Is subliminal learning really passive?

Seitz, Aaron R.; Watanabe, Takeo

doi:10.1038/422036a

Cited by 274 publications

(355 citation statements)

References 8 publications

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“…However, training in clear displays requires the system to work to reduce internal limitations (or equivalently amplify the stimulus) but also provides good information about the nature of the target stimulus or the perceptual template. Although an optimized template is far more critical in noisy displays, template tuning may nonetheless occur in low-noise training by optimization of information about the stimulus, ¶ or possibly by repetitive task-relevant exposure to the signal stimuli (25,26). Training the template in (lowcontrast) low-noise displays may be sufficient to optimize the filtering of external noise substantially, especially when the external noise is white.…”

Section: Discussionmentioning

confidence: 99%

Perceptual learning in clear displays optimizes perceptual expertise: Learning the limiting process

Dosher

Lü

2005

Proc. Natl. Acad. Sci. U.S.A.

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Human operators develop expertise in perceptual tasks by practice or perceptual learning. For noisy displays, practice improves performance by learned external-noise filtering. For clear displays, practice improves performance by improved amplification or enhancement of the stimulus. Can these two mechanisms of perceptual improvement be trained separately? In an orientation task, we found that training with clear displays generalized to performance in noisy displays, but we did not find the reverse to be true. In noisy displays, the noise in the stimulus limits performance. In clear displays, performance is limited by noisiness of internal representations and processes. Our results suggest that training in one display condition optimizes the limiting factor(s) in performance in that condition and that noise filtering is also improved by exposure to the stimulus in clear displays. The asymmetric pattern of transfer implies the existence of two independent mechanisms of perceptual learning, which may reflect channel reweighting in adult visual system. These results also suggest that training operators with clear stimuli may suffice to improve performance in a range of clear and noisy environments by simultaneous learning by two mechanisms.training procedures ͉ noisy displays ͉ observer models P erceptual learning (1-4) can improve the performance of observers significantly, creating perceptual expertise (5, 6). Practice on a stimulus and task often results in very substantial improvements reflected in improved accuracy or in an ability to perform at a given threshold accuracy level with a more difficult (e.g., lower contrast) stimulus. Also, perceptual learning accrued by practice often exhibits specificities to stimulus characteristics such as retinal position (2, 7), orientation (8, 9), or scale (10, 11), demonstrating perceptual rather than strategic knowledge. Refs. 1 and 12 proposed the possible existence of three independent mechanisms of perceptual learning and empirically documented the existence of two of the mechanisms: external-noise filtering and stimulus amplification. The mechanisms are characterized and tested by a perceptual template model (PTM) of the human observer with titrated external-noise manipulations (12-14). The two mechanisms are important in different environments: external-noise filtering or exclusion in noisy environments and stimulus amplification in clear environments. Perceptual learning in these two mechanisms reflects the improvements in different kinds of limitations: improvements in the quality of the information in the stimulus by external-noise filtering and improvements in the intrinsic limitations in the processing of the human observer by stimulus enhancement.In these investigations of perceptual learning in visual tasks (1, 12, 15), training in high and low noise have always been intermixed, so that the two documented mechanisms have often occurred together as mixtures (for exception, see ref. 16). Single-mechanism models have provided one explanation for simultaneous lea...

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Section: Discussionmentioning

confidence: 99%

Perceptual learning in clear displays optimizes perceptual expertise: Learning the limiting process

Dosher

Lü

2005

Proc. Natl. Acad. Sci. U.S.A.

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“…Similarly, Ahissar and Hochstein (2002) found that subjects' learning to detect a target within a horizontally or vertically elongated array does not transfer to a feature of the array they do not attend, namely, the orientation of the array. Although Watanabe and his colleagues (Watanabe et al, 2001(Watanabe et al, , 2002Seitz and Watanabe, 2003) have shown that it is not necessary for attention to be directed to a visual feature for that feature to be learned, they admit that attention plays an important role in perceptual learning (Seitz and Watanabe, 2005).…”

Section: Changes In Attention-related Areas During Perceptual Learningmentioning

confidence: 99%

Activations in Visual and Attention-Related Areas Predict and Correlate with the Degree of Perceptual Learning

Mukai¹,

Kim²,

Fukunaga³

et al. 2007

J. Neurosci.

149

131

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Repeated experience with a visual stimulus can result in improved perception of the stimulus, i.e., perceptual learning. To understand the underlying neural mechanisms of this process, we used functional magnetic resonance imaging to track brain activations during the course of training on a contrast discrimination task. Based on their ability to improve on the task within a single scan session, subjects were separated into two groups: "learners" and "nonlearners." As learning progressed, learners showed progressively reduced activation in both visual cortex, including Brodmann's areas 18 and 19 and the fusiform gyrus, and several cortical regions associated with the attentional network, namely, the intraparietal sulcus (IPS), frontal eye field (FEF), and supplementary eye field. Among learners, the decrease in brain activations in these regions was highly correlated with the magnitude of performance improvement. Unlike learners, nonlearners showed no changes in brain activations during training. Learners showed stronger activation than nonlearners during the initial period of training in all these brain regions, indicating that one could predict from the initial activation level who would learn and who would not. In addition, over the course of training, the functional connectivity between IPS and FEF in the right hemisphere with early visual areas was stronger for learners than nonlearners. We speculate that sharpened tuning of neuronal representations may cause reduced activation in visual cortex during perceptual learning and that attention may facilitate this process through an interaction of attention-related and visual cortical regions.

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“…However, independently of these considerations further research is available indicating that substantially shorter training times are possible (Seitz and Watanabe 2003) and even weak motion signals (3 or 5% of coherently moving dots) are detectable and might thus be accompanied by conscious percepts Tsushima et al 2008). …”

Section: Explaining Behavioral Resultsmentioning

confidence: 99%

“…In Seitz and Watanabe (2003) the authors modified the setup during the training or ''exposure'' phase, respectively, with respect to the central letter discrimination task in such a way that only the target letters were paired with a target motion signal of 5% coherently moving dots while the remaining six letters were paired with distractor directions of the same coherence level. However, the learning effect was reduced substantially: Whereas in Watanabe et al (2001) the performance increase for the motion discrimination task turned out to be about 20% (and approximately 7% for the motion detection task, as depicted in Fig.…”

Section: Explaining Behavioral Resultsmentioning

confidence: 99%

Perceptual learning with perceptions

2010

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In this work we present an approach to understand neuronal mechanisms underlying perceptual learning. Experimental results achieved with stimulus patterns of coherently moving dots are considered to build a simple neuronal model. The design of the model is made transparent and underlying behavioral assumptions made explicit. The key aspect of the suggested neuronal model is the learning algorithm used: We evaluated an implementation of Hebbian learning and are thus able to provide a straight-forward model capable to explain the neuronal dynamics underlying perceptual learning. Moreover, the simulation results suggest a very simple explanation for the aspect of ''sub-threshold'' learning (Watanabe et al. in Nature 413:844-884, 2001) as well as the relearning of motion discrimination after damage to primary visual cortex as recently reported (Huxlin et al. in J Neurosci 29:3981-3991, 2009) and at least indicate that perceptual learning might only occur when accompanied by conscious percepts.

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Is subliminal learning really passive?

Cited by 274 publications

References 8 publications

Perceptual learning in clear displays optimizes perceptual expertise: Learning the limiting process

Perceptual learning in clear displays optimizes perceptual expertise: Learning the limiting process

Activations in Visual and Attention-Related Areas Predict and Correlate with the Degree of Perceptual Learning

Perceptual learning with perceptions

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