A neural reward prediction error revealed by a meta-analysis of ERPs using great grand averages

Economic approaches to decision making assume that people attach values to prospective goods and act to maximize their obtained value. Neuroeconomics strives to observe these values directly in the brain. A widely used valuation term in formal learning and decision-making models is the reward prediction error: the value of an outcome relative to its expected value. An influential theory (Holroyd & Coles, 2002) claims that an electrophysiological component, feedback related negativity (FRN), codes a reward prediction error in the human brain. Such a component should be sensitive to both the prior likelihood of reward and its magnitude on receipt. A number of studies have found the FRN to be insensitive to reward magnitude, thus questioning the Holroyd and Coles account. However, because of marked inconsistencies in how the FRN is measured, a meaningful synthesis of this evidence is highly problematic. We conducted a meta-analysis of the FRN’s response to both reward magnitude and likelihood using a novel method in which published effect sizes were disregarded in favor of direct measurement of the published waveforms themselves, with these waveforms then averaged to produce “great grand averages.” Under this standardized measure, the meta-analysis revealed strong effects of magnitude and likelihood on the FRN, consistent with it encoding a reward prediction error. In addition, it revealed strong main effects of reward magnitude and likelihood across much of the waveform, indicating sensitivity to unsigned prediction errors or “salience.” The great grand average technique is proposed as a general method for meta-analysis of event-related potential (ERP). (PsycINFO Database Record (c) 2016 APA, all rights reserved

Principal components analysis of reward prediction errors in a reinforcement learning task

Models of reinforcement learning represent reward and punishment in terms of reward prediction errors (RPEs), quantitative signed terms describing the degree to which outcomes are better than expected (positive RPEs) or worse (negative RPEs). An electrophysiological component known as feedback related negativity (FRN) occurs at frontocentral sites 240-340 ms after feedback on whether a reward or punishment is obtained, and has been claimed to neurally encode an RPE. An outstanding question however, is whether the FRN is sensitive to the size of both positive RPEs and negative RPEs. Previous attempts to answer this question have examined the simple effects of RPE size for positive RPEs and negative RPEs separately. However, this methodology can be compromised by overlap from components coding for unsigned prediction error size, or "salience", which are sensitive to the absolute size of a prediction error but not its valence. In our study, positive and negative RPEs were parametrically modulated using both reward likelihood and magnitude, with principal components analysis used to separate out overlying components. This revealed a single RPE encoding component responsive to the size of positive RPEs, peaking at similar to 330ms, and occupying the delta frequency band. Other components responsive to unsigned prediction error size were shown, but no component sensitive to negative RPE size was found. (C) 2015 Elsevier Inc. All rights reserved

Model-free and model-based reward prediction errors in EEG

Learning theorists posit two reinforcement learning systems: model-free and model-based. Model-based learning incorporates knowledge about structure and contingencies in the world to assign candidate actions with an expected value. Model-free learning is ignorant of the world’s structure; instead, actions hold a value based on prior reinforcement, with this value updated by expectancy violation in the form of a reward prediction error. Because they use such different learning mechanisms, it has been previously assumed that model-based and model-free learning are computationally dissociated in the brain. However, recent fMRI evidence suggests that the brain may compute reward prediction errors to both model-free and model-based estimates of value, signalling the possibility that these systems interact. Because of its poor temporal resolution, fMRI risks confounding reward prediction errors with other feedback-related neural activity. In the present study, EEG was used to show the presence of both model-based and model-free reward prediction errors and their place in a temporal sequence of events including state prediction errors and action value updates. This demonstration of model-based prediction errors questions a long-held assumption that model-free and model-based learning are dissociated in the brain

Parent or community: Where do 20-month-olds exposed to two accents acquire their representation of words?

The recognition of familiar words was evaluated in 20-month-old children raised in a rhotic accent environment to parents that had either rhotic or non-rhotic accents. Using an Intermodal Preferential Looking task children were presented with familiar objects (e.g. 'bird') named in their rhotic or non-rhotic form. Children were only able to identify familiar words pronounced in a rhotic accent, irrespective of their parents' accent. This suggests that it is the local community rather than parental input that determines accent preference in the early stages of acquisition. Consequences for the architecture of the early lexicon and for models of word learning are discussed

Categorization of regional and foreign accent in 5- to 7-year-old British children

This study examines children's ability to detect accent-related information in connected speech. British English children aged 5 and 7 years old were asked to discriminate between their home accent from an Irish accent or a French accent in a sentence categorization task. Using a preliminary accent rating task with adult listeners, it was first verified that the level of accentedness was similar across the two unfamiliar accents. Results showed that whereas the younger children group behaved just above chance level in this task, the 7-year-old group could reliably distinguish between these variations of their own language, but were significantly better at detecting the foreign accent than the regional accent. These results extend and replicate a previous study (Girard, Floccia, & Goslin, 2008) in which it was found that 5-year-old French children could detect a foreign accent better than a regional accent. The factors underlying the relative lack of awareness for a regional accent as opposed to a foreign accent in childhood are discussed, especially the amount of exposure, the learnability of both types of accents, and a possible difference in the amount of vowels versus consonants variability, for which acoustic measures of vowel formants and plosives voice onset time are provided. © 2009 The International Society for the Study of Behavioural Development

Vocabulary of 2-Year-Olds Learning English and an Additional Language: Norms and Effects of Linguistic Distance. I: Introduction

The majority of the world's children grow up learning two or more languages. The study of early bilingualism is central to current psycholinguistics, offering insights into issues such as transfer and interference in development. From an applied perspective, it poses a universal challenge to language assessment practices throughout childhood, as typically developing bilingual children usually underperform relative to monolingual norms when assessed in one language only. We measured vocabulary with Communicative Development Inventories for 372 24?month?old toddlers learning British English and one Additional Language out of a diverse set of 13 (Bengali, Cantonese, Dutch, French, German, Greek, Hindi?Urdu, Italian, Mandarin, Polish, Portuguese, Spanish, and Welsh). We furthered theoretical understanding of bilingual development by showing, for the first time, that linguistic distance between the child's two languages predicts vocabulary outcome, with phonological overlap related to expressive vocabulary, and word order typology and morphological complexity related to receptive vocabulary, in the Additional Language. Our study also has crucial clinical implications: we have developed the first bilingual norms for expressive and receptive vocabulary for 24?month?olds learning British English and an Additional Language. These norms were derived from factors identified as uniquely predicting CDI vocabulary measures: the relative amount of English versus the Additional Language in child?directed input and parental overheard speech, and infant gender. The resulting UKBTAT tool was able to accurately predict the English vocabulary of an additional group of 58 bilinguals learning an Additional Language outside our target range. This offers a pragmatic method for the assessment of children in the majority language when no tool exists in the Additional Language. Our findings also suggest that the effect of linguistic distance might extend beyond bilinguals’ acquisition of early vocabulary to encompass broader cognitive processes, and could constitute a key factor in the study of the debated bilingual advantage