Biology subjects

Gold, J. M.

Publications and source records attributed to Gold, J. M..

3 recordsLinked to original sources

bioRxiv2018

Impaired expected value computations in schizophrenia are associated with a reduced ability to integrate reward probability and magnitude of recent outcomes.

ABSTRACTO_ST_ABSBackgroundC_ST_ABSMotivational 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 \"value-less\" stimulus-response associations, in lieu of expected value (EV)-based learning, could drive probability-magnitude integration deficits in PSZ with motivational deficits.\n\nMethodsHealthy volunteers (n= 38) and PSZ (n=49) completed a reinforcement learning paradigm consisting of four stimulus pairs. Reward magnitude (3/2/1/0 points) and probability (90%/80%/20%/10%) together determined each stimulus EV. Following a learning phase, new and familiar stimulus pairings were presented. Participants were asked to select stimuli with the highest reward value.\n\nResultsPSZ 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 SANS 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.\n\nConclusionsProbability-magnitude deficits in PSZ with motivational deficits arise from underutilization of EV in favor of reliance on value-less stimulus-response associations. Consistent with previous work and confirmed by our computational hybrid framework, probability-magnitude integration deficits were driven specifically by a failure to represent reward magnitude. This work reconfirms the importance of decreased Q-learning/increased actor-critic-type learning as an explanatory framework for a range of EV deficits in PSZ.

neuroscience↗

bioRxiv2018

The Impact of Reward on Attention in Schizophrenia

Traditionally, attention was thought to be directed by either top-down goals or bottom-up salience. Recent studies have shown that the reward history of a stimulus feature also acts as a powerful attentional cue. This is particularly relevant in schizophrenia, which is characterized by motivational and attentional deficits. Here, we examine the impact of reward on selective attention.\n\nForty-eight people with schizophrenia (PSZ) and 34 non-psychiatric control subject (NCS) discriminated the location of a target dot appearing inside a left circle or right circle. The circles were different colors, one of which was associated with reward via pre-training. In the first 2 blocks, targets were equally likely to appear in the left or right circle. In the last 4 blocks, the target was 75% likely on one side, thus allowing us to separately examine how attention was impacted by reward (color) and probability (location).\n\nPSZ had slower overall reaction times (RTs) than NCS. Both groups showed robust effects of spatial probability and reward history, with faster RTs for the rewarded color and for the more probable location. These effects were similar in PSZ and NCS. Negative symptom severity correlated with overall RT slowing, but there were no correlations between symptoms and reward-associated biasing of attention.\n\nPSZ demonstrated RT slowing but normal reward history and spatial probability-driven RT facilitation. These results are conceptually similar to prior findings showing intact implicit reward effects on response bias, and suggest that implicit processing of reward and probability is intact in PSZ.

neuroscience↗

bioRxiv2017

Impaired expected value computations coupled with overreliance on prediction error learning in schizophrenia

BackgroundWhile many have emphasized impaired reward prediction error (RPE) signaling in schizophrenia, multiple studies suggest that some decision-making deficits may arise from overreliance on RPE systems together with a compromised ability to represent expected value. Guided by computational frameworks, we formulated and tested two scenarios in which maladaptive representation of expected value should be most evident, thereby delineating conditions that may evoke decision-making impairments in schizophrenia.\n\nMethodsIn a modified reinforcement learning paradigm, 42 medicated people with schizophrenia (PSZ) and 36 healthy volunteers learned to select the most frequently rewarded option in a 75-25 pair: once when presented with more deterministic (90-10) and once when presented with more probabilistic (60-40) pairs. Novel and old combinations of choice options were presented in a subsequent transfer phase. Computational modeling was employed to elucidate contributions from RPE systems (\"actor-critic\") and expected value (\"Q-leaming\").\n\nResultsPSZ showed robust performance impairments with increasing value difference between two competing options, which strongly correlated with decreased contributions from expected value-based (\"Q-leaming\") learning. Moreover, a subtle yet consistent contextual choice bias for the \"probabilistic\" 75 option was present in PSZ, which could be accounted for by a context-dependent RPE in the \"actor-critic\".\n\nConclusionsWe provide evidence that decision-making impairments in schizophrenia increase monotonically with demands placed on expected value computations. A contextual choice bias is consistent with overreliance on RPE-based learning, which may signify a deficit secondary to the maladaptive representation of expected value. These results shed new light on conditions under which decisionmaking impairments may arise.

neuroscience↗