Trends in Cognitive Sciences
ReviewDynamic speech representations in the human temporal lobe
Introduction
How does the human brain generate phenomenologically rich representations of words from the complex and noisy acoustic speech signal? This is not a new question, with many of our current theories and observations heavily influenced by those nearly 140 years old 1, 2. In this review, we consider the implications of progress that has been made in redefining some of the issues central to speech perception. Recent advances have allowed researchers to examine the functioning human brain with an unprecedented level of detail, paying particular attention to decoding the representations contained in speech-evoked neural responses 3, 4, 5, an important step beyond localizing task-dependent activity. Combined with a growing and productive interaction between linguistics and neuroscience [6], new recording and analysis methods have created a pivotal moment for understanding the neural basis of speech perception.
Section snippets
Organization of the ventral stream
Human neuroimaging and neurophysiology studies support the concept of an information processing hierarchy for speech perception in the temporal lobe. Responses evoked by speech sounds, words, and sentences show activity that spreads primarily from posterior to anterior temporal areas 7, 8, 9, 10, 11, 12, 13, 14. This dominant direction of information flow is facilitated by anatomical connections between the superior temporal plane and anteroventral temporal areas [15] and is commonly referred
Early cortical auditory encoding
To examine the specific roles that the STG plays in the speech perception hierarchy, it is important to understand the inputs to this region. A large body of work has established important aspects of sensory processing that occur in the ascending auditory system en route to the primary auditory cortex in several mammalian species 25, 26, 27. A1 in humans, located on the posteromedial portion of Heschl's gyrus, is characterized by at least one major tonotopic axis [28]. An important aspect of
Stimulus and early linguistic representations in the STG
Despite it showing stimulus- and context-dependent modulations in neural activity, few would argue that A1 exhibits responses that are specific to speech. By contrast, a major target of primary auditory outputs is the STG, which is one of the best-characterized regions in the speech perception system and which shows responses that suggest the earliest stages of speech-tuned representation. Like its upstream neighbors, the STG is highly sensitive to the spectrotemporal content of the acoustic
Cognitive and linguistic modulation
The studies described thus far provide compelling evidence that the STG is a major hub for sublexical processing in the speech perception hierarchy. Like many other brain regions, responses in the STG are nonlinear not only along physical stimulus dimensions (such as categorical phoneme perception), but also according to complex cognitive contexts and task demands. For example, several recent studies have demonstrated that STG activity is powerfully modulated by the attentional constraints of
Lexical representations in the ventral stream
The studies reviewed above suggest that activity in the temporal lobe during speech perception is nondeterministic. That is, it is impossible to predict activity at a given site with a high degree of precision simply based on the physical characteristics of the stimulus. This principle is a defining feature of abstract representations and historically has made it difficult to study the underlying representations of neural systems beyond early sensory cortices. It also makes it potentially even
Concluding remarks
We have discussed evidence that representations of speech information cannot be understood in a strictly linear or deterministic hierarchical framework, even for spectrotemporal representations in the STG. This presents a challenge for understanding more complex and abstract forms of representation such as words (Box 1), but it also potentially provides a means for major advances in neurolinguistics that parallel those in sensory neuroscience. We believe that machine learning and dynamical
Acknowledgments
M.K.L. was funded by National Institutes of Health (NIH) National Research Service Award F32-DC013486. E.F.C. was funded by the NIH grants R00-NS065120, DP2-OD00862, and R01-DC012379 and the Ester A. and Joseph Klingenstein Foundation.
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