back and P300 response

Often, in single task experiments (e.g., Sternberg task), processing capacity is not fully occupied, and therefore, participants can waste resources on irrelevant stimuli. However, during dual-task studies participants must perform two qualitatively different tasks

simultaneously: a primary and a secondary task. An increase in difficulty of the primary task will lead to a decrease of resources available for the secondary task. Similar to previous studies that manipulated cognitive load, experimenters introducing a secondary task found a decrease in P300 amplitude (e.g., Isreal et al., 1980; Kramer et al., 1983; 1987). Watter,

Geffen and Geffen (2001) argue that the N-back task is a dual task. The primary task

involves comparing a currently presented stimulus, with one which is already active in WM.

The secondary task is WM processing, where the individual has to encode, manipulate, search and select information in WM, in order to complete the primary task.

In order to support the dual nature of the N-back task, Watter, Geffen and Geffen (2001) employed a visual N-back paradigm, with four different memory loads (0, 1, 2 and 3).

Their predictions were that if participants select the N-back position in WM in preparation for upcoming trials, then P300 latency should not differ across N-back trials. This would

confirm the similarity of the matching subtask across all N-back conditions. However, amplitude should decrease with increasing WM conditions, in accordance with the Resource Allocation account of the P300. The authors argue that in a unitary task, such as the Sternberg task (Sternberg, 1966), latency will increase but amplitude will decrease, as WM load

increases. This is because participants view the probe, and then engage in a stimulus

evaluation process. As WM load increases, this task becomes more demanding, leading to an increase in P300 latency. Furthermore, amplitude of the P300 decreases, due to increased task demands.

The results provided strong evidence for the dual task nature of the N-back paradigm.

P300 latency was consistent in the 1, 2 and 3 back tasks, indicating that the cognitive

requirements for stimulus evaluation (primary task) were equivalent for the different N-back conditions. However, as the difficulty of the secondary task has increased due to increased WM load resources are allocated away from the primary task. Thus, Watter, Geffen and Geffen (2001) demonstrated that the P300 amplitude reduces as N increased. This amplitude modulation was consistent with previous findings, with both the N-back tasks (Gevins et al.,

1996; McEvoy, Smith & Gevins, 1998). Thus, the authors suggest that there is a reallocation of attention and processing capacity away from the primary task.

Furthermore, the authors found larger P300 amplitudes in the target trial condition. In these conditions, the stimulus matches the stimulus presented N items back. Different

electrophysiological activity between target and non-target N-back trials, is in line with the aforementioned oscillatory analysis conducted by Krause et al., (2000), and Pesonen, Hämäläinen, and Krause (2007). Therefore, the electrophysiological analysis conducted in this thesis will focus on target trials only, where the P300 is maximal. Further discussion of the literature, examining the P300 and the N-back task will also be provided in each chapter of this thesis. In this thesis, both a dual task (N-back), and a unitary task (spatial delayed response task) are employed. This is because participants either have to reallocate resources, or actively manipulate items in WM (Chapter 4). Thus, the electrophysiological results will be discussed in relation to the Resource Allocation, and Context Updating account of the P300.

Statistical analysis of the P300

Within the literature, the P300 has typically been analysed by taking the most positive point in a specific time window. For analysis of the P300 during the N-back task, Watter, Geffen and Geffen (2001) define this window from 300-500ms. In order to control type-1 error rate, the 300-500ms window is used for all ERP experiments in this thesis. This time window was an a-priori choice. Once a time window is defined, the P300 can be analysed by searching for the most positive voltage within this region, or by taking a peak to peak measure. Typically, researchers have analysed the most maximal positive point within this window. However, given that ERPs are a signal to noise ratio, taking a single point within a window risks the analysis of a spurious positivity. A way to avoid this, is to take an average greatest activity

occurring within a time window. For example, one can take the greatest average 50ms activity that occurs within 300-500ms. In addition, researchers have also used a peak- peak measure. After the P300 has peaked, there is a rebound of the component, which results in a negative-going deflection. This allows researchers to analyse the P300 using a peak-peak measure. This peak-peak measure is highlighted in Figure 3. The positive value is the largest 50ms average within the green window (300-500ms), and the negative value is the smallest value in the orange window (500-1000ms). The peak-peak value is the difference between these two points.

Typically, the peak-peak analysis is a difference measure between the maximum and minimum waveform of the P300 peak. Soskins, Rosenfeld and Niendam (2000) provide a convincing argument for analyzing the P300 using a peak-peak measure. The authors

compared a base-peak, with the peak-peak measure for classifying trials in an oddball task as oddball or frequent. The researchers suggest that although the negative waveform is probably not a real component, the peak-peak method using a high pass filter that is greater than 0.1 Hz, but no more than 0.3 Hz, will yield on average 20% superioriority to base-peak method detection. In their paper, Soskins, Rosenfeld and Niendam compared effects of 0.3Hz with 0.01Hz settings of the high pass amplifier filter, and baseline-to-peak, and peak-to-peak measurements of the P300. The key dependent variable was the intraindividual rate of accuracy in discrimination of oddball vs. frequent evoked P300 responses. The authors argue that the combination of the 0.3 Hz filter setting, and the peak-peak measurement of the P300 correctly diagnosed oddball vs. frequent in 100% of cases. Thus, the work in the current thesis uses a 0.3Hz high pass filter, and adopts a peak-peak measurement. In terms of p-value type-1 error rate, in group ANOVAs, the peak-peak measure yields virtually identical results

with the base-peak measures. There is a correlation of 0.93 between the base-peak and the peak – peak methods (Ellwanger, 1987).

In this thesis, all P300 analysis is conducted relative to the onset of a target trial stimulus. After pre-processing, the peak-peak analysis involves finding the most positive 50ms average voltage between 300-500ms, and the most negative 50ms average between 500- 1000ms. While 300-500ms was taken from Watter, Geffen and Geffen (2001), the negative window was defined by the trial length. In experiments 1 and 2, 1000ms marked the end of the trial. Thus, the negative window involves any point after the positive value has been calculated (500ms), to the end of the trial (1000ms). To make fair comparisons between all ERP experiments in this thesis, the same time windows were maintained.

The N-back task experiments conducted in this thesis are analysed using the peak-peak measure of the P300. However, when analyzing the SDRT, the P300 analysis involves the positive region of the P300 only. This is because the SDRT involves analyzing the P300 during stimulus presentation (encoding; S1), and at the probe (retrieval; S2). Previous research has demonstrated a distinct component, called the negative slow wave (NSW), which is present during delayed response paradigms. This component is the result of

maintaining information in visual spatial WM. The NSW appears after S1 presentation, and is strongest during the retention periods. Riby and Orme (2013) examine the NSW from 500-1000 during the target presentation, a time window which overlaps with the P300 negative rebound period. Thus, at S1, it is difficult to distinguish the NSW from the P300 recovery phase. This issue is not as apparent during S2 (retrieval) or in the N-back task, where trials are often analysed to represent matching to the N-back stimuli. Therefore, analysis of the SDRT involves investigating the positive time window only.

Overall, the experiments conducted in this thesis were designed to compare WM processing in adult dyslexic and non-dyslexic participants, across different sensory modalities of WM. In Chapter 3, an overview of research examining WM processing in developmental dyslexia is provided, whereby it is argued that there is debate in the literature regarding the extent to which dyslexic individuals are solely impaired in phonological loop processing, or VSSP processing also. The application of behavioral and ERP techniques for helping to resolve this debate is detailed in Chapter 2, and each experimental chapter in this thesis.

Chapter Summary

Chapter 2 provided a theoretical background to WM, introducing the WM model of Baddeley and Hitch (1974; 1986; Baddeley, 2000), which forms the basis of empirical testing in this thesis. This model has been repeatedly tested and validated in the literature, and is cited consistently in research examining WM impairments in neuro-developmental disorders (see Alloway & Gathercole, 2006 for a full review). The latter half of the chapter included a review of task paradigms and analysis methods, which underlie the empirical work conducted in this thesis. Thus, the chapter ended with a review of research investigating WM and the P300. The rationale for applying these techniques to research examining WM and

developmental dyslexia is developed in chapter 3, along with a full description of the experiments conducted in each empirical chapter. The application of ERP techniques allows for a careful and temporally accurate consideration of WM processing in developmental dyslexia. Critically, as described in chapter 2, research debates the extent to which

individuals with dyslexia have a phonological loop impairment, or a general central executive impairment. Assessing the latency and amplitude of the P300, in different task contexts, allow us to examine resource allocation, as well as updating (central executive processes), in dyslexia. Unlike pure behavioural measures, the temporal precision of ERP analysis, can be

applied at encoding (where no behavioural response is generally necessary) and retrieval, in order to identify the stage at which individuals with dyslexia are impaired. Thus, these measures are critical for examining the nature of the WM impairment in developmental dyslexia.