Decoding evidence for policies on each individual test task.
<p>Each panel shows average decoding evidence during a specific test task (y-axis) as a function of brain region (x-axis). Colors denote different policy categories, and small translucent circles show data from individual participants. Panel <b>B</b> is especially relevant for test...
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2025
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| Summary: | <p>Each panel shows average decoding evidence during a specific test task (y-axis) as a function of brain region (x-axis). Colors denote different policy categories, and small translucent circles show data from individual participants. Panel <b>B</b> is especially relevant for testing whether the neural data are more consistent with SF&GPI or Universal Value Function Approximation (UVFA). The theoretical choice profiles for SF&GPI and UVFA are distinct for test task <i>w</i> = [1,1,−1] (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.3003174#pbio.3003174.s004" target="_blank">S4 Fig</a>). The theoretical choice profile for UVFA predicts: 1) that the more rewarding training policy and objective best policy will be activated on test task <i>w</i> = [1,1,−1] and 2) that the decoding evidence for these two policies will be comparable in magnitude. In contrast, we found that the more rewarding training policy was activated in OTC (<i>t</i>(37) = 3.87, corrected <i>p</i> = 0.003) but did not detect evidence that the objective best policy was activated (<i>t</i>(37) = −0.90, corrected <i>p</i> = 0.750). Decoding evidence for the more rewarding training policy was also significantly higher than the objective best policy (<i>t</i>(37) = 3.21, corrected <i>p</i> = 0.014). Equivalent tests in DLPFC were not significant (<i>t</i>-values < 1.66, corrected <i>p</i>-values > 0.094). These follow-up tests were restricted to OTC and DLPFC based on the decoding results in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.3003174#pbio.3003174.g003" target="_blank">Fig 3</a> of the main text. <i>p</i>-values were corrected for six tests using the Bonferroni–Holm correction (2 policy categories × 2 ROIs and two follow-up tests assessing the difference in decoding evidence in each ROI). Materials to reproduce this figure are available at <a href="https://gin.g-node.org/sam.hall-mcmaster/sfgpi-neural-analysis" target="_blank">https://gin.g-node.org/sam.hall-mcmaster/sfgpi-neural-analysis</a>.</p> <p>(TIF)</p> |
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