Analysis method

This section presents and clarifies the analytical method applied as part of the analysis related to the experiment. This includes a set of preliminary and necessary data transformations, the chosen statistical model, and its main variables.

4.7.1. PRELIMINARY DATA TRANSFORMATIONS

Transformations of data were applied so that data more closely meet the assumptions of the statistical inference procedure that was to be applied and to improve the interpretability as well as appearance of the produced graphs related to the data.

Accordingly, due to a highly skewed DKK Amount variable it is essential to apply a logarithmic (log) transformation in order to respond to the preliminary premise that the data should produce a more parametric distribution. The goal of the log transformation is to change the numeric scale that we use to represent the variable so that the values, on the transformed scale, more closely approximate the desired distribution. In Figure 10 the distribution of the DKK Amount data is depicted before and after the log transformation.

Figure 10: Log Transformation of DKK Amount Value Data Predictive coding of consumer choice

Project document, preliminary results

Participants: Thomas Zoëga Ramsøy (PI), Carsten Stahlhut, Maiken Klindt Christensen

The aim of the study was to test whether prefrontal asymmetry at the time of viewing a product, as indexed by EEG, would be predictive of subsequent Willingness to Pay (WTP).

Hypothesis: WTP is related to increased prefrontal asymmetry during product viewing. In particular, we expect that increased left (versus right) prefrontal engagement is related to increased WTP.

The study was conducted using Attention Tool v. 4.5 (www.imotionsglobal.com) running on a Tobii T60 XL tracker running at 60 Hz with a 1920x1200 pixel screen resolution and an approximate viewing angle of 60 cm. EEG was assessed using an Emotiv 14-channel system (www.emotiv.com). Stimulus presentation and data collection was performed using Attention Tool v 4.5.

A few model checks demonstrates that the DKK Amount variable is highly skewed, and needs to be transformed:

0 200 500 800 1100 1400 1 2 3 4 5 6 7

! ! Before! ! ! ! ! ! Log transformed

First, we run a General Linear Model with prefrontal laterality (AF3 minus AF4) as the independent variable, and with LOGvalue as the dependent variable.

Since the laterality index is the AF3:AF4 ratio (AF3 (left) divided by AF4 (right)), higher

A similar log transformation was carried out for the calculated laterality index values, as introduced and described in the following section; and for the same reasons. The distribution of the laterality index data has thus been transformed to produce a more parametric representation of the data, and in consequence, improved the interpretability of the resultant figures generated and presented as part of the analysis.

4.7.2. STATISTICAL MODEL AND MAIN VARIABLES

The first step of the analysis relies on an application of the statistical model, specifically, the General Linear Model (GLM). The chosen method and model give rise to the possibility to allow for several variables of the measured effect, which accordingly is reflected and explained through out the analysis.

The application of the model was carried out with prefrontal laterality as the independent variable and with DKK Amount as the dependent variable. Prefrontal laterality is expressed as the resultant value of AF3-AF4, that is, the value of the AF3 electrode subtracted the value of the AF4 electrode. The DKK Amount is in the following denoted as logAmount due to the earlier mentioned preliminary data transformation.

Figure 11: The position of the chosen and measured electrodes⁹.

9 The EPOC has a total of 16 electrodes, however, this figure only depicts AF3 and AF4 due to the limited scope in question. The figure is adapted from the source www.emotiv.com, 2012.

Furthermore, the laterality index is expressed as the AF3:AF4 ratio, that is, as the value of the AF3 electrode (left) divided by the value of the AF4 electrode (right). Consequently, higher scores on this metric indicate that left frontal activity is greater than right frontal activation, as opposed to negative scores, which indicate right frontal activity greater than left frontal activity. Figure 11 provides the inter-correlations among the AF3 and AF4 electrodes.

As shown in Figure 11, AF3 and AF4 are placed at the prefrontal cortex also presented earlier in this thesis, which is the primary focus point of this analysis. However, due to the relatively close space of the electrodes it may be meaningful in future studies to compare other electrodes pairs farther apart, as for instance F7 and F8. Furthermore, for every analysis all other electrodes have been utilised as covariates. This has been done to ensure a high specificity for the laterality effect assessed by the primary AF3 and AF4 electrodes.

The following step of the overall analysis addresses, and thus includes, the effects of laterality, related to the frequencies alpha (α), beta (β) and gamma (γ), on WTP. The combined approach has been carried out in order to better take each effect into account, and to better assess the contribution to each individual component effeting WTP.

It is important to note a subtle, however, crucial matter related to the interpretation of the alpha frequency, which is that the alpha frequency was used so that higher alpha values means less activation. This is the result of the fact that cortical alpha power is inversely related to cortical activity (Davidson, 1988). Accordingly, negative values indicate more left-sided alpha power, that is, greater right frontal activity. Conversely, positive values indicate relatively right-sided alpha power, that is, greater left prefrontal activation (Schutter, De Haan, and Van Honk, 2004).

Several necessary steps, some of which has already been mentioned and discussed in the above, were taken in analysing the combined effects of laterality on WTP. They have all been summarised here:

• In order to ensure a general representative of the measures for the same stimuli, the data sets were joined using common denominators such as log number, image number, time stamps, etc.

• For each frequency, the laterality index was implemented by subtracting AF4 from AF3, and subsequently, log transforming the data to produce a more parametric distribution

• Due to a skewed distribution of the DKK Amount data, a similar log transformation, as with the laterality index, was applied to all measured values

• The subject was used as a random factor in the analysis, to avoid the issue of individual differences, and to further look for general effects across the entire data sample.

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