Vulnerability, Development, and Individual Differences: Why Not Everyone Becomes Addicted
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1CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference The computational models evaluated in{ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin... -
2CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference The computational models evaluated in{ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin... -
3CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference The computational models evaluated in{ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin... -
4CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference The computational models evaluated in{ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin... -
5CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference The computational models evaluated in{ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin... -
6CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference The computational models evaluated in{ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin... -
7CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference The computational models evaluated in{ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin... -
8CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference The computational models evaluated in{ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin... -
9CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference The computational models evaluated in{ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin... -
2CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference Adolescence is the period of highest risk for substance use initiation, and the prevailing model attributes this to a temporal mismatch between early-maturing limbic reward circuits and late-maturing prefrontal control systems 2CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference0. The neuroanatomical evidence for this mismatch is substantial: prefrontal cortex undergoes protracted synaptic pruning and myelination that continues t... -
2CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference1 Adolescence is the period of highest risk for substance use initiation, and the prevailing model attributes this to a temporal mismatch between early-maturing limbic reward circuits and late-maturing prefrontal control systems 2CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference2. The neuroanatomical evidence for this mismatch is substantial: prefrontal cortex undergoes protracted synaptic pruning and myelination that continues t... -
2CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference3 Adolescence is the period of highest risk for substance use initiation, and the prevailing model attributes this to a temporal mismatch between early-maturing limbic reward circuits and late-maturing prefrontal control systems 2CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference4. The neuroanatomical evidence for this mismatch is substantial: prefrontal cortex undergoes protracted synaptic pruning and myelination that continues t... -
2CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference5 Adolescence is the period of highest risk for substance use initiation, and the prevailing model attributes this to a temporal mismatch between early-maturing limbic reward circuits and late-maturing prefrontal control systems 2CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference6. The neuroanatomical evidence for this mismatch is substantial: prefrontal cortex undergoes protracted synaptic pruning and myelination that continues t... -
2CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference7 Adolescence is the period of highest risk for substance use initiation, and the prevailing model attributes this to a temporal mismatch between early-maturing limbic reward circuits and late-maturing prefrontal control systems 2CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference8. The neuroanatomical evidence for this mismatch is substantial: prefrontal cortex undergoes protracted synaptic pruning and myelination that continues t... -
2CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference9 Adolescence is the period of highest risk for substance use initiation, and the prevailing model attributes this to a temporal mismatch between early-maturing limbic reward circuits and late-maturing prefrontal control systems 3CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference0. The neuroanatomical evidence for this mismatch is substantial: prefrontal cortex undergoes protracted synaptic pruning and myelination that continues t... -
3CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference1 Adolescence is the period of highest risk for substance use initiation, and the prevailing model attributes this to a temporal mismatch between early-maturing limbic reward circuits and late-maturing prefrontal control systems 3CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference2. The neuroanatomical evidence for this mismatch is substantial: prefrontal cortex undergoes protracted synaptic pruning and myelination that continues t... -
3CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference3 Adolescence is the period of highest risk for substance use initiation, and the prevailing model attributes this to a temporal mismatch between early-maturing limbic reward circuits and late-maturing prefrontal control systems 3CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference4. The neuroanatomical evidence for this mismatch is substantial: prefrontal cortex undergoes protracted synaptic pruning and myelination that continues t... -
3CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference5 Adolescence is the period of highest risk for substance use initiation, and the prevailing model attributes this to a temporal mismatch between early-maturing limbic reward circuits and late-maturing prefrontal control systems 3CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference6. The neuroanatomical evidence for this mismatch is substantial: prefrontal cortex undergoes protracted synaptic pruning and myelination that continues t... -
3CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference7 Two mechanistic accounts compete for explaining adolescent vulnerability. The enhanced reward sensitivity account proposes that adolescent dopaminergic systems are hyperreactive to novel and rewarding stimuli 3CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference8. The immature cognitive control account emphasizes deficient prefrontal inhibition as the primary risk factor 3CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference9. The relative contributio... -
4CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference0 Two mechanistic accounts compete for explaining adolescent vulnerability. The enhanced reward sensitivity account proposes that adolescent dopaminergic systems are hyperreactive to novel and rewarding stimuli 4CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference1. The immature cognitive control account emphasizes deficient prefrontal inhibition as the primary risk factor 4CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference2. The relative contributio... -
4CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference3 Two mechanistic accounts compete for explaining adolescent vulnerability. The enhanced reward sensitivity account proposes that adolescent dopaminergic systems are hyperreactive to novel and rewarding stimuli 4CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference4. The immature cognitive control account emphasizes deficient prefrontal inhibition as the primary risk factor 4CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference5. The relative contributio... -
4CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference6 Two mechanistic accounts compete for explaining adolescent vulnerability. The enhanced reward sensitivity account proposes that adolescent dopaminergic systems are hyperreactive to novel and rewarding stimuli 4CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference7. The immature cognitive control account emphasizes deficient prefrontal inhibition as the primary risk factor 4CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference8. The relative contributio... -
4CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference9 Two mechanistic accounts compete for explaining adolescent vulnerability. The enhanced reward sensitivity account proposes that adolescent dopaminergic systems are hyperreactive to novel and rewarding stimuli 5CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference0. The immature cognitive control account emphasizes deficient prefrontal inhibition as the primary risk factor 5CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference1. The relative contributio... -
5CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference2 The mismatch model, while intuitively appealing, faces important limitations. 5CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference3 showed that adolescent THC exposure in mice (n = 40) downregulated drd2 and adora2a gene expression in NAc and hippocampus — suggesting that substance exposure during adolescence does not merely exploit pre-existing immaturity but actively alters the developmental trajectory of dopamine receptor systems. Early-ons... -
5CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference4 The mismatch model, while intuitively appealing, faces important limitations. 5CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference5 showed that adolescent THC exposure in mice (n = 40) downregulated drd2 and adora2a gene expression in NAc and hippocampus — suggesting that substance exposure during adolescence does not merely exploit pre-existing immaturity but actively alters the developmental trajectory of dopamine receptor systems. Early-ons... -
5CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference6 The mismatch model, while intuitively appealing, faces important limitations. 5CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference7 showed that adolescent THC exposure in mice (n = 40) downregulated drd2 and adora2a gene expression in NAc and hippocampus — suggesting that substance exposure during adolescence does not merely exploit pre-existing immaturity but actively alters the developmental trajectory of dopamine receptor systems. Early-ons... -
5CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference8 The mismatch model, while intuitively appealing, faces important limitations. 5CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference9 showed that adolescent THC exposure in mice (n = 40) downregulated drd2 and adora2a gene expression in NAc and hippocampus — suggesting that substance exposure during adolescence does not merely exploit pre-existing immaturity but actively alters the developmental trajectory of dopamine receptor systems. Early-ons... -
6CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference0 The mismatch model, while intuitively appealing, faces important limitations. 6CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference1 showed that adolescent THC exposure in mice (n = 40) downregulated drd2 and adora2a gene expression in NAc and hippocampus — suggesting that substance exposure during adolescence does not merely exploit pre-existing immaturity but actively alters the developmental trajectory of dopamine receptor systems. Early-ons... -
6CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference2 The mismatch model, while intuitively appealing, faces important limitations. 6CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference3 showed that adolescent THC exposure in mice (n = 40) downregulated drd2 and adora2a gene expression in NAc and hippocampus — suggesting that substance exposure during adolescence does not merely exploit pre-existing immaturity but actively alters the developmental trajectory of dopamine receptor systems. Early-ons... -
6CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference4 The mismatch model, while intuitively appealing, faces important limitations. 6CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference5 showed that adolescent THC exposure in mice (n = 40) downregulated drd2 and adora2a gene expression in NAc and hippocampus — suggesting that substance exposure during adolescence does not merely exploit pre-existing immaturity but actively alters the developmental trajectory of dopamine receptor systems. Early-ons... -
6CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference6 Developmental timeline of reward circuit maturation. Limbic structures (VTA, NAc, amygdala) mature earlier than prefrontal cortex, creating a proposed period of imbalanced risk-reward processing during adolescence. Green annotations indicate components supported by converging neuroimaging and animal data 6CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference7. Orange annotation indicates the dopamine hyperreactivity hypothesi... -
6CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference8 Developmental timeline of reward circuit maturation. Limbic structures (VTA, NAc, amygdala) mature earlier than prefrontal cortex, creating a proposed period of imbalanced risk-reward processing during adolescence. Green annotations indicate components supported by converging neuroimaging and animal data 6CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference9. Orange annotation indicates the dopamine hyperreactivity hypothesi... -
7CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference0 Developmental timeline of reward circuit maturation. Limbic structures (VTA, NAc, amygdala) mature earlier than prefrontal cortex, creating a proposed period of imbalanced risk-reward processing during adolescence. Green annotations indicate components supported by converging neuroimaging and animal data 7CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference1. Orange annotation indicates the dopamine hyperreactivity hypothesi... -
7CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference2 The nonhuman primate literature adds important nuance. 7CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference3 found no statistical sex differences in fixed-ratio cocaine self-administration among adolescent monkeys (n = 6), but when retested as adults, monkeys with prior adolescent exposure (MALT monkeys) showed higher peak response rates — suggesting that the behavioral consequences of adolescent exposure may emerge only after further maturation 7CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference4... -
7CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference5 The nonhuman primate literature adds important nuance. 7CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference6 found no statistical sex differences in fixed-ratio cocaine self-administration among adolescent monkeys (n = 6), but when retested as adults, monkeys with prior adolescent exposure (MALT monkeys) showed higher peak response rates — suggesting that the behavioral consequences of adolescent exposure may emerge only after further maturation 7CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference7... -
7CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference8 Adverse childhood experiences (ACEs) show one of the most robust epidemiological relationships with later addiction risk 7CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference9. In a longitudinal cohort study, 8CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference0 reported that 14.9% of 7,200 participants were arrested by age 26, with ACE exposure as a significant predictor of criminal-legal involvement and substance use outcomes (n = 20... -
8CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference1 Adverse childhood experiences (ACEs) show one of the most robust epidemiological relationships with later addiction risk 8CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference2. In a longitudinal cohort study, 8CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference3 reported that 14.9% of 7,200 participants were arrested by age 26, with ACE exposure as a significant predictor of criminal-legal involvement and substance use outcomes (n = 20... -
8CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference4 Adverse childhood experiences (ACEs) show one of the most robust epidemiological relationships with later addiction risk 8CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference5. In a longitudinal cohort study, 8CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference6 reported that 14.9% of 7,200 participants were arrested by age 26, with ACE exposure as a significant predictor of criminal-legal involvement and substance use outcomes (n = 20... -
8CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference7 Adverse childhood experiences (ACEs) show one of the most robust epidemiological relationships with later addiction risk 8CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference8. In a longitudinal cohort study, 8CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference9 reported that 14.9% of 7,200 participants were arrested by age 26, with ACE exposure as a significant predictor of criminal-legal involvement and substance use outcomes (n = 20... -
9CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference0 Adverse childhood experiences (ACEs) show one of the most robust epidemiological relationships with later addiction risk 9CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference1. In a longitudinal cohort study, 9CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference2 reported that 14.9% of 7,200 participants were arrested by age 26, with ACE exposure as a significant predictor of criminal-legal involvement and substance use outcomes (n = 20... -
9CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference3 Adverse childhood experiences (ACEs) show one of the most robust epidemiological relationships with later addiction risk 9CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference4. In a longitudinal cohort study, 9CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference5 reported that 14.9% of 7,200 participants were arrested by age 26, with ACE exposure as a significant predictor of criminal-legal involvement and substance use outcomes (n = 20... -
9CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference6 Adverse childhood experiences (ACEs) show one of the most robust epidemiological relationships with later addiction risk 9CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference7. In a longitudinal cohort study, 9CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference8 reported that 14.9% of 7,200 participants were arrested by age 26, with ACE exposure as a significant predictor of criminal-legal involvement and substance use outcomes (n = 20... -
9CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference9 Adverse childhood experiences (ACEs) show one of the most robust epidemiological relationships with later addiction risk 2CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference0. In a longitudinal cohort study, 2CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference1 reported that 14.9% of 7,200 participants were arrested by age 26, with ACE exposure as a significant predictor of criminal-legal involvement and substance use outcomes (n = 20... -
2CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference2 Adverse childhood experiences (ACEs) show one of the most robust epidemiological relationships with later addiction risk 2CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference3. In a longitudinal cohort study, 2CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference4 reported that 14.9% of 7,200 participants were arrested by age 26, with ACE exposure as a significant predictor of criminal-legal involvement and substance use outcomes (n = 20... -
2CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference5 Adverse childhood experiences (ACEs) show one of the most robust epidemiological relationships with later addiction risk 2CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference6. In a longitudinal cohort study, 2CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference7 reported that 14.9% of 7,200 participants were arrested by age 26, with ACE exposure as a significant predictor of criminal-legal involvement and substance use outcomes (n = 20... -
2CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference8 Adverse childhood experiences (ACEs) show one of the most robust epidemiological relationships with later addiction risk 2CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference9. In a longitudinal cohort study, 2CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference00 reported that 14.9% of 7,200 participants were arrested by age 26, with ACE exposure as a significant predictor of criminal-legal involvement and substance use outcomes (n = 20... -
2CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference01 While human epidemiological data robustly link ACEs to addiction risk, animal models of early stress show highly variable effects on drug self-administration. Maternal separation paradigms yield inconsistent results depending on timing, duration, sex, and drug class 2CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference02. The mechanisms linking early adversity to addiction circuits are inferred from animal mode... -
2CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference03 While human epidemiological data robustly link ACEs to addiction risk, animal models of early stress show highly variable effects on drug self-administration. Maternal separation paradigms yield inconsistent results depending on timing, duration, sex, and drug class 2CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference04. The mechanisms linking early adversity to addiction circuits are inferred from animal mode... -
2CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference05 While human epidemiological data robustly link ACEs to addiction risk, animal models of early stress show highly variable effects on drug self-administration. Maternal separation paradigms yield inconsistent results depending on timing, duration, sex, and drug class 2CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference06. The mechanisms linking early adversity to addiction circuits are inferred from animal mode... -
2CitationThe computational models evaluated in {ref}
sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference07 The stress–addiction intersection operates through multiple neurobiological pathways. Chronic stress exposure sensitizes the HPA axis and alters mesolimbic dopamine signaling, creating a neurobiological substrate for enhanced drug reinforcement 2CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference08. 2CitationThe computational models evaluated in {ref}sec-computational-modelsformalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...content/10_plasticity_development.md:line 5Open reference09 reviewed evidence that early life stress disrupts medial prefrontal cortex (mPFC) matura... -
... 112 additional anchors in refs_json
References
- [Lin2025] “The computational models evaluated in {ref}`sec-computational-models` formalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...”
- [AmancioBelmont2025] “The computational models evaluated in {ref}`sec-computational-models` formalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...”
- [Rough2025] “The computational models evaluated in {ref}`sec-computational-models` formalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...”
- [BorregoRuiz2025] “The computational models evaluated in {ref}`sec-computational-models` formalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...”
- [Blum2026] “The computational models evaluated in {ref}`sec-computational-models` formalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...”
- [FlanaganBurt2026] “The computational models evaluated in {ref}`sec-computational-models` formalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...”
- [Diotaiuti2025] “The computational models evaluated in {ref}`sec-computational-models` formalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...”
- [Sinha2024] “The computational models evaluated in {ref}`sec-computational-models` formalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...”
- [Zeng2026] “The computational models evaluated in {ref}`sec-computational-models` formalize addiction as a process — from initial drug learning through compulsive seeking — yet their parameters must be tuned to individual subjects. Temporal-difference models require a learning rate; allostatic frameworks require an opponent-process gain; Bayesian accounts require a precision weighting. None of these can be set without specifyin...”
- [Gonzalez2026] “Adolescence is the period of highest risk for substance use initiation, and the prevailing model attributes this to a temporal mismatch between early-maturing limbic reward circuits and late-maturing prefrontal control systems [AmancioBelmont2025, Lin2025, Zeng2026]. The neuroanatomical evidence for this mismatch is substantial: prefrontal cortex undergoes protracted synaptic pruning and myelination that continues t...”
- [Goodpaster2026] “Adolescence is the period of highest risk for substance use initiation, and the prevailing model attributes this to a temporal mismatch between early-maturing limbic reward circuits and late-maturing prefrontal control systems [AmancioBelmont2025, Lin2025, Zeng2026]. The neuroanatomical evidence for this mismatch is substantial: prefrontal cortex undergoes protracted synaptic pruning and myelination that continues t...”
- [Pochapski2024] “Adolescence is the period of highest risk for substance use initiation, and the prevailing model attributes this to a temporal mismatch between early-maturing limbic reward circuits and late-maturing prefrontal control systems [AmancioBelmont2025, Lin2025, Zeng2026]. The neuroanatomical evidence for this mismatch is substantial: prefrontal cortex undergoes protracted synaptic pruning and myelination that continues t...”
- [Cherif2026] “Adolescence is the period of highest risk for substance use initiation, and the prevailing model attributes this to a temporal mismatch between early-maturing limbic reward circuits and late-maturing prefrontal control systems [AmancioBelmont2025, Lin2025, Zeng2026]. The neuroanatomical evidence for this mismatch is substantial: prefrontal cortex undergoes protracted synaptic pruning and myelination that continues t...”
- [CajiaoManrique2023b] “The mismatch model, while intuitively appealing, faces important limitations. [CajiaoManrique2023b] showed that adolescent THC exposure in mice (n = 40) downregulated *drd2* and *adora2a* gene expression in NAc and hippocampus — suggesting that substance exposure during adolescence does not merely exploit pre-existing immaturity but actively alters the developmental trajectory of dopamine receptor systems. Early-ons...”
- [Swaim2026] “The mismatch model, while intuitively appealing, faces important limitations. [CajiaoManrique2023b] showed that adolescent THC exposure in mice (n = 40) downregulated *drd2* and *adora2a* gene expression in NAc and hippocampus — suggesting that substance exposure during adolescence does not merely exploit pre-existing immaturity but actively alters the developmental trajectory of dopamine receptor systems. Early-ons...”
- [Olsson2025] “The mismatch model, while intuitively appealing, faces important limitations. [CajiaoManrique2023b] showed that adolescent THC exposure in mice (n = 40) downregulated *drd2* and *adora2a* gene expression in NAc and hippocampus — suggesting that substance exposure during adolescence does not merely exploit pre-existing immaturity but actively alters the developmental trajectory of dopamine receptor systems. Early-ons...”
- [YoungWolff2026] “The mismatch model, while intuitively appealing, faces important limitations. [CajiaoManrique2023b] showed that adolescent THC exposure in mice (n = 40) downregulated *drd2* and *adora2a* gene expression in NAc and hippocampus — suggesting that substance exposure during adolescence does not merely exploit pre-existing immaturity but actively alters the developmental trajectory of dopamine receptor systems. Early-ons...”
- [Ogilvie2026] “The mismatch model, while intuitively appealing, faces important limitations. [CajiaoManrique2023b] showed that adolescent THC exposure in mice (n = 40) downregulated *drd2* and *adora2a* gene expression in NAc and hippocampus — suggesting that substance exposure during adolescence does not merely exploit pre-existing immaturity but actively alters the developmental trajectory of dopamine receptor systems. Early-ons...”
- [Sofi2026] “The mismatch model, while intuitively appealing, faces important limitations. [CajiaoManrique2023b] showed that adolescent THC exposure in mice (n = 40) downregulated *drd2* and *adora2a* gene expression in NAc and hippocampus — suggesting that substance exposure during adolescence does not merely exploit pre-existing immaturity but actively alters the developmental trajectory of dopamine receptor systems. Early-ons...”
- [Allen2025] “The nonhuman primate literature adds important nuance. [Allen2025] found no statistical sex differences in fixed-ratio cocaine self-administration among adolescent monkeys (n = 6), but when retested as adults, monkeys with prior adolescent exposure (MALT monkeys) showed higher peak response rates — suggesting that the behavioral consequences of adolescent exposure may emerge only after further maturation [Allen2025]...”
- [LaCharite2026] “Adverse childhood experiences (ACEs) show one of the most robust epidemiological relationships with later addiction risk [LaCharite2026, Ogilvie2026, Snipes2026, HerreroRoldan2025, Furlong2026]. In a longitudinal cohort study, [LaCharite2026] reported that 14.9% of 7,200 participants were arrested by age 26, with ACE exposure as a significant predictor of criminal-legal involvement and substance use outcomes (n = 20...”
- [Snipes2026] “Adverse childhood experiences (ACEs) show one of the most robust epidemiological relationships with later addiction risk [LaCharite2026, Ogilvie2026, Snipes2026, HerreroRoldan2025, Furlong2026]. In a longitudinal cohort study, [LaCharite2026] reported that 14.9% of 7,200 participants were arrested by age 26, with ACE exposure as a significant predictor of criminal-legal involvement and substance use outcomes (n = 20...”
- [HerreroRoldan2025] “Adverse childhood experiences (ACEs) show one of the most robust epidemiological relationships with later addiction risk [LaCharite2026, Ogilvie2026, Snipes2026, HerreroRoldan2025, Furlong2026]. In a longitudinal cohort study, [LaCharite2026] reported that 14.9% of 7,200 participants were arrested by age 26, with ACE exposure as a significant predictor of criminal-legal involvement and substance use outcomes (n = 20...”
- [Furlong2026] “Adverse childhood experiences (ACEs) show one of the most robust epidemiological relationships with later addiction risk [LaCharite2026, Ogilvie2026, Snipes2026, HerreroRoldan2025, Furlong2026]. In a longitudinal cohort study, [LaCharite2026] reported that 14.9% of 7,200 participants were arrested by age 26, with ACE exposure as a significant predictor of criminal-legal involvement and substance use outcomes (n = 20...”
- [Alghamdi2026] “Adverse childhood experiences (ACEs) show one of the most robust epidemiological relationships with later addiction risk [LaCharite2026, Ogilvie2026, Snipes2026, HerreroRoldan2025, Furlong2026]. In a longitudinal cohort study, [LaCharite2026] reported that 14.9% of 7,200 participants were arrested by age 26, with ACE exposure as a significant predictor of criminal-legal involvement and substance use outcomes (n = 20...”
- [Sloan2026] “Adverse childhood experiences (ACEs) show one of the most robust epidemiological relationships with later addiction risk [LaCharite2026, Ogilvie2026, Snipes2026, HerreroRoldan2025, Furlong2026]. In a longitudinal cohort study, [LaCharite2026] reported that 14.9% of 7,200 participants were arrested by age 26, with ACE exposure as a significant predictor of criminal-legal involvement and substance use outcomes (n = 20...”
- [MartinezCaballero2025] “While human epidemiological data robustly link ACEs to addiction risk, animal models of early stress show highly variable effects on drug self-administration. Maternal separation paradigms yield inconsistent results depending on timing, duration, sex, and drug class [Goodpaster2026, MartinezCaballero2025, GarciaCabrerizo2024]. The mechanisms linking early adversity to addiction circuits are inferred from animal mode...”
- [GarciaCabrerizo2024] “While human epidemiological data robustly link ACEs to addiction risk, animal models of early stress show highly variable effects on drug self-administration. Maternal separation paradigms yield inconsistent results depending on timing, duration, sex, and drug class [Goodpaster2026, MartinezCaballero2025, GarciaCabrerizo2024]. The mechanisms linking early adversity to addiction circuits are inferred from animal mode...”
- [Torres2026] “The stress–addiction intersection operates through multiple neurobiological pathways. Chronic stress exposure sensitizes the HPA axis and alters mesolimbic dopamine signaling, creating a neurobiological substrate for enhanced drug reinforcement [MartinezCaballero2025, GarciaCabrerizo2024, Sinha2024, Torres2026]. [Goodpaster2026] reviewed evidence that early life stress disrupts medial prefrontal cortex (mPFC) matura...”
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