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I'm not a psychology student, but I'm doing a subjective experiment which is similar to magnitude estimation: simply measure the effect of different stimulus through a number from 1-10.

Now I need to build a model to explain those numbers, i.e. why different patterns corresponds to different numbers. I find it so difficult and vague that I don't really know how to proceed. Any complicated mathematical model can explain data very well. Are you based on biological evidence?

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closed as unclear what you're asking by Artem Kaznatcheev, Chuck Sherrington, Keegan Keplinger, AsheeshR, Nick Stauner Jan 18 at 21:21

Please clarify your specific problem or add additional details to highlight exactly what you need. As it's currently written, it’s hard to tell exactly what you're asking. See the How to Ask page for help clarifying this question.If this question can be reworded to fit the rules in the help center, please edit the question.

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It's not clear to me what your question is. –  Chuck Sherrington Oct 21 '13 at 19:10
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Your description pretty much sounds like Steven's Power Law could be a good starting point for your endeavor: en.wikipedia.org/wiki/Stevens%27_power_law –  H.Muster Oct 22 '13 at 6:31
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Can you give a detailed description of your experiment and your hypotheses that you test? –  what Oct 22 '13 at 6:55

1 Answer 1

up vote 3 down vote accepted

It's a little unclear what you're asking. In general, psychologists try to build models that are parsimonious; this often means only introducing new parameters (particularly free parameters) into a model when they are absolutely necessary. You are right that with a sufficient number of free parameters, one can build a model that fits the data perfectly. But such a model has no generalizability: if you overfit your model to your data, the model will predict novel data very poorly.

Additionally, a model is preferred when its parameters have some psychological plausibility. That is, a parameter of the model should correspond to some psychological construct (e.g. working memory capacity, neural noise, etc). This is particularly important because it tells psychologists something about cognition and behavior, which is often the goal. If the parameters of a model are completely arbitrary, it's not always clear what we learn from it.

You are again correct that the choice in how to model psychological data is still vague. One many choose between any number of formalisms, such as neural networks or symbolic systems, and it is common for a phenomenon to be modeled in many different ways. Indeed, much of the cognitive modeling literature is full of numerous approaches to the same problem, with each author arguing in favor of their own approach.

The best way to figure out how to model your data is to read other papers that model the same or similar tasks. Look at what other researchers have done, and assess the merits of their approach. If possible, read multiple papers that model the same task in different ways (often researchers from opposing camps will cite each other).

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