Shared Mechanisms of Perceptual Learning and Decision Making

Law, Chi-Tat; Gold, Joshua I.

doi:10.1111/j.1756-8765.2009.01044.x

Cited by 28 publications

(29 citation statements)

References 92 publications

(152 reference statements)

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“…rapid recalibration) result from changes at low-level sensory processes (Van der Burg et al 2015a). Furthermore, changes at decisional stages of perception have been recognized to contribute to other forms of perceptual learning (Law and Gold 2008; Law and Gold 2010). Future studies will be necessary to determine the contribution of changes in top-down and bottom-up processing to the changes in temporal acuity we observe following perceptual training.…”

Section: Discussionmentioning

confidence: 99%

Multisensory perceptual learning is dependent upon task difficulty

2016

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There has been a growing interest in developing behavioral tasks to enhance temporal acuity as recent findings have demonstrated changes in temporal processing in a number of clinical conditions. Prior research has demonstrated that perceptual training can enhance temporal acuity both within and across different sensory modalities. Although certain forms of unisensory perceptual learning have been shown to be dependent upon task difficulty, this relationship has not been explored for multisensory learning. The present study sought to determine the effects of task difficulty on multisensory perceptual learning. Prior to and following a single training session, participants completed a simultaneity judgment (SJ) task, which required them to judge whether a visual stimulus (flash) and auditory stimulus (beep) presented in synchrony or at various stimulus onset asynchronies (SOAs) occurred synchronously or asynchronously. During the training session, participants completed the same SJ task but received feedback regarding the accuracy of their responses. Participants were randomly assigned to one of three levels of difficulty during training: easy, moderate and hard, which were distinguished based on the SOAs used during training. We report that only the most difficult (i.e., hard) training protocol enhanced temporal acuity. We conclude that perceptual training protocols for enhancing multisensory temporal acuity may be optimized by employing audiovisual stimuli for which it is difficult to discriminate temporal synchrony from asynchrony.

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Section: Discussionmentioning

confidence: 99%

Multisensory perceptual learning is dependent upon task difficulty

2016

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“…Some forms of perceptual learning are thought to reflect experience-dependent calibration of these kinds of task-specific readout strategies (Law & Gold, 2009b). One proposed mechanisms involves changes in weights that result in an increasingly selective readout of the most sensitive and informative sensory neurons to form the decision variable (Chung, Levi & Tjan, 2005; Dosher & Lu, 1998; Dosher & Lu, 1999; Gold, Bennett & Sekuler, 1999; Gold, Sekuler & Bennett, 2004; Jacobs, 2009; Law & Gold, 2009b; Li, Levi & Klein, 2004; Petrov et al, 2005).…”

Section: Effects Of Readout On Specific Features Of the Psychometrmentioning

confidence: 99%

“…One proposed mechanisms involves changes in weights that result in an increasingly selective readout of the most sensitive and informative sensory neurons to form the decision variable (Chung, Levi & Tjan, 2005; Dosher & Lu, 1998; Dosher & Lu, 1999; Gold, Bennett & Sekuler, 1999; Gold, Sekuler & Bennett, 2004; Jacobs, 2009; Law & Gold, 2009b; Li, Levi & Klein, 2004; Petrov et al, 2005). Correlates of these kinds of changes in readout have been identified in the parietal cortex of monkeys learning a coarse motion discrimination task (Law & Gold, 2008).…”

Section: Effects Of Readout On Specific Features Of the Psychometrmentioning

confidence: 99%

How mechanisms of perceptual decision-making affect the psychometric function

Gold

Ding

2013

Progress in Neurobiology

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Psychometric functions are often interpreted in the context of Signal Detection Theory, which emphasizes a distinction between sensory processing and non-sensory decision rules in the brain. This framework has helped to relate perceptual sensitivity to the "neurometric" sensitivity of sensory-driven neural activity. However, perceptual sensitivity, as interpreted via Signal Detection Theory, is based on not just how the brain represents relevant sensory information, but also how that information is read out to form the decision variable to which the decision rule is applied. Here we discuss recent advances in our understanding of this readout process and describe its effects on the psychometric function. In particular, we show that particular aspects of the readout process can have specific, identifiable effects on the threshold, slope, upper asymptote, time dependence, and choice dependence of psychometric functions. To illustrate these points, we emphasize studies of perceptual learning that have identified changes in the readout process that can lead to changes in these aspects of the psychometric function. We also discuss methods that have been used to distinguish contributions of the sensory representation versus its readout to psychophysical performance.

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“…For instance, Law and Gold (2008) found that there were no changes in the firing rates of MT neurons after training monkeys on a motion discrimination task that varied both motion direction and motion coherence (Figure 5a), but there were changes in the firing rates of the neurons in lateral intraparietal area (LIP), an area implicated in accumulating sensory evidence during decision making (Figure 5b; Law & Gold, 2008; 2009). The authors conclude that learning results from a change in read-out of the most informative sensory neurons and that these read-out changes are driven by feedback, which guides the selective enhancement of the connections between the most sensitive populations in MT and LIP in order to optimize performance (Law & Gold, 2009; 2010). This model is akin to similar proposals in the attention literature (Eckstein et al, 2000; Palmer & Moore, 2009; Palmer, Verghese, & Pavel, 2000; Pestilli et al, 2011), in which responses from the most sensitive sensory neurons are pooled and responses from uninformative sensory neurons are filtered out, leading to overall improvements in the ability to discriminate target features from distracters (Gold, Law, & Bennur, 2010; Palmer and Moore, 2009; Pestilli et al, 2011).…”

Section: Mechanisms Of Perceptual Learning Mirror Those Of Attentionmentioning

confidence: 99%

Exploring the relationship between perceptual learning and top-down attentional control

Byers

Serences

2012

Vision Research

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Here, we review the role of top-down attention in both the acquisition and the expression of perceptual learning, as well as the role of learning in more efficiently guiding attentional modulations. Although attention often mediates learning at the outset of training, many of the characteristic behavioral and neural changes associated with learning can be observed even when stimuli are task irrelevant and ignored. However, depending on task demands, attention can override the effects of perceptual learning, suggesting that even if top-down factors are not strictly necessary to observe learning, they play a critical role in determining how learning-related changes in behavior and neural activity are ultimately expressed. In turn, training may also act to optimize the effectiveness of top-down attentional control by improving the efficiency of sensory gain modulations, regulating intrinsic noise, and altering the read-out of sensory information.

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Shared Mechanisms of Perceptual Learning and Decision Making

Cited by 28 publications

References 92 publications

Multisensory perceptual learning is dependent upon task difficulty

Multisensory perceptual learning is dependent upon task difficulty

How mechanisms of perceptual decision-making affect the psychometric function

Exploring the relationship between perceptual learning and top-down attentional control

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