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14

I'd suggest checking out the Linear Ballistic Accumulator (Donkin et al., 2011) model for a scenario like this. While LBA can be used to model any number of alternatives in a speeded choice task, to model signal detection you'd want to model just two accumulators, one for the "signal" response and one for the "no signal" response. With this scenario, ...


9

In addition to Mike's suggestion, see the Ratcliff diffusion model and variants thereof. E.g.: Ratcliff, R., & Rouder, J. N. (1998). Modeling response times for two–choice decisions. Psychological Science, 9, 347–356. Ratcliff, R., & Tuerlinckx, F. (2002). Estimating parameters of the diffusion model: Approaches to dealing with contaminant reaction ...


6

Technically it can go either way, and both situations would be conceptually and statistically equivalent. But it is conventional to fix the mean of the noise distribution at 0, so that increased discriminability corresponds graphically with the signal+noise distribution moving rightward along the latent axis.


6

You should probably also check out: Pleskac & Busemeyer (2010). Two-stage dynamic signal detection: A theory of choice, decision time, and confidence. Psychological Review. Also, I believe Busemeyer has a dynamic signal detection theory paper but I don't know that it has been published. The Pleskac & Busemeyer paper probably draws on this ...


4

Yes, there are several alternatives to d'. First of all, why do we need alternatives to d'? d' assumes the distribution of internal representation of signal and noise are Gaussian. This assumption is common, but may not be true, and a measure that does not make this assumption is more robust. There are technical problems computing d' when the Hit Rate or ...


3

I've never looked at the GNAT before but, even though it's just about as transparent as IAT, the critical thing making it difficult to fake bias is the deadlining. In order to fake one would would have to respond in no-go trials where one would not and that also misrepresented their association. That calculation would be difficult to do in the time window ...


3

As per self-regulatory theory people can have two types of regulatory focus: promotion and prevention. Promotion focus is an eagerness-strategy where if we draw an analogy with SDT, one is more eager to detect signals even though there may be a few false alarms. One is ok with errors of commission. This type of motivation will reflect in seeded responses ...


2

Now I think I have understood your mental model of human perception, I can give you an answer. Correct me if this is not what you meant to ask. If I understood you, you think that the human brain functions like a robotic brain. A sensor captures an image, sends it to the brain (which is comparable to a central processing unit), then the next one, etc. The ...


2

I'm going to leave some commentary on your question—because you actually asked a lot of questions. Disclaimer—my answer is based on my understanding of human cognition, and I am not citing sources because I don't want this to be construed as a scientific answer. First question: does the brain have different "sampling rates" for the various senses? Answer: ...


2

A simple explanation for the phenomenon is top-down feedback. As the bottom-up acoustic/phonological input is coming in, there is top-down feedback based on your knowledge of the language, the situation, and all of the other contextual information, which is helping to constrain or inform your interpretation of the bottom-up signal. A classic example that is ...


2

I believe you may be looking for Fuzzy SDT which allows for the incorporation of response time into SDT, among other things: Hancock, P.A., Masalonis, A.J., & Parasuraman, R. (2000) "On the theory of fuzzy signal detection: theoretical and practical considerations" Theor. Issues In Ergon. Sci. 1(3):207-230 pdf



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