31 SUmmary of Data Sets

31.1 Rat maze data

Consider an experiment in which a set of rats run through a set of mazes some number of times each. The researchers running this experiment are interested in how quickly rats can run these mazes, and so they record the time in seconds each rat takes to complete each run of each maze.

rm(list=ls())

d <- fread('https://crossley.github.io/book_stats/data/maze/maze.csv')

The resulting data.table contains the following columns:

  • rat: Rat identifier (i.e., different numbers correspond to different rats).

  • maze: Maze identifier (i.e., different numbers correspond to different mazes).

  • run: Run identifier (i.e., different numbers correspond to different runs).

  • time: The time in seconds taken to complete the maze.

31.2 Criterion learning data

  • Does feedback-delay impair criterion learning?

  • What is criterion learning? Here is an example trial.

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  • In this case, thin bars belong to category A, and thick bars belong to category B.

  • Bar thickness is continuous… when exactly does thick become thin?

  • Where is the category boundary (i.e., criterion) that separates thick from thin?

  • Criterion learning is the process that allows our brains to figure this out.

rm(list=ls())

d <- fread('https://crossley.github.io/book_stats/data/criterion_learning/crit_learn.csv')

The resulting data.table contains the following columns:

  • t: Trial number across the entire experiment.

  • t_prob: Trial number per problem.

  • sub: Subject identifier (i.e., different numbers correspond to different subjects).

  • cnd: Condition identifier.

  • prob_num: Problem number (gets increased by 1 every time a participant solves a problem).

  • t2c: Trials to criterion (i.e., the number of trials it took a participant to solve a particular problem.)

  • exp: Experiment indicator. Overall, this study was broken down into two experiments – one using sine-wave grating stimuli and the other using a different type of stimuli.

  • nps: Number of problems solved. This is the same as max(prob_num)

31.3 NHP data

The data file can be found here: https://crossley.github.io/book_stats/data//nhp_cat_learn/ii_gabor.csv

This data file contains the results from a monkey performing a category learning experiment similar to those that we have seen a handful of times already in this class. On each trial of the experiment the monkey sees a sine-wave grating and must learn through trial and error whether that grating is a member of category A or category B.

rm(list=ls())

d <- fread('https://crossley.github.io/book_stats/data//nhp_cat_learn/ii_gabor.csv')

col_names <- c(
  'cat',
  'x',
  'y',
  'resp',
  'rt',
  'phase'
)

setnames(d, col_names)

d[, trial := 1:.N]
  • trial: Trial number.
  • cat: Category label.
  • x: The spatial frequency of the sine-wave grating.
  • y: The orientation of the sine-wave grating.
  • resp: The response made by the monkey (i.e., category A or B).
  • rt: Reaction time (time from stimulus onset to button press).
  • phase: Indicates the phase of the experiment. In phase==2, the category labels are swapped relative to phase==1.

31.4 Minimally invasive surgery (MIS) data

  • This data is from a motor learning and motor control experiment.

  • On each trial, participants simply rested their hand in the middle of a desk, and then tried to move their hand quickly and accurately to a visual target somewhere on a circle centred at their hand position and with a radius of about 8 cm.

  • We tested two different groups of people. One group were college students, and the other group were professional surgeons.

  • The data file can be found here: https://crossley.github.io/book_stats/data/mis/mis_data.csv

rm(list=ls())

d <- fread('https://crossley.github.io/book_stats/data/mis/mis_data.csv')
  • subject: Anonymous subject ID
  • group: Indicates college student or surgeon
  • phase: Indicates the phase of the experiment
  • trial: Trial number
  • target: The visual target was reached to for this trial
  • error: The mismatch between the centre of the target and the final hand position
  • movement_time: How long the movement lasted
  • reaction_time: How long until the movement began
  • peak_velocity: The maximum velocity reached during the reach

31.5 MEG data

We also have magnetoencephalography (MEG) data collected from a single participant while they performed a category learning experiment. On each trial of the category learning experiment, the participant viewed a circular sine wave grating, and had to push a button to indicate whether they believed the stimulus belonged to category A or category B. We have seen and worked with this type of category learning experiment many times throughout this course, and it is further described by the following figure.

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MEG is used to record the time-series of magnetic and electric potentials at the scalp, which are generated by the activity of neurons. There are many sensors, each configured to pick up signal from a different position on the scalp. This is shown in the following figure (the text labels indicate the channel name and are placed approximately where the MEG sensor is located on a real head).

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The data file that we will be working with is arranged into epochs aligned to stimulus presentation. This means that every time a stimulus is presented we say that an epoch has occurred. We then assign a time of \(t=0\) to the exact moment the stimulus appeared. We then typically look at the neural time series from just before the stimulus appeared to a little while after the stimulus has appeared. For this data, each epoch starts 0.1 seconds before stimulus onset, and concludes 0.3 seconds after stimulus onset. The following figure shows the MEG signal at every sensory location across the entire scalp for 5 time points within this \([-0.1s, 0.3s]\) interval.

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The data can be located here: https://crossley.github.io/book_stats/data/eeg/epochs.txt

rm(list=ls())

## d <- fread('https://crossley.github.io/book_stats/data/eeg/epochs.txt')

  • The time column contains times in seconds relative to stimulus onset. Stimulus onset always occurs at \(0\) seconds.

  • The condition column indicates which category the stimulus belonged to for the given epoch. We won’t make use of this column here, and we will remove it below.

  • The epoch column is the epoch number. You can think of this like we have usually thought of trial columns in examples throughout the course.

  • The many different MEG xyz columns contain the actual neural time series signals for each sensor. See the above figure for how these column names map onto scalp positions.