Summary: A new computational model reveals the impaired ability to integrate size and probability of reward in those with schizophrenia may be the result of a lack of consideration of the magnitude of the reward.
People with schizophrenia have a hard time integrating information about a reward–the size of the reward and the probability of receiving it–when assessing its value, according to a study in Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. The inability to integrate the two factors correlated with more severe motivational deficits, suggesting that the impairment may contribute to decreased value placed on a reward, and thus reduced motivation to complete the task required to receive it.
Using a computational model to tease apart components of reward estimation for decision making, the researchers, based at the Maryland Psychiatric Research Center, found that the impaired ability to integrate the size and probability of a reward was primarily caused by a lack of consideration of the reward magnitude.
“The paper is a valuable contribution to the new field of Computational Psychiatry,” said Cameron Carter, MD, Editor of Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, adding that computational models, such as the one used in the study, can lead to new insights into the nature of impaired processing in the brain that forms the basis of symptoms and deficits in mental disorders.
“In this study the authors show that people with schizophrenia, whose motivational deficits lead to much of the social and occupational disability in the illness, perform poorly on reward-based decision making because they fail to compute the expected value of a specific action and instead rely on non-value based information available in the task,” said Dr. Carter.
To assess the information that participants used to make their decision, the researchers developed a learning task that required participants to consider both the size of a reward and the probability of receiving it. Participants were presented with two choices, and asked to select the choice with the highest reward value.
“Using the rewarded learning task, we showed that people with motivational deficits focus too much on how often a reward is presented (i.e., probability), at the cost of learning about the size of the reward (i.e., magnitude). Our mathematical model helped determine that these ‘reward integration’ deficits–that is, an inability to combine information about reward probability and magnitude–were linked to a decreased ability to precisely represent reward value, which is thought to involve a brain area called the orbitofrontal cortex,” said lead author Dennis Hernaus, PhD, now based at Maastricht University, The Netherlands.
Participants with schizophrenia who had motivational deficits did worse when the choice was easier (when the objective value between the choices was larger), indicating that their performance declined as the demands on this brain region to assess value increased.
The findings suggest that the mathematical model used in the study could be useful as a diagnostic tool to help identify motivational deficits in patients. The association between poor motivation and reward integration deficits identified in the study helps explain why problems with motivation arise, and hints at deficits within a specific brain region. The researchers plan to study deficits within this brain region and the link to motivational deficits in future studies using neuroimaging.
Source: Rhiannon Bugno – Elsevier
Publisher: Organized by NeuroscienceNews.com.
Image Source: NeuroscienceNews.com image is in the public domain.
Original Research: Abstract for “Impaired Expected Value Computations in Schizophrenia Are Associated With a Reduced Ability to Integrate Reward Probability and Magnitude of Recent Outcomes” by Dennis Hernaus, Michael J. Frank,Elliot C. Brown, Jaime K. Brown, James M. Gold, and James A. Waltz in Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. Published December 7 2018.
[cbtabs][cbtab title=”MLA”]Elsevier”Inability to Integrate Reward Information Contributes to Undervalued Rewards in Schizophrenia.” NeuroscienceNews. NeuroscienceNews, 22 January 2019.
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Impaired Expected Value Computations in Schizophrenia Are Associated With a Reduced Ability to Integrate Reward Probability and Magnitude of Recent Outcomes
Motivational deficits in people with schizophrenia (PSZ) are associated with an inability to integrate the magnitude and probability of previous outcomes. The mechanisms that underlie probability-magnitude integration deficits, however, are poorly understood. We hypothesized that increased reliance on “valueless” stimulus-response associations, in lieu of expected value (EV)-based learning, could drive probability-magnitude integration deficits in PSZ with motivational deficits.
Healthy volunteers (n = 38) and PSZ (n = 49) completed a learning paradigm consisting of four stimulus pairs. Reward magnitude (3, 2, 1, 0 points) and probability (90%, 80%, 20%, 10%) determined each stimulus’s EV. Following a learning phase, new and familiar stimulus pairings were presented. Participants were asked to select stimuli with the highest reward value.
PSZ with high motivational deficits made increasingly less optimal choices as the difference in reward value (probability × magnitude) between two competing stimuli increased. Using a previously validated computational hybrid model, PSZ relied less on EV (“Q-learning”) and more on stimulus-response learning (“actor-critic”), which correlated with Scale for the Assessment of Negative Symptoms motivational deficit severity. PSZ specifically failed to represent reward magnitude, consistent with model demonstrations showing that response tendencies in the actor-critic were preferentially driven by reward probability.
Probability-magnitude deficits in PSZ with motivational deficits arise from underutilization of EV in favor of reliance on valueless stimulus-response associations. Confirmed by our computational hybrid framework, probability-magnitude integration deficits were driven specifically by a failure to represent reward magnitude. This work provides a first mechanistic explanation of complex EV-based learning deficits in PSZ with motivational deficits that arise from an inability to combine information from different reward modalities.