Back

People with a tobacco use disorder misattribute non-drug cues as worse predictors of positive outcomes compared to drug cues

Kalhan, S.; Schwartenbeck, P.; Hester, R.; Garrido, M. I.

2023-03-28 neuroscience
10.1101/2023.03.27.534463 bioRxiv
Show abstract

Adaptive behaviours depend on dynamically updating internal representations of the world based on the ever-changing environmental contingencies. People with a substance use disorder (pSUD) show maladaptive behaviours with high persistence in drug-taking, despite severe negative consequences. We recently proposed a salience misattribution model for addiction (SMMA; Kalhan et al., (2021)), arguing that pSUD have aberrations in their updating processes where drug cues are misattributed as strong predictors of positive outcomes, but weaker predictors of negative outcomes. We also argue that conversely, non-drug cues are misattributed as weak predictors of positive outcomes, but stronger predictors of negative outcomes. However, these hypotheses need to be empirically tested. Here we used a multi-cue reversal learning task, with reversals in whether drug or non-drug cues are currently relevant in predicting the outcome (monetary win or loss). We show that compared to controls, people with a tobacco use disorder (pTUD), do form misaligned internal representations. We found that pTUD updated less towards learning the drug cues relevance in predicting a loss. Further, when neither drug nor non-drug cue predicted a win, pTUD updated more towards the drug cue being relevant predictors of that win. Our Bayesian belief updating model revealed that pTUD had a low estimated likelihood of non-drug cues being predictors of wins, compared to drug cues, which drove the misaligned updating. Overall, several hypotheses of the SMMA were supported, but not all. Our results implicate that strengthening the non-drug cue association with positive outcomes may help restore the misaligned internal representation in pTUD.

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

1
Computational Psychiatry
12 papers in training set
Top 0.1%
10.1%
2
Cognitive, Affective, & Behavioral Neuroscience
25 papers in training set
Top 0.1%
6.8%
3
Psychological Medicine
74 papers in training set
Top 0.3%
6.4%
4
eLife
5422 papers in training set
Top 13%
6.3%
5
Addiction Biology
47 papers in training set
Top 0.3%
4.9%
6
Psychopharmacology
59 papers in training set
Top 0.2%
4.9%
7
Nature Human Behaviour
85 papers in training set
Top 0.9%
3.6%
8
European Journal of Neuroscience
168 papers in training set
Top 0.2%
2.6%
9
Frontiers in Behavioral Neuroscience
46 papers in training set
Top 0.3%
2.4%
10
Behavioral Neuroscience
25 papers in training set
Top 0.1%
2.4%
50% of probability mass above
11
Neuroscience
88 papers in training set
Top 0.7%
2.1%
12
PLOS Computational Biology
1633 papers in training set
Top 14%
1.9%
13
The Journal of Neuroscience
928 papers in training set
Top 5%
1.9%
14
Scientific Reports
3102 papers in training set
Top 55%
1.8%
15
Psychological Review
19 papers in training set
Top 0.1%
1.7%
16
NeuroImage: Clinical
132 papers in training set
Top 2%
1.7%
17
Brain Sciences
52 papers in training set
Top 0.8%
1.7%
18
Addiction Neuroscience
17 papers in training set
Top 0.3%
1.5%
19
PLOS ONE
4510 papers in training set
Top 60%
1.2%
20
Biological Psychiatry
119 papers in training set
Top 2%
1.2%
21
Neuropsychologia
77 papers in training set
Top 0.8%
1.2%
22
Nature Communications
4913 papers in training set
Top 56%
1.2%
23
eneuro
389 papers in training set
Top 7%
1.1%
24
Drug and Alcohol Dependence
37 papers in training set
Top 0.5%
1.0%
25
Biological Psychology
18 papers in training set
Top 0.1%
1.0%
26
Translational Psychiatry
219 papers in training set
Top 4%
0.9%
27
Schizophrenia Research
29 papers in training set
Top 0.5%
0.9%
28
Biological Psychiatry: Cognitive Neuroscience and Neuroimaging
62 papers in training set
Top 1%
0.9%
29
Frontiers in Neuroscience
223 papers in training set
Top 7%
0.8%
30
Neurobiology of Learning and Memory
35 papers in training set
Top 0.3%
0.8%