Event-related potentials

In the ERP approach, many segregated epochs are averaged depending on experimental manipulations to exclude the background noise of EEG signals that are random

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fluctuations and not related to the cognitive tasks (Rugg & Coles, 1995). To increase the signal-to-noise ratio, there usually need to be sufficient EEG trials in each experimental condition. The signal-to-noise ratio increases as a function of the square root of the trial number (Luck, 2005). The averaged waveforms are the ERP, which are thought to represent changes attributable to the experimental manipulations over time. Taking the subsequent memory effect as an example, ERP waveforms belonging to subsequently remembered stimuli might have different deflections than waveforms that belong to subsequently forgotten stimuli over different time courses.

In order to interpret the relationships between cognitive processes and ERP waveforms, one can use prior knowledge such as ERP components. An ERP waveform is represented by positive and negative deflections of different amplitudes across time series. ERP components are used to decompose such continuous waveforms into discrete parts in terms of polarity, amplitude and latency to try to associate them with underlying cognitive or neural processes (Kappenman & Luck, 2012). Although there have been many arguments on defining the term ERP component (for reviews, see Kappenman & Luck, 2012; Luck, 2005; Coles & Rugg, 1995), in practice an ERP component can be defined in terms of its specific polarity, duration, scalp distribution and sensitivity to experimental manipulations (Donchin & Heffley, 1978). For example, as discussed above, the CNV is a component that indicates anticipation to an upcoming event. It was first defined in a study by Walter et al. (1964) as a negative deflection over frontocentral scalp sites before S2 when S1 is a warning stimulus. Apart from the CNV, three components that are thought to be related to attention are also of interest. When comparing the cue-related activity elicited by informative or non-informative cues irrespective of memory performance, the three components N1, P2, and P300 can provide evidence about whether the processing of informative cues attracts more attention than the processing of non-informative cues, which can indicate participants’

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overall preparation for an upcoming stimulus guided by more amount of advance information. The N1 is a component that has a negative peak elicited by a stimulus and is thought to have many subcomponents (for a review, see Näätänen & Picton, 1987). In this doctoral thesis, the N1 component is defined as a negative deflection and considered to reflect visual selective attention (Mangun, 1995). In a time window after the N1 component, a positive deflection over posterior scalp sites also reflects attention to the visual stimulus. This is termed the P2 component (Kotchoubey, Wascher, &

Verleger, 1997). The P300 component is defined by an ‘oddball’ paradigm, which the P300 is a large positive deflection elicited by a salient stimulus compared with another identical stimulus within an experimental series (Coles & Rugg, 1995). The P300 component is suggested to reflect higher-level cognitive processes such as context updating (Donchin, 1981) or the level of effort devoted to a task (Isreal, Chesney, Wickens, & Donchin, 1980).

Although it seems difficult to define an ERP component, ERP components provide a way to bridge across experiments (Otten & Rugg, 2005). If no prior knowledge about ERP components is available, the interpretations between cognitive processes and ERP waveforms can be made by considering amplitudes, polarities, scalp distributions and time courses (Otten & Rugg, 2005). Differences in amplitudes of ERP waveforms often reflect the same cognitive processes but engaged in quantitatively different ways. For example, larger P300 amplitudes may reflect greater updating processes. However, if the amplitude differences are in reversed polarities, it might indicate some qualitative differences as the reversed scalp polarities might be generated from different neural sources (e.g. Otten & Rugg, 2001b). In addition, differences in scalp distribution point to qualitative differences in cognitive processes. Along with the information from the time course, the differences between two ERP waveforms can be related to a certain cognitive process (Otten & Rugg, 2005). For example, retrieval

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processes can be reflected by differences between the ERP waveforms elicited by correctly remembered old items and correctly rejected new items, which are called old / new effects (Rugg, 1995). The start of old / new effects, as any ERP differences, indicates an upper bound on differences occurring in underlying processes of discriminating a remembered item from new items. As discussed in the previous chapter, two old/ new effects differ in time and scalp distribution. The first old / new effect is between 300 and 500 ms after event onset and largest over frontal scalp sites. The second old / new effect is between 500 and 800 ms and largest over left parietal scalp sites. The two effects reflect the involvement of different neural patterns underlying recognition memory over time due to their different time courses and scalp distributions.

The two effects indicate that there may be qualitatively different cognitive processes that happen at different times in recognition memory (Otten & Rugg, 2005).

There are usually two ways to quantify ERP amplitudes. Peak amplitude measures consist of finding the maximum amplitude of a waveform in a defined time window. The other one is averaging the amplitudes in a defined time window of the waveform (Luck, 2005). The ERP analyses in this doctoral thesis used the mean amplitude method to quantify the ERPs. Peak amplitude measures assume that ERP components are reflected by maximum voltage values. Therefore, when measuring an ERP component, the peak amplitude measures usually need to involve a relatively wide measuring time window because the latency of a peak varies across subjects, electrode sites or conditions (Luck, 2005). The mean amplitude method does not measure the peak amplitude, but the average amplitude in a specified interval. This type of measurement is less affected by noise in the signal, although at the expense of being able to assess latency differences across conditions. Use of a relatively narrow time window can remedy this problem to some extent. Care should be taken that the selection of a time window is justified by existing knowledge, and not arbitrary and without

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justification since this has been shown to lead to biased statistics (Kilner, 2013). As different temporal intervals were used in Experiment 1 from Experiments 2 and 3, the chosen time windows will be introduced in each Results section in Chapter 3.

The statistical analyses of subsequent memory effects or other comparisons were done via analyses of variance (ANOVA). When an ANOVA was done for any factor with more than two levels, the Greenhouse-Geisser correction was incorporated to avoid violations of sphericity (Keselman & Rogan, 1980). The Greenhouse-Geisser correction can decrease the degrees of freedom when there are more than two levels in one factor. Then the p value is increased to protect the analyses from type I errors caused by nonsphericity. The results of this thesis will report the corrected degrees of freedom and p values when ANOVAs were done with factors more than two levels. In addition, the data were scaled by the max/min method to remove overall amplitude differences between conditions (McCarthy & Wood, 1985). McCarthy and Wood (1985) suggested that a significant interaction between scalp sites and experimental conditions does not necessarily indicate that the effects in the two experimental conditions are qualitatively different due to different scalp distributions. Such significant interactions might be caused by overall larger amplitudes in one condition than amplitudes in the other condition because EEG data are multiplicative and ANOVAs are additive.

Therefore, McCarthy and Wood (1985) suggested scaling the data by the data point value – minimum value / the maximum value – the minimum value. If the interaction between experimental condition and electrode site for the scaled data is significant, at least the significant interaction is not confounded by an overall amplitude difference. In that case, it can be concluded the effects in the two experimental conditions have different scalp distributions. The results of this doctoral thesis reported ANOVAs for the original data and scaled data.

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