Category learning is constrained by training context and prior experience

Everyday behaviors like interpreting a child’s squeal as thrilled or terrified or understanding

diverse acoustic signals from different talkers to each be the word “thanks” rely on

categorization. Learning to treat perceptually distinct objects as functionally equivalent changes

how we structure our knowledge about the world. Category learning is a major area

of research that spans from cellular neuroscience to human behavioral methods to philosophy.

Despite the breadth of research on category learning, much still remains unknown about how

humans create and reorganize mental representations of categories. Even so, the majority

of research on perceptual category learning focuses on visual categories. In this dissertation, I

will focus on auditory category learning. Sound presents unique learning challenges that are

important for understanding speech learning, music perception, and everyday listening.

This dissertation investigates how humans build on existing knowledge to learn new sound

categories. Chapter 1 presents a theoretical framework on the interaction of sensory experience,

perceptual representations, and category learning. Chapters 2 and 3 uncover how factors of the

current learning context affect category learning. Chapter 4 examines how existing perceptual

representations influence how learners form categories within a perceptual environment.

Chapters 5 and 6 investigate how experience with statistically structured sensory information

influences similarity-based perceptual representations and the effect of this experience on

subsequent category learning. A neural network model presented in Chapter 7 serves as a starting

point in understanding the underlying computational mechanisms that allow sensory experience

to shape perceptual representations, which then influence higher-level cognition, such as the

processes involved in category learning. This research advances understanding of both auditory

and visual categorization by revealing how category learning is both constrained by prior

experience and influenced by regularities and the context of the current learning environment.