Commit 75a12cbc authored by Simon Ciranka's avatar Simon Ciranka

the Markdown file is always messed up

parent 1e6050b8
...@@ -69,7 +69,6 @@ be interpreted as an adaptive learning rate. ...@@ -69,7 +69,6 @@ be interpreted as an adaptive learning rate.
# g squared (fancy name for changing the sign of the LL) # g squared (fancy name for changing the sign of the LL)
G2 <- sum(subjectLevel$ProbCorrect, na.rm = TRUE) G2 <- sum(subjectLevel$ProbCorrect, na.rm = TRUE)
G2 <- -2*G2 G2 <- -2*G2
#Upper bounds - I also had beta max 1 min -1 as in your script but I wonder could be even more ambig averse so I think larger range is also fine...
G2 G2
} }
...@@ -138,7 +137,6 @@ estimate of the participants to create logliks. ...@@ -138,7 +137,6 @@ estimate of the participants to create logliks.
# g squared (fancy name for changing the sign of the LL) # g squared (fancy name for changing the sign of the LL)
G2 <- sum(subjectLevel$ProbCorrect, na.rm = TRUE) G2 <- sum(subjectLevel$ProbCorrect, na.rm = TRUE)
G2 <- -2*G2 G2 <- -2*G2
#Upper bounds - I also had beta max 1 min -1 as in your script but I wonder could be even more ambig averse so I think larger range is also fine...
G2 G2
} }
``` ```
...@@ -183,7 +181,6 @@ amount of time. ...@@ -183,7 +181,6 @@ amount of time.
# g squared (fancy name for changing the sign of the LL) # g squared (fancy name for changing the sign of the LL)
G2 <- sum(subjectLevel$ProbCorrect, na.rm = TRUE) G2 <- sum(subjectLevel$ProbCorrect, na.rm = TRUE)
G2 <- -2*G2 G2 <- -2*G2
#Upper bounds - I also had beta max 1 min -1 as in your script but I wonder could be even more ambig averse so I think larger range is also fine...
G2 G2
} }
...@@ -309,6 +306,8 @@ fitted Parameters in the same dataFrame ...@@ -309,6 +306,8 @@ fitted Parameters in the same dataFrame
``` ```
#### Model Comparison #### Model Comparison
---------------- ----------------
...@@ -337,21 +336,34 @@ Seqential Updating is bad. ...@@ -337,21 +336,34 @@ Seqential Updating is bad.
# So now lets look at the learning rates. # So now lets look at the learning rates.
--------------------------------------- ---------------------------------------
### Marble Estimate Distribution ### Marble Estimate Distribution
This plot shows how the learning rates are distributed in all subjects. This plot shows how the learning rates are distributed in all subjects.
We can see that most of the subjects seem to overweight new pieces of We can see that most of the subjects seem to overweight new pieces of
information relative to an Ideal observer. information relative to an Ideal observer.
![](HalfHGF_files/figure-markdown_strict/ShowPlot-1.png) ![](HalfHGF_files/figure-markdown_strict/ShowPlot-1.png)
### Marble Estimate Distribution Exponential ### Marble Estimate Distribution Exponential
What i get now if i look at the parameter estimate What i get now if i look at the parameter estimate
of the exponential model is an **underweghting** of all information of the exponential model is an **underweghting** of all information
considered. considered.
![](HalfHGF_files/figure-markdown_strict/Show%20second%20Plot-1.png) ![](HalfHGF_files/figure-markdown_strict/Show%20second%20Plot-1.png)
## Marble Estimation Distributin HGF_Like ## Marble Estimation Distributin HGF_Like
The HGF like model has no Benchmark and the “learning rate" can not be super easily interpreted. But i like it :) The HGF like model has no Benchmark and the “learning rate" can not be super easily interpreted. But i like it :)
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