Found 17 images.
| ID | Name | Collection(s) | Description |
|---|---|---|---|
| 563792 | Midfrontal beta oscillations | Biased credit assignment in motivational learning biases arises through prefrontal influences on striatal learning | Parametric regressor: trial-by-trial frontal (t-weighted mean of channels Cz/ FCz/ Fz) beta oscillations (300–1,250 ms relative to outcome onset, 13 - 30 Hz) measured using scalp EEG. This beta response is significantly higher for positive outcomes (rewards/ no punishments) than negative outcomes (no rewards/ punishments). This regressor is added on top of GLM2 regressors (featuring 8 outcome x response regressors plus standard prediction errors and the difference term to biased prediction errors), yielding GLM3C. Motivational Go/NoGo task (Swart et al., 2017; 2018; van Nuland et al., 2020). |
| 440381 | Net Value | The neural basis of effort valuation: A meta-analysis of functional magnetic resonance imaging studies | Unthresholded z-score map of main Net Value meta-analysis (N=15) |
| 440385 | Net Value Supp_EffortXReward | The neural basis of effort valuation: A meta-analysis of functional magnetic resonance imaging studies | Unthresholded z-score map of supplementary Net Value meta-analysis including studies with EffortXReward as net value parameter (N=17). |
| 42831 | Optimist vs. Unbiased (RPE) | Behavioural and neural characterization of optimistic reinforcement learning | difference between optimist (greater learning rate for positive prediction errors) and unbiased concerning Reward prediction error representation |
| 42832 | Optimist vs. Unbiased (Q-chosen) | Behavioural and neural characterization of optimistic reinforcement learning | Different representation of Q-chosen when comparing Optimist and Unbiased subjects. |
| 440382 | Net Value Supp_1SV | The neural basis of effort valuation: A meta-analysis of functional magnetic resonance imaging studies | Unthresholded z-score map of supplementary Net Value meta-analysis limited to studies using parameters that represent net value of only 1 effortful reward option (N=11). |
| 42406 | Reward Prediction Error | Behavioural and neural characterization of optimistic reinforcement learning | Reward Prediction Error at outcome onset (positive correlation with RPE) |
| 440386 | RawEffort | The neural basis of effort valuation: A meta-analysis of functional magnetic resonance imaging studies | Unthresholded z-score map showing neural activity associated with prospective effort (N=22) |
| 550248 | Standard reward prediction errors | Biased credit assignment in motivational learning biases arises through prefrontal influences on striatal learning | Parametric regressor: reward prediction errors computed with a standard Rescorla Wagner model. This is GLM1. The GLM contains the following 10 regressors: 1-4) 4 regressors crossing the performed action (Go/ NoGo) with the valence of the cue (Win/ Avoid). At the time of cue onset. 5) Response hand: +1 for left and response, 0 for no response, -1 for right hand response. At the time of cue onset. 6) Incorrect response. At the time of responses. 7) Outcome Onset (any outcome). At the time of outcomes. 8) Standard reward prediction errors computed with the standard Rescorla-Wagner learning model. 9) Difference between biased and standard reward prediction errors, respectively computed with a) a Rescorla-Wagner models assuming an increased learning rate for rewarded Go responses and a decreased learning rate after punished NoGo responses, and b) a standard Rescorla-Wagner learning model. At the time of outcomes. 10) Invalid outcomes (non-instructed key pressed, returning error message). At the time of outcomes. This contrast just reflecting the standard reward prediction errors (regressor 8). |
| 550249 | Biased minus standard reward prediction errors | Biased credit assignment in motivational learning biases arises through prefrontal influences on striatal learning | Parametric regressor: reward prediction errors computed with a standard Rescorla Wagner model. This is GLM1. The GLM contains the following 10 regressors: 1-4) 4 regressors crossing the performed action (Go/ NoGo) with the valence of the cue (Win/ Avoid). At the time of cue onset. 5) Response hand: +1 for left and response, 0 for no response, -1 for right hand response. At the time of cue onset. 6) Incorrect response. At the time of responses. 7) Outcome Onset (any outcome). At the time of outcomes. 8) Standard reward prediction errors computed with the standard Rescorla-Wagner learning model. 9) Difference between biased and standard reward prediction errors, respectively computed with a) a Rescorla-Wagner models assuming an increased learning rate for rewarded Go responses and a decreased learning rate after punished NoGo responses, and b) a standard Rescorla-Wagner learning model. At the time of outcomes. 10) Invalid outcomes (non-instructed key pressed, returning error message). At the time of outcomes. This contrast just reflects the difference of biased minus standard reward prediction errors (regressor 9). A conjunction of this contrast and the standard reward prediction error contrast captures regions for which BOLD signal is significantly better explained by biased prediction errors compared to standard prediction errors (see approach in Wittmann et al., 2006; Daw et al., 2011). |
| 19156 | Reward | Predicting local striatal reward signals from corticostriatal connectivity | Parametric modulator on the outcome regressor |
| 23 | Guilt Aversion | Triangulating the Neural, Psychological, and Economic Bases of Guilt Aversion | This contrast compares trials in which participants matched expectations (i.e., reciprocated the amount they believed their partner expected) to trials in which they returned less than they believe their partner expected at the decision phase epoch. This contrast compares trials in which the guilt model predicts guilt aversion and guilt inaversion. Guilt inaversion trials by definition are when players make more money and thus are associated with increased financial value. Images are thresholded using cluster correction p < 0.05 with an initial z threshold of 2.3. |
| 9633 | Moral Action observation | mtliuzza's temporary collection | Action Observation for Moral vs Immoral actions. Unthresholded map. |
| 43850 | Psychophysiological interaction (PPI) of hippocampus and memory encoding success during reward learning | Episodic Memory Encoding Interferes with Reward Learning and Decreases Striatal Prediction Errors | PPI connectivity analysis: memory success-related modulation of connectivity between the left hippocampus and whole-brain activity. |
| 3076 | maia pujara | maiapujara's temporary collection | VS activity for reward anticipation in normal subjects (n=17) |
| 508302 | NetValueSupp_physicalEffortOnly_z | The neural basis of effort valuation: A meta-analysis of functional magnetic resonance imaging studies | Unthresholded z-score map of Net Value with only studies that used physical effort (N=13). Please note that this includes studies with RewardXEffort parameters. |
| 550239 | Outcome Valence | Biased credit assignment in motivational learning biases arises through prefrontal influences on striatal learning | Go actions (irrespective of left vs. right) minus Go actions at the time people receive an outcome. This is GLM2. The GLM contains the following 13 regressors: 1-8) 8 regressors crossing the performed action (Go/ NoGo) with the obtained outcome (reward/ no reward = neutral/ no punishment = neutral/ punishment). At the time of outcomes. 9) Left hand response. At the time of responses. 10) Right hand response. At the time of responses. 11) Incorrect response. At the time of responses. 12) Outcome Onset (any outcome). At the time of outcomes. 13) Invalid outcomes (non-instructed key pressed, returning error message). At the time of outcomes. This contrast is based on the first 8 regressors, taking all 4 positive outcome regressors (reward/ no punishment) minus all 4 negative outcome regressors (no reward/ punishment) (note that all regressors at the times of outcomes). Motivational Go/NoGo task (Swart et al., 2017; 2018; van Nuland et al., 2020). |