# Tag Info

16

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, ...

10

In addition to Mike's answer, see the Ratcliff diffusion model 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 times and parameter ...

8

A study by D’Zmura et al. (2009) in which two syllables were spoken in imagination showed that imagined speech information was present in EEG alpha, beta and theta bands. The beta band (13-18 Hz) proved most informative. The most informative electrodes were located mainly near the top of the head (vertex) where electromyographic artifacts had least influence....

7

The field of study you should focus on is the one for which you have already identified in your paragraph above which is EEG based "brain-computer interface". EEG signals are compared by their "features". Each of the signal you have provided above have different features. These features can be mean, variance, frequency, kurtosis, skewness of each of the ...

7

Short Answer It appears that stimulation of the thalamus would invoke feeling of pain: Direct deep brain stimulation (DBS) in the VP thalamus from patients without pain typically evoked nonpainful, paraesthetic sensation. DBS at the core and posterior inferior region of the VP thalamus can evoke pain sensation without specific topographic distribution. ...

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

There was a special issue of the journal Perception and Psychophysics in 2001, titled Psychometric Functions and Adaptive Methods. It contains several papers relevant to your question. Klein's paper [1] references all the others and reviews what each is about. It should serve as a good starting point. An excerpt from Klein's summary: The simple up–...

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 ...

5

There are a huge number of paradigms that SDT can be applied to. The simplest is probably the so-called yes/no paradigm. You present a single stimulus (typically noise alone or signal plus noise) and ask was the signal present. The subject if forced to respond with either yes or no. This type of paradigm typically leads to a response bias. In a 2-interval, 2-...

5

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 ...

5

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 ...

4

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: ...

4

If I understand your question correctly, yes, since ERP involves averaging over similar events, very accurate time-locking to stimulus onset is critical. The idea of an ERP is that activity relevant to the stimulus will happen at a similar timepoint after similar stimuli--if you don't have an accurate time stamp (usually to the level of single milliseconds ...

4

For these types of questions I really like Detection Theory: A User's Guide by Macmillan and Creelman. They consider 3 types of bias. The criterion location $c$ is calculated relative to the zero-bias point and expressed in units of standard deviations, such that a $c$ of 1 means the criterion is 1 standard deviation to the right of the zero-bias location ...

4

I worked out the answer to the question just as I was about to post it. Swets (1973) writes of a 1954 conference that Peterson and Birdsall had presented it a year earlier in a technical report. So it is fair to say, from the vantage point of psychology, that Peterson and Birdsall showed us how to plot the data. Unless Swets is mistaken, the ...

3

The EEG signals between these regions will not be independent. The electrical activity that is being measured at the scalp, is the result of electrical flow inside the brain. This flow does not go in just one direction. It 'smears out' a little bit to surrounding areas, since the entire scalp/body is conductive. Another reason that the activity measured at ...

3

The biggest drawback of your procedure, also point of discussion previously here is the limited number of trials. Apparently, you obtained binary data, namely one answer (yes/no) per trial, basically leaving you with a sparsely sampled, binary data set. Binary data will not allow a curve fitting, which is the conventional way to determine a threshold. To ...

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 ...

3

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

3

Signals and System by Oppenheim (and others) was developed while he was teaching 6.003 at MIT. Similarly Foundations of Analog and Digital Electronic Circuits by Agarwal (and others) was developed while he was teaching 6.002 at MIT. Circuits, Signals and Systems by Siebert was written while he was teaching at MIT. Siebert was before my time, but I believe 6....

3

You can't. Flatline EEG is how brain death is defined, and while it may be possible to induce an eye blink in a deceased human, I am sure ethics will not approve, and it won't be scientifically satisfactory. even in the case of a real flatline EEG, there will always be some remaining residual activity (albeit very weak), such as line noise. However, ...

3

First of all, I would recommend working with a domain specific (EEG) and established analysis package of your choice. You mentioned python, so I would advise you to check out MNE-Python. There is a tutorial regarding eyeblink detection/correction/rejection from EEG data that you could work through. To answer your general question, ICA and PCA are typically ...

3

In order to estimate d' for a particular signal level, we need an estimate of the hit rate at that signal level and an estimate of the false alarm (FA) rate. Typically, the FA rate is estimated from the no-signal catch trials. With a paradigm like the method of constant stimuli, the assumption is that the false alarm rate is constant and doesn't depend on ...

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 find that it is the signal strength of the second signal that more so determines correct detection (0,1) The ideal unbiased observer bases the decision on $X1-X2$ while it sounds like your subjects are basing their decision on $aX1-X2$ where $a < 1$. While it is suboptimal, it is not a big deal. The fact that you know it means either report it or train ...

2

There's actually a very simple answer to this question: calculate criterion of confidence level = 3 by counting as hits only those trials where the answer was correct (and the target was there), and confidence was equal or higher than 3. This is also what Rahnev et al did in the following paper: Rahnev, D., Koizumi, A., McCurdy, L. Y., D’Esposito, M., &...

2

First lets write down some math for the above figures assuming Gaussian distributions with means $\mu_{n}$ and $\mu_{s}$ both with a standard deviation of unity. In this case the probability of a detection is $$P_D=1/(\sqrt{2\pi})\int_{c}^{\infty}e^{-(x-\mu_{s})^2/2}dx$$ and the probability of a false alarm is P_F=1/(\sqrt{2\pi})\int_{c}^{\infty}e^{-(x-\...

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