Attentional Control and Working Memory

Attentional Control and Working Memory Attentional control and working memory over top-down, bottom-up factors Complicated activities rely on attention to selectively focus on task-relevant stimuli while overlooking salient distractive stimuli. For instance, drivers need to able to attend to oncoming traffic while simultaneously ignoring distracting stimuli such as eating, looking after children, or hearing the bell of a cellphone receive a message. Most models pertaining to the selectivity of attention suggest that our attention is biased to either stimulus-based factors (bottom-up selection) and/or goal-driven factors (top-down selection) (Theeuwes, 2010). Physically salient properties of objects that draw attention involuntarily are bottom-up factors, in contrast, past knowledge, goals, and future plans are top-down factors that automatically guide our attention (Katsuki Constantinidis, 2014). Attentional control researchers have continuously argued whether goal-driven factors or stimulus-based factors have a larger influence on attentional control. However, this assumes that attention co ntrol involves a dichotomous selection between stimulus-based factors and goal-driven factors. This is an assumption that is incorrect and does not consider attentional control research that exists beyond this dichotomic viewpoint (Vecera et al, 2014). Past theories of attention focusing on the biases between goal-driven (top-down) and physically salient stimuli (bottom-up) do not take into consideration findings that persist outside of these factors, such as, the influence of experience with distractors on future search tasks. Attentional control, using working memory of distractor experience and strong biases, is a more effective posit than the dichotomic bias between goal-driven factors and physically salient factors. Although the dichotomy of bottom-up and top-down does not account for selection biases that are not goal-related nor physically salient, it still provides a highly acceptable theory of attentional control. The first visual sweep is completely driven by stimuli (Theeuwes, 2010). Theeuwes (2010) claims that the most physically salient item drives attention during the first visual scan, it is not until later in time that visual selection is biased in a top-down manner. This top-down manner involves feedback processing and voluntary control based on willful plans and current goals. Theeuwes (1992) found that when looking for a circle among diamonds of all the same color, the response time was a lot slower when one of the diamonds was red. Their study demonstrated that salience has an impact on visual attentional control. Goal driven selection matches targets that most fit the observers goal template. For example, when at the supermarket, if the goal is to buy a red apple, the observer wi ll prioritize red items. Overall, the bottom-up and top-down model offers a much more simplistic approach to attention and is one that can be easily accepted due to its lack of complexity in reasoning. For instance, it is easy to comprehend that items that pop out are more likely to grab attention, as well as, current selection goals of the on looker. However, this theory suggests that irrelevant items are not learned and cannot be used in future search tasks. Both stimulus-based and goal-driven factors influence attentional control, however, researchers have recently started to notice the impact experience has on the selective nature of attention (Awh et al., 2012). For example, participants point out noticeable, color targets quickly if the target-color is repeated throughout subsequent trials (Maljkovic Nakayama, 1994). They found that even when observers have a strong stimulus-based bias towards the target, experience strengthens this bias. Accordingly, priming of pop out of targets in repeated trials demonstrates the ability of experience to change the efficiency and overall efficacy of attentional control (Lee, Mozer, Vecera, 2009). These findings further support the idea that experience can influence attentional control, an idea that is not supported by bottom-up and top-down theories. In contrast to research done in favor of bottom-up, top-down posits, one memory system that falls in favor of experience and attentional control is priming of pop out (PoP). PoP occurs when individuals can point out a target faster if the essential feature of that target is constant in subsequent trials (Maljkovic Nakayama, 1994). In their study, they had their participants look for a colored diamond and had them identify if the diamond had a feature missing from either side. They found that PoP helped individuals and increased their response times. Their findings suggest that by continually showing a targets defining features, it reinforces the selective bias towards that targets features. In a similar vein, Tulving and Schacter (1990) found that representation systems based on perception allow for perceptual priming to occur. These representation system process new information in short-term memory. This short-term memory hastens the processing of similar information in future task s. Thus, when the visual information sweep frequently encounters similar items to process, these items are processed in a faster manner because short-term memory already has a memory trace of that item. Priming of pop out further demonstrates how learned experience with physically salient items benefits subsequent search tasks. It demonstrates that passive priming can provoke strong selection biases that have nothing to do with goal-driven selection. The bottom-up, top-down attentional control model does not consider these findings. Large amounts of research on attentional selection cannot be accounted for by the tendency to group attentional control in either top-down or bottom-up factors (Awh et al., 2012), for example, memory. There are two types of memory that have different roles and first need to be distinguished. Visual working memory depictions are different from visual long-term memories (VLTM). Visual working memory depictions are held for a limited amount of time, while visual long-term depictions continue throughout time (Luck, 2008). The constant maintenance of information limits the length of time for which visual working memory (VWM) depictions are upheld in memory. Lastly, VWM can only hold three to four items at the same time, while VLTM depictions are not bounded to a specific amount of objects (Brady et al., 2008). Although VWM is important in memory, VWM, in regards to attentional control, is specifically important for building experience with distractor rejections, but, is not useful for fut ure use. Visual long term memory (VLTM) uses information (information that is no longer relevant to the task) encoded in the past to guide attention (Fan Turk-Browne, 2016). In their first experiment, Fan and Turk-Browne (2016) found that VLTM for the associated location of a target guided spatial attention during visual search for the target, even when this location was not relevant to the task. Their second experiment expanded on these findings by discovering that VLTM for the associated color of a target influenced attentional capture in a different task. Memories can guide attention toward associated features, even when these features were encoded incidentally and were never relevant to any task (Fan Turk-Browne, 2016). An items features are automatically retrieved from long-term memory based on environmental cues encoded into working memory. These working memory representations bias selection toward items perceived in the world that match with features in memory through react ivation. An example of this would be shopping at a supermarket frequently gone to. When shopping at the local supermarket looking for your favorite cereal, for example, you are less likely to be distracted by other grocery items because you know where youre going and do not have to scan the visual area as often as opposed to it being the first time at that specific store. Observers find targets more easily when knowledge is given beforehand concerning the physical features of the target, like location, identity, and color (Moher Egeth, 2012). This is a process known as visual cueing. Observers find targets more easily, when they are told beforehand, not to look at certain irrelevant areas of the display areas that will not have any targets pop up. For example, an individual is more often than not to find their friend at a mall if told that their friend will be wearing a bright yellow shirt. In the same manner, Woodman and Luck (2007) found that targets were located faster if distractor items that were in the color that had to be ignored were present versus the distractors not being there at all. They concluded that participants used a template for rejection wherein items that match any beforehand features that had to be ignored, could be avoided during search, thus, items possessing the feature that had to be ignored were quickly rejected, ultimatel y, minimizing the size of the search. Knowing what not to look for reduces the number of items needed to be scanned, inadvertently reducing the time it takes to search through items. Further extending current research on the theory that individuals can use cues to bias attention away from salient distractors, individuals need experience with distractors before the distractors can actually be ignored (Cunningham Egeth, 2016). Experience with irrelevant stimuli can improve search in tasks. Learning to ignore features can result in a benefit in search tasks because time spent learning about these features, that need to be ignored, enhances its ability to be used by individuals in future search tasks (Cunningham Egeth, 2016). Results from their experiment found that within the same task, observers only benefited from cues that were consistent and not by cues that changed trial by trial. This demonstrates that cues can only be beneficial in search tasks if the cues are repeatedly shown ; developing a more concrete trace in long term memory in which participants can use. The mentioned studies establish that memory is an important part of the attentional selection process. The concept of memory cannot be put into a category that is either stimulus-driven or goal-driven, but rather makes its own valid case in the plethora of selection phenomena. Biased competition proposes that attentional control mechanisms occur when several neuronal axons land in the same receptive vicinity (Desimone Duncan, 1995). They found that when several stimuli fall into one receptive field, a neuron has multiple choices as to which of these stimuli it should respond to; this is quite an uncertain process. However, attentional mechanisms solve this uncertainty through two processes: attention is biased towards matching target objects with templates held in VWM. And, attention is biased towards items that are physically salient. Objects that are held in VWM are preferred over objects that are not because cells that have the objects features show higher rates of activity (Miller Desimone, 1994). Features of items in the external world are represented by these cells held in VWM, thus, the higher the activation rate, the more probable these neurons are to reach supra-threshold and fire an action potential when an external item matches that of the ite m in working memory. In support of experience and attentional control, biased competition reveals that past experience directs learning towards novel characteristics in settings and plays an important role forming the long-term memory system (Hutchinson et al., 2016). Frequent studies of attention have looked at task-related goals and its effect on memory encoding, but not much research has investigated the role of memory guiding itself during selection (Awh et al., 2012). According to Hutchinson et al. (2016), memory allows for the brain to differentiate between old information (information in which the individual has already encountered) and new information that will give the best representation of the surroundings. Thus, in circumstances that involve both the presence of old and new information, old information will affect how new information is processed and interpreted. Biased competition further supports that experience has an effect on what enters the memory system, which then, subsequently affect s the attentional systems use of templates in the prioritization of certain items. Cases that cannot be explained by the traditional dichotomy of attentional control can be further expanded by reward control. Although attentional selection can be voluntary, in the case of goal-driven tasks, subsequent selection can be provoked be rewards. Hickey et al. (2010) had participants look for a diamond shape while also ignoring irrelevant color stimuli at the same time. Participants were given a low or a high monetary reward depending on whether they answered right. The researchers found that rewards could bias attentional selection to either the target or to the irrelevant stimuli trial after trial.ÂÂ   For instance, if the target color stayed the same on subsequent trials, participants had a fast response time after given a high monetary reward. However, when the distractor had the same color as the previous target, reaction times were slow after given a high monetary reward. This study suggests that monetary reward influenced attention towards the color that was gi ven the high reward, irrespective of whether the color was associated with the distractor or the target. Several studies have shown that attentional selection is biased towards monetary reward. These findings cannot be explained by the voluntary, top-down or the physically salient, bottom-up attentional control dichotomy. Monetary reward further demonstrates that the dichotomic posit of attentional control is one that is incomplete and that monetary reward only expands on the present findings related to selection phenomena. Rewards are one of the strong biases that have a significant influence on selective processes. When encountering physically noticeable distractors, the experiences built on these distractors allows individuals to focus in future search tasks. This finding reveals that experience with physically noticeable distractors, and not only target templates held in working memory, benefits the high functionality of attentional control. Like further posits of attentional controls dependence on experience, learning to reject irrelevant stimuli depends on visual long term memory. This is an acceptable finding to grasp because long term memory possesses the ability to direct attention to target items in the present and later on, and, away from distractors. This finding further validates that attentional control cannot be explained by purely using the dichotomy of goal-driven and physically-salient-driven efforts. Rather, attentional control is an active process founded on creating experience with specific objects. Consequently, attentional control is a skill that is increasingly sharpened a s we gain experience out in the world. By not having much experience, the skills used in controlling attention is rather basic and depends on the simple use of the physical noticeability of object features. However, as individuals experience increases with certain tasks, the skills involved in attentional control sharpens and focuses on specific features. Once our attention is focused on a specific set of features, top-down control of attention can operate more efficiently. The importance of attentional control can be further seen in everyday life, especially in the realm of mental health. Several findings have found that there is a high correlation between those who suffer with mental illnesses and levels of attentional control. Individuals who have Alzheimers disease, for example, have trouble maintaining goal-directedness (Coubard, et al., 2011). They found that Alzheimers disease affects the ability of switching attention, suppressing, and preparing attention for random events. Further, individuals who suffer from schizophrenia and attention deficit hyperactivity disorder (ADHD) have a fast response time in tasks when levels of anxiety and depression are lessened (Sarter and Paolone, 2011). Emotional processing is an important of human interaction and communication. Low attentional control would hinder the ability to shift attention away from potentially threating information which would increase ones susceptibility of developing harmful psychological effects (Fergus et al., 2012). Post-traumatic stress disorder (PTSD) is another mental illness that is also affected by attentional control. Individuals with PTSD and low attentional control show attentional avoidance (Schoorl et al., 2014). Attentional avoidance is the concept of biasing attention away from threatening situations. These threatening situations serve as triggers that remind individuals with PTSD of the traumatic events they have experienced. This cognitive avoidance can be dysfunctional becaus e individuals with PTSD do not face threatening stimuli head on and avoid it, which, deprive them of the chance to realize that the traumatic event will not occur again (Schoorl et al., 2014). This was only the case when post-traumatic stress disorder symptoms were high and attention control levels were low. Works Cited 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. doi:10.1016/j.tics.2012.06.010 Brady, T.F., Konkle, T., Alvarez, G.A., Oliva, A. (2008). Visual long-term memory has a massive storage capacity for object details. Proceedings of the National Academy of Sciences, 105(38), 14325-14329. doi: 10.1073/pnas.0803390105 Cunningham, C. A., Egeth, H. E. (2016). Taming the white bear: Initial costs and eventual benefits of distractor inhibition. Psychological Science, 27(4), 476-485. doi:10.1177/0956797615626564 Coubard, O. A., Ferrufino, L., Boura, M., Gripon, A., Renaud, M., Bherer, L. (2011). Attentional control in normal aging and Alzheimers disease. Neuropsychology, 25(3), 353-367. doi:10.1037/a0022058 Desimone, R., Duncan, J. (1995). Neural mechanisms of selective visual attention. Annual Reviews of Neuroscience, 18(1), 193-222. doi: 10.1146/annurev.ne.18.0030195.001205 Fan, J. E., Turk-Browne, N. B. (2016). Incidental biasing of attention from visual long-term memory. Journal Of Experimental Psychology: Learning, Memory, And Cognition, 42(6), 970-977. doi:10.1037/xlm0000209 Fergus, T. A., Bardeen, J. R., Orcutt, H. K. (2012). Attentional control moderates the relationship between activation of the cognitive attentional syndrome and symptoms of psychopathology. Personality And Individual Differences, 53(3), 213-217. doi:10.1016/j.paid.2012.03.017 Hickey, C., Chelazzi, L., Theeuwes, J. (2010). Reward Changes Salience in Human Vision via the Anterior Cingulate. Journal of Neuroscience, 30(33), 11096-11103. doi:10.1523/jneurosci.1026-10.2010 Hutchinson, J. B., Pak, S. S., Turk-Browne, N. B. (2016). Biased competition during long- term memory formation. Journal Of Cognitive Neuroscience, 28(1), 187-197. doi:10.1162/jocn_a_00889 Katsuki, F., Constantinidis, C. (2014). Bottom-up and top-down attention: Different processes and overlapping neural systems. The Neuroscientist, 20(5), 509-521. doi:10.1177/1073858413514136 Lee, H., Mozer, M.C., Vecera, S.P. (2009). Mechanisms of priming of pop-out: Stored representations or feature-gain modulations? Attention, Perception, Psychophysics, 71(5), 1059-1071. doi: 10.3758/APP.71.5.1059 Luck, S.J. (2008). Visual short-term memory. In S.J. Luck A. Hollingworth (Eds.), Visual Memory (pp. 43-85). New York: Oxford University Press. Maljkovic, V., Nakayama, K. (1994). Priming of pop-out: I. Role of features. Memory Cognition, 22(6), 657-72. doi: 10.3758/BF03209251 Miller, E.K., Desimone, R. (1994). Parallel neuronal mechanisms for short-term memory. Science, 263((5146), 520-522. doi: 10.1126/science.8290960 Moher, J., Egeth, H.E. (2012). The ignoring paradox: Cueing distractor features leads first to selection, then to inhibition of to-be-ignored items. Attention, Perception, Psychophysics, 74(8), 1590-1605. doi: 10.3758/s13414-012-0358-0 Sarter, M., Paolone, G. (2011). Deficits in attentional control: Cholinergic mechanisms and circuitry-based treatment approaches. Behavioral Neuroscience, 125(6), 825-835. doi:10.1037/a0026227 Schoorl, M., Putman, P., Van Der Werff, S., Van Der Does, A. W. (2014). Attentional bias and attentional control in posttraumatic stress disorder. Journal Of Anxiety Disorders, 28(2), 203-210. doi:10.1016/j.janxdis.2013.10.001 Theeuwes, J. (1992). Perceptual selectivity for color and form. Perception Psychophysics, 51(6), 599-606. doi:10.3758/BF03211656 Theeuwes, J. (2010). Top-down and bottom-up control of visual selection. Acta Psychologica, 135(2), 77-99. doi:10.1016/j.actpsy.2010.02.006 Tulving, E., Schacter, D.L. (1990). Priming and human memory systems. Science, 247(4940), 301-306. doi: 10.1126/science.2296719 Vecera, S. P., Cosman, J. D., Vatterott, D. B., Roper, Z. J. (2014). The control of visual attention: Toward a unified account. In B. H. Ross, B. H. Ross (Eds.) , The psychology of learning and motivation, Vol. 60 (pp. 303-347). San Diego, CA, US: Elsevier Academic Press. Vogel, E.K., Woodman, G.F., Luck, S.J. (2006). The time course of consolidation in visual working memory. Journal of Experimental Psychology: Human Perception and Performance,32(6), 1436-1451. doi: 10.1037/0096-1523.32.6.1436