Neural Changes with Tactile Learning Reflect Decision-Level Reweighting of Perceptual Readout

Abstract

Despite considerable work, the neural basis of perceptual learning remains uncertain. For visual learning, although some studies suggested that changes in early sensory representations are responsible, other studies point to decision-level reweighting of perceptual readout. These competing possibilities have not been examined in other sensory systems, investigating which could help resolve the issue. Here we report a study of human tactile microspatial learning in which participants achieved >six-fold decline in acuity threshold after multiple training sessions. Functional magnetic resonance imaging was carried out during performance of the tactile microspatial task and a control, tactile temporal task. Effective connectivity between relevant brain regions was estimated using multivariate, autoregressive models of hidden neuronal variables obtained by deconvolution of the hemodynamic response. Training-specific increases in task-selective activation assessed using the task-by-session interaction, and associated changes in effective connectivity, primarily involved subcortical and anterior neocortical regions implicated in motor and/or decision processes, rather than somatosensory cortical regions. A control group of participants tested twice, without intervening training, exhibited neither threshold improvement nor increases in task-selective activation. Our observations argue that neuroplasticity mediating perceptual learning occurs at the stage of perceptual readout by decision networks. This is consonant with the growing shift away from strictly modular conceptualization of the brain towards the idea that complex network interactions underlie even simple tasks. The convergence of our findings on tactile learning with recent studies of visual learning reconciles earlier discrepancies in the literature on perceptual learning.

The impact of hemodynamic variability and signal mixing on the identifiability of effective connectivity structures in BOLD fMRI

“…In many applications, lagged methods for effective connectivity in fMRI are applied to the deconvolved BOLD time series, with an example of GC (David et al., 2008; Goodyear et al., 2016; Hutcheson et al., 2015; Ryali, Supekar, Chen, & Menon, 2011; Ryali et al., 2016; Sathian, Deshpande, & Stilla, 2013; Wheelock et al., 2014). However, as demonstrated in Figure 5b, the natural variability in the neuronal dynamics results with an upper bound on the accuracy of the lagged based methods: even assuming a perfect deconvolution (which is never the case in practice), which would allow for perfect retrieval of the neuronal time series from the BOLD time series, for the TR = 0.70 s, the accuracy rate of ∆ would be <100% (for the parameter space we are exploring in this study, this accuracy would be on the level of 90%).…”

Modulation of somatosensory perception and multisensory integration as a function of habitual patterns of action

“…Supporting this, Sathian et al (2013) found no evidence for SI changes following tactile perceptual learning when looking at changes in functional connectivity in the human brain. They trained subjects in a microspatial tactile task producing a six-fold reduction in tactile acuity thresholds.…”

“…I will discuss how these experimental modulations of perception have informed our understanding of supportive neural mechanisms, and what they reveal about dynamic plasticity of somatosensory perception in response to experience (Adab & Vogels, 2011;Law & Gold, 2008A. Reed et al, 2011;Sathian, Deshpande, & Stilla, 2013). Finally, I will outline areas where gaps still exist in our understanding and how research conducted in the current thesis attempts to address outstanding questions.…”

“…Other theories state sensory representations of stimuli in the cortex remain unchanged, and it is the neural 'read-out' of these lower-order representations by higher-order areas at the decision-making or interpretation stage that change with training (DiCarlo, Johnson, & Hsiao, 1998;Petrov, Dosher, & Lu, 2005;Sathian et al, 2013).…”

“…A strictly higher-order account of perceptual learning would predict global transfer of tactile learning to any body location tested, due to learning occurring in either 'later' somatosensory areas where neurons encode larger or multiple body parts (RF properties reviewed in Iwamura, Tanaka, Iriki, Taoka, & Toda, 2002) or higher-order, non-topographic brain areas (Sathian et al, 2013). …”