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I am using the tapas_hgf_whatworld family of scripts (edited, edits explained below) to model the perceptual states of a pSRTT task (adapted from Marshall et al, 2016).
I am facing an awkward problem where the more accurate my subject's mu1hat is, the higher their da1 becomes.
Below is a graph to illustrate the problem:
the correct transitional probability of a high-frequency previous trial to a low-frequency current trial is 0.3 of the time, hence younger adults are marginally and significantly better at predicting mu1hat as compared to older adults in correct high-to-low frequency trials
However, younger adults have higher da1 as compared to older adults for correctly answered high-to-low frequency trials.
Have I done something wrong? Can anyone explain this?
Below is a description of my task and what I have done.
Description of my task
Briefly, this task contains 4 visual targets, appearing in different sequences of 50 trials each. For each trial within the 50 trials, one of the 4 visual targets will appear most frequently (70% of the time, high-frequency stimulus), while the rest of the 3 visual targets at 10% of the time (a total of 30%; low-frequency stimulus). The high-frequency stimulus changes every 50 trials, while the frequency of appearance stays the same between high and low-frequency stimuli. Low-frequency visual targets are looked at together as one whole low-frequency stimulus (appearing at an accumulative sum of 30% of the time).
To summarise, below is the transitional matrix between the previous trial (column) and the current trial (row)
()
Additionally, I have a 5th kind of trial-to-trial transition that is random (each visual targets is shown at 25% of the time) and used for dummy trials.
This is how I have set up my ttms in the tapas_hgf_whatworld_config.m, and how I have set up my input variables:
Hi experts,
I am using the tapas_hgf_whatworld family of scripts (edited, edits explained below) to model the perceptual states of a pSRTT task (adapted from Marshall et al, 2016).
I am facing an awkward problem where the more accurate my subject's mu1hat is, the higher their da1 becomes.
Below is a graph to illustrate the problem:
the correct transitional probability of a high-frequency previous trial to a low-frequency current trial is 0.3 of the time, hence younger adults are marginally and significantly better at predicting mu1hat as compared to older adults in correct high-to-low frequency trials
However, younger adults have higher da1 as compared to older adults for correctly answered high-to-low frequency trials.
Have I done something wrong? Can anyone explain this?
Below is a description of my task and what I have done.
Description of my task
Briefly, this task contains 4 visual targets, appearing in different sequences of 50 trials each. For each trial within the 50 trials, one of the 4 visual targets will appear most frequently (70% of the time, high-frequency stimulus), while the rest of the 3 visual targets at 10% of the time (a total of 30%; low-frequency stimulus). The high-frequency stimulus changes every 50 trials, while the frequency of appearance stays the same between high and low-frequency stimuli. Low-frequency visual targets are looked at together as one whole low-frequency stimulus (appearing at an accumulative sum of 30% of the time).
To summarise, below is the transitional matrix between the previous trial (column) and the current trial (row)
()
Additionally, I have a 5th kind of trial-to-trial transition that is random (each visual targets is shown at 25% of the time) and used for dummy trials.
This is how I have set up my ttms in the tapas_hgf_whatworld_config.m, and how I have set up my input variables:
ttms
My input into the model consists of 2 columns.
Thank you!
Regards.
Vae
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