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

Abstract

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.

Auditory and Cognitive Effects of Aging on Perception of Environmental Sounds in Natural Auditory Scenes

“…This is only partially a function of the energetic masking of the background auditory scenes as evidenced by a moderate correlation of threshold and slope, r = 0.48. That is, functions with higher (i.e., worse) thresholds tended to have steeper slopes and vice versa, a finding that has occasionally occurred in previous psychoacoustic research (Gold, Law, Connolly, & Bennur, 2010). This might account for the relatively shallower slopes of the experienced YNH for both the Congruent and Incongruent conditions (since the threshold for that group was much lower than for any other group).…”

Dichotomy in perceptual learning of interval timing: calibration of mean accuracy and precision differ in specificity and time course

“…5B), however, the apparent enhancement in timing variability at those ⌬Ts is actually quite detrimental in a real-world situation because veridical targets are missed in a highly reliable manner for the short or long ⌬Ts. Our findings underscore the importance of assessing effects of PL by measuring the two error metrics simultaneously and interpreting them jointly in a coherent manner (Buonomano et al 2009;Gold et al 2010;Johnston et al 2006;Meegan et al 2000;Stetson et al 2006).…”

“…1C): "mean accuracy (constant error)"-the degree of match between physical (⌬T) and subjective [(⌬t)] time intervals on averageand "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).…”

Perceptual Learning: Toward a Comprehensive Theory

“…As discussed in the mid stage model section, the model proposed by this group assume reweighting in connections between MT and LIP (Gold et al 2009). However, another possibility that is not mutually exclusive to the reweighting model is that at least VPL of coarse motion direction discrimination is associated with changes in decision factors including a decision criterion, which is reflected in LIP response changes (Shadlen 2014).…”

“…Most mid-stage and late-stage models assume that what is changed is weighting connecting different areas (Chowdhury & DeAngelis 2008, Dosher et al 2004, Dosher & Lu 1998, Dosher & Lu 1999, Gold et al 2009, Gu et al 2012, Kahnt et al 2011, Lewis et al 2009, Liu et al 2010, Petrov et al 2005, Xiao et al 2008, Zhang et al 2008, Zhang et al 2010a, Zhang et al 2010b). However, these models on their own cannot explain the results that V1 is changed in association with task-relevant VPL or incomplete inter-ocular transfer (transfer of VPL trained with one eye to the other eye) (Karni & Sagi 1991, Karni & Sagi 1993, Shibata et al 2012b, Xu et al 2012b).…”