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Attention has been the center of attention for scientists and thinkers across the centuries. It has been studied in great depths, but there remains a disagreement even as to the very definition of it. From a means for managing metaphysical representations (Descartes, 1641) to a means for sensory data management (Mole, 2017), it has assumed many different roles. For the purpose of this assignment, we will define it as a “set of processes that leads to the selection of behaviorally relevant information from our sensory environment” (Gazzaniga, Ivry, & Mangun, 2014). The world around us contains many sets of information and we choose from the transduced messages what we want to perceive.
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A great body of research has gone into determining the very nature of this selection, its underlying mechanisms, and how to control them for the optimal behavioural or cognitive outcome. One of the first models of divided attention in experimental psychology was generated by Broadbent (1958). In this model, the attention systems attribute a gain to each sensory feature which consists of all the bottom-up and top-down factors that contribute to focusing on the feature. Top-down processes can be active sampling of information and bottom-up processes can be salient features of the sensory information, strengthened by emotional/informational factors. The sensory information with the highest gain is transduced (1) through the attention filter and the rest are attenuated (0).
Newer models were presented afterwards, for instance by Treisman (1964), to explain the gaps left by this model. In Triesman’s model, the brain would integrate a separate attenuator which is tasked with keeping the gain scores. The sensory information that passes the gain threshold is then brought to active attention. Both of those models assume that there is a filter that includes or removes specific information based on a gain function. The models were critiqued for their hardships explaining certain phenomena such as the cocktail-party problem (Cherry, 1953). This problem points out that a person is more likely to respond to their own names being called in a party even though there is ample noise. A recent study by Mesgarani & Chang (2012) investigated selective hearing in a noisy situation, not for evolutionarily important salient stimuli such as a person’s own name but for more typical speech patterns, concluding that different areas in the auditory cortex are activated for each of the parallel sounds. By changing the attended speech of choice, the listener switched the activated area in the auditory cortex. This shows that processing for each of the attended stimuli are happening in separate locations and hence refutes models of selective attention that assume all perceptive processes similar.
Another issue that was brought up for these filter models of attention was by on the same experiment as Cherry 1953, but showing that some of the subjects had recall of the unattended stimuli when it connected contextually with the attended stimulus and was recalled for up to 15 seconds (Wood & Cowan, 1995, p. 195). This led to the popularity of some models of attention that were brought forward in the 1960s, postulating that attention is a matter of limited resources, and the sensory information will not interfere so long as they are using different banks of resources (Navon & Gopher, 1979; Wickens, 2002). The experimental support for the resource theory of attention includes Duncan, Martens, & Ward (1997) where presenting one auditory and one visual stimulus has much less interference effect than presenting two auditory or two visual stimuli. Later experiments also confirmed its result that the visual and auditory stimuli use separate attentional pools that are not shared (Arrighi, Lunardi, & Burr, 2011). Other similar experiments have been done with different combinations of sensory information such as Wahn & König (2016) where visual and tactile attention are shown to be separate in attentional resources. In this experiment, we seek to divide the attention between auditory language perception and mathematical perception to observe their interference patterns.
71 undergraduate students (47 female + 24 male) at the University of Toronto took part in this experiment (mean age 20.13 ± 1.40 s.d., years in university 2.18 ± 0.81 s.d.). The informed consent was acquired through an online portal and the experiment was conducted using the TopHat portal (Top Hat Inc., Toronto, ON, CA) during a second-year cognitive psychology lecture. The experiment was conducted by the lecturer. They were then divided into a divided attention (DA) group (nDA = 31), and a Full Attention (FA) group (nFA = 42).
The students observed a sequence of 57 words on a Panasonic PT-RQ22K projector (Panasonic Corp., Kadoma, Osaka, Japan) using Microsoft Office 2010 PowerPoint (Microsoft Corp., Redmond, WA, USA). All the presented words were accompanied by a coloured dot. In 50 out of the 57 instances the dot was green and in the other 7 it was red (Spataro, Mulligan, & Rossi-Arnaud, 2013). Each word was presented for a period of 1 second. 500 milliseconds after the word appeared, the coloured dot was presented, which stayed on for 250 milliseconds. The FA group was only instructed to attend to the presented sequence of words whereas the DA group was instructed to think about the name of the colour of the accompanying dot.
After the encoding phase was finished, the participants were shown a string of words on the TopHat interface, where they answered yes/no to whether they have seen the word, on their personal device (phone, tablet, or laptop). The data was collected using TopHat and then analyzed using SPSS (IBM Inc. Armonk, NY, USA). An independent t-test was done for each set of trials (for green trials altogether and red trials altogether) to see the percentage of correct recall in each of the cases.
The independent sample t-tests were undertaken to compare the rate of correct recalls in DA and FA during the green and red trials. In the green trials, the DA showed a remarkably lower recall rate than the FA (meanDA = 3.45 ± 1.71, meanFA = 4.33 ± 1.59, p-value ≈ 0.03, df = 71, t-statistic = -2.27). But in the red trials the tables turned, and no significant difference was observed between the FA and DA groups (meanDA = 4.94 ± 1.81, meanFA= 4.79 ± 1.51; p-value ≈ 0.701, df = 71, t-statistic = 0.386).
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This experiment shows a similar response to encoding the words in red trials between the DA and FA groups. This supports the limited resource models of attention that posit each sensory or cognitive system has a specific attention system which is limited in capacity. When there are several features competing for the same resource, we might see a decrease in the quality and quantity of performance (Wickens, 2002). Both remembering a word and recounting a word (the name of the colour in this case) need to engage the lexical representations of the brain. This causes a different outcome for the normal encoding during the green trials. But in the red trials, the oddball detection system is also engaged in the encoding. Extensive research has gone into oddball detection in the brain (since Donald & Goff, 1971). It has been demonstrated that the parietal regions of the brain respond positively to observing visual or hearing auditory stimuli with a surprising feature, about 300 milliseconds after the surprising stimulus (oddball) is presented. This brainwave is titled P3 or P300, and is robustly observed in many different experiments (reviewed by Huang, Chen, & Zhang, 2015) and even in nonhuman primates (Arthur & Starr, 1984; Paller, Zola-Morgan, Squire, & Hillyard, 1988). Hence we can draw the conclusion that the short-term memory system is separate than the region that codes for novelty (as previously demonstrated by Barbeau, Chauvel, Moulin, Regis, & Liégeois-Chauvel, 2017). Further research can be done on similar structures in auditory stimuli (e.g. where there are words accompanied by low- or high-pitched tones, where one of the tones is frequent ant the other is the oddball).
However, it is worth noting that the undertaking of such an experiment during a classroom has clear hinderances. It is possible that the students might be confused due to the division of attention with other collegiate commitments. We can observe this possible confusion by the fact that the DA and FA groups total to more population (nDA + nFA = 31 + 42 = 73) than the whole classroom (n = 71); possibly, more than one of the students had responded to both the DA and FA sections. It is nearly impossible to find out the anomalous entries for removal, as different combinations of the ground truths can give rise to this observation. Next experiments can attempt to control the populations taking part in the experiment and control the background activity or noise involved in the performance as well.
To recapitulate, this experiment demonstrates that the recall performance is worse when there is more than one factor competing for a similar resource. It further confirms the separation of oddball and normal memory encoding.
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