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Nested hierarchies of structures using Active inference for language recognition and reading
Each level of the hierarchy has a default structure if the lowest observable is a letter o(1)=letter a simple hierarchy can be described level 1 two factor: states = [composition of letters (e.g. word), locations of letters in the word] obs = [ letter, locations of letters in the word] than level 2 states = [composition of words (e.g. sentence), locations of words in the sentence] obs = [ word, locations of words in the sentence]
Extending the composition and the meaning of the composition it is possible to iterate this structure on N-levels
It is, also, possible to add classes for each level, that gives a context for each state recognized. For example for level 2 becomes: states = [sentence, locations of words in the sentence, context] obs = [ word, locations of words in the sentence, report]
Type HAI_LANGUAGE_pathsLoad; in main the directory to add necessary subpaths. then choose one of the main in directory "MAINS" to see a demonstration of code features
Execute reading simulation (in folder simulation) to produce the outputs
- four letters reading simulation: Sim1_CM_4l, Sim1_DM1_4l, Sim1_DM2_4l, Sim1_DM_4l
- eight letters reading simulation: Sim1_CM_8l, Sim1_DM1_8l, Sim1_DM2_8l, Sim1_DM_8l Then execute Sim1_Fig5a to produce Fig5(a) of the paper.
HAI example with a simple dictionary 2-level hierarchy level 1 states = [ word, locations of letters in the word] obs = [ letter, locations of letters in the word] level 2 states = [sentence, locations of words in the sentence] obs = [ word, locations of words in the sentence]
DICTIONARY: Simple dictionary of 6 words composed by A B C D
2-level hierarchy level 1 states = [ word, locations of letters in the word] obs = [ letter, locations of letters in the word] level 2 states = [sentence, locations of words in the sentence] obs = [ word, locations of words in the sentence]
DICTIONARY: Simple dictionary of English words (two syllable words of six letters)
3-level hierarchy level 1 states = [syllable, locations of letter in the syllable] obs = [ letter, locations of letter in the syllable] level 2 states = [ word, locations of syllable in the sentence] obs = [syllable, locations of syllable in the sentence] level 2 states = [sentence, locations of word in the sentence] obs = [ word, locations of word in the sentence]
DICTIONARY: Simple dictionary of English words (two syllable words of six letters)
2-level hierarchy level 1 states = [syllable, locations of letters in the syllable] obs = [ letter, locations of letters in the syllable] level 2 states = [ word, locations of syllable in the sentence] obs = [syllable, locations of syllable in the sentence]
DICTIONARY: Simple dictionary of English words (two syllable words of six letters)
three level structure, Dictionary provided by BERT
Read the produced BERT sentence 'THIS PAPER IS ALSO MENTIONED IN THE FAMOUS ENGLISH HISTORICAL NOVEL BY SIR ROBERT DE LA HAY'
level 1 states = [syllable, locations of letters in the syllable] obs = [ letter, locations of letters in the syllable] level 2 states = [ word, locations of syllables in the word] obs = [syllable, locations of syllables in the word] level 3 states = [sentence, locations of words in the sentence] obs = [ word, locations of words in the sentence]
Same as previous but given a context "We present a novel computational model that uses hierarchical active inference to simulate the reading process and eye movements during reading." read the BERT produced sentence:
THE COMPUTATIONAL MODEL IS ABLE TO PREDICT A TIME HORIZON FOR READING DURING A GIVEN TIME OR PLACE PERIOD
Loop with BERT reading a sentence that has a word (BUTTER) that BERT does not provide
LOOP of HAI Code on a DICTIONARY predicted by BERT https://it.mathworks.com/matlabcentral/fileexchange/107375-transformer-models?s_tid=FX_rc3_behav add the corresponding path to use BERT model git clone https://github.com/matlab-deep-learning/transformer-models
provided an API-KEY.txt for OPEN-AI chatGPT, given the same context of (6) "We present a novel computational model that uses hierarchical active inference to simulate the reading process and eye movements during reading."
produce a random sentence: e.g. THIS MODEL HAS BEEN DESIGNED TO ENABLE THE ACCOMMODATION OF A COMPREHENSIVE SET OF ADAPTIVE BEHAVIORS TO ACHIEVE BEST ACCURACY
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matlab-tree package Package needed to enable tree visualization and computation on MDP https://tinevez.github.io/matlab-tree/index.html
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spm12 statistical parametric mapping version 12 https://www.fil.ion.ucl.ac.uk/spm/software/download/
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export_fig enahanced routines for saving figures in MATLAB https://github.com/altmany/export_fig
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utilities https://github.com/donnarumma/utilities/
HAI_disp -> print tree structure HAI_compare -> compare two hierarchical structures
VB_MDP.m and spm12 spm_MDP_VB_X.m
- hidden states X are updated from t on. -Bayesian model averaging of hidden states- in spm_MDP_VB_X.m the second cycle is from 1 to S in VB_MDP.m it is possible to set it from t to S
- this consequently means that in section
- check for residual uncertainty (in hierarchical schemes) - the state on which the Entropy is computed in t and not 1
Francesco Donnaruma francesco.donnarumma@istc.cnr.it
Mirco Frosolone mirco.frosolone@istc.cnr.it
Giovanni Pezzulo giovanni.pezzulo@istc.cnr.it
COgnition iN ActioN Laboratory (CONAN) https://www.istc.cnr.it/it/group/conan-0
This code is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 1, or (at your option) any later version.
This code is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. A copy of the GNU General Public License can be obtained from the Free Software Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.