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The involvement of visual and verbal representations in a quantitative and a qualitative visual change detection task. Laura Jenkins, and Dr Colin Hamilton.

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Presentation on theme: "The involvement of visual and verbal representations in a quantitative and a qualitative visual change detection task. Laura Jenkins, and Dr Colin Hamilton."— Presentation transcript:

1 The involvement of visual and verbal representations in a quantitative and a qualitative visual change detection task. Laura Jenkins, and Dr Colin Hamilton Department of Psychology, Northumbria University

2 Aims of this presentation...
To give an overview of the background models associated with the PhD Thesis. Background Models Multi-component Model (Logie, 2011) Working Memory Model (Baddeley, 2000, 2012) Methods of Study 1 and 2 (including background models) Discrete Slot Model (Luck and Vogel, 1997) – study 1 (quantitative stimuli) Shared Resource Model (Bays et al., 2009) – study 2 (qualitative stimuli) To give an overview of the results of both studies, including ideas for future research. Mention both studies the same except the stimuli

3 Figure 1: Logie’s (2011) Multi-Component Model of Working Memory.
Introduction - models Figure 1: Logie’s (2011) Multi-Component Model of Working Memory. Figure 2: Baddeley’s (2000, 2012 ) modification of the original working memory model. Initially thought to be a domain specific model, however filtering using the Episodic Buffer would suggest a more domain general approach A domain specific account. Information is derived through perceptual input directly into the visuospatial sketch pad. No initially episodic influences

4 Visual Patterns Test Representation
Logie (2011) would suggest that the Visual Patterns Test uses the visual cache component only. This information would have to be inputted through the episodic buffer on an object level, a more domain general approach. Other research would suggest the use of a verbal resource as verbal interference effected the VPT (Hamilton et al., 2003). the use of an executive resource as the executive interference removed the advantage of the high verbal coding VPT condition (Brown and Wesley, 2013). What about change detection tasks? A domain specific approach by Luck and Vogel (1997, 2013) with perceptually derived representation. Luck and Vogel (2013), P392. “First, to qualify as VWM, it is not sufficient that the information was acquired through the visual modality; the representation of the information must be visual in nature. If the observer stores a verbal or amodal conceptual representation of the sensory input, we no longer consider it to be a visual memory.” Briefly state hamlton and brow and Wesley methods

5 Aim Aim of this research is to identify whether quantitative and qualitative change detection task uses domain specific or domain general resources. To do this, three interference procedures will be employed – Visual Attention (Bar Fit Task), Verbal Attention (Parity Task) and Dynamic Visual Noise. Why use a verbal interference task? An attentional verbal secondary task should not use the visual domain specific resources and if it has an impact, this is more likely to be upon the domain general resources. Hypothesis: The verbal interference task will not have any effect upon ANY of the change detection tasks indicating the use of domain specific resources, for example the visual cache only – Luck and Vogel (2013) view. Aim is to question change detection to see if results are similar to the vpt. 3 interference tasks used – explained soon. Verbal task used as this supposedly hits domain general resources or not visual specific. We predict Luck and Vogel visual only representations

6 Method – Study 1 and 2 Design: repeated measures with two factors:
array set size (4, 6 for Quantitative study 1 and 1, 2 for Qualitative study 2) interference (Bar Fit, Verbal Parity and Dynamic Visual Noise). Study 2 (qualitative) used smaller array sizes as Bae and Flombaum (2013) suggested that these were enough to detect smaller qualitative changes. Study 1 Participants:19 young adult undergraduates (5 males, 14 females, mean = 23, SD = 4.48). Study 2 Participants:15 young adult undergraduates (13 females, 2 males, mean 22, SD = 4.19) and 11 young adults (all females, mean 21, SD = 3.13). Two repetitions were made at study 2 as it was found that ceiling level effects were present in the interference task performance levels. The second repetition (2b) made the tasks more demanding by adding in an extra interference item per trial.

7 Method – Study 1 and 2 Primary Tasks
Is the square colour the same? Quantitative stimuli Encoding Array (500 ms) Maintenance/ interference (4000 ms) Retrieval Array (3000 ms) Figure 3: Example of the quantitative visual change detection task (Luck and Vogel, 1997). This is in line with the Discrete Slot Model from Luck and Vogel (1997) which looks at large changes. Study 2 Is the shape bigger or smaller? Qualitative Stimuli Encoding Array (500 ms) Maintenance/ interference (4000 ms) Retrieval Array (3000 ms) Figure 4: Example of the qualitative visual change detection task (Bae and Flombaum, 2013). This is in line with the Shared Resource Model from Bays et al. (2009) which looks at small changes – 5, 10, 15, 20% changes.

8 “5” “2” “8” Method – Secondary Tasks
1) Bar Fit Task – Visuospatial Interference – 3 sequential images. Does the bar fit? 2) Verbal Parity Task – Verbal Interference – 3 sequential numbers. Even or odd? “5” “2” “8” 3) Dynamic Visual Noise– Visual Specific Interference – look at the moving/flickering screen Figure 5: Examples of the three interference tasks used in study 1 and 2. Each task is designed to target a specific working memory component.

9 Method 500ms 4000ms 500ms 4000ms 3000ms 3000ms Figure 6: An example of one trial from a baseline primary task (left) and a visual dual task paradigm (right)

10 Results – Study 1 & 2 – Initial ANOVAs
DVN - no significant difference between baseline and either array size 4 or array size 6. Bar Fit Task - only significant for array size 4 F(1,9)= 9.474, p = .013, partial η² = Verbal Parity Task - significant for array size 4 F(1,9)= 9.704, p = .012, partial η² = and array size 6 F(1,9)= 9.228, p = .014, partial η² = .506 DVN - a significant difference was found between the DVN condition and baseline condition for array size 1, F(1,9)= 6.503, p=.031, partial η² = Increase not decrease. Detailed analysis conducted. Bar Fit Task - no significant difference between baseline and either array size 1 or array size 2. Verbal Parity Task - no significant difference between baseline and either array size 1 or array size 2. 1 (baseline) x 1 (interference) repeated measures ANOVAs on each separate set size. Study 1: Quantitative Study 2: Qualitative Mu scores: One samples t-tests – differences from mean of 1 – significant differences in both visual (p=.013) and verbal (p<.001, p=.002) conditions for study 1, however study 2 only demonstrated a significant difference with verbal interference at set size 1 (p=.021)

11 Initial Conclusions Initial ANOVAs and Mu Scores
The experimental hypothesis was NOT supported – the interference was not visual specific. Study 1 demonstrated an effect of visuospatial and verbal interference – Mu scores and ANOVAs suggest this. Domain general approach (Logie, 2011). Study 2 was in need for further analyses (percentage change analysis) to see if the verbal and DVN secondary tasks were hitting the small or large changes…so that’s what we did!!

12 Results – Study 2(b) only
Small vs Large Percentage Change (error rates) Small changes had higher error rates F(1,9)=12.352, p=.007, partial η²=.578. A significant interaction was also found between percentage change and interference type F(3,7)=4.461, p=.047, partial η²=.657. What does the graph suggest: The DVN is hitting the larger changes, making higher error rates. A 4 (interference) x 2 (array size) x 2 (small/large change) repeated measures ANOVA was conducted on the data. Figure 7: An interaction between percentage change (large or small) and interference type.

13 Results – Small vs Large Percentage Change
One way repeated measures ANOVAs were conduced on both the large and small change error totals. This was to see if any differences occurred between the baseline primary task, and the primary task which included the interference type (at each separate array size). Main Findings study 2: Array Size 1 Verbal interference hitting large changes (p=.006) Array Size 2 DVN interference hitting large changes (p=.002) DVN interference hitting small changes (p=.039) – but an improvement?? Larger error rate – lower score – more interference

14 Overall Study Discussion (1)
Study 1 (quantitative) – visuospatial and verbal interference. Study 2 (qualitative) – DVN and verbal interference. DVN has been shown to effect the large changes in a qualitative task but not a quantitative task. This is seen as visual specific interference and should have hit both tasks. Questioned at end of PowerPoint!! Verbal interference (parity task) was shown to effect both visual memory tasks, however, this effect was shown at large changes on the qualitative task (and not small changes). Potentially verbal interference only hits large changes. Visuospatial interference (bar fit task) was shown to effect the quantitative stimuli in study 1 but not qualitative stimuli of study 2. Future research area! Study 1 supports the domain general research with the use of a potential executive resource or episodic buffer component – Brown and Wesley, (2013); Logie (2011), Hamilton et al. (2003). For study 2, this needs to be considered with caution. Look at how tasks are created – small changes!!

15 Overall Study Discussion (2)
Limitations Very long 1 hour testing session. Fatigued participants by the end. Counterbalancing tasks tried to help avoid fatigue effects. Potential limitation - lack of visuospatial interference in study 2 qualitative stimuli. To look at this, either use a different visuospatial secondary task (tapping/tracking) or look at the primary task to see if this does use visual representations (activation of the N200 ERP component in next PhD study). Future Research Use electrophysiological data to try and support behavioural data. Increased N400 activity would suggest the use of verbal representations (Riby & Orme, 2013). Increased N200 activity would suggest visual representation use (Luck, 2005). Activation of both = episodic buffer use!!

16 Email: laura.jenkins@northumbria.ac.uk
2 Questions Why did the DVN only have an effect on the large changes in the qualitative task but not in the large changes of the quantitative task? Why did the DVN demonstrate an improvement with some of the small changes in the qualitative task? Thank you! Any questions??


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