Algorithmic flow chart highlighting a possible path of NGM behavior under sequential-presentation.

<p>Typical path of meaning extracted from sequential-presentation trial. Subsequent “training” stimuli do not affect initial hypothesis so long as consistent. If inconsistent, then a new hypothesis is generated (not depicted here).</p>

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Main Author: Spencer Caplan (10188380) (author)
Published: 2025
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author Spencer Caplan (10188380)
author_facet Spencer Caplan (10188380)
author_role author
dc.creator.none.fl_str_mv Spencer Caplan (10188380)
dc.date.none.fl_str_mv 2025-07-03T17:46:35Z
dc.identifier.none.fl_str_mv 10.1371/journal.pone.0327615.g004
dc.relation.none.fl_str_mv https://figshare.com/articles/figure/Algorithmic_flow_chart_highlighting_a_possible_path_of_NGM_behavior_under_sequential-presentation_/29472490
dc.rights.none.fl_str_mv CC BY 4.0
info:eu-repo/semantics/openAccess
dc.subject.none.fl_str_mv Neuroscience
Sociology
Science Policy
Mental Health
Biological Sciences not elsewhere classified
existing data demonstrates
presented &# 8212
poodle &# 8221
na &# 239
sce )&# 8212
factors influencing generalization
mechanistic processes rather
guiding learner behavior
training objects facilitates
&# 8220
sce seems
learner end
generalization model
dogs rather
algorithmic processes
training objects
training items
rational behavior
independent behavior
human behavior
xlink ">
vice versa
unified way
tuned output
temporal manner
statistical inference
specified hypotheses
specific subset
semantic generalizations
sample size
quantitative parameter
qualitative parameter
previously referred
possible interactions
models based
independent effect
hierarchical relation
global optimization
fundamental question
either simultaneously
computational model
category formation
dc.title.none.fl_str_mv Algorithmic flow chart highlighting a possible path of NGM behavior under sequential-presentation.
dc.type.none.fl_str_mv Image
Figure
info:eu-repo/semantics/publishedVersion
image
description <p>Typical path of meaning extracted from sequential-presentation trial. Subsequent “training” stimuli do not affect initial hypothesis so long as consistent. If inconsistent, then a new hypothesis is generated (not depicted here).</p>
eu_rights_str_mv openAccess
id Manara_32043cdc56d19cf0ecd4e171eaa9d389
identifier_str_mv 10.1371/journal.pone.0327615.g004
network_acronym_str Manara
network_name_str ManaraRepo
oai_identifier_str oai:figshare.com:article/29472490
publishDate 2025
repository.mail.fl_str_mv
repository.name.fl_str_mv
repository_id_str
rights_invalid_str_mv CC BY 4.0
spelling Algorithmic flow chart highlighting a possible path of NGM behavior under sequential-presentation.Spencer Caplan (10188380)NeuroscienceSociologyScience PolicyMental HealthBiological Sciences not elsewhere classifiedexisting data demonstratespresented &# 8212poodle &# 8221na &# 239sce )&# 8212factors influencing generalizationmechanistic processes ratherguiding learner behaviortraining objects facilitates&# 8220sce seemslearner endgeneralization modeldogs ratheralgorithmic processestraining objectstraining itemsrational behaviorindependent behaviorhuman behaviorxlink ">vice versaunified waytuned outputtemporal mannerstatistical inferencespecified hypothesesspecific subsetsemantic generalizationssample sizequantitative parameterqualitative parameterpreviously referredpossible interactionsmodels basedindependent effecthierarchical relationglobal optimizationfundamental questioneither simultaneouslycomputational modelcategory formation<p>Typical path of meaning extracted from sequential-presentation trial. Subsequent “training” stimuli do not affect initial hypothesis so long as consistent. If inconsistent, then a new hypothesis is generated (not depicted here).</p>2025-07-03T17:46:35ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0327615.g004https://figshare.com/articles/figure/Algorithmic_flow_chart_highlighting_a_possible_path_of_NGM_behavior_under_sequential-presentation_/29472490CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/294724902025-07-03T17:46:35Z
spellingShingle Algorithmic flow chart highlighting a possible path of NGM behavior under sequential-presentation.
Spencer Caplan (10188380)
Neuroscience
Sociology
Science Policy
Mental Health
Biological Sciences not elsewhere classified
existing data demonstrates
presented &# 8212
poodle &# 8221
na &# 239
sce )&# 8212
factors influencing generalization
mechanistic processes rather
guiding learner behavior
training objects facilitates
&# 8220
sce seems
learner end
generalization model
dogs rather
algorithmic processes
training objects
training items
rational behavior
independent behavior
human behavior
xlink ">
vice versa
unified way
tuned output
temporal manner
statistical inference
specified hypotheses
specific subset
semantic generalizations
sample size
quantitative parameter
qualitative parameter
previously referred
possible interactions
models based
independent effect
hierarchical relation
global optimization
fundamental question
either simultaneously
computational model
category formation
status_str publishedVersion
title Algorithmic flow chart highlighting a possible path of NGM behavior under sequential-presentation.
title_full Algorithmic flow chart highlighting a possible path of NGM behavior under sequential-presentation.
title_fullStr Algorithmic flow chart highlighting a possible path of NGM behavior under sequential-presentation.
title_full_unstemmed Algorithmic flow chart highlighting a possible path of NGM behavior under sequential-presentation.
title_short Algorithmic flow chart highlighting a possible path of NGM behavior under sequential-presentation.
title_sort Algorithmic flow chart highlighting a possible path of NGM behavior under sequential-presentation.
topic Neuroscience
Sociology
Science Policy
Mental Health
Biological Sciences not elsewhere classified
existing data demonstrates
presented &# 8212
poodle &# 8221
na &# 239
sce )&# 8212
factors influencing generalization
mechanistic processes rather
guiding learner behavior
training objects facilitates
&# 8220
sce seems
learner end
generalization model
dogs rather
algorithmic processes
training objects
training items
rational behavior
independent behavior
human behavior
xlink ">
vice versa
unified way
tuned output
temporal manner
statistical inference
specified hypotheses
specific subset
semantic generalizations
sample size
quantitative parameter
qualitative parameter
previously referred
possible interactions
models based
independent effect
hierarchical relation
global optimization
fundamental question
either simultaneously
computational model
category formation