Francisco Garre Frutos
Mind, Brain, and Behavior Research Center, Universidad de Granada, España
(pixabay) Tumisu.
In recent years, numerous studies have explored the impact of learning on our attention. Recent research suggests that our attentional system prioritizes stimuli that predict positive or negative outcomes, implying that what we like, or dislike, tends to capture our attention. In this article, we review studies suggesting that these attentional biases occur automatically, even when people consciously try to avoid them.
Our world constantly bombards us with millions of stimuli, so it is important to distinguish what is relevant from what is not. For example, if we go to the supermarket to buy green apples, it makes sense that our attention is drawn to stimuli that share characteristics with what we are looking for, such as green and round objects. At the same time, we are also likely to pay attention to items that stand out because of their perceptual characteristics, even if we are not looking for them, such as a banana mistakenly placed in the apple section. Finally, we may focus on products that we simply like because of our previous experience with them, such as our favorite brand of chocolate, even though we don’t intend to buy them, or they are not particularly salient.
Although it is intuitive to think that what we like can influence how we pay attention, this phenomenon has only recently begun to be studied in the laboratory (Anderson et al., 2021). Research has shown that it is possible to manipulate the amount of attention an arbitrary stimulus receives by varying its ability to predict positive or negative outcomes. It appears that this attentional bias is inflexible and automatic. A clear example of the automaticity of this attentional bias can be found in the study by Le Pelley et al. (2015). In this study, participants performed a task in which they had to search for a stimulus with a different shape, for example, a diamond among the circles as in Figure 1.
Figure 1. Schematic representation of the task used by Le Pelley et al. (2015).
Participants could earn money if they quickly directed their gaze to the diamond. However, the amount of money they earned depended on the color of one of the circles that also appeared on the screen, which could be presented in two possible colors: one indicating a low reward (blue in Figure 1) and another displaying a high reward (red in Figure 1). Participants were informed that they could earn rewards if they managed to fixate on the diamond, but if participants looked at the colored distractors, they would not earn any money. The results showed that they looked at the distractor more often when it was the high-reward color. In other words, even though it went against their interests and was not necessarily more «salient» than the low-reward distractor, people paid more attention to the high-reward distractor.
Le Pelley et al. (2015) showed that this attentional bias occurs relatively automatically. This result appears to occur under conditions where the distractors predict the intensity of an electric shock rather than money (Nissens et al., 2016), and even when people are explicitly told that looking at distractors is completely counterproductive (Pearson et al., 2015). This tendency persists even after the distractor is no longer predictive of the likelihood of receiving a reward (Watson et al., 2019).
Surprising as it may seem, people try to avoid paying attention to these distractors, but they fail to do so. When participants are no longer punished for looking at the distractors, these attentional biases become stronger (Pearson et al., 2020), what suggests that participants try to avoid attending the high-reward distractor when it is counterproductive. It seems that people are trying to control their attention to distractors in a reactive way. That is, while participants may not be able to proactively avoid looking at the high-reward distractor, they seem to be able to counteract its influence when the eye movement starts later (Pearson et al., 2021). In other words, if they have enough time they are able to inhibit fixating the distractor, suggesting that the amount of control people can exert over this form of attention is limited.
Thus, seemingly irrelevant stimuli can become powerful attentional magnets if they acquire the ability to predict appetitive or aversive outcomes. People vary in their tendency to be attracted by these magnets, what may have important consequences, for example, in addictions (Anderson, 2021b). The automatic attention to stimuli related to the object of addiction may help perpetuate these maladaptive habits. If a smoker is trying to quit but encounters cigarette shops and other people smoking on the street, the tendency to attend to these stimuli may increase the likelihood of relapse.
In summary, the influence of learning on attention is becoming increasingly important in psychological research. Understanding the role of these biases has not only theoretical implications but also important applications to psychopathological conditions in which attention may function abnormally.
Referencias
Anderson, B. A. (2021). Relating value-driven attention to psychopathology. Current Opinion In Psychology, 39, 48-54.
Anderson, B. A., et al. (2021). The past, present, and future of selection history. Neuroscience & Biobehavioral Reviews, 130, 326-350.
Le Pelley, M. E., Pearson, D., Griffiths, O., & Beesley, T. (2015). When goals conflict with values: counterproductive attentional and oculomotor capture by reward-related stimuli. Journal of Experimental Psychology: General, 144, 158-171.
Nissens, T., Failing, M., & Theeuwes, J. (2016). People look at the object they fear: oculomotor capture by stimuli that signal threat. Cognition and Emotion, 31, 1707–1714.
Pearson, D., & Le Pelley, M. E. (2020). Learning to avoid looking: Competing influences of reward on overt attentional selection. Psychonomic Bulletin & Review, 27, 998-1005.
Pearson, D., & Le Pelley, M. E. (2021). Reward encourages reactive, goal-directed suppression of attention. Journal of Experimental Psychology: Human Perception and Performance, 47, 1348.
Pearson, D., et al. (2015). Cognitive control and counterproductive oculomotor capture by reward-related stimuli. Visual Cognition, 23, 41–66.
Manuscrito recibido el 27 de septiembre de 2024.
Aceptado el 21 de octubre de 2024.
This is the English version of
Garre Frutos, F. (2025). ¿Querer es poder? Control y automaticidad en sesgos atencionales aprendidos. Ciencia Cognitiva, 19:1, 5-7.
