Making Inferences from ERPs

When components have been identified, their reliability has to be assessed with

statistical analyses. The optimal statistical test depends on the experimental design (e.g.

within or between participants design), however the most commonly used test is the

repeated measures analysis of variance (ANOVA). The research reported in this thesis used a repeated measures ANOVA for the within age comparison, and a mixed

ANOVA for the between age comparison. The ANOVA calculates individual p-values for all factors and the likelihood that one reaches significance by chance increases with the number of factors. Therefore it is sensible to not include factors that are not

necessary. To characterise the amplitude and scalp distribution of the ERP component, electrodes are divided into separate factors that correspond to different spatial locations:

anterior and posterior location, left and right hemisphere, and superior, mid and inferior sites.

3.4.1 Making Inferences from Quantitative Differences

Significant quantitative (or magnitude) differences between two ERP waveforms suggest that the component underlying the difference is reliable. The first point in time at which the waveforms diverge, however, only provides an upper-bound estimate of the onset time of the component. Earlier differences may have been present in brain regions where the signal cannot propagate to the scalp (Rugg and Coles, 1995; Otten and Rugg, 2005). When reliable components are identified, it is important to assess whether the distributions of the components are equivalent, because qualitative differences in the distribution of components are assumed to reflect the operation of different cognitive processes across conditions.

3.4.2 Making Inferences from Qualitative Differences

Significant qualitative (or topographic) differences between two ERP difference waveforms refer to changes in scalp distribution across conditions. The inverse problem states that a dipole detected at the scalp is compatible with an infinite number

of underlying generators, therefore firm conclusions cannot be drawn about the actual neural sources of scalp recorded activity. However, a qualitative difference between two difference waveforms generally indicates that at least partially non-overlapping neural populations are engaged and therefore different components are present.

As with quantitative differences, inferential statistics are used to assess the reliability of apparent qualitative differences. However, the ANOVA model assumes that ERP data is additive (i.e. a twofold increase in the strength of a neural generator adds a constant voltage to each electrode), whereas in reality ERP data is multiplicative (i.e. a twofold increase in source strength produces a twofold increase in voltage at each electrode).

The ANOVA model interprets the multiplicative voltage increase as a qualitative difference rather than a quantitative difference, resulting in spurious topographic differences. To circumvent this issue, ERP data are normalised prior to topographic analyses (McCarthy and Wood, 1985). Normalisation eliminates amplitude differences that reflect changes in source strength between conditions but preserves the topographic differences across electrodes.

There is debate as to whether normalisation is necessary. For example, Haig et al.

(1997) and Urbach and Kutas (2002) argue that the procedure fails to consider

differences in variance between conditions, and normalisation can therefore obscure, or produce misleading distributional differences. Other authors (Ruchkin et al., 1999;

Wilding, 2006), however, promote that normalisation should be performed prior to topographic analyses, but that significant results should only be interpreted as

confirming the presence of distributional differences between conditions. The nature of these differences should then be inferred from the pattern observed in the unscaled data.

While aware that normalisation may produce conservative results, the topographic analyses reported in this thesis employ the maximum/minimum method recommended

by McCarthy and Wood (1985). This method finds the maximum and minimum values in each condition, subtracts the minimum from every data point, and divides the data point by the difference between the maximum and minimum.

The ANOVAs assessing quantitative and qualitative differences employ the

Greenhouse-Geisser correction for non-sphericity of data (Greenhouse and Geisser, 1959), and corrected df values and associated F ratios are reported where appropriate.

A data set is spherical if the variances within all levels of any repeated-measures factor are equal and the covariance between the levels is the same. However, EEG data is often non-spherical because the degree of shared variance between any two EEG electrodes depends on their relative locations, therefore as the distance between the electrodes increases, so shared variance and homogeneity of covariance decreases. As the ANOVA model assumes that the data set is spherical, and the probability of a Type-1 error increases if this assumption is violated, the Greenhouse-Geisser correction is necessary prior to analyses.

3.5 Conclusion

This chapter commenced with a discussion of the neural origin of ERPs. The ERP reflects the dipole of the summed post-synaptic potentials of populations of neurons somewhere in the brain, when the requirements of synchronicity and open field

configurations are met. This electrical activity is conducted to the scalp and is recorded from a montage of electrodes, which are linked to a suitable reference site. Once the signal is recorded, amplified, filtered and digitised, the small ERP signal must be extracted from the background EEG noise using artefact reduction and averaging techniques. Once the ERP signal is extracted, the subtraction method is used to isolate

the ERP component that reflects the underlying cognitive process of interest between two experimental conditions. The reliability of the ERP components has to be

confirmed with statistical analyses. Significant quantitative (or magnitude) differences between two ERP waveforms suggest that the component underlying the difference is reliable. In contrast, significant qualitative (or topographic) differences between two ERP difference waveforms suggests that different components are present across conditions.