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All participants remembered the name of the experimenter, thus the low accuracy rate did not occur because they had forgotten the information; rather the experimenter s name was not personally signi cant enough to the participants to evoke clear distinguishable P300 brain waves EEG-P300 lie detection tests can thus only be used with critical details that are highly meaningful to the participants One could further argue that EEG-P300 examinations using a GKT paradigm have additional disadvantages compared to GKT polygraph examinations EEG examinations are highly intrusive, as examinees have to wear an uncomfortable cap containing as many as 128 electrodes Such caps are not really suitable yet for using outside the laboratory, which is one reason why practitioners do not measure P300 brain waves to date (Nakayama, 2002).

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SAQ 5.5 The mid-infrared spectrum of the clay kaolinite in KBr is illustrated below in Figure 5.9. Assign the infrared bands for kaolinite. What information does this spectrum provide about the structure of this clay

In addition, the P300 brain wave is typically a very small signal, and so to measure the P300 brain waves reliably, many trials are needed For example, Rosenfeld, Shue, and Singer (2007) showed their participants the critical items and ller alternatives 30 times In other words, EEG-P300 examinations are considerably more dif cult to conduct than polygraph examinations..

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Now that we have outlined both the benefits and pitfalls of optimization studies, we can proceed with an examination of the preferred mechanics to employ in attempt to ensure the greatest utility of our optimization studies. To reiterate, the primary goal in our optimization studies is not pinpoint accuracy in forecasting of future performance. Instead, it is merely identification of historically robust parameters and parameter sets in the hope that such trade system criteria will continue to display positive correlations to past performance. To review, consistency and data integrity in our optimization studies are crucial prerequisites in obtaining meaningful conclusions. I define consistency as the application of the same rules regarding entry, exit, and transaction costs throughout the entire backtested data series. Regarding data integrity, ideally our backtested data series should cover a diversified portfolio of assets and include all types of market environments: bullish, bearish, trending, choppy, neutral, and volatile. Once this preliminary groundwork has been firmly established, we need to determine criteria for our choice of parameters and parameter sets. In choosing trading system parameters, we are seeking those that display the greatest propensity of enabling our participation in general principles of market behavior (e.g., mean reversion and/or trending). Regarding our testing of particular parameter sets in our optimization studies, the key here is inclusion of a broad and diverse group of parameter sets. There are two reasons for this: Broad and diversified parameter sets improve our odds of identifying a robust set that will have a high probability of future positive correlations to our backtested history.10 2. Perhaps more important, the broader and more diversified our parameter sets, the greater the probability of our identification and subsequent elimination of suboptimal parameter sets.11

Are Trained CBCA Evaluators Better than Laypersons To answer the question of whether CBCA experts are better at classifying truth tellers and liars than laypersons, it is useful to know how someone becomes a CBCA expert. Unfortunately, little is known about what kind of training is actually required for this. According to Raskin and Esplin (1991b), a two- or three-day workshop is advisable, whereas K hnken (1999, 2004) recommends a three-week training course. Alo though it is debateable how much training is needed, it probably needs to be a rather extensive training programme. Making CBCA/SVA assessments is never a straightforward task. The CBCA list is extensive and several criteria are dif cult to grasp. After the CBCA coding, one needs to assess the issues listed on the Validity Checklist, and this is often a complicated task (Steller, 1989; Wegener, 1989). It is impossible to require the necessary skills to do this without extensive training, and even a two- or three-day workshop may not be suf cient. Several researchers have directly compared the truth/lie detection skills of trained CBCA coders with those of laypersons (Akehurst, Bull, Vrij, & K hnken, 2004; K hnken, 1987; Landry & Brigham, 1992; o o Ruby & Brigham, 1998; Santtila, Roppola, Runtti, & Niemi, 2000; Steller, Wellershaus, & Wolf, 1988; Tye, Amato, Honts, Kevitt, & Peters, 1999). However, all of these studies clearly fell short of the minimum two- or three-day workshop requirement to become a CBCA expert. The shortest training session (45 minutes) was given by Landry and