The Neural Integration of Speaker and Message

نویسندگان

  • Jos J. A. Van Berkum
  • Danielle van den Brink
  • Cathelijne M. J. Y. Tesink
  • Miriam Kos
  • Peter Hagoort
چکیده

When do listeners take into account who the speaker is? We asked people to listen to utterances whose content sometimes did not match inferences based on the identity of the speaker (e.g., "If only I looked like Britney Spears" in a male voice, or "I have a large tattoo on my back" spoken with an upper-class accent). Event-related brain responses revealed that the speaker's identity is taken into account as early as 200-300 msec after the beginning of a spoken word, and is processed by the same early interpretation mechanism that constructs sentence meaning based on just the words. This finding is difficult to reconcile with standard "Gricean" models of sentence interpretation in which comprehenders initially compute a local, context-independent meaning for the sentence ("semantics") before working out what it really means given the wider communicative context and the particular speaker ("pragmatics"). Because the observed brain response hinges on voice-based and usually stereotype-dependent inferences about the speaker, it also shows that listeners rapidly classify speakers on the basis of their voices and bring the associated social stereotypes to bear on what is being said. According to our event-related potential results, language comprehension takes very rapid account of the social context, and the construction of meaning based on language alone cannot be separated from the social aspects of language use. The linguistic brain relates the message to the speaker immediately.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

شبکه عصبی پیچشی با پنجره‌های قابل تطبیق برای بازشناسی گفتار

Although, speech recognition systems are widely used and their accuracies are continuously increased, there is a considerable performance gap between their accuracies and human recognition ability. This is partially due to high speaker variations in speech signal. Deep neural networks are among the best tools for acoustic modeling. Recently, using hybrid deep neural network and hidden Markov mo...

متن کامل

Augmented Higher Cognition: Enhancing Speech Recognition Through Neural Activity Measures

The goal of communication is delivery of the content of a message. Vocalizations carry a symbolic representation of a message from the mind of the speaker to a listener (human or machine) who must decode the representation. Machine-based speech interpreters have libraries of sound templates and word and grammar logic systems that assist in decoding a message. Human listeners have some additiona...

متن کامل

Integration of Color Features and Artificial Neural Networks for In-field Recognition of Saffron Flower

ABSTRACT-Manual harvesting of saffron as a laborious and exhausting job; it not only raises production costs, but also reduces the quality due to contaminations. Saffron quality could be enhanced if automated harvesting is substituted. As the main step towards designing a saffron harvester robot, an appropriate algorithm was developed in this study based on image processing techniques to recogn...

متن کامل

Integration of artificial neural network and geographic information system applications in simulating groundwater quality

 Background: Although experiments on water quality are time consuming and expensive, models are often employed as supplement to simulate water quality. Artificial neural network (ANN) is an efficient tool in hydrologic studies, yet it cannot predetermine its results in the forms of maps and geo-referenced data. Methods: In this study, ANN was applied to simulate groundwater quality ...

متن کامل

Performance Analysis of a New Neural Network for Routing in Mesh Interconnection Networks

Routing is one of the basic parts of a message passing multiprocessor system. The routing procedure has a great impact on the efficiency of a system. Neural algorithms that are currently in use for computer networks require a large number of neurons. If a specific topology of a multiprocessor network is considered, the number of neurons can be reduced. In this paper a new recurrent neural ne...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • Journal of cognitive neuroscience

دوره 20 4  شماره 

صفحات  -

تاریخ انتشار 2008