If the experimenter comes in to the lab with the paper already written and expects the lab results to confirm his modelling he is overconfident.

Nice one!

If it is overconfidence in participants, use an incentivized belief elicitation task. The participants indicate their level of confidence, and then this confidence level is scored against subsequently observed behavior.

Overconfidence is actually tricky to measure, ex-ante, and how to measure it will depend on which definition of overconfidence you want to use. E.g., if you are interested in overconfidence defined as believing your information is more precise than it is, then you could simply elicit each participant's belief distribution for an objectively-known random event (e.g., state lottery outcome) and compare the elicited distribution to the actual distribution using some measure of dispersion (e.g., variance) for statistical testing. If you are interested in a more intuitive measure of overconfidence, then you could match pairs of participants to compete on a task where performance is orthogonal to ability so that, objectively there is a 50/50 chance (or, whatever probability you want to design) of winning. Then you could elicit participant's beliefs about the chances of winning. Labeling individuals reporting values sufficiently higher than 50/50 as overconfident would be warranted here, I think, where the definition of "sufficiently" is up to the experimenter.

Here's a recent paper addressing the issue written by some of the leading exponents of decision theory and experimental economics:

http://www.gsm.pku.edu.cn/resource/uploadfiles/docs/20121218/201212180425335237.pdf