Data and Analysis Unit: EXE10

Description

fMRI contingency learning experiment, based on the Shanks-Darby procedure. This DAU contains raw data, and an analysis script, in open cross-platform formats (see the file format notes).

Citation

The APA-format citation for this resource is: Wills, A.J. and Milton, F. (2016). Data and Analysis Unit: EXE10. Retrieved from http://www.willslab.co.uk/exe10

If you make use of these resources, please drop me an email: andy@willslab.co.uk.

Resources

• scans - Sub-page containing MR raw data.
• exe10sots.zip - Updated 2016-09-26, now matches published paper. Archive containing stimulus onset time files required for fMRI analysis; one per participant. The files are in .mat format. Each file contains the vectors listed below. Each vector contains a set of stimulus onset times, expressed in seconds from the beginning of the run. Where a vector would otherwise be empty (e.g. for a participant who had no timeout trials), the vector contains a single onset time, set to a value after the end of the run (equal to, or greater than, 770 seconds).
  • training.1.correct: Trials during training phase, run 1, on which the participant made the correct response.
  • training.1.incorrect: Trials during training phase, run 1, on which the participant made the incorrect response.
  • training_1_timeout: Trials during training phase, run 1, on which the participant made no response.
  • training.2.correct: Trials during training phase, run 2, on which the participant made the correct response.
  • training.2.incorrect: Trials during training phase, run 2, on which the participant made the incorrect response.
  • training_2_timeout: Trials during training phase, run 2, on which the participant made no response.
  • old.test.correct: Trials during the test phase, on which a familiar stimulus was presented, and the participant made the correct response.
  • old.test.incorrect: Trials during the test phase, on which a familiar stimulus was presented, and the participant made the incorrect response.
  • critical.correct: Trials for critical test items in which the response is consistent with rule-based generalization (see Milton et al., submitted).
  • critical.incorrect: Trials for critical test items in which the response is consistent with similarity-based generalization (see Milton et al., submitted).
  • Compound_rule: Trials for the critical test compound items in which the response is consistent with rule-based generalization.
  • Compound_similarity: Trials for the critical test items in which the response is consistent with similarity-based generalization.
  • Element_rule: Trials for the critical test elements in which the response is consistent with rule-based generalization.
  • Element_similarity: Trials for the critical test element items in which the response is consistent with similarity-based generalization.
  • timeout: Trials during the test phase in which the participant made no response.
• exe10data.txt - Trial-level behavioural raw data. Column headings are as follows:
  • date: Date paricipant was tested (in MM-DD-YYYY format)
  • cond: Experimental condition ( "rule" - rule instructions; "sim" - similarity instructions).
  • cb: Counterbalance condition (see exe10code.csv).
  • subj: Participant ID (unique within EXE10).
  • phase: Experiment phase (1 = training, 2 = test)
  • blk: Block (Range 1-10; does not reset between phases).
  • trial: Trial number (resets for each block)
  • stim: Presented stimulus (e.g. 'cheese and garlic')
  • resp: Participant's response to presented meal ( 0 = No reaction , 1 = Reaction, -1 = timed out )
  • cresp: Correct response.
  • rt: Participant's reaction time to presented meal in millisecond ( 0 = timed out)
  • stim2: Stimulus ID number (see exe10code.csv, 'stim' column).
• exe10code.csv - Shows mapping from food stimulus ID ('stim2' in exe10data.txt), physical food names, and logical food labels (A,B, etc). The last of these is contingent on counter-blance condition. Column headings are as follows:
  • stim: Stimulus ID number (used in exe10data.txt)
  • Food: Physical food name(s) presented
  • CB1code: Logical food labels (A,B,...) where in first counterbalance condition (cb=1 in exe10data.txt)
  • CB1type: Logical food category where in first counterbalance condition. Categories are as follows:
    • famP: 'familiar positive' i.e. item present during training that is followed by an allergic reaction.
    • famN: 'familiar negative'
    • MN, OP, K/L, O/P: As described in Wills et al. (2011).
  • CB1corr: Correct response where cb=1 (0 = No reaction, 1 = Reaction). For novel test items, correct = the rule-based response (e.j. 0 for IJ)
  • CB2code: As CB1code for cb = 2
  • CB2type: As CB1type for cb = 2
  • CB2corr: As CB1corr for cb = 2
• exe10analysis.R - R script for behavioural analyses reported in Milton et al. (submitted). Requires R (R Core team, 2016), and the following scripts:
  • exe10preprocess.R - R script for behavioural data pre-processing.
  • exe10graphs.R - R script for graph production. Includes add_label function, written by Anderson (2013).
  • bsci.R - R function for calculation of difference-adjusted between-subject confidence intervals. Implements same calculation as bs.ci() from Baguley (2012). Written by Thom Baguley.
  • dienes2008.R - R script for Bayes Factor calculations as per Dienes (2011). Ported to R by Baugely and Kaye (2010).

Further resources

The following resources are not formally part of the archive, either because they do not use open formats, or because they have not been thoroughly bug-checked, or both. They are provided in case others find them useful, with absolutely no warranty!

• exe10eprime.tbz - Eprime 1.x scripts. The archive contains two .es2 scripts; one for each of the two conditions. Both are for counterblance condition 1.
• eprime_convert_exe010.R - Script to convert multiple E-prime .edat files to exe10data.txt.
• rawdata - Directory (local copy only, available on request). Contains .edat files for this experiment, plus combination of them into a single .XLS file, and some notes on the file structure (file_struct.txt).

References

• Anderson, S. C. (2013). Labelling panels in R. http://seananderson.ca/2013/10/21/panel-letters.html
• Baguley, T. (2012). Calculating and graphing within-subject confidence intervals for ANOVA. Behavior Research Methods, 44, 158-175.
• Baguley, T. & Kaye, D. (2010). Book review: Understanding psychology as a science: An introduction to scientific and statistical inference. British Journal of Mathematical and Statistical Psychology, 63, 695-698. (Despite the title, this paper also discusses between-subject confidence intervals, which is what bsci.R uses).
• Dienes, Z. (2011). Bayesian versus orthodox statistics: Which side are you on? Perspectives on Psychological Science, 6, 274-290.
• R Core Team (2016) R: A Language and Environment for Statistical Computing. http://www.R-project.org/, R Foundation for Statistical Computing Vienna, Austria.