1. Introduction
Driving on an empty stomach can potentially trigger a food craving, and a burger can serve as a convenient option for a fast meal (what’s your idea?). Nevertheless, when on the lookout for a roadside burger joint, the blaring sound of a truck horn might cause the driver to momentarily lose focus, resulting in unintentionally missing the nearby burger shop. In such instances, being attentive plays a crucial role in enabling the driver to effectively engage with the surrounding environment, which abounds with diverse cues and stimuli.
The primary experiments have demonstrated that attention, as an emergent property of brain activity (Courtney, 2004), is allocated in various ways. These ways include the subject's expectations, their behavioral goals, and the physical salience of the stimuli present in the environment (Groner & Groner, 1989). The allocation of attention can occur in two distinct ways (Jonides. J, 1981; Posner, 1980; Posner & Petersen, 1990): top-down (goal-directed) and bottom-up (stimulus-driven), as initially conceptualized by William James over a century ago. James referred to these two forms of attention as active and passive, respectively (James, 1890).
During the 1970s, several years after Helmholtz postulated that attentional focus could be voluntarily shifted, the first experiments attempting to demonstrate the top-down deployment of attention were conducted (Eriksen & Hoffman, 1972, 1973; Posner, 1980). Then, theories about attention characterization emerged as the feature integration theory (Treisman & Gelade, 1980), the biased competition model (Desimone & Duncan, 1995), the normalization model (Reynolds & Heeger, 2009), etc.
Top-down and bottom-up attention represent two distinctive forms of attention regulation. The key defining feature that distinguishes them is the extent to which the captured attention is related to the organism's behavior (Baluch & Itti, 2011). Specifically, top-down attention, also known as goal-directed or endogenous attention, operates as a filtering mechanism that selectively captures information relevant to the current behavioral goal, thus reflecting voluntary control. In contrast, bottom-up attention, also referred to as stimulus-driven or exogenous attention, is triggered automatically in response to salient or unexpected stimuli in the environment. Hence, it is considered to be involuntary or reflexive. In other words, top-down attention reflects the organism's proactive orientation toward achieving a specific goal, while bottom-up attention reflects the organism's reactive response to the external world. (Carrasco, 2011; Corbetta & Shulman, 2002; Desimone & Duncan, 1995; Kastner & Ungerleider, 2000).
The primary focus of this study revolves around the correlation between an organism's present behavioral objectives and the instinctual top-down and bottom-up models of attention control. Recently, incorporating the subject’s prior experiences has led to an evolved model, expanding it into a trichotomy (Awh et al., 2012). Therefore, the primary debate in this review centers on the relationship between these three components and how they interplay to influence an organism's behavior.
2. Where Both Mechanisms Meet
The "tabula rasa" concept, originating from the Latin term meaning "blank slate," posits that a newborn individual begins with no preexisting knowledge. According to this viewpoint, all mental characteristics and knowledge will be acquired through experiences. (Petryszak, 1981). On the contrary, certain characteristics of our mental makeup have been developed through evolution and operate much like a learned skill, such as driving. Initially, a conscious effort is required to develop these functions into reflexive behaviors that require minimal effort to execute (Hasher & Zacks, 1979). In this regard, while it is commonly believed that some actions are reflexive and therefore beyond conscious control, it is important to recognize that all behavior is ultimately directed by the nervous system. Whether or not an individual is consciously aware of their actions, they are still voluntary responses of the whole nervous system to the stimuli it encounters. It should be noted, however, that the debate surrounding the inherent freedom of this voluntary behavior is outside the scope of this review.
In the first paragraph, it is not explicitly clear what the driver's primary objective is. Is he solely focused on driving and navigating the road? Or is he more interested in finding a burger shop? Alternatively, is his number one priority to ensure his safety and avoid accidents? In actuality, the driver has various goals that he is attempting to accomplish all at once, with finding the burger shop appearing to be at the forefront of his mind. However, it is important to remember that he is also driving and navigating the road and attempting to avoid any potential hazards that may come his way. In terms of attentional deployment, it is useful to think of it as an analogy. As we engage in performing a task, our attention shifts based on the most relevant information to our current task's demands. In fact, our attentional priorities are updated dynamically depending on the situation we encounter (Todd & Manaligod, 2018). At the same time, we are constantly engaged in the long-lasting task of staying alive - a task that every living organism has evolved to prepare for and learn from. This task cannot be considered involuntary; it is a struggle that we undertake consciously or unconsciously every moment of our lives. In this sense, the long-standing terms "bottom-up" and "top-down" orienting of attention in the literature can be viewed as a single mechanism: selective attention.
3. Master Maps of Integration
The proposed computational model of attention states that the process of selecting the next target for attention and gaze relies on a master map, known as the saliency map, which represents the relative strength of the scene's stimuli based on their features. At any given moment, the most active area in the saliency map determines the most salient location in the space and directs the locus of attention in a winner-takes-all (WTA) manner (Itti et al., 1998; Itti & Koch, 2000, 2001; Koch & Ullman, 1984, 1985). Although the concept of a saliency map was initially associated with bottom-up influences, researchers have since adopted a new term that encompasses both bottom-up and top-down effects: the priority map (Bisley & Goldberg, 2010; Bisley & Mirpour, 2019; Fecteau & Munoz, 2006). In other words, neither top-down nor bottom-up can solely describe the next behavior of the organism. Then, a master integrative map comes into play (Baluch & Itti, 2011).
Extensive studies conducted on monkeys have shed light on the crucial role played by certain brain areas in the formation of the saliency/priority map within the neural system. These areas include the primary visual cortex, the posterior parietal cortex, the frontal eye field, the superior colliculus, and the substantia nigra (Basso & Wurtz, 2002; Bisley & Goldberg, 2010; Bisley & Mirpour, 2019; Li, 2002; McPeek & Keller, 2002; Schall & Hanes, 1993; Thompson & Bichot, 2005; Veale et al., 2017; White et al., 2017). The selection process ultimately integrates available information about the current task with the physical salience of surrounding stimuli. This mechanism helps the organism determine the most relevant location or object (Narhi-Martinez et al., 2023) in alignment with its current life goals. This is an evolutionary advantage rather than the distraction it might initially appear to be. Importantly, these goals can be ones of which the subject is either consciously aware or not. Therefore, according to the biased competition theory, the won location/object representation becomes dominant in all feature dimensions (Desimone & Duncan, 1995; Duncan et al., 1997; Petersen & Posner, 2012; Shipp, 2004).
The features of a stimulus that are deemed salient are those that have gained significance over time due to the subject's life experiences and inherited traits through evolution. Such features are essential for the organism's survival or hold a rewarding nature, which adds value to the stimuli (Anderson, 2013; Theeuwes, 2019). Studies have demonstrated that learning induces changes in the sensory processing of the cortex in response to learned features through training. These changes occur by altering the signal-to-noise ratio, encoding of neural populations, and response threshold, resulting in increased engagement of the organism in the task at hand (Caras & Sanes, 2017; Choi et al., 2018; Makino & Komiyama, 2015; Poort et al., 2015).
4. What Do the Brain and Behavior Define?
In this section, I will briefly review some electrophysiological and behavioral studies that highlight how the traditional top-down/bottom-up dichotomy has led to confusion in interpreting results. It's important to recognize that while simplifying studies by isolating different aspects or factors can strengthen speculative power, it can also be misleading if those aspects or factors are inherently inseparable and represent a single, unified process.
Many experiments conducted so far have focused on distinguishing between top-down and bottom-up mechanisms and have studied them independently (Saalmann et al., 2007). As a result, there have been opposing findings, with some studies suggesting that the physical characteristics of the stimulus have a greater impact on where an individual directs their attention (Theeuwes, 1994, 2004), whereas others indicate that the individual's intentions and goals play a more prominent role (Folk et al., 1992) depending on the context. Furthermore, it is worth noting that emotional stimuli, particularly those of a threatening nature, possess a potent ability to regulate attention. Such stimuli hold a stronger sway over individuals prone to anxiety as they exert a bias towards threatening stimuli during the process of attentional competition (Bishop, 2008; Mathews & Mackintosh, 1998; Mulckhuyse, 2018). Hence, the debate regarding the significance of each mechanism in regulating the focus of attention continues (Anderson et al., 2021; Theeuwes, 2010). In addition, it has been established that bottom-up factors play a crucial role in shaping the initial stages of processing. Conversely, the latter stages appear to be more heavily influenced by top-down factors (Theeuwes, 1994, 2004; Van Der Stigchel et al., 2009). Although behavioral studies have suggested a specific timing window influenced by attentional mechanisms in the brain, fMRI and ERP studies have not entirely agreed with this conclusion. Some researchers have argued that top-down signals play a significant role in visual search, and they attribute bottom-up priming effects to attentional guidance (see Van Der Stigchel et al., 2009).
Behavioral studies examining the endogenous and exogenous orienting of attention yield conflicting results. Certain studies support a unified mechanism for attentional control (Jonides. J, 1981; Warner et al., 1990), while others propose two distinct mechanisms (Müller & Rabbitt, 1989). Nevertheless, the ultimate outcome is cohesive - directing attention to the most pertinent location for an organism's objectives. Additionally, psychologically, the attentional system's selection process is governed by the dynamic interplay between top-down and bottom-up signals (Connor et al., 2004).
The current review explores the concepts of bottom-up and top-down attention and the conflicting results observed in behavioral experiments. Some researchers, like Corbetta and Shulman, have proposed that these processes are partially segregated within the brain's network (Corbetta & Shulman, 2002). However, electrophysiological studies have not always supported this idea. For example, research by Katsuki and Constantinidis has failed to clearly distinguish between the neural activities associated with these attention processes within the frontoparietal network (Katsuki & Constantinidis, 2012, 2014). This network is crucial for both target selection and distractor suppression in areas such as the prefrontal cortex (Cosman et al., 2018) and the extrastriate cortex (Klink et al., 2023). Additionally, certain studies using functional magnetic resonance imaging (fMRI) have demonstrated that in the extrastriate cortex, both top-down and bottom-up elements play significant roles in perceptual grouping competition (Beck & Kastner, 2007; Kastner et al., 1998). Similarly, recent findings in spatial and feature-based attention have uncovered several similarities in the core mechanisms underlying visual selective attention, particularly in the ways spatial, feature-, and object-based attention operate (Chapman & Störmer, 2024). It has been suggested that these two attentional orienting mechanisms are components of the same neural networks, rather than being separate from one another, due to the similarities in the way they influence stimulus representation (Sarter et al., 2001; Treue, 2001).
Although a growing body of evidence supports the idea of top-down biasing competition in various cortical regions, this understanding largely stems from considering top-down and bottom-up mechanisms separately (Buschman & Miller, 2007). It would be valuable to explore other biasing mechanisms, particularly bottom-up factors, to see if they have similar effects across multiple brain regions (Beck & Kastner, 2009). Stated differently, attention may be regarded as an emerging feature of the comprehensive system, wherein the constituent mechanisms are indivisible (Deco et al., 2002). In this regard, the lateral prefrontal cortex and pulvinar serve as cortical and subcortical central hubs responsible for integrating bottom-up and top-down factors, which collectively form a single pathway that facilitates selection (Asplund et al., 2010; Shipp, 2004).
5. Conclusion
According to the framework introduced by Eric I. Knudsen, four processes are fundamental to attentional control: working memory, top-down control, competitive selection, and bottom-up filtering (Knudsen, 2007). Based on this framework, the last three components determine which input information can access working memory by taking into account of the current biases. The output of the working memory then affects subsequent biases based on the most recently evaluated information (Gazzaley & Nobre, 2012). Various studies have shown that working memory influences attention in both voluntary and involuntary ways, depending on the specific task at hand (see Soto et al., 2008). The attentional system plays a significant role in resolving competition in favor of accessing goals information in the working memory (Courtney, 2004). This process ultimately helps the organism to interact with its environment effectively and appropriately.
To sum up, the dichotomy of top-down/bottom-up attention has driven researchers to design and conceptualize experiments and explore findings at both the behavioral and neural network levels. Nevertheless, despite mounting evidence, concerns have been raised about the similarities between these two attention mechanisms and their impact on behavior and related neural networks. Addressing these issues will be crucial for future research.
In this perspective, all behaviors are intricately linked to the demands of the nervous system in every single moment. As a result, there is no singular objective that can be used to categorize behaviors as either relevant or irrelevant. In essence, it can be inferred that in every moment, there exists an additional goal, which may not be immediately apparent, that is, TO BE ALIVE. The prerequisites of this inherent longing are acquired either through evolutionary processes or the past encounters of the organism.
It is crucial to understand that this perspective yields two significant advantages in the field. Firstly, it provides a framework to carefully contemplate the organism itself and its behavior before delving into the description and analysis of any observed behavior and associated neural activities. This approach encompasses a comprehensive examination from the cellular level to the circuit level. Secondly, it emphasizes the importance of conceptualizing experiments in both the design phase and when discussing the results. For instance, if we were to examine the results of a trained subject solely based on a top-down task without taking into account its training history (spanning multiple days), our conclusions regarding brain networks responsible for top-down attention control might be skewed. By disregarding other pertinent factors, we risk overlooking valuable insights that could be derived from a more holistic approach.
Notably, this perspective challenges the adequacy of the current dichotomy in attentional mechanisms to fully explain the neural and behavioral underpinnings of selection. Unlike other perspectives that address the limitations of the traditional dichotomy by incorporating additional elements (Awh et al., 2012; Teufel & Fletcher, 2020), this perspective emphasizes that various factors influence a unified mechanism rather than separate, discrete processes. In fact, it aims to elucidate how these factors are interwoven with the current goals of an organism throughout its life. The review emphasizes that an organism's present goals are shaped by its history of learned experiences, immediate demands, and innate needs—even if the organism is not consciously aware of all these influences at any given moment.
Acknowledgments
I would like to thank Prof. Jan Theeuwes and Prof. Jude Mitchell for their invaluable feedback and helpful comments on the manuscript.
Declaration of Interests
The author declares no competing interests.
References
- Anderson, B. A. (2013). A value-driven mechanism of attentional selection. Journal of Vision, 13(3). [CrossRef]
- Anderson, B. A., Kim, H., Kim, A. J., Liao, M. R., Mrkonja, L., Clement, A., & Grégoire, L. (2021). The past, present, and future of selection history. Neuroscience and Biobehavioral Reviews, 130(April), 326–350. [CrossRef]
- Asplund, C. L., Todd, J. J., Snyder, A. P., & Marois, R. (2010). A central role for the lateral prefrontal cortex in goal-directed and stimulus-driven attention. Nature Neuroscience, 13(4), 507–512. [CrossRef]
- Awh, E., Belopolsky, A. V., & Theeuwes, J. (2012). Top-down versus bottom-up attentional control: a failed theoretical dichotomy. Trends in Cognitive Sciences, 16(8), 437–443. [CrossRef]
- Baluch, F., & Itti, L. (2011). Mechanisms of top-down attention. Trends in Neurosciences, 34(4), 210–224. [CrossRef]
- Basso, M. A., & Wurtz, R. H. (2002). Neuronal activity in substantia nigra pars reticulata during target selection. Journal of Neuroscience, 22(5), 1883–1894. [CrossRef]
- Beck, D. M., & Kastner, S. (2007). Stimulus similarity modulates competitive interactions in human visual cortex. Journal of Vision, 7(2), 19–19. [CrossRef]
- Beck, D. M., & Kastner, S. (2009). Top-down and bottom-up mechanisms in biasing competition in the human brain. Vision Research, 49(10), 1154. [CrossRef]
- Bishop, S. J. (2008). Neural mechanisms underlying selective attention to threat. Annals of the New York Academy of Sciences, 1129, 141–152. [CrossRef]
- Bisley, J. W., & Goldberg, M. E. (2010). Attention, intention, and priority in the parietal lobe. Annual Review of Neuroscience, 33(1), 1–21. [CrossRef]
- Bisley, J. W., & Mirpour, K. (2019). The neural instantiation of a priority map. Current Opinion in Psychology, 29, 108–112. [CrossRef]
- Buschman, T. J., & Miller, E. K. (2007). Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices. Science, 315(5820), 1860–1864. [CrossRef]
- Caras, M. L., & Sanes, D. H. (2017). Top-down modulation of sensory cortex gates perceptual learning. Proceedings of the National Academy of Sciences of the United States of America, 114(37), 9972–9977. [CrossRef]
- Carrasco, M. (2011). Visual attention: The past 25 years. Vision Research, 51(13), 1484–1525. [CrossRef]
- Chapman, A. F., & Störmer, V. S. (2024). Representational structures as a unifying framework for attention. Trends in Cognitive Sciences, 28(5), 416–427. [CrossRef]
- Choi, I., Lee, J. Y., & Lee, S. H. (2018). Bottom-up and top-down modulation of multisensory integration. Current Opinion in Neurobiology, 52, 115–122. [CrossRef]
- Connor, C. E., Egeth, H. E., & Yantis, S. (2004). Visual Attention: Bottom-Up Versus Top-Down. Current Biology, 14(19), R850–R852. [CrossRef]
- Corbetta, M., & Shulman, G. L. (2002). Control of goal-directed and stimulus-driven attention in the brain. Nature Reviews Neuroscience, 3(3), 201–215. [CrossRef]
- Cosman, J. D., Lowe, K. A., Woodman, G. F., & Schall, J. D. (2018). Prefrontal Control of Visual Distraction. Current Biology, 28(3), 414-420.e3. [CrossRef]
- Courtney, S. M. (2004). Emergent Properties of Information Representation. Cognitive, Affective & Behavioral Neuroscience, 4(4), 501–516.
- Deco, G., Pollatos, O., & Zihl, J. (2002). The time course of selective visual attention: theory and experiments. Vision Research, 42(27), 2925–2945. [CrossRef]
- Desimone, R., & Duncan, J. (1995). Neural mechanisms of selective visual attention. Annual Review of Neuroscience, 18(1), 193–222. [CrossRef]
- Duncan, J., Humphreys, G., & Ward, R. (1997). Competitive brain activity in visual attention. Current Opinion in Neurobiology, 7(2), 255–261. [CrossRef]
- Eriksen, C. W., & Hoffman, J. E. (1972). Temporal and spatial characteristics of selective encoding from visual displays *. Perception & Psychophysics, 12(August), 201–204.
- Eriksen, C. W., & Hoffman, J. E. (1973). The extent of processing of noise elements during selective encoding from visual displays. Perception & Psychophysics, 14(1), 155–160. [CrossRef]
- Fecteau, J. H., & Munoz, D. P. (2006). Salience, relevance, and firing: a priority map for target selection. Trends in Cognitive Sciences, 10(8), 382–390. [CrossRef]
- Folk, C. L., Remington, R. W., & Johnston, J. C. (1992). Involuntary Covert Orienting Is Contingent on Attentional Control Settings. Journal of Experimental Psychology: Human Perception and Performance, 18(4), 1030–1044. [CrossRef]
- Gazzaley, A., & Nobre, A. C. (2012). Top-down modulation: Bridging selective attention and working memory. Trends in Cognitive Sciences, 16(2), 129–135. [CrossRef]
- Groner, R., & Groner, M. T. (1989). Attention and eye movement control: An overview. European Archives of Psychiatry and Neurological Sciences, 239(1), 9–16. [CrossRef]
- Hasher, L., & Zacks, R. T. (1979). Automatic and effortful processes in memory. Journal of Experimental Psychology: General, 108(3), 356–388. [CrossRef]
- Itti, L., & Koch, C. (2000). A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Research, 40(10–12), 1489–1506. [CrossRef]
- Itti, L., & Koch, C. (2001). Computational modelling of visual attention. Nature Reviews Neuroscience, 2(3), 194–203. [CrossRef]
- Itti, L., Koch, C., & Niebur, E. (1998). A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11), 1254–1259. [CrossRef]
- James, W. (1890). The principles of psychology. Henry Holt and Co.
- Jonides. J. (1981). Voluntary versus automatic control over the mind’s eye’s movement. In Attention and Performance IX (Long, J.B. and Baddeley, A.D., eds) (pp. 187–283). Lawrence Erlbaum Associates.
- Kastner, S., De Weerd, P., Desimone, R., & Ungerleider, L. G. (1998). Mechanisms of Directed Attention in the Human Extrastriate Cortex as Revealed by Functional MRI. Science, 282(5386), 108–111. [CrossRef]
- Kastner, S., & Ungerleider, L. G. (2000). Mechanisms of visual attention in the human cortex. Annual Review of Neuroscience, 23, 315–341. [CrossRef]
- Katsuki, F., & Constantinidis, C. (2012). Unique and shared roles of the posterior parietal and dorsolateral prefrontal cortex in cognitive functions. Frontiers in Integrative Neuroscience, 6(APRIL), 1–53. [CrossRef]
- Katsuki, F., & Constantinidis, C. (2014). Bottom-up and top-down attention: Different processes and overlapping neural systems. Neuroscientist, 20(5), 509–521. [CrossRef]
- Klink, P. C., Teeuwen, R. R. M., Lorteije, J. A. M., & Roelfsema, P. R. (2023). Inversion of pop-out for a distracting feature dimension in monkey visual cortex. Proceedings of the National Academy of Sciences of the United States of America, 120(9). [CrossRef]
- Knudsen, E. I. (2007). Fundamental components of attention. Annual Review of Neuroscience, 30(1), 57–78. [CrossRef]
- Koch, C., & Ullman, S. (1984). Selecting one among the many: A simple network implementing shifts in selective visual attention. Massachusetts Inst of Tech Cambridge Artificial Intelligence Lab, 1–18.
- Koch, C., & Ullman, S. (1985). Shifts in selective visual attention: Towards the underlying neural circuitry. Human Neurobiology, 4(4), 219–227. [CrossRef]
- Li, Z. (2002). A saliency map in primary visual cortex. Trends in Cognitive Sciences, 6(1), 9–16. [CrossRef]
- Makino, H., & Komiyama, T. (2015). Learning enhances the relative impact of top-down processing in the visual cortex. Nature Neuroscience 2015 18:8, 18(8), 1116–1122. [CrossRef]
- Mathews, A., & Mackintosh, B. (1998). A cognitive model of selective processing in anxiety. Cognitive Therapy and Research, 22(6), 539–560. [CrossRef]
- McPeek, R. M., & Keller, E. L. (2002). Saccade target selection in the superior colliculus during a visual search task. Journal of Neurophysiology, 88(4), 2019–2034. [CrossRef]
- Mulckhuyse, M. (2018). The influence of emotional stimuli on the oculomotor system: A review of the literature. Cognitive, Affective and Behavioral Neuroscience, 18(3), 411–425. [CrossRef]
- Müller, H. J., & Rabbitt, P. M. A. (1989). Reflexive and Voluntary Orienting of Visual Attention: Time Course of Activation and Resistance to Interruption. Journal of Experimental Psychology: Human Perception and Performance, 15(2), 315–330. [CrossRef]
- Narhi-Martinez, W., Dube, B., & Golomb, J. D. (2023). Attention as a multi-level system of weights and balances. Wiley Interdisciplinary Reviews: Cognitive Science, 14(1), e1633. [CrossRef]
- Petersen, S. E., & Posner, M. I. (2012). The attention system of the human brain: 20 years after. Annual Review of Neuroscience, 35, 73–89. [CrossRef]
- Petryszak, N. G. (1981). Tabula rasa–its origins and implications. Journal of the History of the Behavioral Sciences, 17(1), 15–27. [CrossRef]
- Poort, J., Khan, A. G., Pachitariu, M., Nemri, A., Orsolic, I., Krupic, J., Bauza, M., Sahani, M., Keller, G. B., Mrsic-Flogel, T. D., & Hofer, S. B. (2015). Learning Enhances Sensory and Multiple Non-sensory Representations in Primary Visual Cortex. Neuron, 86(6), 1478–1490. [CrossRef]
- Posner, M. I. (1980). Orienting of attention. The Quarterly Journal of Experimental Psychology, 32(1), 3–25. [CrossRef]
- Posner, M. I., & Petersen, S. E. (1990). The attention system of the human brain. Annual Review of Neuroscience, 13(1), 25–42. [CrossRef]
- Reynolds, J. H., & Heeger, D. J. (2009). The Normalization Model of Attention. Neuron, 61(2), 168–185. [CrossRef]
- Saalmann, Y. B., Pigarev, I. N., & Vidyasagar, T. R. (2007). Neural mechanisms of visual attention: how top-down feedback highlights relevant locations. Science (New York, N.Y.), 316(5831), 1612–1615. [CrossRef]
- Sarter, M., Givens, B., & Bruno, J. P. (2001). The cognitive neuroscience of sustained attention: where top-down meets bottom-up. Brain Research Reviews, 35(2), 146–160. [CrossRef]
- Schall, J. D., & Hanes, D. P. (1993). Neural basis of saccade target selection in frontal eye field during visual search. Nature, 366(6454), 467–469. [CrossRef]
- Shipp, S. (2004). The brain circuitry of attention. Trends in Cognitive Sciences, 8(5), 223–230. [CrossRef]
- Soto, D., Hodsoll, J., Rotshtein, P., & Humphreys, G. W. (2008). Automatic guidance of attention from working memory. Trends in Cognitive Sciences, 12(9), 342–348. [CrossRef]
- Teufel, C., & Fletcher, P. C. (2020). Forms of prediction in the nervous system. Nature Reviews Neuroscience, 21(4), 231–242. [CrossRef]
- Theeuwes, J. (1994). Stimulus-Driven Capture and Attentional Set: Selective Search for Color and Visual Abrupt Onsets. Journal of Experimental Psychology: Human Perception and Performance, 20(4), 799–806. [CrossRef]
- Theeuwes, J. (2004). Top-down search strategies cannot override attentional capture. Psychonomic Bulletin & Review, 11(1), 65–70. [CrossRef]
- Theeuwes, J. (2010). Top-down and bottom-up control of visual selection. Acta Psychologica, 135(2), 77–99. [CrossRef]
- Theeuwes, J. (2019). Goal-driven, stimulus-driven, and history-driven selection. Current Opinion in Psychology, 29, 97–101. [CrossRef]
- Thompson, K. G., & Bichot, N. P. (2005). A visual salience map in the primate frontal eye field. Progress in Brain Research, 147(SPEC. ISS.), 249–262. [CrossRef]
- Todd, R. M., & Manaligod, M. G. M. (2018). Implicit guidance of attention: The priority state space framework. Cortex, 102, 121–138. [CrossRef]
- Treisman, A. M., & Gelade, G. (1980). A feature-integration theory of attention. Cognitive Psychology, 12(1), 97–136. [CrossRef]
- Treue, S. (2001). Neural correlates of attention in primate visual cortex. Trends in Neurosciences, 24(5), 295–300. [CrossRef]
- Van Der Stigchel, S., Belopolsky, A. V., Peters, J. C., Wijnen, J. G., Meeter, M., & Theeuwes, J. (2009). The limits of top-down control of visual attention. Acta Psychologica, 132(3), 201–212. [CrossRef]
- Veale, R., Hafed, Z. M., & Yoshida, M. (2017). How is visual salience computed in the brain? Insights from behaviour, neurobiology and modeling. Philosophical Transactions of the Royal Society B: Biological Sciences, 372(1714), 20160113. [CrossRef]
- Warner, C. B., Juola, J. F., & Koshino, H. (1990). Voluntary allocation versus automatic capture of visual attention. Perception & Psychophysics, 48(3), 243–251. [CrossRef]
- White, B. J., Berg, D. J., Kan, J. Y., Marino, R. A., Itti, L., & Munoz, D. P. (2017). Superior colliculus neurons encode a visual saliency map during free viewing of natural dynamic video. Nature Communications, 8, 14263. [CrossRef]
|
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).