Modeling changes of age and gender during face morphing.

<p>Both were time-binned at the video frame rate (24 fps) and scaled to maxima of 1.0 arbitrary units [a.u.]. (A) Differential age change encoded according to Stevens' law of psychophysics (using a power exponent of 0.3; <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049451#pone-0049451-g002" target="_blank">Figure 2A</a>). Note that relative facial aging was up-weighted to initial periods of the example morph (also see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049451#pone-0049451-g001" target="_blank">Figure 1A</a>; here: solid red line) and, for identical age differences, to younger absolute ages, i.e. aging from 10 to 26 was assumed to provide a stronger stimulus with more visual cues than aging from 64 to 80 years (dashed vs. double-dotted/dashed line). (B) Differential gender change expressed by the first derivative of the function plotted in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049451#pone-0049451-g002" target="_blank">Figure 2C</a>. Note that peak androgyny was defined as the effective stimulus-of-interest, i.e. the transition of facial gender was emphasized at the center of the morph (see also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049451#pone-0049451-g001" target="_blank">Figure 1A</a> and Movie S1). Half of the morphs contained no gender transitions, retaining a flat line at zero level to indicate the lack of gender change (dashed line).</p

Synopsis of functional activations related to age, gender and motion/optical flow.

<p>Clusters significantly activated by changes of facial age, gender and motion/optical flow (FWER-corrected p<0.05 for n = 24 subjects)^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049451#nt102" target="_blank">1</a>}.</p>1<p>hemi, hemisphere; size in [mm²], CWP, cluster-wise probability (non-parametric cluster mass inference over the entire surface; [ ]); Max, peak activation probability (absolute log10-maximum of uncorrected p-values: −log10(p); [ ]); VtxMax, vertex of Max on Freesurfer's average surface; MNI, coordinates in MNI standard space [mm]; vE/BA, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049451#pone.0049451-vonEconomo1" target="_blank">[113]</a>/<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049451#pone.0049451-Brodmann1" target="_blank">[114]</a> area; annotation, anatomical labels; pANG, posterior angular gyrus area (*sub-cluster related to high age-rating competence); pITS, posterior inferior temporal sulcus; DLPFC, dorsolateral prefrontal cortex; LOT, lateral occipitotemporal area; FFG, fusiform gyrus; hMT+, human motion-sensitive MT+ (V5 or MT/MST) area.</p

Left-hemispheric nodes of the presumed brain network processing the age of faces.

<p>3D model illustrating how the ventral stream, pITS in particular, may interact via Wernicke's perpendicular fasciculus (WpF) with the posterior magnitude-encoding and approximate number system <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049451#pone.0049451-Walsh1" target="_blank">[98]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049451#pone.0049451-Cantlon1" target="_blank">[99]</a>, pANG in particular, to quantify the varying age of faces. FFG exhibits some connectivity to pANG (cf. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049451#pone-0049451-g006" target="_blank">Figure 6</a>) but is primarily engaged in processing fixed face attributes such as categorical gender (even if continuously changed over variable androgyny levels like in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049451#pone-0049451-g001" target="_blank">Figure 1A</a>; see also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049451#pone-0049451-g002" target="_blank">Figures 2C</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049451#pone-0049451-g003" target="_blank">3B</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049451#pone-0049451-g004" target="_blank">4A</a>). pANG, posterior angular gyrus area; pITS, posterior inferior temporal sulcus; DLPFC, dorsolateral prefrontal cortex; LOT, lateral occipitotemporal area; FFG, fusiform gyrus; 17–19, Brodmann's areas forming three visual tiers; hMT+, human motion-sensitive temporal cortex; ITG/MTG/STG, inferior/middle/superior temporal gyrus; ITS/STS, inferior/superior temporal sulcus.</p

Continuous face morphing, optical flow and associated functional activations.

<p>(A) Exemplary keyframes of a video sequence (see Movie S1) morphing a 20 year-old female into a 60 year-old male. Both gradual age and gender changes are illustrated at intervals of 1 second. (B) Line magnitude images of optical flow velocities computed by the Horn-Schunck algorithm. Differential motion/optical flow was quantified as an overall parameter by the sum of flow magnitudes between successive keyframes. (C) Motion-/flow-related activations of hMT+ derived from the group-level analyses (n = 24 subjects, FWER-corrected p<0.05, [−log10 (p)] colorbar) on posterior cortical flat maps of both hemispheres. Additionally, ventral (v) and dorsal (d) visuotopic labels (V1–8, Vp, LO, hMT+) of the SuMS database, transformed from Caret's PALS atlas into FreeSurfer's average surface space, are displayed. Note that according to recent data <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049451#pone.0049451-Wandell1" target="_blank">[112]</a>, V4v and V8 are labeled together as hV4 while VP has been labeled V3v.</p

Functional activations associated with changes of facial age and gender.

<p>(A) Group-level (n = 24) functional activations¹ related to age and gender change, respectively. (B) Quantification and between-cluster/-hemisphere comparisons of observed effect sizes evoked by facial age and gender changes across (n = 24) subjects. Individual values of each cluster's mean activation (± error bars across subjects) were normalized to the lowest average of corresponding response magnitudes (as extracted from the first-level analyses). (C) Increased age-related activations¹ of the most accurate (n = 5) above average age-raters (n = 14). The corresponding cortical flat map is outlined by the borders of the left age-responsive pANG cluster. (D) Relative to average post-hoc raters (avg, n = 14), high explicit age-rating accuracy (upper quintile P80, n = 5) was accompanied by almost five times the response magnitude during implicit age-change processing within left pANG (p<0.001, based on mean individual activation levels of the sub-cluster shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049451#pone-0049451-g004" target="_blank">Figure 4C</a>, as back-projected to native subject space). Activations of lower quintile raters (P20, n = 5) were more variable but not statistically different from the average (P20–80). ¹Significant activations (FWER-corrected p<0.05) displayed on FreeSurfer's average inflated surface (color bars depict uncorrected activation probabilities [−log10 (p)]). pANG, posterior angular gyrus area; pITS, posterior inferior temporal sulcus; DLPFC, dorsolateral prefrontal cortex; LOT, lateral occipito-temporal area; FFG, fusiform gyrus; orientation labels: L, left; R, right.</p

Brain regions associated with the dynamic forward model.

<p>Activity correlated with the precision of prediction from the dynamic forward model. Cortical activity and subcortical activity in cerebellum and caudate. The figure shows group Z-maps for the 22 participants, thresholded at <i>p</i><0.05 corrected (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001662#s4" target="_blank">Methods</a>). A full table of activation peaks is given in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001662#pbio.1001662.s005" target="_blank">Table S2</a>.</p

Association pathways subserving facial age processing.

<p>Ventral portion of Wernicke's perpendicular fasciculus (WpF) connecting pANG and pITS (average probabilistic path distributions connecting the functional clusters; n = 24, 3D-tract volume rendering thresholded at ≥100 connecting samples passing through each voxel, displayed on sagittal [x = −36 mm] and coronal [y = −54 mm] projection view planes in MNI standard space). pANG, posterior angular gyrus area; pITS, posterior inferior temporal sulcus; orientation labels: L, left; R, right.</p

Activity associated with the statistical model and with accuracy.

<p>(A) Activity correlated with the precision of prediction from the statistical model. The figure shows group Z-maps for the 22 participants, thresholded at <i>p</i><0.05 corrected (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001662#s4" target="_blank">Methods</a>). (B) Parameter estimates for the effect of precision of the statistical model, precision of the trajectory estimate, and trial-to-trial accuracy, for a region of interest in the orbitofrontal cortex, defined based on a meta-analysis <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001662#pbio.1001662-Kringelbach1" target="_blank">[65]</a>. Bars show group mean, and error bars show s.e.m. Note that although there is a significant effect of precision for the statistical model (<i>p</i> = 0.036, one sample <i>t</i> test against zero), there is no effect of accuracy per se (<i>p</i> = 0.82) or of the precision of the dynamic model (<i>p</i> = 0.55); note, in the region of interest analysis, accuracy is not orthogonalised with respect to model precisions, so the effects of model precision are independent of variance that could also be explained by overall accuracy. This is why effect sizes look slightly different to in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001662#pbio-1001662-g006" target="_blank">Figure 6</a>. (C) Activity relating to trial-to-trial accuracy. The figure shows group Z-maps for the 22 participants, thresholded at <i>p</i><0.05 corrected (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001662#s4" target="_blank">Methods</a>). Note the strong peak in the ventral striatum. Slice location is y = 6, peak effect at 20, 6, −10, Z = 4.8. In the whole brain analysis, accuracy was orthogonalised with respect to the model precisions, with which it was correlated (as in panel E). (D) Parameter estimates as in (B), but for a region of interest in the ventral striatum, defined using the nucleus accumbens mask from the Harvard-Oxford atlas, available in FSL (<a href="http://www.fmrib.ox.ac.uk/fsl" target="_blank">www.fmrib.ox.ac.uk/fsl</a>). Note that this ROI is strongly affected by overall accuracy (<i>p</i> = 0.0015, one sample <i>t</i> test against zero) but not by the precision of the statistical (<i>p</i> = 0.23) or dynamic (<i>p</i> = 0.61) models. (E) Behavioral effects of precision of the statistical model and trajectory estimate on accuracy. Bars show group mean ± s.e.m. effect size from a multiple regression of accuracy on precisions for the two models. The effect of precision for both the statistical and dynamic models were significant (<i>t</i> test versus zero, <i>p</i><0.01 and <i>p</i><0.0001, respectively), but the effect of dynamic model precision was much greater (paired <i>t</i> test, <i>p</i><0.0001).</p

Summary of the characteristics of two classes of predictive model.

<p>The equations are typical instantiations of each model class—for the statistical endpoints distribution model, a temporal difference learning rule in which the value of an item on iteration (V_t+1) is equal to the value on iteration t, plus some proportion of the prediction error (δ) times learning rate (α). Dynamic forward models would typically be captured by a set of differential equations, where the rate of change of some parameters (Θ), such as position, is a function of time, f(t).</p

LPNec : laboratório de psicologia experimental, neurociências e comportamento

<div><p>A computational approach to functional specialization suggests that brain systems can be characterized in terms of the types of computations they perform, rather than their sensory or behavioral domains. We contrasted the neural systems associated with two computationally distinct forms of predictive model: a reinforcement-learning model of the environment obtained through experience with discrete events, and continuous dynamic forward modeling. By manipulating the precision with which each type of prediction could be used, we caused participants to shift computational strategies within a single spatial prediction task. Hence (using fMRI) we showed that activity in two brain systems (typically associated with reward learning and motor control) could be dissociated in terms of the forms of computations that were performed there, even when both systems were used to make parallel predictions of the same event. A region in parietal cortex, which was sensitive to the divergence between the predictions of the models and anatomically connected to both computational networks, is proposed to mediate integration of the two predictive modes to produce a single behavioral output.</p></div