of an emotion-associated color. In Experiments 1 and 2, this color could only appear in a distractor, while in Experiment 3, it appeared in the target. Analyzing the standard, averaged RT measures, we found no significant valence association or valence-modulated attentional capture. However, correlational analyses revealed a positive relationship between individual participants' color-valence preference during the association phase and their valence-based effect during the test phase. Moreover, most individuals favored red over green in the association phase, leading to marked color-related asymmetries in the average measures. Crucially, the presence of the valence-preferred color anywhere in the test display facilitated RTs. This effect persisted even when the color appeared in one of the distractors (Experiments 1 and 2), at variance with this distractor capturing attention. These findings suggest that task-irrelevant valence-preferred color signals were registered pre-attentively and boosted performance, likely by raising the general (non-spatial) alertness level. However, these signals were likely kept out of attentional-priority computation to prevent inadvertent attentional capture.

Value-driven attention and associative learning models: a computational simulation analysis

Value-driven attentional capture (VDAC) refers to a phenomenon by which stimulus features associated with greater reward value attract more attention than those associated with smaller reward value. To date, the majority of VDAC research has revealed that the relationship between reward history and attentional allocation follows associative learning rules. Accordingly, a mathematical implementation of associative learning models and multiple comparison between them can elucidate the underlying process and properties of VDAC. In this study, we implemented the Rescorla-Wagner, Mackintosh (Mac), Schumajuk-Pearce-Hall (SPH), and Esber-Haselgrove (EH) models to determine whether different models predict different outcomes when critical parameters in VDAC were adjusted. Simulation results were compared with experimental data from a series of VDAC studies by fitting two key model parameters, associative strength (V) and associability (α), using the Bayesian information criterion as a loss function. The results showed that SPH-V and EH- α outperformed other implementations of phenomena related to VDAC, such as expected value, training session, switching (or inertia), and uncertainty. Although V of models were sufficient to simulate VDAC when the expected value was the main manipulation of the experiment, α of models could predict additional aspects of VDAC, including uncertainty and resistance to extinction. In summary, associative learning models concur with the crucial aspects of behavioral data from VDAC experiments and elucidate underlying dynamics including novel predictions that need to be verified.