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FirstWordAttemptSampleIncreasingVocab.m
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FirstWordAttemptSampleIncreasingVocab.m
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Core Model, 2022
% Written by Maya Davis
% Concept by Maya Davis and Melissa A. Redford
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [VocabularySequences, GoalSequences, AllSelectedSequences, ...
VocabularySizes, AllDistanceVectors, DistanceVectorsAveraged, ...
AllAverageDistances, AverageDistancesAveraged] = ...
FirstWordAttemptSampleIncreasingVocab(space, ConsonantMins, ...
ConsonantMaxes, VowelMins, VowelMaxes, CVPattern, SilhouetteRadius, ...
VocabularySizes, NumNeighbors, NumTrials)
% EX | CVPattern = "CVC"
% EX | There are 2 consonants and 2 vowels
% EX | SilhouetteRadius = 2
% EX | VocabularySizes = [1 2 4 7]
% EX | NumNeighbors = 2
% EX | NumTrials = 3
% Assume that the motor silhouettes are 2n - 1 time steps long
TimeLength = 2 * length(char(CVPattern)) - 1;
AllDistanceVectors = nan(length(VocabularySizes), TimeLength, NumTrials);
% EX | AllDistanceVectors = nan(4, 5, 3)
AllAverageDistances = nan(NumTrials, length(VocabularySizes));
% EX | AllAverageDistances = nan(3, 4)
DistanceVectorsAveraged = nan(length(VocabularySizes), TimeLength);
% EX | DistanceVectorsAveraged = nan(5, 4)
AverageDistancesAveraged = nan(1, length(VocabularySizes));
% EX | AverageDistancesAveraged = nan(1, 4)
VocabularySequences = cell(NumTrials, length(VocabularySizes));
% EX | VocabularySequences = cell(3, 4)
AllSelectedSequences = cell(NumTrials, length(VocabularySizes));
% EX | AllSelectedSequences = cell(3, 2)
GoalSequences = cell(NumTrials, length(VocabularySizes));
% EX | GoalSequences = cell(3, 4)
for n = 1:NumTrials
fprintf("Starting trial %d\n", n);
[VocabSequences, GoalSequence, SelectedSequences, ...
~, ~, DistanceVectors, AverageDistances] = ...
FirstWordAttemptRandomVocabularyIncreasing(space, ...
ConsonantMins, ConsonantMaxes, VowelMins, VowelMaxes, ...
CVPattern, SilhouetteRadius, VocabularySizes, NumNeighbors);
% EX | SUPPOSE EVERY TRIAL IS THE SAME:
% EX | VocabSequences = {[1 1 1; 1 1 2] [1 1 1; 1 1 2; 1 2 1]
% EX | [1 1 1; 1 1 2; 1 2 1; 1 2 2]
% EX | [1 1 1; 1 1 2; 1 2 1; 1 2 2; 2 1 1; 2 1 2; 2 2 2]}
% EX | GoalSequences = {[2 2 1] [2 2 1] [2 2 1] [2 2 1]}
% EX | SelectedSequences = {[1 1 1; 1 1 2] [1 2 1; 1 1 1]
% EX | [1 2 1; 1 1 1] [2 1 1; 1 2 1]}
% EX | DistanceVectors = [6 6 3; 6 3 0; 6 3 0; 3 3 0]
% EX | AverageDistances = [5 3 3 2]
VocabularySequences(n, :) = VocabSequences;
AllSelectedSequences(n, :) = SelectedSequences;
for v = 1:size(GoalSequences, 2)
GoalSequences{n, v} = GoalSequence;
end
AllDistanceVectors(:, :, n) = DistanceVectors;
AllAverageDistances(n, :) = AverageDistances;
fprintf("Done with trial %d\n", n);
end
% EX | VocabularySequences = {[1 1 1; 1 1 2] [1 1 1; 1 1 2; 1 2 1]
% EX | [1 1 1; 1 1 2; 1 2 1; 1 2 2]
% EX | [1 1 1; 1 1 2; 1 2 1; 1 2 2; 2 1 1; 2 1 2; 2 2 2];
% EX | [1 1 1; 1 1 2] [1 1 1; 1 1 2; 1 2 1]
% EX | [1 1 1; 1 1 2; 1 2 1; 1 2 2]
% EX | [1 1 1; 1 1 2; 1 2 1; 1 2 2; 2 1 1; 2 1 2; 2 2 2];
% EX | [1 1 1; 1 1 2] [1 1 1; 1 1 2; 1 2 1]
% EX | [1 1 1; 1 1 2; 1 2 1; 1 2 2]
% EX | [1 1 1; 1 1 2; 1 2 1; 1 2 2; 2 1 1; 2 1 2; 2 2 2]}
% EX |
% EX | AllSelectedSequences = {
% EX | [1 1 1; 1 1 2] [1 2 1; 1 1 1] [1 2 1; 1 1 1] [2 1 1; 1 2 1];
% EX | [1 1 1; 1 1 2] [1 2 1; 1 1 1] [1 2 1; 1 1 1] [2 1 1; 1 2 1];
% EX | [1 1 1; 1 1 2] [1 2 1; 1 1 1] [1 2 1; 1 1 1] [2 1 1; 1 2 1]}
% EX |
% EX | GoalSequences = {[2 2 1] [2 2 1] [2 2 1] [2 2 1];
% EX | [2 2 1] [2 2 1] [2 2 1] [2 2 1];
% EX | [2 2 1] [2 2 1] [2 2 1] [2 2 1]}
% EX |
% EX | AllDistanceVectors(:,:,1) = [6 6 6; 6 6 6; 6 6 6; 3 3 3]
% EX | AllDistanceVectors(:,:,2) = [6 6 6; 3 3 3; 3 3 3; 3 3 3]
% EX | AllDistanceVectors(:,:,2) = [3 3 3; 0 0 0; 3 3 3; 3 3 3]
% EX |
% EX | AllAverageDistances = [5 3 3 2; 5 3 3 2; 5 3 3 2]
for s = 1:length(VocabularySizes)
DistanceVectorsAveraged(s, :) = mean(AllDistanceVectors(s, :, :), 3);
AverageDistancesAveraged(s) = mean(AllAverageDistances(:, s));
end
% EX | DistanceVectorsAveraged = [6 6 3; 6 3 0; 6 3 3; 3 3 3]
% EX | AverageDistancesAveraged = [5 3 3 2]
end
function [VocabSequences, GoalSequence, SelectedSequences, ASilhouette, ...
ResultingJunctures, DistanceVectors, AverageDistances] = ...
FirstWordAttemptRandomVocabularyIncreasing(space, ConsonantMins, ...
ConsonantMaxes, VowelMins, VowelMaxes, CVPattern, SilhouetteRadius, ...
VocabularySizes, NumNeighbors)
% EX | space.SpaceTransformation is the identity
% EX | ConsonantMins = [3 5; 4 6]
% EX | ConsonantMaxes = [3 5; 4 6]
% EX | VowelMins = [15 20; 18 25]
% EX | VowelMaxes = [15 20; 18 25]
% EX | CVPattern = "CVC"
% EX | SilhouetteRadius = 1
% EX | VocabularySizes = [2 3 4 7]
% EX | NumNeighbors = 2
for n = 1:length(VocabularySizes) - 1
assert(VocabularySizes(n) < VocabularySizes(n + 1), "The " + ...
"vocabulary sizes need to be listed in increasing order")
end
BiggestSize = VocabularySizes(length(VocabularySizes));
% EX | BiggestSize = 7
[space, AllSequences, MotorSilhouetteList, ExemplarList, ...
GoalExemplar, GoalSequence] = Make2DCVWords(space, ...
ConsonantMins, ConsonantMaxes, VowelMins, VowelMaxes, ...
CVPattern, SilhouetteRadius, BiggestSize);
% EX | AllSequences = [1 1 1; 1 1 2; 1 2 1; 1 2 2;
% EX | 2 1 1; 2 1 2; 2 2 2]
% EX | MotorSilhouetteList is of length 7 such that, for example,
% EX | MotorSilhouetteList(4) is the motor silhouette corresponding
% EX | to the sequence 1 2 2
% EX | ExemplarList is of length 7 such that, for example,
% EX | ExemplarList(4) is the exemplar corresponding to the
% EX | sequence 1 2 2
% EX | GoalExemplar is the trajectory w/ matrix
% EX | [5 12. 5 20 11.5 3; 6 15.5 25 14.5 4]
% EX | GoalSequence = [2 2 1]
VocabSequences = cell(1, length(VocabularySizes));
% EX | VocabSequences = cell(1, 4)
SelectedSequences = cell(1, length(VocabularySizes));
% EX | SelectedSequences = cell(1, 2)
GoalSequences = cell(1, length(VocabularySizes));
% EX | GoalSequences = cell(1, 4)
DistanceVectors = nan(length(VocabularySizes), ...
length(MotorSilhouetteList(1).Regions));
% EX | DistanceVectors = [nan nan nan nan; nan nan nan nan;
% EX | nan nan nan nan; nan nan nan nan; nan nan nan nan]
AverageDistances = nan(1, length(VocabularySizes));
% EX | AllAverageDistances = [nan nan nan nan]
for s = 1:length(VocabularySizes)
% EX | s = 1
% EX | s = 2
% EX | s = 3
% EX | s = 4
vSize = VocabularySizes(s);
% EX | vSize = 2
% EX | vSize = 3
% EX | vSize = 4
% EX | vSize = 7
CurrentVocabSequences = AllSequences(1:vSize, :);
% EX | CurrentVocabSequences = [1 1 1; 1 1 2]
% EX | CurrentVocabSequences = [1 1 1; 1 1 2; 1 2 1]
% EX | CurrentVocabSequences = [1 1 1; 1 1 2; 1 2 1; 1 2 2]
% EX | CurrentVocabSequences = [1 1 1; 1 1 2; 1 2 1; 1 2 2;
% EX | 2 1 1; 2 1 2; 2 2 2]
CurrentMotorSilhouetteList = MotorSilhouetteList(1:vSize);
% EX | CurrentMotorSilhouetteList = [MS1]
% EX | CurrentMotorSilhouetteList = [MS1 MS2]
% EX | CurrentMotorSilhouetteList = [MS1 MS2 MS3 MS4]
% EX | CurrentMotorSilhouetteList = [MS1 MS2 MS3 MS4 MS5 MS6 MS7]
CurrentExemplarList = ExemplarList(1:vSize);
% EX | CurrentExemplarList = [E1 E2]
% EX | CurrentExemplarList = [E1 E2 E3]
% EX | CurrentExemplarList = [E1 E2 E3 E4]
% EX | CurrentExemplarList = [E1 E2 E3 E4 E5 E6 E7]
[CurrentSelectedSequences, GoalSequence, ASilhouette, ...
ResultingJunctures, DistanceVector, AverageDistance] = ...
FirstWordAttempt(space, CurrentVocabSequences, ...
CurrentMotorSilhouetteList, CurrentExemplarList, ...
GoalExemplar, GoalSequence, NumNeighbors);
% EX | Closest sequences to [2 2 1] in order of decreasing
% EX | closeness: 2 1 1, 1 2 1, 2 2 2, 1 1 1, 2 1 2, 1 2 2, 1 1 2
% EX | CurrentSelectedSequences = [1 1 1; 1 1 2]
% EX | GoalSequence = [2 2 1]
% EX | ASilhouette = 11 - btwn - 11 - btwn - 12
% EX | ResultingJunctures = [JC1 JB11 JV1 JB21 JC2]
% EX | DistanceVector = [6 6 6 4.5 3]
% EX | AverageDistance = 5
% EX |
% EX | CurrentSelectedSequences = [1 2 1; 1 1 1]
% EX | GoalSequence = [2 2 1]
% EX | ASilhouette = 11 - btwn - 12 - btwn - 11
% EX | ResultingJunctures = [JC1 JB12 JV2 JB12 JC1]
% EX | DistanceVector = [6 4.5 3 1.5 0]
% EX | AverageDistance = 3
% EX |
% EX | CurrentSelectedSequences = [1 2 1; 1 1 1]
% EX | GoalSequence = [2 2 1]
% EX | ASilhouette = 11 - btwn - 12 - btwn - 11
% EX | ResultingJunctures = [JC1 JB12 JV2 JB12 JC1]
% EX | DistanceVector = [6 4.5 3 1.5 0]
% EX | AverageDistance = 3
% EX |
% EX | CurrentSelectedSequences = [2 1 1; 1 2 1]
% EX | GoalSequence = [2 2 1]
% EX | ASilhouette = 12 - btwn - 12 - btwn - 11
% EX | ResultingJunctures = [JC2 JB22 JV2 JB12 JC1]
% EX | DistanceVector = [3 3 3 1.5 0]
% EX | AverageDistance = 2
VocabSequences{1, s} = CurrentVocabSequences;
% EX | VocabSequences = {[1 1 1; 1 1 2] [1 1 1; 1 1 2; 1 2 1]
% EX | [1 1 1; 1 1 2; 1 2 1; 1 2 2]
% EX | [1 1 1; 1 1 2; 1 2 1; 1 2 2; 2 1 1; 2 1 2; 2 2 2]}
SelectedSequences{1, s} = CurrentSelectedSequences;
% EX | SelectedSequences = {[1 1 1; 1 1 2] [1 2 1; 1 1 1]
% EX | [1 2 1; 1 1 1] [2 1 1; 1 2 1]}
GoalSequences{1, s} = GoalSequence;
% EX | GoalSequences = {[2 2 1] [2 2 1] [2 2 1] [2 2 1]}
DistanceVectors(s, :) = DistanceVector;
% EX | DistanceVectors = [6 6 6 4.5 3; 6 4.5 3 1.5 0; 6 4.5 3 1.5 0; 3 3 3 1.5 0]
AverageDistances(1, s) = AverageDistance;
% EX | AverageDistances = [5.1 3 3 2.1]
fprintf("Done with vocabulary #%d\n", s);
end
end