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Beyond pain: modeling decision-making deficits in chronic pain.

Hess LE, Haimovici A, Muñoz MA, Montoya P - Front Behav Neurosci (2014)

Bottom Line: In the present study, we developed a simple heuristic model to simulate individuals' choice behavior by varying the level of decision randomness and the importance given to gains and losses.By contrast, the best account of the available data in HCs was obtained when decisions were based on previous experiences and losses loomed larger than gains.In conclusion, our model seems to provide useful information to measure each individual participant extensively, and to deal with the data on a participant-by-participant basis.

View Article: PubMed Central - PubMed

Affiliation: Training and Research for Argentina Medical Association (CIMA), Faculty of Medical Sciences, National University of Rosario (UNR) Rosario, Santa Fe, Argentina ; Research Institute on Health Sciences (IUNICS), University of Balearic Islands (UIB) Palma, Spain.

ABSTRACT
Risky decision-making seems to be markedly disrupted in patients with chronic pain, probably due to the high cost that impose pain and negative mood on executive control functions. Patients' behavioral performance on decision-making tasks such as the Iowa Gambling Task (IGT) is characterized by selecting cards more frequently from disadvantageous than from advantageous decks, and by switching often between competing responses in comparison with healthy controls (HCs). In the present study, we developed a simple heuristic model to simulate individuals' choice behavior by varying the level of decision randomness and the importance given to gains and losses. The findings revealed that the model was able to differentiate the behavioral performance of patients with chronic pain and HCs at the group, as well as at the individual level. The best fit of the model in patients with chronic pain was yielded when decisions were not based on previous choices and when gains were considered more relevant than losses. By contrast, the best account of the available data in HCs was obtained when decisions were based on previous experiences and losses loomed larger than gains. In conclusion, our model seems to provide useful information to measure each individual participant extensively, and to deal with the data on a participant-by-participant basis.

No MeSH data available.


Related in: MedlinePlus

Goodness-of-fit of the model computed as the distance between predicted and observed behavioral persistence for chronic pain patients and healthy controls at the group- and the individual-level. Data modeling of chronic pain patients (FM) show that the distance between predicted and observed data is minimized at ρ = 0, whereas the distance is minimal at ρ = 0.6 for HCs (Panel A). The parameter Tmax (degree of decision randomness) was held constant at 50, as in Figure 4. The model is able to separate both groups by fitting the persistence of each single subject in the space of the free parameters in our model (ρ and Tmax) (Panel B). The labels for chronic pain patients (numbered red crosses) and HCs (numbered white circles) are placed where the difference between predicted and observed persistence is minimized. The persistent behavior of most chronic pain patients (numbered from 1 to 11 in red) is best reproduced by the model when Tmax is high (ramdom decision) and ρ is low (more importance is given to gains than to losses). On the other hand, performance of most HCs is best reproduced with lower Tmax (decision guided by the last experience) and higher values of ρ (more importance is given to losses than to gains).
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Figure 6: Goodness-of-fit of the model computed as the distance between predicted and observed behavioral persistence for chronic pain patients and healthy controls at the group- and the individual-level. Data modeling of chronic pain patients (FM) show that the distance between predicted and observed data is minimized at ρ = 0, whereas the distance is minimal at ρ = 0.6 for HCs (Panel A). The parameter Tmax (degree of decision randomness) was held constant at 50, as in Figure 4. The model is able to separate both groups by fitting the persistence of each single subject in the space of the free parameters in our model (ρ and Tmax) (Panel B). The labels for chronic pain patients (numbered red crosses) and HCs (numbered white circles) are placed where the difference between predicted and observed persistence is minimized. The persistent behavior of most chronic pain patients (numbered from 1 to 11 in red) is best reproduced by the model when Tmax is high (ramdom decision) and ρ is low (more importance is given to gains than to losses). On the other hand, performance of most HCs is best reproduced with lower Tmax (decision guided by the last experience) and higher values of ρ (more importance is given to losses than to gains).

Mentions: Finally, the average distance between persistence values predicted by the model and those collected from behavioral performance in both groups were computed to test the goodness-of-fit of our model at the group-level (Figure 6A). Results indicated that distance between predicted and observed data was minimized at ρ = 0.6 in HCs, whereas distance was minimal at ρ close to 0 in chronic pain patients. A similar result was obtained when the best fitted distances between predicted and observed data were computed for each participant to visualize the goodness-of-fit of our model at the individual-level (Figure 6B). Our mathematical model provides the best account for the observed data in HCs when Tmax levels were low (decision guided by previous experience) and ρ values were high (losses loom larger than gains), corresponding to high persistent choice behavior in the IGT (Figure 6B, numbers from 4 to 15 in white). By contrast, high Tmax levels (high decision randomness) and low ρ values (gains are more relevant than losses) were the parameters of our mathematical model that best fitted behavioral performance in most patients with chronic pain (Figure 6, numbers from 1 to 11 in red).


Beyond pain: modeling decision-making deficits in chronic pain.

Hess LE, Haimovici A, Muñoz MA, Montoya P - Front Behav Neurosci (2014)

Goodness-of-fit of the model computed as the distance between predicted and observed behavioral persistence for chronic pain patients and healthy controls at the group- and the individual-level. Data modeling of chronic pain patients (FM) show that the distance between predicted and observed data is minimized at ρ = 0, whereas the distance is minimal at ρ = 0.6 for HCs (Panel A). The parameter Tmax (degree of decision randomness) was held constant at 50, as in Figure 4. The model is able to separate both groups by fitting the persistence of each single subject in the space of the free parameters in our model (ρ and Tmax) (Panel B). The labels for chronic pain patients (numbered red crosses) and HCs (numbered white circles) are placed where the difference between predicted and observed persistence is minimized. The persistent behavior of most chronic pain patients (numbered from 1 to 11 in red) is best reproduced by the model when Tmax is high (ramdom decision) and ρ is low (more importance is given to gains than to losses). On the other hand, performance of most HCs is best reproduced with lower Tmax (decision guided by the last experience) and higher values of ρ (more importance is given to losses than to gains).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4117932&req=5

Figure 6: Goodness-of-fit of the model computed as the distance between predicted and observed behavioral persistence for chronic pain patients and healthy controls at the group- and the individual-level. Data modeling of chronic pain patients (FM) show that the distance between predicted and observed data is minimized at ρ = 0, whereas the distance is minimal at ρ = 0.6 for HCs (Panel A). The parameter Tmax (degree of decision randomness) was held constant at 50, as in Figure 4. The model is able to separate both groups by fitting the persistence of each single subject in the space of the free parameters in our model (ρ and Tmax) (Panel B). The labels for chronic pain patients (numbered red crosses) and HCs (numbered white circles) are placed where the difference between predicted and observed persistence is minimized. The persistent behavior of most chronic pain patients (numbered from 1 to 11 in red) is best reproduced by the model when Tmax is high (ramdom decision) and ρ is low (more importance is given to gains than to losses). On the other hand, performance of most HCs is best reproduced with lower Tmax (decision guided by the last experience) and higher values of ρ (more importance is given to losses than to gains).
Mentions: Finally, the average distance between persistence values predicted by the model and those collected from behavioral performance in both groups were computed to test the goodness-of-fit of our model at the group-level (Figure 6A). Results indicated that distance between predicted and observed data was minimized at ρ = 0.6 in HCs, whereas distance was minimal at ρ close to 0 in chronic pain patients. A similar result was obtained when the best fitted distances between predicted and observed data were computed for each participant to visualize the goodness-of-fit of our model at the individual-level (Figure 6B). Our mathematical model provides the best account for the observed data in HCs when Tmax levels were low (decision guided by previous experience) and ρ values were high (losses loom larger than gains), corresponding to high persistent choice behavior in the IGT (Figure 6B, numbers from 4 to 15 in white). By contrast, high Tmax levels (high decision randomness) and low ρ values (gains are more relevant than losses) were the parameters of our mathematical model that best fitted behavioral performance in most patients with chronic pain (Figure 6, numbers from 1 to 11 in red).

Bottom Line: In the present study, we developed a simple heuristic model to simulate individuals' choice behavior by varying the level of decision randomness and the importance given to gains and losses.By contrast, the best account of the available data in HCs was obtained when decisions were based on previous experiences and losses loomed larger than gains.In conclusion, our model seems to provide useful information to measure each individual participant extensively, and to deal with the data on a participant-by-participant basis.

View Article: PubMed Central - PubMed

Affiliation: Training and Research for Argentina Medical Association (CIMA), Faculty of Medical Sciences, National University of Rosario (UNR) Rosario, Santa Fe, Argentina ; Research Institute on Health Sciences (IUNICS), University of Balearic Islands (UIB) Palma, Spain.

ABSTRACT
Risky decision-making seems to be markedly disrupted in patients with chronic pain, probably due to the high cost that impose pain and negative mood on executive control functions. Patients' behavioral performance on decision-making tasks such as the Iowa Gambling Task (IGT) is characterized by selecting cards more frequently from disadvantageous than from advantageous decks, and by switching often between competing responses in comparison with healthy controls (HCs). In the present study, we developed a simple heuristic model to simulate individuals' choice behavior by varying the level of decision randomness and the importance given to gains and losses. The findings revealed that the model was able to differentiate the behavioral performance of patients with chronic pain and HCs at the group, as well as at the individual level. The best fit of the model in patients with chronic pain was yielded when decisions were not based on previous choices and when gains were considered more relevant than losses. By contrast, the best account of the available data in HCs was obtained when decisions were based on previous experiences and losses loomed larger than gains. In conclusion, our model seems to provide useful information to measure each individual participant extensively, and to deal with the data on a participant-by-participant basis.

No MeSH data available.


Related in: MedlinePlus