Auditory and visual category learning in musicians and nonmusicians.

Abstract

Across three experiments, we compare the ability of amateur musicians and nonmusicians in learning artificial auditory and visual categories that can be described as either rulebased (RB) or information-integration (II) category structures. RB categories are optimally learned using a reflective reasoning process, whereas II categories are optimally learned by integrating information from two stimulus dimensions at a reflexive, pre-decisional processing stage. We found that musicians have selective advantages for learning auditory RB categories, specifically when they are instructed about the dimensions that define the categories. In Experiment 1, musicians enrolled in a music college demonstrated advantages over nonmusicians in learning auditory RB categories defined on frequency and duration dimensions but did not demonstrate differences in learning auditory II categories or either visual RB or II categories. In Experiment 2, a broader online sample of musicians who were not instructed about the dimensions did not demonstrate any advantage in auditory or visual learning. In Experiment 3, an online sample of musicians when given dimension instructions demonstrated early advantages over non-musicians for auditory RB but not visual RB categories. Musicians do not demonstrate a global categorization advantage. Musicians' category learning advantage is limited to their modality of expertise, is enhanced with dimension instructions, and is specific to categories that can be described with verbalizable rules.

Auditory and visual category learning in children and adults

“…Prior work has also demonstrated that children have enhanced learning for auditory over visual information (Budoff & Quinlan, 1964), whereas adults have enhanced learning for visual over auditory information (Roark, Smayda, et al, 2021;Sloutsky & Napolitano, 2003). We found that children do not have modality bias for category learning and performed similarly across modalities.…”

Impaired and Spared Auditory Category Learning in Developmental Dyslexia

“…A power analysis (calculated using the pwr package in the R programming environment; Version 3.6.1.; R Core Team, 2019; see Champely, 2020) indicated that a sample of 21 participants per group would be needed to obtain statistical power at a 0.80 level (α = .05) to detect a small-to-medium difference among conditions ( d = 0.37 or f = 0.185). The effect size was estimated from the smallest between-groups difference from a recent study of auditory categorization (Roark et al, 2022).…”

Impaired and Spared Auditory Category Learning in Developmental Dyslexia

“…A power analysis (calculated using the pwr package in R; Champely, 2020) indicated that a sample of 21 participants per group would be needed to obtain statistical power at a 0.80 level (alpha= .05) to detect a small-medium difference among conditions (d = 0.37 or f = 0.185). The effect size was estimated from the smallest between-group difference from a recent study of auditory categorization (Roark, Smayda, et al, 2021).…”

“…The tasks were similar to those used in previous studies that examined RB and II category learning in the auditory domain (Roark, Smayda, et al, 2021). Each participant completed both RB and II tasks, with the order counterbalanced across participants.…”

“…To assess participants' learning strategies we applied decision-bound computational models (Ashby, 1992;. Four classes of decision-bound models were applied to category response data: hypothesis-testing models, a procedural-based model, and a guessing model, as in previous work (e.g., Roark, Smayda, et al, 2021;Maddox & Ashby, 2004).…”