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The prior density the top row corresponds to the level of activity in each prediction unit from a lexical system representing the likely identity of the next speech segment predicted from the current speech input using the CELEX database. The difference between the predicted pattern of activity and the pattern arising from sensory evidence determines the level of activity in each prediction error unit the middle row.

Our results show that differential MEG responses to familiar and consolidated novel spoken words match computations of segment prediction error. These data provide an important additional constraint for neurobiological theories of spoken word recognition. Although most computational theories of spoken word recognition propose competitive evaluation of multiple lexical hypotheses [ 7—9 ], we saw no evidence for neural computations correlated with lexical uncertainty. Instead, our results strongly favor computational accounts [ 10—12 ] in which the difference between lexical predictions and the current speech input is coded in the STG.

Our findings are also consistent with other proposals for predictive coding [ 25—28 ], which have become increasingly prominent in various domains of perception including object vision [ 29 ] and multimodal integration [ 30—32 ]. Our results also complement previous findings on semantic and phonological contextual expectations [ 33 ], which appear to have a similar effect on MEG responses as the lexical expectations studied here.

Existing neural simulations of segment prediction for speech have been implemented in connectionist networks [ 11, 12 ] that abstract from details of the underlying neurophysiology or in models that have not included lexical-level computations e. These cells then project this prediction error to higher levels in the hierarchy to update previously compatible, and hence partially activated, lexical representations. Prediction error cells that feed forward action potentials to higher levels within neocortical hierarchies are the large supragranular, pyramidal neurons [ 25, 26 ], which are also believed to be the main contributors to the MEG signal given that the dendrites of these neurons tend to be aligned.

Our model, therefore, mimics probabilistic accounts of spoken word recognition [cf.

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Instead, according to this predictive coding account, coactivated lexical candidates compete by making incompatible predictions for the speech segments that will be heard next. Instead, segment prediction acts to support lexical processing, because segmental predictions that are disconfirmed can be used to directly rule out incompatible lexical hypotheses and accurate segment predictions conversely used to increase the activation of compatible lexical items.

Nonetheless, a limitation of our current model is that it does not include a fully specified lexical system; rather, segment predictions were estimated from lexical probabilities derived from the CELEX database. Future work should extend the model to include multiple levels in the speech processing hierarchy.

A related question concerns the nature of the lexical representations. We have confined our experiment and simulations to monomorphemic words. However, response time data suggest that a morphemic entropy measure has the opposite relationship with behavior than does lexical competition i. Further extension of our model may also be necessary to explain the unexpected greater MEG signal for baseline items in the day 1 than day 2 condition.

This could reflect, for example, additional interaction with an episodic memory system, which affects processing of baseline items that have not been recently perceived cf. Finally, our inability to detect neural effects predicted by a lexical entropy account does not entirely rule out traditional accounts based on lexical competition that might occur, for example, in brain regions to which MEG is simply not sensitive.

Our point is that we did find evidence consistent with a predictive coding account and that such an account appears able to achieve the same functions without requiring lexical competition. Nonetheless, further evidence and simulations will be required to establish whether or not previously proposed neural correlates of lexical competition e. We further combined frequency measures over homophonic forms of these spoken words.

We assume a set of error-free phone recognizers and an optimal recognition process i. The addition of noise and variability in the speech signal and the suboptimal nature of human speech perception may introduce additional variance, or delays between the speech signal and neural responses, but will not negate the qualitatively different predictions of lexical competition and segment prediction computations.

In both cases, we assume that MEG measures the aggregate activity of neural circuits that contribute to segment perception and lexical identification for multiple items. This conditional probability is computed for each segment in a speech sequence using the phonological transcriptions and word frequencies in CELEX divided by the summed frequency of the set of matching words.


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Novel words trained on day 1 were assigned a frequency of occurrence equal to the frequency of the source words in the experimental item set; this is the amount of learning assumed for novel words. This equation is identical to that explicitly specified in Bayesian models of word recognition such as Shortlist-B see Equation 5 in [ 8 ] , although these conditional probabilities are also approximated by output activity in localist and distributed neural network models [ 7, 9 ]. We then combine the conditional probability of all activated words using the Entropy measure proposed in Information Theory [ 45 ]:.

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To turn these predictions into an error measure, we can then compute the summed absolute error between the conditional probabilities for predicted segments and the observed probability of the speech segment. Given an ideal observer, hence error-free segment recognizers, these observed probabilities are one or zero for segments that are heard or absent, respectively.

We would like to thank our volunteers for their participation, Gareth Gaskell and Jakke Tamminen for help in creating novel words, Claire Cook and Maarten van Casteren for help in collecting MEG data, Ruth Cumming and Hae-Sung Jeon for help in marking up speech files, William Marslen-Wilson for encouragement and support, and four anonymous reviewers for their helpful comments. Supplemental Information includes three figures, Supplemental Results, and Supplemental Experimental Procedures and can be found with this article online at doi National Center for Biotechnology Information , U.

Sponsored Document from. Curr Biol. Author information Article notes Copyright and License information Disclaimer. Davis: ku. This article has been cited by other articles in PMC. Summary Humans can recognize spoken words with unmatched speed and accuracy. Results Computational Simulations of Spoken Word Recognition All current accounts of spoken word recognition propose that identification occurs once speech segments that uniquely identify a single item are heard i.

Open in a separate window. Effects of Training Day on Pre-DP Neural Responses We first computed the main effect of day 1 versus day 2 training on pre-DP neural responses, averaging responses over all three item types source, novel, baseline because these are lexically indistinguishable using pre-DP speech segments given that the phoneticians who marked DP took account of coarticulation [ 22 ]; Supplemental Experimental Procedures , Section B. Neural Generators of Post-DP Responses Results of the sensor analyses clearly suggest that changes to the neural response to spoken words and pseudowords reflect computations of segment prediction error rather than lexical entropy.

Temporal Predictive Coding Model A Speech responsive cortex in the STG has been divided into multiple local patches that code different segments phonemes here for convenience illustrated by little Gaussian kernels. Discussion Our results show that differential MEG responses to familiar and consolidated novel spoken words match computations of segment prediction error. Acknowledgments We would like to thank our volunteers for their participation, Gareth Gaskell and Jakke Tamminen for help in creating novel words, Claire Cook and Maarten van Casteren for help in collecting MEG data, Ruth Cumming and Hae-Sung Jeon for help in marking up speech files, William Marslen-Wilson for encouragement and support, and four anonymous reviewers for their helpful comments.

Notes Published online: March 15, Footnotes Supplemental Information includes three figures, Supplemental Results, and Supplemental Experimental Procedures and can be found with this article online at doi Supplemental Information Document S1. References 1. Marslen-Wilson W. The temporal structure of spoken language understanding.

Zwitserlood P. The locus of the effects of sentential-semantic context in spoken-word processing. O'Rourke T. Electrophysiological evidence for the efficiency of spoken word processing. Tracking speech comprehension in space and time. Revill K. Neural correlates of partial lexical activation. Righi G.


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Neural systems underlying lexical competition: an eye tracking and fMRI study. McClelland J. Norris D. Shortlist B: a Bayesian model of continuous speech recognition. Gaskell M. Integrating form and meaning: a distributed model of speech perception. Harris Z. From phoneme to morpheme. Cairns P. Bootstrapping word boundaries: a bottom-up Corpus-based approach to speech segmentation. Elman J. Finding Structure in time. Davis M. Learning and consolidation of novel spoken words.

Dumay N. Sleep-associated changes in the mental representation of spoken words. Lexical competition and the acquisition of novel words. Tamminen J. Sleep spindle activity is associated with the integration of new memories and existing knowledge. Hickok G. The cortical organization of speech processing. Chang E.

Speaking Up and Speaking Out

Categorical speech representation in human superior temporal gyrus. Jacquemot C. Phonological grammar shapes the auditory cortex: a functional magnetic resonance imaging study. The Power of the Spoken Word was far advanced for the time it was written, and obviously part of the foundation for the modern law of attraction books currently making the rounds. Sometimes it's good to go back to our roots and find that these powerful techniques of manifesting were known before we were born. I enjoyed the perspectives and the reminders that we need to be aware of what we say in all aspects of life.

This book rivals any metaphysical book on the subject. It is written plainly and straightforwardly, an attribute often missing in modern writing. I would recommend it to anyone who is exploring their spiritual power. The fact that it was written by a woman in a time when women's power was often curtailed is particularly impressive to me. Emerson, William James and others, Ms. Shinn offers sage and practical advice on a wide range of topics common to all people. She is especially astute when it comes to prescribing ways of nurturing relationships.

Her work has a Christian emphasis, invoking the words of Christ to make a point. Yet she tempers the religiosity with very real daily challenges that rely on universal qualities of compassion, love and understanding. She offers some good advice on money matters: how to gain wealth and hold on to it. Well-written by one whose soul and love shine throughout. I enjoyed this book because it empowered me in showing me how my word can attract as well as change situations and emotions.

We ask in prayer 'Just say the word, and my soul will be healed Think it, say it, believe it! It is an added bonus to coin her writing in the context that the Bible "talks of thoughts and states of consciousness". This encourages new ways of seeing the Bible with more understanding.

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If I could have only one book this is the one I would choose. This book has everything you need to change your life and it has many examples of situations where following her ideas has worked for others. Florence was a spiritual person who believed God wants us to be happy and her books are so easy to read. You will love the happy happy feelings you get from the book. I listen to this book often on speech to text on my KIndle as I sleep, I wake up happy and full of hope for the future. This is the best book for anyone into Metaphysical, LOA.

There is no need to read any other book on this topic. One can re-read this and gain the knowledge all over again. One person found this helpful. This book has power to change thought patterns positively. We coast through life believing that some things are too good to be true ; yet, with faith ,we can truly have all that we ask for.

God promises are true alway. There is a tremendous library of self-help books available today. Much of the advice and wisdom you find in these books can be traced to the works of Florence Scovel Shinn. Like her other books, Florence wrote The Power of the Spoken Word in very simple terms, reflecting the simplicity of the philosophy behind the ideas. The language may appear outdated in some passages, but the messages are timeless. Anyone who is struggling to understand life and how it works will find answers here. As they say, open your mind and "Be ears that hear! They all have the same message but Florence really captures the essence of truth and makes it accessible to all.

Perhaps that is why so many have tried to repackage her ideas. Why bother with those? Go to the source. See all reviews. Amazon Giveaway allows you to run promotional giveaways in order to create buzz, reward your audience, and attract new followers and customers. Learn more about Amazon Giveaway. This item: The Power of the Spoken Word. Set up a giveaway.

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