Dataset Information

Probability and magnitude evaluation in schizophrenia.

ABSTRACT: Alterations in reinforcement learning and decision making in schizophrenia have been linked with orbitofrontal cortex (OFC) dysfunction, a region critical for weighing reward magnitude in the calculation of expected value (EV). However, much of this work has used complex tasks that require combined learning and EV calculation. Here we used a simple "Roulette" task that examined the calculation of EV directly through a combination of text and/or pictorial representation of reward probability and magnitude. Forty-four people with schizophrenia and 30 controls were recruited. Patients were less sensitive to adjustments in a parameter combining probability and magnitude into one EV construct. Breaking down the construct into independent contributions of probability and magnitude, we found that negative symptoms were associated with magnitude sensitivity. This is consistent with the hypothesized role of OFC in actively representing magnitude and the notion that negative symptoms may involve a failure to appropriately estimate and use future reward magnitude to guide decision making.

SUBMITTER: Albrecht MA

PROVIDER: S-EPMC5514296 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

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Probability and magnitude evaluation in schizophrenia.

Albrecht Matthew A MA Waltz James A JA Frank Michael J MJ Gold James M JM

Schizophrenia research. Cognition 20160720

Alterations in reinforcement learning and decision making in schizophrenia have been linked with orbitofrontal cortex (OFC) dysfunction, a region critical for weighing reward magnitude in the calculation of expected value (EV). However, much of this work has used complex tasks that require combined learning and EV calculation. Here we used a simple "Roulette" task that examined the calculation of EV directly through a combination of text and/or pictorial representation of reward probability and ...[more]

PMID: 28740816

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Project description:BACKGROUND: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. METHODS: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. RESULTS: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. CONCLUSIONS: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.

Project description:Determining which demographic and ecological parameters contribute to variation in population growth rate is crucial to understanding the dynamics of declining populations. This study aimed to evaluate the magnitude and mechanisms of an apparent major decline in an Atlantic Puffin Fratercula arctica population. This was achieved using a 27-year dataset to estimate changes in population size and in two key demographic rates: adult survival and breeding success. Estimated demographic variation was then related to two ecological factors hypothesised to be key drivers of demographic change, namely the abundance of the main predator at the study site, the Great Skua Stercorarius skua, and Atlantic Puffin chick food supply, over the same 27-year period. Using a population model, we assessed whether estimated variation in adult survival and reproductive success was sufficient to explain the population change observed. Estimates of Atlantic Puffin population size decreased considerably during the study period, approximately halving, whereas Great Skua population estimates increased, approximately trebling. Estimated adult Atlantic Puffin survival remained high across all years and did not vary with Great Skua abundance; however, Atlantic Puffin breeding success and quantities of fish prey brought ashore by adults both decreased substantially through the period. A population model combining best possible demographic parameter estimates predicted rapid population growth, at odds with the long-term decrease observed. To simulate the observed decrease, population models had to incorporate low immature survival, high immature emigration, or increasingly high adult non-breeding rates. We concluded that reduced recruitment of immatures into the breeding population was the most likely cause of population decrease. This study showed that increase in the size of a predator population does not always impact on the survival of adult prey and that reduced recruitment can be a crucial determinant of seabird population size but can easily go undetected.

Dataset Information

Probability and magnitude evaluation in schizophrenia.

Publications

Probability and magnitude evaluation in schizophrenia.

Similar Datasets

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