Task 2 Flashcards
What is a psychometric function?
-> It is a Graph , which is showing when the participant is capable of percieving something (Y) axis, while increasing physical intensity (tone X).
What are two response baises / reponse criterion ?
- liberal responder
- Conservative responder
Waht is meant by liberal responder ? (Also explain how they would respond in a signal detction task)
- response to even the slightest possibility of hearing a tone
- In SDT = high hit high false alarms but low CR and low misses
- Xc = left shifted
What is meant by conservitive responder ? (Also explain how they would respond in a signal detction task)
- wants to be totally sure that she hears the tone before saying “yes.”
- In SDT = low hit low false alarms But high CR and high misses
- Xc right shifted
How to overcome the response criterion ?
- Via the signal detection theory = introducing trails with no tone
- Also introduce Payoffs = No error = 100 Euro
how does the signal detection tehory work ? (Method)
- Two stimuli are present
o Tone = signal
o noise is all the other stimuli in the environment (often mistakes as signals) - The tone is in 50% of the trials present and 50% not present while the noise is always present.
-> Randome order between tone and noise
-> Meassures sensitivty of the subject!!
When is the tone easy detctable in a SDT task? Why is that bad ?
- High intensity tone
- Low level of noise (less pread) -> (the graphs become more narrow)
- > In these trails we could not measure sensitivity we must always work around the threshold
What types of noises do exist ?
- > Internal noises
- > External noises
What is meant by internal noise ?
-> brain / neural activity
What is meant by external noise ?
-> random variations in the environment ( a fly / headphone moving)
How can an Error occur in a trial where the tone was present and in a trail where no tone was present ?
- > Tone present = weak interanl respond ->Stmuli is weak and noise is weak
- > Tone not present = Strong internal respond -> high noise
What are the Genral response /decison criterion ?
-> XC = stands for answering convincively that a reponse is cuased by a signal and not by noise
Xc = internal response
Everything to the right of the XC = Yes caused by signal
Everything to the left of the Xc = “NO” caused by noise
What are the two responses which we can get in a SDT task with NO SIGNAL BEING PRESNET?
- Correct rejection = no tone present -> answer no -> correct
- False Alarm = no tone present -> answer yes -> wrong
What are the two responses which we can get in a SDT task with A SIGNAL BEING PRESNET?
- Hit = Tone present -> answer yes -> correct
- Miss = Tone present -> answer NO -> wrong
What is the overall goal of SDT ?
- To identify the true sensitivy d’
What does d’ stand for ?
- Sensitvity
What is an assumption of the STD ?
- > We assume a normal distribution to make use of Z scores
- > Internal noise has a specific distribution across trials (usually standard Gaussian = normal)
- > Same noise distribution for N trials and S+N trials
- > Signal and noise add up linearly (i.e., do not interact)
- > Decision is based on two consecutive processing stages (sensory stage and decision stages)
How do we compute d’ ?
- P(FA) /Total number of no signal
- P (H) / total number of signals
- d’= Z (P(H)) – Z (P(FA))
What are other options to identitfy if good sensitivty is present ?
- The overlap between the two graphs (best method)
- > If the overlap is small = d’ large -> high sensitivity - Or the distance between the two mean (peaks)
How do we interpretet different d’ values ?
- d’= 0 -> poor sensitivity / detectability -> just guessing
- d’ > 0 -> good sensitivity / detectability -> the best
- d’ < 0 -> good sensitivity / detectability -> but systematic error
What does Beta (ß) stand for ?
-> strategy
How do we compute the strategy / beta?
- > Z(P(FA)) and Z(P(H)) as before (see previous slides)
- > Y(Z(P(FA))) = height of N distribution at Xc
- > Y(Z(P(H))) = height of S+N distribution at Xc
- > β = Y(Z(P(H))) / Y(Z(P(FA)))
What is a important computing rule ?
- > If we know What P (CR) is then we know P (FA)
- > 1 - P (CR) = P (FA)
How do we more simply identify Beta ?
- > It is the relative height at the two distributions (graphs) at the criterion (Xc)
