Psych 45: N-back stats



In [1]:

    
%matplotlib inline

import pandas as pd
import seaborn as sns
sns.set(style='ticks', context='poster', font_scale=1.5)









    



/Users/zeynepenkavi/anaconda/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')

Import data file



In [2]:

    
data = pd.read_csv('http://web.stanford.edu/class/psych45/demos/Psych45-nback_stats_2017.csv')

# filter RT column so just a number (ms and comma not included)
data.avg_rt = data.avg_rt.str.strip(' ms').str.replace(',', '').astype(float)

data.percent_correct = data.percent_correct.astype(float)
data.combined = data.combined.str.replace(',', '').astype(float)

# task_list = ['2-back', '3-back', '4-back', '5-back', '6-back', '7-back', '8-back']
task_list = ['2-back', '3-back', '4-back', '5-back']
task_list_subset = ['2-back', '3-back', '4-back']



In [3]:

    
data.head()









    Out[3]:






  
    
      
      task
      percent_correct
      avg_rt
      combined
      when
      date_downloaded
    
  
  
    
      0
      2-back
      73.33
      651.12
      887.88
      14 hours ago
      4/8/2017 9:52 AM
    
    
      1
      2-back
      66.67
      640.53
      960.80
      14 hours ago
      4/8/2017 9:52 AM
    
    
      2
      2-back
      40.00
      787.44
      1968.61
      14 hours ago
      4/8/2017 9:52 AM
    
    
      3
      2-back
      80.00
      848.13
      1060.16
      17 hours ago
      4/8/2017 9:52 AM
    
    
      4
      2-back
      86.67
      809.40
      933.92
      17 hours ago
      4/8/2017 9:52 AM

Analyses

Number of data points:



In [4]:

    
data.groupby(['task']).count().when









    Out[4]:





task
2-back    151
3-back    103
4-back     27
5-back      7
Name: when, dtype: int64

Mean accuracy and response time:



In [5]:

    
data.groupby(['task']).mean().reset_index()









    Out[5]:






  
    
      
      task
      percent_correct
      avg_rt
      combined
    
  
  
    
      0
      2-back
      71.627815
      895.365232
      1497.726291
    
    
      1
      3-back
      52.760388
      987.278350
      2094.078932
    
    
      2
      4-back
      45.577778
      1002.651481
      2472.497778
    
    
      3
      5-back
      52.875714
      1113.028571
      2194.667143

How does task affect percent of trials correct?



In [6]:

    
g = sns.factorplot(x='task', y='percent_correct', 
                   order=task_list,
                   data=data, 
                   ci=95, 
                   aspect=2.3,
                   palette=sns.color_palette("Blues_r", 
                                             n_colors=len(task_list)))
g.set_xlabels('')
g.set_ylabels('% correct')
g.set_xticklabels(rotation=30)
sns.despine(trim=True)



In [7]:

    
g = sns.factorplot(x='task', y='percent_correct', 
                   order=task_list_subset,
                   data=data, 
                   palette=sns.color_palette("Blues_r", 
                                             n_colors=len(task_list_subset)), 
                   ci=95, 
                   aspect=1.3)
g.set_ylabels('% correct')
g.set_xlabels('')
g.set_xticklabels(rotation=30)
sns.despine(trim=True)

How does task affect average response time on correct trials?



In [8]:

    
g = sns.factorplot(x='task', y='avg_rt', 
                   order=task_list,
                   data=data, 
                   palette=sns.color_palette("Blues_r", 
                                             n_colors=len(task_list)), 
                   ci=95, 
                   aspect=2.3)
g.set_ylabels('avg RT (ms)')
g.set_xlabels('')
g.set_xticklabels(rotation=30)
sns.despine(trim=True)



In [9]:

    
g = sns.factorplot(x='task', y='avg_rt', 
                   order=task_list_subset,
                   data=data, 
                   palette=sns.color_palette("Blues_r", 
                                             n_colors=len(task_list_subset)), 
                   ci=95, 
                   aspect=1.3)
g.set_ylabels('avg RT (ms)')
g.set_xlabels('')
g.set_xticklabels(rotation=30)
sns.despine(trim=True)

What about combined (correct and incorrect trials) RT?



In [10]:

    
g = sns.factorplot(x='task', y='combined', 
                   order=task_list,
                   data=data, 
                   palette=sns.color_palette("Blues_r", 
                                             n_colors=len(task_list)), 
                   ci=95, 
                   aspect=2.3)
g.set_ylabels('combined RT (ms)')
g.set_xlabels('')
g.set_xticklabels(rotation=30)
sns.despine(trim=True)



In [ ]:

	task	percent_correct	avg_rt	combined	when	date_downloaded
0	2-back	73.33	651.12	887.88	14 hours ago	4/8/2017 9:52 AM
1	2-back	66.67	640.53	960.80	14 hours ago	4/8/2017 9:52 AM
2	2-back	40.00	787.44	1968.61	14 hours ago	4/8/2017 9:52 AM
3	2-back	80.00	848.13	1060.16	17 hours ago	4/8/2017 9:52 AM
4	2-back	86.67	809.40	933.92	17 hours ago	4/8/2017 9:52 AM

	task	percent_correct	avg_rt	combined
0	2-back	71.627815	895.365232	1497.726291
1	3-back	52.760388	987.278350	2094.078932
2	4-back	45.577778	1002.651481	2472.497778
3	5-back	52.875714	1113.028571	2194.667143