Feature reliability determines specificity and transfer of perceptual learning in orientation search

Abstract

Training can modify the visual system to produce a substantial improvement on perceptual tasks and therefore has applications for treating visual deficits. Visual perceptual learning (VPL) is often specific to the trained feature, which gives insight into processes underlying brain plasticity, but limits VPL’s effectiveness in rehabilitation. Under what circumstances VPL transfers to untrained stimuli is poorly understood. Here we report a qualitatively new phenomenon: intrinsic variation in the representation of features determines the transfer of VPL. Orientations around cardinal are represented more reliably than orientations around oblique in V1, which has been linked to behavioral consequences such as visual search asymmetries. We studied VPL for visual search of near-cardinal or oblique targets among distractors of the other orientation while controlling for other display and task attributes, including task precision, task difficulty, and stimulus exposure. Learning was the same in all training conditions; however, transfer depended on the orientation of the target, with full transfer of learning from near-cardinal to oblique targets but not the reverse. To evaluate the idea that representational reliability was the key difference between the orientations in determining VPL transfer, we created a model that combined orientation-dependent reliability, improvement of reliability with learning, and an optimal search strategy. Modeling suggested that not only search asymmetries but also the asymmetric transfer of VPL depended on preexisting differences between the reliability of near-cardinal and oblique representations. Transfer asymmetries in model behavior also depended on having different learning rates for targets and distractors, such that greater learning for low-reliability distractors facilitated transfer. These findings suggest that training on sensory features with intrinsically low reliability may maximize the generalizability of learning in complex visual environments.

Exogenous attention facilitates perceptual learning in visual acuity to untrained stimulus locations and features

“…Indeed, the benefit for the attention group is more noteworthy when considering that during the post-test session observers in the neutral group were tested in exactly the same conditions in which they trained, whereas for observers in the attention group the post-test was not identical to the training sessions. Because training procedures for both groups were identical other than the attentional manipulation, our findings are not due to other factors known to influence VPL transfer, such as sensory adaptation (Harris et al, 2012), variability in task difficulty (Hung & Seitz, 2014), task precision (Jeter et al, 2009), length of training (Jeter et al, 2010), stimulus feature reliability (Yashar & Denison, 2017) and exposure to stimuli at untrained locations prior to training (T. . We interpret our findings to demonstrate that the selective allocation of exogenous attention during training facilitates fine perceptual discrimination across locations and features.…”

“…Learning specificity is often attributed to plasticity in sensory areas that encode precise stimulus locations and features, such as V1 (Ghose, Yang, & Maunsell, 2002;Gu et al, 2011;Schoups, Vogels, Qian, & Orban, 2001;Watanabe et al, 2002;Zhang, Cong, Song, & Yu, 2013), but the degree of specificity depends on training conditions and task demands (Hung & Seitz, 2014;Jeter, Dosher, Petrov, & Lu, 2009;Xiao et al, 2008;Yashar & Denison, 2017). Perceptual learning has also been related to higher cortical regions, such as the lateral intraparietal cortex (LIP) and anterior cingulate cortex (Adab & Vogels, 2011;Chowdhury & DeAngelis, 2008;Jeter, Dosher, Liu, & Lu, 2010;Kahnt, Grueschow, Speck, & Haynes, 2011;Law & Gold, 2008).…”

“…For example, expert radiologists employ efficient search strategies when viewing medical images, such that they are faster and make fewer perceptual errors in discriminating abnormal from healthy tissue compared to novices ( Waite, Grigorian, Alexander, Macknik, Carrasco, Heeger, & Martinez-Conde, 2019 ). Robust learning has generally been thought to require extensive and repeated training ( Frank, Reavis, Tse, & Greenlee, 2014 ; Karni & Sagi, 1993 ; Watanabe, Náñez, Koyama, Mukai, Liederman, & Sasaki, 2002 ; Yotsumoto, Watanabe, & Sasaki, 2008 ), but recent studies have demonstrated long-lasting performance enhancements with short training periods ( Hussain, Sekuler, & Bennett, 2011 ; Yashar & Carrasco, 2016 ; Yashar, Chen, & Carrasco, 2015 ).…”

Endogenous spatial attention during perceptual learning facilitates location transfer

“…This effect was verified by both repeated-measures ANOVAs and group-level Bayesian model comparison. Because groups only differed in the way in which attention was allocated during training—distributed in the neutral group or selective in the attention group—and stimulus and task were constant, we can rule out the possible role of other factors that contribute to location transfer: length of training (Jeter et al, 2010 ), task precision (Jeter et al, 2009 ), sensory adaptation (Harris et al, 2012 ), sensory uncertainty of stimulus features (Yashar & Denison, 2017 ), exposure to stimuli at untrained locations prior to training (T. Zhang et al, 2010 ), and variability in task difficulty (Hung & Seitz, 2014 ).…”

“…Many studies have specifically focused on exploring the conditions leading to location specificity versus transfer. Some factors reported to influence specificity include the length of training (Jeter et al, 2010 ), task precision (Jeter, Dosher, Petrov, & Lu, 2009 ), sensory adaptation (Harris et al, 2012 ), sensory uncertainty of stimulus features in visual search (Yashar & Denison, 2017 ), exposure to stimuli at untrained locations prior to training (T. Zhang et al, 2010 ), and variability in task difficulty (Hung & Seitz, 2014 ; but see Discussion of Wang et al, 2014 ).…”