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Mean landing position (ϕ) plotted over time (blocks) for the high and low reward distractor condition. Error bars represent 95% within-subject confidence intervals.
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Salient stimuli and stimuli associated with reward have the ability to attract both
attention and the eyes. The current study exploited the effects of reward on the wellknown
global effect in which two objects appear simultaneously in close spatial
proximity. Participants always made saccades to a predefined target, while the colour of
a nearby dis...
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Context 1
... there was no significant interaction between reward and block (F(1,17) = 1.13, p = .35), indicating that the difference between saccades made in the high and low reward conditions remained constant over the course of the experiment (see Figure 4). Interestingly, the reward information already showed a near significant effect in the first block of the experiment (t(17) = 1.88, p = .077, ...
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The role of different spatial frequency bands in threat detection has been explored extensively. However, most studies use manual responses and the results are mixed. Here, we aimed to investigate the contribution of spatial frequency information to threat detection by using three response types, including manual responses, eye movements, and reach...
Citations
... Since Anderson et al.'s (2011a) seminal work, this type of attentional bias has been observed in both overt and covert attention measures (Anderson, 2015;Bucker et al., 2015;Le Pelley et al., 2015;Watson et al., 2020;Watson et al., 2019b); it seems to be robust to extinction and resistant to cognitive control (e.g. explicit instructions to ignore distractors, Pearson et al., 2015). ...
Stimuli predicting rewards are more likely to capture attention, even when they are not relevant to our current goals. Individual differences in value-modulated attentional capture (VMAC) have been associated with various psychopathological conditions in the scientific literature. However, the claim that this attentional bias can predict individual differences requires further exploration of the psychometric properties of the most common experimental paradigms. The current study replicated the VMAC effect in a large online sample (N = 182) and investigated the internal consistency, with a design that allowed us to measure the effect during learning (rewarded phase) and after acquisition, once feedback was omitted (unrewarded phase). Through the rewarded phase there was gradual increase of the VMAC effect, which did not decline significantly throughout the unrewarded phase. Furthermore, we conducted a reliability multiverse analysis for 288 different data preprocessing specifications across both phases. Specifications including more blocks in the analysis led to better reliability estimates in both phases, while specifications that removed more outliers also improved reliability, suggesting that specifications with more, but less noisy, trials led to better reliability estimates. Nevertheless, in most instances, especially those considering fewer blocks of trials, reliability estimates fell below the minimum recommended thresholds for research on individual differences. Given the present results, we encourage researchers working on VMAC to take into account reliability when designing studies aimed at capturing individual differences and provide recommendations to improve methodological practices.
... The comparable performance observed with pleasant and neutral-valence feedback during the association phase might appear surprising, as emotional stimuli are generally perceived as attention attractors (Bradley et al., 2012;Dominguez-Borràs & Vuilleumier, 2013;Hinojosa et al., 2015) and are often favored over neutral ones (Alpers, 2008;Calvo et al., 2007). Additionally, emotional stimuli ('distractors') have been found to influence the oculomotor system at an early stage, automatically and involuntarily directing eye movements (Bucker et al., 2015;Le Pelley et al., 2015;Nissens et al., 2017;Watson et al., 2019). However, the lack of a color-valence association effect (i.e., faster RTs to targets associated with pleasant vs. neutral valence) during the association phase is not uncommon. ...
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.
... Previous studies have confirmed that VDAC resists extinction even over the course of several hundred unrewarded trials Della Libera & Chelazzi, 2009;Stankevich & Geng, 2014). Although the classical conditioning theory of learning proposed that a previously conditioned response to a reward-predictive stimulus will vanish in the absence of reinforcement (Pavlov, 1927;Wagner, 1961), most studies on VDAC have shown no significant reduction in attentional capture by the reward-related distractors in the test phase (Anderson et al., 2011b;Anderson & Yantis, 2012Bucker et al., 2015;Failing & Theeuwes, 2014;Rothkirch et al., 2013;Sali et al., 2014;Stankevich & Geng, 2014;Theeuwes & Belopolsky, 2012). These findings suggest that reward learning generates a persistent attentional priority in favor of the previously rewardassociated feature even when no longer predictive of reward (Milner et al., 2023). ...
... For instance, VDAC has been observed to persist for several days up to as much as 9 months after reward learning in the absence of additional reinforcement, and it resists extinction even over several hundred unrewarded trials (Anderson et al., 2011b; Della Libera & Chelazzi, 2009;Stankevich & Geng, 2014). Apart from the prediction based on classical conditioning, where a previously conditioned response to a reward-predictive stimulus is expected to vanish in the absence of reinforcement (Pavlov, 1927), most results in the VDAC literature report no significant reduction in impairment over the course of a test phase (Anderson et al., 2011b;Anderson & Yantis, 2012Bucker et al., 2015;Failing & Theeuwes, 2014;Rothkirch et al., 2013;Sali et al., 2014;Stankevich & Geng, 2014;Theeuwes & Belopolsky, 2012). These findings strongly indicate that reward learning forms an unusually persistent and highly extinction-resistant change in attentional priority that is biased in favor of previously reward-associated features even when they are no longer predictive of reward (Milner et al., 2023). ...
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.
... It is typically found that performance in the test phase is significantly impaired when one of the distractor items is a previously high-value-associated stimulus, compared to when it is a neutral color stimulus or a previously low-value-associated stimulus. Eye-tracking studies suggest that the performance cost is due to attentional capture by the value-associated stimulus: Participants' first saccades are more likely to land on the previously high-value-associated than the low-value-associated stimulus before redirecting to the target shape required by the task Bucker et al., 2014;Hickey & van Zoest, 2012;Pearson et al., 2015;. ...
Attention tends to be attracted to visual features previously associated with reward. To date, nearly all existing studies examined value-associated stimuli at or near potential target locations, making such locations meaningful to inspect. The present experiments examined whether the attentional priority of a value-associated stimulus depends on its location-wise task relevance. In three experiments we used an RSVP task to compare the attentional demands of a value-associated peripheral distractor to that of a distractor associated with the top-down search goal. At a peripheral location that could never contain the target, a value-associated color did not capture attention. In contrast, at the same location, a distractor in a goal-matching color did capture attention. The results show that value-associated stimuli lose their attentional priority at task-irrelevant locations, in contrast to other types of stimuli that capture attention.
... It is interesting to note that, almost exclusively, the influence of selection history on attention has been observed for previously taskrelevant (e.g., Anderson et al., 2011a, b;Anderson and Halpern, 2017;Chun andJiang, 1998, 2003;Jiang et al., 2013b;Kyllingsbaek et al., 2001;Sha and Jiang, 2016;Theeuwes and Belopolsky, 2012) or physically salient stimuli (e.g., Anderson et al., 2011a;Bucker and Theeuwes, 2017;Horstmann, 2002;Le Pelley et al., 2015;Neo and Chua, 2006;Vatterott and Vecera, 2012;Wang and Theeuwes, 2018a, b, c), or participants are informed of the relationship between certain stimuli and valent task outcomes, thereby highlighting the information value of such stimuli (e.g., Bucker et al., 2015a, b;. This is perhaps unsurprising if one approaches the learning that underlies selection history effects on attention from the perspective of biased competition; if attention is not directed to a stimulus, it will not be distinguished from other, competing stimuli in the visual system (Desimone and Duncan, 1995;Reynolds et al., 1999;Serences and Yantis, 2006). ...
... Such an influence is evident early in the process of saccade generation, with even the fastest saccades being biased toward valent stimuli (e.g., Bucker et al., 2015a,b;Mulckhuyse et al., 2013;Pearson et al., 2016;Schmidt et al., 2017). At the same time, the influence of reward learning and aversive conditioning is not restricted to rapid initial orienting, also being evident for slower-to-generate saccades (e.g., Bucker et al., 2015a, b;Mulckhuyse and Dalmaijer, 2016;Pearson et al., 2016;Schmidt et al., 2017) and, as described above, can also influence the disengagement of attention Koster et al., 2004aKoster et al., , 2004bMuller et al., 2016). Contextual cueing effects can be observed with only very brief exposure to the stimulus array (Chun and Jiang, 1998;Kobayashi and Ogawa, 2020). ...
The last ten years of attention research have witnessed a revolution, replacing a theoretical dichotomy (top-down vs. bottom-up control) with a trichotomy (biased by current goals, physical salience, and selection history). This third new mechanism of attentional control, selection history, is multifaceted. Some aspects of selection history must be learned over time whereas others reflect much more transient influences. A variety of different learning experiences can shape the attention system, including reward, aversive outcomes, past experience searching for a target, target‒non-target relations, and more. In this review, we provide an overview of the historical forces that led to the proposal of selection history as a distinct mechanism of attentional control. We then propose a formal definition of selection history, with concrete criteria, and identify different components of experience-driven attention that fit within this definition. The bulk of the review is devoted to exploring how these different components relate to one another. We conclude by proposing an integrative account of selection history centered on underlying themes that emerge from our review.
... More particularly, one class of phenomena that belongs to selection history is related to reward history (Anderson, 2015;Failing and Theeuwes, 2018;Theeuwes, 2018). Indeed, many studies have shown that stimuli (previously) associated with reward outcomes could trigger attentional capture in spite of being neither salient nor relevant in the task at hand (e.g., Hickey et al., 2010;Anderson et al., 2011aBourgeois et al., 2015;Bucker et al., 2015;Le Pelley et al., 2015;Munneke et al., 2015;Pearson et al., 2015;Anderson, 2016;Failing and Theeuwes, 2017). ...
Smartphones are particularly likely to elicit driver distraction with obvious negative repercussions on road safety. Recent selective attention models lead to expect that smartphones might be very effective in capturing attention due to their social reward history. Hence, individual differences in terms of Fear of Missing Out (FoMO) – i.e., of the apprehension of missing out on socially rewarding experiences – should play an important role in driver distraction. This factor has already been associated with self-reported estimations of greater attention paid to smartphones while driving, but the potential link between FoMO and smartphone-induced distraction has never been tested empirically. Therefore, we conducted a preliminary study to investigate whether FoMO would modulate attentional capture by reward distractors displayed on a smartphone. First, participants performed a classical visual search task in which neutral stimuli (colored circles) were associated with high or low social reward outcomes. Then, they had to detect a pedestrian or a roe deer in driving scenes with various levels of fog density. The social reward stimuli were displayed as distractors on the screen of a smartphone embedded in the pictures. The results showed a significant three-way interaction between FoMO, social reward distraction, and task difficulty. More precisely, under attention-demanding conditions (i.e., high-fog density), individual FoMO scores predicted attentional capture by social reward distractors, with longer reaction times (RTs) for high rather than low social reward distractors. These results highlight the importance to consider reward history and FoMO when investigating smartphone-based distraction. Limitations are discussed, notably regarding our sample characteristics (i.e., mainly young females) that might hamper the generalization of our findings to the overall population. Future research directions are provided.
... The crucial question is whether stimuli that are physically not salient (and therefore do not capture attention) can acquire capturing qualities when associated with reward. To address this drawback, Failing and colleagues (Failing, Nissens, Pearson, Le Pelley & Theeuwes, 2015;Failing & Theeuwes, 2017; see also Bucker, Belopolsky & Theeuwes, 2015) developed a procedure in which the reward-signaling distractor was never task relevant and also never physically salient (see Figure 6). Yet in spite of the important change in the experimental procedure, they also found that observers' eyes were captured by a stimulus signaling relatively high reward. ...
In this Element, a framework is proposed in which it is assumed that visual selection is the result of the interaction between top-down, bottom-up and selection-history factors. The Element discusses top-down attentional engagement and suppression, bottom-up selection by abrupt onsets and static singletons as well as lingering biases due to selection-history entailing priming, reward and statistical learning. We present an integrated framework in which biased competition among these three factors drives attention in a winner-take-all-fashion. We speculate which brain areas are likely to be involved and how signals representing these three factors feed into the priority map which ultimately determines selection.
... In the test of experiment 1, no difference was confirmed between the high reward-fear distractor condition and the no reward-neutral condition, but the RTs of the high reward-fear condition were longer than those of the low reward-happy condition. It is well known that threatening stimuli (e.g., fear, anger) could capture the individuals' attention, despite the fact that they were irrelevant to the current goal (Batty and Taylor, 2003;Barratt and Bundesen, 2012;Ikeda et al., 2013;Bucker et al., 2014). However, the current results suggest that processing a fearful face does not generate attention disengagement difficulties. ...
The aim of this study was to explore whether reward learning would affect the processing of targets when an emotional stimulus was task irrelevant. In the current study, using a visual search paradigm to establish an association between emotional faces and reward, an emotional face appeared as a task-irrelevant distractor during the test after reward learning, and participants were asked to judge the orientation of a line on the face. In experiment 1, no significant difference was found between the high reward-fear distractor condition and the no reward-neutral condition, but the response times of the high reward-fear condition were significantly longer than those of the low reward-happy condition. In experiment 2, there was no significant difference in participants’ performance between high reward-happy and no reward-neutral responses. In addition, response times of the low reward-fear condition wear significantly longer than those of the high reward-happy and no reward-neutral conditions. The results show that reward learning affects attention bias of task-irrelevant emotional faces even when reward is absent. Moreover, the high reward selection history is more effective in weakening the emotional advantage of the processing advantage than the low reward.
... They hypothesized that if reward reinforces the spatial attentional orienting toward the target stimulus in an instrumental manner, the target would be selected primarily, but a distractor signaling high reward would be more easily ignored than a distractor signaling low reward, resulting in a less interference effect for the distractor associated with high reward than one associated with low or no reward. Interestingly, however, they found significantly greater interference with the high-reward distractor than with the low-reward distractor, consistent with the findings of other studies with a similar method (Bucker et al., 2015;Pearson et al., 2015;Munneke et al., 2016). These results suggest that the value-driven attentional bias was obtained depending on the Pavlovian association between reward and stimulus feature rather than the reinforcement of spatial attentional orienting toward the target (see Bucker and Theeuwes, 2017). ...
... Because the attentional capture by the distractor (i.e., Pavlovian response) was opposite to the attentional orienting to the target (i.e., instrumental response), the competition between the two responses seemed to be intense ( Figure 8A). Consistent with other similar studies (Bucker et al., 2015;Pearson et al., 2015; FIGURE 8 | (A) An example of the competitive relationship between the Pavlovian and instrumental response-based orienting. (B) An example of the confounding relationship between them. ...
It has been demonstrated that a reward-associated stimulus feature captures attention involuntarily. The present study tested whether spatial attentional orienting is biased via reinforcement learning. Participants were to identify a target stimulus presented in one of two placeholders, preceded by a non-informative arrow cue at the center of the display. Importantly, reward was available when the target occurred at a location cued by a reward cue, defined as a specific color (experiments 1 and 3) or a color–direction combination (experiment 2). The attentional bias of the reward cue was significantly increased as trials progressed, resulting in a greater cue-validity effect for the reward cue than the no-reward cue. This attentional bias was still evident even when controlling for the possibility that the incentive salience of the reward cue color modulates the cue-validity effect (experiment 2) or when the reward was withdrawn after reinforcement learning (experiment 3). However, it disappeared when the reward was provided regardless of cue validity (experiment 4), implying that the reinforcement contingency between reward and attentional orienting is a critical determinant of reinforcement learning-based spatial attentional modulation. Our findings highlight that a spatial attentional bias is shaped by value via reinforcement learning.
... These studies demonstrated that participants have only partial control over the attraction of gaze towards the cue (Hallett, 1978;Munoz & Everling, 2004). Such partial control was evident also when the salient object was determined by orientation (van Zoest, Donk, & Theeuwes, 2004), abrupt onset (Theeuwes, Kramer, Hahn, & Irwin, 1998), reward value (Bucker, Belopolsky, & Theeuwes, 2015) and even task-irrelevant gaze cues (Kuhn & Kingstone, 2009). However, all these studies used a simple task in which participants were only instructed to prepare a saccade in the direction (or the opposite direction in the case of the anti-saccade task) of the target. ...
Background
What can theories regarding memory-related gaze preference contribute to the field of deception detection? While abundant research has examined the ability to detect concealed information through physiological responses, only recently has the scientific community started to explore how eye tracking can be utilized for that purpose. However, previous attempts to detect deception through eye movements have led to relatively low detection ability in comparison to physiological measures. In the current study, we demonstrate that the modulation of gaze behavior by familiarity, changes considerably when participants perform a visual detection task in comparison to a short-term memory task (that was used in a previous study). Thus, we highlight the importance of theory-based selection of task demands for improving the ability to detect concealed information using eye-movement measures.
Results
During visual exploration of four faces (some familiar and some unfamiliar) gaze was allocated preferably on familiar faces, manifested by more fixations. However, this preference tendency vanished once participants were instructed to convey countermeasures and conceal their familiarity by deploying gaze equally to all faces. This gaze behavior during the visual detection task differed significantly from the one observed during a short-term memory task used in a previous study in which a preference towards unfamiliar faces was evident even when countermeasures were applied.
Conclusions
Different tasks elicit different patterns of gaze behavior towards familiar and unfamiliar faces. Moreover, the ability to voluntarily control gaze behavior is tightly related to task demands. Adequate ability to control gaze was observed in the current visual detection task when memorizing the faces was not required for a successful accomplishment of the task. Thus, applied settings would benefit from a short-term memory task which is much more robust to countermeasure efforts. Beyond shedding light on theories of gaze preference, these findings provide a backbone for future research in the field of deception detection via eye movements.
Electronic supplementary material
The online version of this article (10.1186/s41235-019-0162-7) contains supplementary material, which is available to authorized users.
