CurrentModule = EcoTrialStructure
EcoTrialStructure provides simple and natural operations to analyze experiments in economic decision making. You can import both behavioral and physiological data.
When the data have already been saved as a .mat file, the first thing to do is import the data.
This package's test/data folder contains a test file that serves as a useful demo:
julia> using EcoTrialStructure
julia> cts, trs, ets = parsemat(joinpath(pkgdir(EcoTrialStructure), "test", "data", "testfile.mat"));
julia> cts
OrderedCollections.OrderedDict{Int64, CellsTrial{Float64}} with 2 entries:
3 => 63 cells with 69 timepoints
201 => 63 cells with 39 timepoints
julia> trs
OrderedCollections.OrderedDict{Int64, TrialResult} with 2 entries:
3 => TrialResult(nA=0, nB=2, leftA=true, choseA=true)
201 => TrialResult(nA=2, nB=0, leftA=false, choseA=true)
julia> ets
OrderedCollections.OrderedDict{Int64, EventTiming} with 2 entries:
3 => EventTiming(trial_start=0.0 ms, offer_on=2003.0 ms, offer_off=4820.0 m…
201 => EventTiming(trial_start=0.0 ms, offer_on=2002.0 ms, offer_off=4812.0 m…
This test data file was extracted from a much larger & more complete experiment with 237 trials, of which just 2 were used for testing purposes.
cts has information about the cells (see CellsTrial), trs about the offers and behavioral decisions (see TrialResult), and ets about the timing of events during each trial (see EventTiming).
Each is indexed with the trial index, i.e., ets[3] extracts the event timing for trial 3.
You can extract additional information from the .mat file; an example is given by positive_cells.
Extracting data can be done by standard Julia methods, for example:
julia> trs[3].nB
2
There are also a number of convenience utilities, like isforced to detect whether a given trial presented a "forced" choice. See more examples in Simple utilities.
CellsTrial provides a number of convenience methods for extracting comparable data from different trials; see the examples in its documentation for a detailed explanation. But as an overall example, here is a demonstration based on the data above in [Matlab-file import](@ref mat). Here, we extract dFoF data triggered at offer_on across all (two) trials, extracting 2 pre-frames and 8 post-frames:
julia> fs = FrameSeq(:offer_on, -2:7) # 0 corresponds to offer_on
FrameSeq(:offer_on, -2:7)
julia> dFoFs = [cts[trialindex][fs(et), :][2] for (trialindex, et) in ets];
julia> dFoFs[1] # trial 3
10×63 OffsetArray(::Matrix{Float64}, -2:7, 1:63) with eltype Float64 with indices -2:7×1:63:
0.146783 0.479383 0.598108 … 0.0408331 0.0357164 0.0741511
0.177113 0.481758 0.579934 0.0181103 0.029064 0.0962147
0.158358 0.318992 0.679291 0.0356008 -0.0285638 0.242723
0.0949043 0.427107 0.792304 0.105977 -0.0537317 0.26273
0.207441 0.387189 0.50655 0.0597971 -0.0613983 0.135856
0.173742 0.240543 0.49942 … 0.0809025 -0.0496011 0.129161
0.0754508 0.292467 0.44907 0.156167 0.00943657 0.143208
0.115572 0.281595 0.356081 0.0406873 0.0155088 0.122557
0.119914 0.193427 0.418782 0.100677 0.288955 0.19112
0.0501042 0.12239 0.416016 0.0648454 0.622751 0.0572375
and where dFoFs[2] returns the data for trial 201.
As explanation, fs(et) "concretizes" the abstract notion of offer_on to the specific time for the trial corresponding
to the events recorded in et. Frame sampling may not be precisely syncronized with behavioral events, so the
frame nearest to the event time is chosen as the basepoint. Because indexing a CellsTrial with a time interval or FrameSeq returns both the time interval and the dFoF data, the final [2] selects just the dFoF data.
parsemat
positive_cells
CellsTrial
FrameSeq
OfferType
TrialResult
EventTiming
madechoice
isforced
iswrong
isdeferred
ncells