Visual search is the process of locating a target among distractors. It is necessary because our visual system has limitations, such as limited visual field and resolution, and the need to bind features of objects. The visual field is limited, and resolution decreases as we move away from the fovea. Additionally, crowding effects make it difficult to identify targets in peripheral vision. The binding problem refers to the challenge of integrating features of an object, which requires attention. Attention allows for more subtle perceptual distinctions and improves spatial discrimination. Preattentive processing occurs before selective attention is deployed, and it is essential for efficient search. However, the term is controversial because it often implies a preattentive brain region. Spatially selective visual attention is necessary because the brain cannot process all visual information simultaneously. This leads to a bottleneck in visual processing, where attention selects which items to focus on. Classic visual search tasks involve searching for a target in an array of items. These tasks have been used to study the efficiency of search, with response time (RT) and accuracy as key measures. RT methods show that search efficiency depends on the number of items in the display (set size). The slope of the RT × set size function indicates the rate of processing. A flat slope suggests parallel processing, while a linear slope suggests serial processing. However, the slope alone is not sufficient to determine the type of processing, as other factors can influence the results. Eye movement methods track where attention is directed during search. Fixation and saccades are linked to attention, and eye movements can reveal how attention is deployed. However, eye tracking data may not always reflect the actual search process, as attention can be deployed without eye movements. Electrophysiological measures, such as event-related potentials (ERPs), provide insights into the neural basis of attention and search. The N2pc component is associated with the deployment of attention. Functional magnetic resonance imaging (fMRI) has shown that different brain regions are involved in visual search, depending on the type of target. However, fMRI lacks the spatiotemporal resolution to track attention in detail. Magnetoencephalography (MEG) offers better resolution but is not yet widely used. The efficiency of search depends on the attributes that guide attention, such as color, motion, and shape. These attributes are limited in number, and their effectiveness varies. For example, color and motion are more effective than orientation or depth cues. The continuum of search efficiency is influenced by the ability to guide attention toward likely targets and the speed of rejecting distractors. Efficient search is characterized by a shallow RT × set size function, while inefficient search has a steeper slope. The efficiency of search can be affected by factors such as expertise and emotional state.Visual search is the process of locating a target among distractors. It is necessary because our visual system has limitations, such as limited visual field and resolution, and the need to bind features of objects. The visual field is limited, and resolution decreases as we move away from the fovea. Additionally, crowding effects make it difficult to identify targets in peripheral vision. The binding problem refers to the challenge of integrating features of an object, which requires attention. Attention allows for more subtle perceptual distinctions and improves spatial discrimination. Preattentive processing occurs before selective attention is deployed, and it is essential for efficient search. However, the term is controversial because it often implies a preattentive brain region. Spatially selective visual attention is necessary because the brain cannot process all visual information simultaneously. This leads to a bottleneck in visual processing, where attention selects which items to focus on. Classic visual search tasks involve searching for a target in an array of items. These tasks have been used to study the efficiency of search, with response time (RT) and accuracy as key measures. RT methods show that search efficiency depends on the number of items in the display (set size). The slope of the RT × set size function indicates the rate of processing. A flat slope suggests parallel processing, while a linear slope suggests serial processing. However, the slope alone is not sufficient to determine the type of processing, as other factors can influence the results. Eye movement methods track where attention is directed during search. Fixation and saccades are linked to attention, and eye movements can reveal how attention is deployed. However, eye tracking data may not always reflect the actual search process, as attention can be deployed without eye movements. Electrophysiological measures, such as event-related potentials (ERPs), provide insights into the neural basis of attention and search. The N2pc component is associated with the deployment of attention. Functional magnetic resonance imaging (fMRI) has shown that different brain regions are involved in visual search, depending on the type of target. However, fMRI lacks the spatiotemporal resolution to track attention in detail. Magnetoencephalography (MEG) offers better resolution but is not yet widely used. The efficiency of search depends on the attributes that guide attention, such as color, motion, and shape. These attributes are limited in number, and their effectiveness varies. For example, color and motion are more effective than orientation or depth cues. The continuum of search efficiency is influenced by the ability to guide attention toward likely targets and the speed of rejecting distractors. Efficient search is characterized by a shallow RT × set size function, while inefficient search has a steeper slope. The efficiency of search can be affected by factors such as expertise and emotional state.