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Jun
16
awarded  Necromancer
Jun
16
comment Research suggesting conscious control over brain region activation?
Search pubmed for "neurofeedback" to get an impressing list of publications concerning your question: ncbi.nlm.nih.gov/pubmed?term=neurofeedback
Jun
13
answered Why do participants prefer to give input values that are “round numbers”?
Jun
12
awarded  Necromancer
Jun
12
revised Positive and negative reinforcement and punishment effectiveness
added 71 characters in body
Jun
11
comment How to obtain Fritz Perls' Eye Witness to Therapy film?
I found three films there: auditorium-netzwerk.de/Fachbereiche/… Don't know whether they are worth the money, though.
Jun
11
revised Positive and negative reinforcement and punishment effectiveness
deleted 1 characters in body
Jun
11
answered Positive and negative reinforcement and punishment effectiveness
Jun
8
comment Are IQ tests “biased” against individuals with Asperger's Syndrome?
Just a note: The fact that you passed the test does not necessarily mean that the test is not biased against you. The bias would just result in lower scores, which nevertheless can be higher than the Mensa inclusion criterion.
Jun
4
comment Fitting a psychometric function when data does not lend itself to a sigmoidal fit [pypsignifit]
Another idea: try to constraint lapsing rate and guessing rate to values between 0.3 and 0.8 (i.e., the minimum and maximum percent correct rate in the data set your picture is based on).
Jun
4
comment Fitting a psychometric function when data does not lend itself to a sigmoidal fit [pypsignifit]
I would not suggest to use different functions for individual fits, but use one functions for all, i.e., choose the function that gives the best overall solutions. Concerning the bad cases: did you try to seed the estimation routine with starting values nearer to 0.5 for the guessing rate and the lapsing rate?
Jun
3
comment Fitting a psychometric function when data does not lend itself to a sigmoidal fit [pypsignifit]
Then I would try different psychometric functions (e.g, logistic, Weibull) until I find one that is fitted to the data as a straight line with a slope of almost zero.
Jun
3
comment Visual search: complexity of positive vs negative search tasks
Actually I think that the reference to Treisman makes your answer the best one. Nevertheless, everything else in your answer that does not come from Treisman is not backed up by academic research and, therefore, is not what a good answer at CogSci should be.
Jun
3
comment Fitting a psychometric function when data does not lend itself to a sigmoidal fit [pypsignifit]
The data look really like the participant performed on chance level. Actually, I would not try to fit them at all, because the fits cannot get better than your example.
May
29
comment What's the name of this visual search task?
Don't know the name, but maybe you can find it in the following paper by Treisman: sciencedirect.com/science/article/pii/S0734189X85800049, as most of it is concerned with such a task.
May
28
awarded  Critic
May
25
comment What is the term for judging based on a simulation of the same parameters on oneself
@JohnPick: Nah, it's too short as an answer.
May
25
comment What is the term for judging based on a simulation of the same parameters on oneself
Especially your second example sounds a lot like egocentrism (en.wikipedia.org/wiki/Egocentrism)
May
25
comment Visual search: complexity of positive vs negative search tasks
That's not what Treisman said. She just observed that people are faster "finding a red square" compared to "finding something that is not a red square". Actually, the fact that the difficulty of the tasks similar makes this observation interesting. If the tasks would differ in difficulty, this observation would be rather trivial, wouldn't it?
May
24
comment Visual search: complexity of positive vs negative search tasks
But your theory explains the phenomenon in terms of differences in task difficulties (you argue that not-Y usually is more difficult to evaluate). However, the phenomenon is about the difference between evaluating X and not-X, that is, there is no difference in task difficulty.