Category learning can alter perception and its neural correlates

Abstract

Learned Categorical Perception (CP) occurs when the members of different categories come to look more dissimilar (“between-category separation”) and/or members of the same category come to look more similar (“within-category compression”) after a new category has been learned. To measure learned CP and its physiological correlates we compared dissimilarity judgments and Event Related Potentials (ERPs) before and after learning to sort multi-featured visual textures into two categories by trial and error with corrective feedback. With the same number of training trials and feedback, about half the subjects succeeded in learning the categories (“Learners”: criterion 80% accuracy) and the rest did not (“Non-Learners”). At both lower and higher levels of difficulty, successful Learners showed significant between-category separation—and, to a lesser extent, within-category compression—in pairwise dissimilarity judgments after learning, compared to before; their late parietal ERP positivity (LPC, usually interpreted as decisional) also increased and their occipital N1 amplitude (usually interpreted as perceptual) decreased. LPC amplitude increased with response accuracy and N1 amplitude decreased with between-category separation for the Learners. Non-Learners showed no significant changes in dissimilarity judgments, LPC or N1, within or between categories. This is behavioral and physiological evidence that category learning can alter perception. We sketch a neural net model predictive of this effect.

Effects of Noise on the Behavioral and Neural Categorization of Speech

“…Indeed, as demonstrated recently in cochlear implant listeners, the sensory input can be highly impoverished, sparse in spectrotemporal detail, and intrinsically noisy (i.e., delivered electrically to the cochlea) yet still offer robust speech categorization (Han et al, 2016). Collectively, our data suggest that both the mere construction of perceptual objects and the natural discrete binning process of CP help category members "pop out" amidst noise (e.g., Nothdurft, 1991;Perez-Gay et al, 2018) to maintain robust speech perception in noisy environments.…”

“…Although noise interference would blur physical acoustic details and create a noisier cortical map, categories would be partially sparedindicated by the remaining "peakedness" in the neural space. Thus, both the construction of perceptual objects and natural discrete binning process of CP might enable category members to "pop out" among a noisy feature space (e.g., Nothdurft, 1991;Perez-Gay et al, 2018). Consequently, the mere process of grouping speech sounds into categories might aid comprehension of speech-in-noise (SIN)-assuming those representations are not too severely compromised and remain distinguishable from noise itself.…”

Functional plasticity coupled with structural predispositions in auditory cortex shape successful music category learning

“…Moreover, these neuroplastic effects are surprisingly fast, occurring rapidly within only 20 minutes of training. Our findings parallel visual category learning where changes in the visual-evoked N1 and late positive component signal successful learning (Perez-Gay Juarez et al, 2019). Our results also align with previous studies using various auditory training tasks including speech (Alain et al, 2010;Alain et al, 2007;Ben-David et al, 2011;Tremblay et al, 2001;Tremblay & Kraus, 2002;Tremblay et al, 2009) and nonspeech sounds (Atienza et al, 2002;Bosnyak et al, 2004;Tong et al, 2009;Wisniewski et al, 2020) suggesting P2 indexes auditory experience that reflects learning success and is not simply a product of the task acquisition process (cf.…”

“…Long-term auditory experiences (e.g., music training, tone language expertise) have also been associated with enhanced P2 during active sound categorization (Bidelman & Alain, 2015;Bidelman & Lee, 2015;Bidelman et al, 2014) as well as learning (Seppanen et al, 2012(Seppanen et al, , 2013Shahin et al, 2003). The ERP decreases we find in successful learners are highly consistent with single-session, rapid learning experiments demonstrating greater efficiency of sensory-evoked neural responses during active task engagement (Alain et al, 2010;Ben-David et al, 2011;Guenther et al, 2004;Perez-Gay Juarez et al, 2019;Sohoglu & Davis, 2016). Consequently, our results reinforce notions that the P2 is a biomarker of learning to classify auditory stimuli and map sounds to labels.…”

“…Categorization of auditory stimuli is also a foundational skill for language development and is believed to arise from both learned and innate factors (Livingston et al, 1998;Mankel, Barber, et al, 2020;Mankel, Pavlik Jr, et al, 2020;Perez-Gay Juarez et al, 2019;Rosen & Howell, 1987). Auditory categories are further shaped by experiences such as speaking a second language (Escudero et al, 2011;Lively et al, 1993;Perrachione et al, 2011) or musical training (Bidelman & Walker, 2019;Bidelman et al, 2014;Wu et al, 2015), suggesting flexibility in categorical perception with learning.…”