Relationships Between the Threshold and Slope of Psychometric and Neurometric Functions During Perceptual Learning: Implications for Neuronal Pooling

Department of Neuroscience, University of Pennsylvania, 3610 Hamilton Walk, Philadelphia, PA 19104-6074, USA.
Journal of Neurophysiology (Impact Factor: 2.89). 10/2009; 103(1):140-54. DOI: 10.1152/jn.00744.2009
Source: PubMed


Perceptual learning involves long-lasting improvements in the ability to perceive simple sensory stimuli. Some forms of perceptual learning are thought to involve an increasingly selective readout of sensory neurons that are most sensitive to the trained stimulus. Here we report novel changes in the relationship between the threshold and slope of the psychometric function during learning that are consistent with such changes in readout and can provide insights into the underlying neural mechanisms. In monkeys trained on a direction-discrimination task, perceptual improvements corresponded to lower psychometric thresholds and slightly shallower slopes. However, this relationship between threshold and slope was much weaker in comparable, ideal-observer "neurometric" functions of neurons in the middle temporal (MT) area, which represent sensory information used to perform the task and whose response properties did not change with training. We propose a linear/nonlinear pooling scheme to account for these results. According to this scheme, MT responses are pooled via linear weights that change with training to more selectively read out responses from the most sensitive neurons, thereby reducing predicted thresholds. An additional nonlinear (power-law) transformation does not change with training and causes the predicted psychometric function to become shallower as uninformative neurons are eliminated from the pooled signal. We show that this scheme is consistent with the measured changes in psychometric threshold and slope throughout training. The results suggest that some forms of perceptual learning involve improvements in a process akin to selective attention that pools the most informative neural signals to guide behavior.

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Available from: Sharath Bennur, Jun 06, 2015
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    • "Behavioral consequences of PL in IT can be probed mainly by changes in two descriptive statistics (Fig. 1C): " mean accuracy (constant error) " —the degree of match between physical (⌬T) and subjective [␮(⌬t)] time intervals on average— and " precision (temporal variance) " —the reciprocal of variance [␴(⌬t)] of perceived time intervals across repeated trials (McAuley and Miller 2007; Merchant et al. 2008b; Zarco et al. 2009). Despite the intimate relationship between the two measurements , which has been demonstrated in many visual tasks (Ahissar and Hochstein 1997; Gold et al. 2010; Herzog et al. 2006; Wenger et al. 2008), simultaneous measurements or conjunctive analyses of mean accuracy and precision data have been rare in studies on IT. Only a few studies have reported concurrent improvement (Meegan et al. 2000) or distinct effects of interstimulus interval or adaptation on discrimination threshold and apparent duration (Buonomano et al. 2009; Johnston et al. 2006; Stetson et al. 2006). "
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    Journal of Neurophysiology 10/2012; 109(2). DOI:10.1152/jn.01201.2011 · 2.89 Impact Factor
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    • "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 et al., 2010; Palmer & Moore, 2009; Pestilli et al., 2011). Other models, such as the Perceptual Template Model (PTM) and the Augmented Hebbian Reweighting Model (AHRM), hold that learning is driven both by improved filtering of internal and external noise as well as by a selective enhancement of the most sensitive sensory inputs (Dosher & Lu, 1998, 1999, 2009; Lu & Dosher, 2004; Lu, Liu, & Dosher, 2010; Petrov, Dosher, & Lu, 2005). "
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    Vision research 07/2012; 74. DOI:10.1016/j.visres.2012.07.008 · 1.82 Impact Factor
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    • "Our results linking attention with selective integration also complement recent findings in perceptual learning. Several studies have suggested that perceptual learning occurs when downstream areas learn to selectively integrate neural activity from the most informative neurons (Dosher and Lu 1998; Gold et al. 2010; Jacobs 2009; Law and Gold 2008). Some of the most direct evidence for this hypothesis comes from a recent perceptual learning experiment by Law and Gold (2008). "
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