Study 2a and 2b Comparison

Larger % changes Smaller % changes

Figure 4.2 The interaction between percentage change and array size in study 2b

4.7.1 Study 2a and 2b Comparison

To compare the results of study 2a and study 2b, a 2(array size) x 5(percentage change) x 2(maintenance internal) ANOVA was conducted. Both array size and percentage change were within-subjects’ factors and the maintenance interval was used as a between-subjects’

factor.

70 75 80 85 90 95 100

25% 20% 15% 10% 5%

Performance Level 100 = 100%, 0 = 0%

Percentage Size Change

Array Size 1 Array Size 2

90 Main Effects

Results demonstrated a significant main effect of array size, F(1,22)= 35.449, p< .001, partial η²^{= .617.} Array size 1 had a significantly larger mean of 91.06 compared to the mean of array size 2 of 84.40.

A main effect of percentage change was also found, F(4,19)= 22.475, p< .001, partial η²= .826. Bonferroni post hoc analyses demonstrated that the 25% change, with a mean performance of 94.64 (SD=11.40) was significantly larger than the mean performance of the 5% change mean of 79.68 (SD=6.73, p<.001). The 20% change, with a mean performance of 92.48 (SD=10.88) was also significantly larger than the mean performance of the 5% change (p<.001). The 25% change was shown to have significantly larger than the mean performance of the 10% change of 85.72 (SD= 12.03, p=.001). The 20% change was significantly larger than the 10% change (p=.014). The 25% change was significantly larger than the 15% change of 86.13 (SD=12.44, p=.004).

No main effect of maintenance interval was found F(1,22)= .045, p= .883, partial η²= .002.

Interactions

A significant interaction between array size and percentage change was found, F(4,19)= 3.811, p= .019, partial η²= .445.

Paired samples t-tests were conducted to look at any differences between the percentage change performance levels of each array size.

Results suggested that there were significant differences between the 15% changes of each array size t(23) = 3.985, p=.001, the 10% changes of each array size, t(23) = 3.495, p= .002 and also the 5% changes of each array size t(23) = 4.756, p= < .001. However, there were no

91 significant differences between the 25% changes of each array size, t(23) = 1.141, p= .226, and also the 20% changes of each array size, t(23) = 1.005, p= .325.

The interaction is being caused by the significant differences of the smaller array sizes whereas opposing results occur with the larger array sizes, presenting no differences. Please see Figure 4.3. for a graph of this interaction.

Figure 4.3. The interaction between array size and percentage change in the comparison of study 2a and 2b.

4.8 Discussion

This current preliminary investigation aimed to identify the extent to which array size and maintenance interval impacted upon change detection performance within a qualitative short term visual memory protocol. In addition, it aimed to establish the parameters for the sizes of changes which would enable performance to be uninfluenced by floor or ceiling effects in

92 Initially a 900 millisecond maintenance interval was used to follow the same procedure as Bae and Flombaum (2013), however, this was then extended to a 4000 millisecond maintenance interval to utilise the change detection in a longer context with regards to the next study of the doctoral thesis. As study 4 will use a 4000 millisecond maintenance interval to look at the representations in working memory, a comparison was made of the original timings of Bae and Flombaum (2013) 900 millisecond interval and also the 4000 millisecond maintenance interval to see if any detrimental effects occur.

Similar to Bae and Flombaum (2013), when a 900 millisecond maintenance interval was used, the current investigation found a significant difference between the percentage changes.

This suggested that in both Bae and Flombaum (2013) and in the current study, participants could detect very small changes (qualitative changes) in shape stimuli. However, one point to note is that Bae and Flombaum (2013) only had a small increase/decrease of 10% and they did not use stimuli that had very small 5% size changes. This is where the current study expands on those findings of Bae and Flombaum (2013) and presents further data to support the detection of small qualitative changes in visual arrays.

The current study also aimed to look at the difference between a 900 millisecond and 4000 millisecond maintenance interval and found that the 4000 millisecond maintenance interval provided participants with a time limit that reduced working memory performance, in particular with regards to array size 2. As Bae and Flombaum (2013) did not use a long 4000 millisecond maintenance interval, it is difficult to compare the current findings to Bae and Flombaum (2013) based on the maintenance interval only. It could be suggested that the interaction between array size and percentage change size reflects the fundamental distinction between Quantitative – categorical (large) change detection representation and Qualitative -

93 high resolution representation within visual short term memory. When the change size is larger, approaching a categorical level or large change in size, the impact of array size is reduced. However, when high resolution representation is required in the context of small size changes, then array is more influential. It should be noted that in these results, even when the stimuli were designed to minimize comparison errors, performance still declined when array size increased from 1 to 2 items.

Thompson et al. (2006) did use small percentage changes, narrowing these changes to a 6%

increase or decrease in the area of a shape. In their 2006 investigation, researchers used a 4000 millisecond maintenance interval to show that the size JND task could work and assess an individual’s visual memory. Although similar size changes and a similar maintenance interval was used, the procedure of the current investigation was slightly different to that of Thompson et al. (2006). Thompson et al. (2006) only presented participants with one square for the encoding arrays and did not make use of any other shape. The current investigation used both array size 1 and array size 2, but also presented participants with a variety of three different shapes.

It could be suggested that the inclusion of two different shapes with a 4000 millisecond maintenance interval made the current task more difficult than that of Thompson et al.

(2006). 2 shapes would mean that less memory resource is shared between each shape (item).

Thompson et al. (2006) only had 1 shape meaning that the full memory resource was allocated to this one item. Looking from a Bays et al. (2009) perspective, the current task could be seen as more difficult due to the decreased precision in memory with the resource being divided between two items. To fully compare the differences between the current investigation and that of Thompson et al. (2006), a separate study would need to be conducted looking only at squares with array size 1.

94 Due to the possible reduction of comparison errors in set size 1 (perspectives proposed by Bae & Flombaum, 2013), task performance across the 900 millisecond and 4000 millisecond maintenance interval may have been similar simply because participants found the task to be too easy. From looking at the averages, participants did not score remarkably less than 74%

across set size 1 in both maintenance intervals. With regards to the current thesis investigation, to increase task difficulty, it may be advisable to have a larger proportion of array size 2 within the change detection procedure if the task is found to not be demanding upon memory. This would make the change detection task more demanding than simply just using array size 1.

The decrease in performance from set size 1 to set size 2 does provide support for the Shared Resource Model (Bays et al., 2009). As there was a decrease in performance, it suggests that although all items are stored in memory with a given array is presented, more resource was allocated to the single item in set size 1 compared to the two individual items in set size 2.

Contrasting models of working memory capacity (Luck and Vogel, 1997, 2013) would suggest that both set size 1 and set size 2 are stored with similar accuracy. These types of slot models would suggest that both set size 1 and set size 2 would not completely fill the 3-4 working memory slots, enabling high accuracy on both set sizes. As this was not the case, the slot model accounts cannot be used to account for the results of the current investigation of this qualitative change detection protocol.

Improvements and Future Research

A potential limitation consideration of the current investigation could be that the task used a variety of shapes, instead of just simple squares as used by Hamilton et al. (2001) and Thompson et al., (2006). The current task was based on the research by Bae and Flombaum

95 (2013) who used a variety of shapes, such as triangles, circles and squares, to look at shape size changes and memory errors in visual arrays. It could be questioned as to whether a similar pattern of results would be found with just using squares as Phillips and Hamilton (2001) did.

One direction for future research could be to make the current change detection paradigm similar to that of Phillips and Hamilton (2001). In this research, only squares were used to assess the size changes in visual arrays, in particular the presentation of one single central square, concluding that small changes could be detected. This would eliminate the use of 2 shapes/squares, meaning that participants would only need for focus upon the one item presented.

Although the current change detection task was created to stop potential memory errors such as correspondence errors, the task unintentionally caused participants to need to use binding abilities instead (Wheeler & Treisman, 2002), making the task more difficult than intended.

As both colour and shape were used in the qualitative change detection tasks, and participants were unaware as to which shape would be presented at retrieval, the binding of the shapes and colours had to be conducted in memory. If participants were informed that the colours of the shapes would not change during the current task, this could have caused less errors within memory as the size of the shapes only would have been focussed on.

Brown and Brockmole (2010) looked at feature binding in an ageing population, suggesting that binding errors in memory can occur especially when the attentional load is increased.

With regards to the next stage of the current doctoral thesis, differences in primary task performance could occur when giving participants a task such as the current qualitative change detection task (which includes binding) plus an attentionally demanding task such as a visuospatial interference task (Vergauwe et al., 2009). Compared to the non-binding

96 quantitative change detection task, the qualitative change detection task could produce more errors within memory simply based on the fact that the disruption of the blinding abilities has occurred. In future, it may be useful to use single coloured shapes, for example, black, so that the participant does not need to use the binding abilities associated with colour and shape and therefore the possibility of such errors can be reduced.