Learning Acts on Distinct Processes for Visual Form Perception in the Human Brain

Abstract

Learning is known to facilitate our ability to detect targets in clutter and optimize brain processes for successful visual recognition. Previous brain-imaging studies have focused on identifying spatial patterns (i.e., brain areas) that change with learning, implicating occipitotemporal and frontoparietal areas. However, little is known about the interactions within this network that mediate learningdependent improvement in complex perceptual tasks (i.e., discrimination of visual forms in clutter). Here we take advantage of the complementary high spatial and temporal resolution of simultaneous EEG-fMRI to identify the learning-dependent changes in spatiotemporal brain patterns that mediate enhanced behavioral sensitivity in the discrimination of global forms after training. We measured the observers' choices when discriminating between concentric and radial patterns presented in noise before and after training. Similarly, we measured the choices of a pattern classifier when predicting each stimulus from EEG-fMRI signals. By comparing the performance of human observers and classifiers, we demonstrated that learning alters sensitivity to visual forms and EEG-fMRI activation patterns related to distinct visual recognition processes. In particular, behavioral improvement after training was associated with changes in (1) early processes involved in the integration of global forms in higher occipitotemporal and parietal areas, and (2) later processes related to categorical judgments in frontal circuits. Thus, our findings provide evidence that learning acts on distinct visual recognition processes and shapes feedforward interactions across brain areas to support performance in complex perceptual tasks.

Neural Changes with Tactile Learning Reflect Decision-Level Reweighting of Perceptual Readout

“…As reviewed in the Introduction, many studies implicated V1 as the locus of neural plasticity underlying visual learning (Schoups et al, 2001;Schwartz et al, 2002; Furmanski et al, 2004; Yotsumoto et al, 2008; Gilbert et al, 2009;Bao et al, 2010; Jehee et al, 2012), while other studies showed plasticity of representations in extrastriate visual (Zohary et al, 1994; Tovee et al, 1996;Raiguel et al, 2006), somatosensory (Recanzone et al, 1992 b,c,d;Harris et al, 1999) and auditory (Recanzone et al, 1993;Alain et al, 2007;van Wassenhove and Nagarajan, 2007) cortex. However, others found that pertinent changes were modest in visual cortical areas (Ghose et al, 2002;Yang and Maunsell, 2004), were not confined to early visual areas (Song et al, 2002; Ding et al, 2003;Mayhew et al, 2012), or affected the decision stage rather than sensory representations (Law and Gold, 2008). These neurophysiological studies, along with psychophysical and modeling studies (Petrov et al, 2005; Bejjanki et al, 2011;Huang et al, 2012) favor an alternative hypothesis, that perceptual learning depends on plasticity of perceptual readout by decision processes.…”

“…Thus, other fMRI methods, e.g., based on multivoxel pattern analysis, or fine-grained analysis of digit maps (Duncan and Boynton, 2007) might demonstrate somatosensory cortical plasticity in our task, as might methods using alternative modalities such as electrophysiology. Indeed, somatosensory cortical plasticity occurs in other tasks (see above), and changes in both sensory representations and decision processes may be relevant (Bejjanki et al, 2011;Mayhew et al, 2012). …”

Deep Hierarchies in the Primate Visual Cortex: What Can We Learn for Computer Vision?

“…We do not have the space here to discuss what is known about the development [193] and learning of biological visual processing hierarchies (e.g., [105], [113]). However, there are some fairly obvious conclusions relevant to computer vision.…”

Spurious correlations in simultaneous EEG-fMRI driven by in-scanner movement

“…Alarmingly, the presented pattern of spurious motion related EEG-BOLD effects was found employing preprocessing steps and analysis steps commonly employed in EEG-fMRI (Baumeister et al, 2014;de Munck et al, 2007;de Munck et al, 2009;Debener et al, 2007;Hanslmayr et al, 2013;Hanslmayr et al, 2011;Jann et al, 2009;Jansen et al, 2012;Laufs et al, 2006b;Laufs et al, 2003;Li et al, 2012;Liu et al, 2012b;Mayhew et al, 2010;Mayhew et al, 2012;Mayhew et al, 2013;Meyer et al, 2013;Novitskiy et al, 2011;Plichta et al, 2013;Regenbogen et al, 2012;White et al, 2013) and could easily pass as neurophysiological plausible results. The relationship of theta oscillatory power and successful memory formation remains controversial, as opposing effects have been reported by recent studies (Hanslmayr and Staudigl, 2014).…”