Independent perceptual learning in monocular and binocular motion systems

Lü, Zhong-Lin; Chu, Wilson; Dosher, Barbara Anne; Lee, Sophia

doi:10.1073/pnas.0501387102

Cited by 31 publications

(25 citation statements)

References 45 publications

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“…Transfer: To compare transfer an ANOVA was conducted with transfer of learning (last training block vs. transfer-test block, within-subjects) and training group (between-subjects). Moreover, transfer of learning was assessed individually by calculating a variation on a commonly used transfer index (e.g., Ahissar & Hochstein, 1997; Hung & Seitz, 2014; Jeter, Dosher, Petrov & Lu, 2009; Lu, Chu, Dosher & Lee, 2005; Zhang et al, 2010). …”

Section: Methodsmentioning

confidence: 99%

Rapid and long-lasting learning of feature binding

Yashar

Carrasco

2016

Cognition

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How are features integrated (bound) into objects and how can this process be facilitated? Here we investigated the role of rapid perceptual learning in feature binding and its long-lasting effects. By isolating the contributions of individual features from their conjunctions between training and test displays, we demonstrate for the first time that training can rapidly and substantially improve feature binding. Observers trained on a conjunction search task consisting of a rapid display with one target-conjunction, then tested with a new target-conjunction. Features were the same between training and test displays. Learning transferred to the new target when its conjunction was presented as a distractor, but not when only its component features were presented in different conjunction distractors during training. Training improvement lasted for up to 16 months, but, in all conditions, it was specific to the trained target. Our findings suggest that with short training observers’ ability to bind two specific features into an object is improved, and that this learning effect can last for over a year. Moreover, our findings show that while the short-term learning effect reflects activation of presented items and their binding, long-term consolidation is task specific.

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Section: Methodsmentioning

confidence: 99%

Rapid and long-lasting learning of feature binding

Yashar

Carrasco

2016

Cognition

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“…Dosher and Lu have built a reweighting model of perceptual learning, which has been highly influential in VPL research (Dosher & Lu 1998, Liu et al 2010, Lu et al 2005, Petrov et al 2005, Petrov et al 2006). The model assumes that VPL occurs due to two independent mechanisms, improved filtering of external noise and removal of internal noise.…”

Section: Task-relevant Visual Perceptual Learningmentioning

confidence: 99%

“…One of the most influential models that assume changes in the mid-stage is the re-weighting model (Dosher & Lu 1998, Gold et al 2008, Liu et al 2010, Lu et al 2005, Petrov et al 2005, Petrov et al 2006). The dual plasticity model does not contradict the reweighting principle.…”

Section: Dual-plasticity Model Of Visual Perceptual Learningmentioning

confidence: 99%

Perceptual Learning: Toward a Comprehensive Theory

Watanabe

Sasaki

2015

Annu. Rev. Psychol.

287

271

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Visual perceptual learning (VPL) is long-term performance increase resulting from visual perceptual experience. Task-relevant VPL of a feature results from training of a task on the feature relevant to the task. Task-irrelevant VPL arises as a result of exposure to the feature irrelevant to the trained task. There are at least two serious problems. First, which stage of information processing is changed in association with task-relevant VPL is controversial. Second, no model has ever explained both task-relevant and task-irrelevant VPL. Here we propose a dual plasticity model, in which there are feature-based plasticity that is a change in a representation of the learned feature and task-based plasticity that is a change in processing of the trained task. While the two types of plasticity underlie task-relevant VPL, only feature-based plasticity lies under task-irrelevant VPL. This model provides a new comprehensive framework in which apparently contradictory results could be explained.

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“…The fact that posttraining residual impairments involved deficits in contrast sensitivity as well as fine discrimination abilities suggest that they could arise from low signal-to-noise ratio and/or a general deficit in suppressing irrelevant signals at retrained blind field locations. Indeed, perceptual deficits have been shown to result solely or jointly from a failure to differentiate signal from both external and internal noise as well as from low information sampling efficiency (Pelli, 1981;Legge, Kersten, & Burgess, 1987;Pelli & Farell, 1999;Simpson, Falkenberg, & Manahilov, 2003;Dakin, Mareschal, & Bex, 2005;Lu, Chu, Dosher, & Lee, 2005;Lu, Chu, & Dosher, 2006;Lu & Dosher, 2008). Visual deficits in normal aging (Bennett, Sekuler, & Ozin, 1999;Bower & Anderson, 2012), following visual cortex damage (Hayes & Merigan, 2006), and in amblyopia (Pelli, Levi, & Chung, 2004;Xu, Lu, Qiu, & Zhou, 2006;Huang, Lu, & Zhou, 2009) are all associated with at least one of these processing limitations.…”

Section: Introductionmentioning

confidence: 99%

Visual recovery in cortical blindness is limited by high internal noise

et al. 2015

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Damage to the primary visual cortex typically causes cortical blindness (CB) in the hemifield contralateral to the damaged hemisphere. Recent evidence indicates that visual training can partially reverse CB at trained locations. Whereas training induces near-complete recovery of coarse direction and orientation discriminations, deficits in fine motion processing remain. Here, we systematically disentangle components of the perceptual inefficiencies present in CB fields before and after coarse direction discrimination training. In seven human CB subjects, we measured threshold versus noise functions before and after coarse direction discrimination training in the blind field and at corresponding intact field locations. Threshold versus noise functions were analyzed within the framework of the linear amplifier model and the perceptual template model. Linear amplifier model analysis identified internal noise as a key factor differentiating motion processing across the tested areas, with visual training reducing internal noise in the blind field. Differences in internal noise also explained residual perceptual deficits at retrained locations. These findings were confirmed with perceptual template model analysis, which further revealed that the major residual deficits between retrained and intact field locations could be explained by differences in internal additive noise. There were no significant differences in multiplicative noise or the ability to process external noise. Together, these results highlight the critical role of altered internal noise processing in mediating training-induced visual recovery in CB fields, and may explain residual perceptual deficits relative to intact regions of the visual field.

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Independent perceptual learning in monocular and binocular motion systems

Cited by 31 publications

References 45 publications

Rapid and long-lasting learning of feature binding

Rapid and long-lasting learning of feature binding

Perceptual Learning: Toward a Comprehensive Theory

Visual recovery in cortical blindness is limited by high internal noise

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