Hemispheric differences in strong versus weak semantic priming: evidence from event-related brain potentials

Dynamic cognitive processes of text-picture integration revealed by event-related potentials

The integration of text and picture is the core of multimedia information processing. Relevant theories suggest that text and picture are processed through different channels in the early stage, and integrated in the late stage of processing. Based on these theories, the current study adopted measures of event-related potentials to examine the cognitive and neural processes of text-picture integration. The results showed that in the early stage of text-picture integration, picture processing evoked a more negative N1 over the occipital area and a N300 over the prefrontal area, which might reflect the discrimination process of visual stimuli and the imagery representation of the picture, respectively; in the late stage, the text-picture induced a N400 in the central area and an LPC over the central, parietal and temporal areas, which might be associated with the semantic activation and integration of text and picture, respectively. These results not only provide support for existing theories, but also further elucidate the dynamic neural processing of text-picture integration in terms of its temporal and spatial characteristics.

Understanding associative vs. abstract pictorial relations: An ERP study

One of the most remarkable human abilities is extracting relations between objects, words or ideas - a process that underlies perception, learning and reasoning. Yet, perhaps due to its complexity, surprisingly little is known about the neural basis of this fundamental ability. Here, we examined EEG waveforms evoked by different types of relations, conveyed by pairs of images. Subjects were presented with the pairs, that were either associatively related, abstractly related or unrelated, and judged if they were related or not. Evidence for a gradual modulation of the amplitude of the N400 and late negativity was found, such that unrelated pairs elicited the most negative amplitude, followed by abstractly-related pairs and lastly associatively-related ones. However, this was confined to first encounter with the pairs, and a different, more dichotomous pattern was observed when the pairs were viewed for the second time. Then, no difference was found between associatively and abstractly related pairs, while both differed from unrelated pairs. Notably, when the pairs were sequentially presented, this pattern was found mostly in right electrodes, while it appeared both in left and right sites during simultaneous presentation of the pairs. This suggests that while two different mechanisms may be involved in generating predictions about an upcoming related/unrelated stimulus, online processing of associative and abstract semantic relations might be mediated by a single mechanism. Our results further support claims that the N400 component indexes multiple cognitive processes that overlap in time, yet not necessarily in neural generators.

The effect of associative strength on semantic priming in schizophrenia

The present research was designed to investigate the pattern of semantic priming in schizophrenia as a function of strength of association (or semantic distance between concepts in the semantic network). Thirty schizophrenia patients, without formal thought disorder, and twenty-nine healthy controls participated in a lexical decision task in which prime-target associative strength (strong, weak and not related) and stimulus-onset asynchrony (SOA: 250ms and 750ms) were manipulated. Patients and controls showed the same associative strength effect on RTs. In the short SOA condition priming effects were obtained for both strong and weak prime-target associative conditions. However in the long SOA priming was only significant for strongly associated pairs. This pattern of priming effects was similar in both groups, with higher priming on the short SOA and strong association conditions. Altogether results suggest that automatic semantic spreading activation is unimpaired in schizophrenia patients without formal thought disorder. These results are in line with the general evidence of impaired implicit priming observed only in patients with formal thought disorder. At the same time patients use context as controls to facilitate word processing. Finally, these findings evidence that, prime-target associative strength could moderate results in studies of semantic memory deficits in schizophrenia.

Semantic integration by pattern priming: experiment and cortical network model

Neural network models describe semantic priming effects by way of mechanisms of activation of neurons coding for words that rely strongly on synaptic efficacies between pairs of neurons. Biologically inspired Hebbian learning defines efficacy values as a function of the activity of pre- and post-synaptic neurons only. It generates only pair associations between words in the semantic network. However, the statistical analysis of large text databases points to the frequent occurrence not only of pairs of words (e.g., "the way") but also of patterns of more than two words (e.g., "by the way"). The learning of these frequent patterns of words is not reducible to associations between pairs of words but must take into account the higher level of coding of three-word patterns. The processing and learning of pattern of words challenges classical Hebbian learning algorithms used in biologically inspired models of priming. The aim of the present study was to test the effects of patterns on the semantic processing of words and to investigate how an inter-synaptic learning algorithm succeeds at reproducing the experimental data. The experiment manipulates the frequency of occurrence of patterns of three words in a multiple-paradigm protocol. Results show for the first time that target words benefit more priming when embedded in a pattern with the two primes than when only associated with each prime in pairs. A biologically inspired inter-synaptic learning algorithm is tested that potentiates synapses as a function of the activation of more than two pre- and post-synaptic neurons. Simulations show that the network can learn patterns of three words to reproduce the experimental results.

FN400 and LPC memory effects for concrete and abstract words

According to dual-process models, recognition memory depends on two neurocognitive mechanisms: familiarity, which has been linked to the frontal N400 (FN400) effect in studies using ERPs, and recollection, which is reflected by changes in the late positive complex (LPC). Recently, there has been some debate over the relationship between FN400 familiarity effects and N400 semantic effects. According to one view, these effects are one and the same. Proponents of this view have suggested that the frontal distribution of the FN400 could be due to stimulus concreteness: recognition memory experiments commonly use highly imageable or concrete words (or pictures), which elicit semantic ERPs with a frontal distribution. In the present study, we tested this claim using a recognition memory paradigm in which subjects memorized concrete and abstract nouns; half of the words changed font color between study and test. FN400 and LPC old/new effects were observed for abstract as well as concrete words, and were stronger over right hemisphere electrodes for concrete words. However, there was no difference in anteriority of the FN400 effect for the two word types. These findings challenge the notion that the frontal distribution of the FN400 old/new effect is fully explained by stimulus concreteness.

ERP evidence for memory and predictive mechanisms in word-to-text integration

During reading, word-to-text integration processes proceed quickly and incrementally through both prospective (predictive) and retrospective (memory) processes. Across a sentence boundary, where prediction may be less functional, memorial processes may be especially important. We tested predictive and memory mechanisms with event-related potentials (ERPs) recorded on the first content word across a sentence boundary by manipulating the direction of association between this word and one from the preceding sentence. For comparison with this text comprehension (TC) task, we tested these same word pairs in a word meaning judgment (MJ) task. In both tasks we found reduced N400 amplitudes over central scalp electrodes when the two words were either forward-associated (FA) or backward-associated (BA), relative to task-specific baseline conditions. In the MJ task, FA pairs produced a greater reduction in the N400 reduction than BA pairs over right parietal areas. However, in the TC task, BA pairs produced a greater N400 reduction than FA pairs over left parietal electrodes. A temporal principal component analysis of TC and MJ data showed a component reflecting the central N400. Additional components from TC data reflected FA-BA differences during early (N200) and late (parietal N400 and LPC) phases of processing. Comprehension skill predicted association effects in the MJ task, especially FA, and the BA central N400 effects in the TC task. The results demonstrate that, beyond N400 indicators of prediction effects, ERPs reflect the role of memory processes in word-to-text integration across sentences, part of a dynamic interplay between anticipatory and memorial processes that support comprehension.

Gradients versus dichotomies: how strength of semantic context influences event-related potentials and lexical decision times

In experiments devoted to word recognition and/or language comprehension, reaction time in the lexical decision task is perhaps the most commonly used behavioral dependent measure, and the amplitude of the N400 component of the event-related potential (ERP) is the most common neural measure. Both are sensitive to multiple factors, including frequency of usage, orthographic similarity to other words, concreteness of word meaning, and preceding semantic context. All of these factors vary continuously. Published results have shown that both lexical decision times and N400 amplitudes show graded responses to graded changes of word frequency and orthographic similarity, but a puzzling discrepancy in their responsivity to the strength of a semantic context has received little attention. In three experiments, we presented pairs of words varying in the strengths of their semantic relationships, as well as unrelated pairs. In all three experiments, N400 amplitudes showed a gradient from unrelated to weakly associated to strongly associated target words, whereas lexical decision times showed a binary division rather than a gradient across strengths of relationship. This pattern of results suggests that semantic context effects in lexical decision and ERP measures arise from fundamentally different processes.

Impairment of homonymous processing in Alzheimer's disease

An important issue in research on language is how concepts are represented and associated with each other in the brain. Many investigations have focused on language ambiguity and the phenomenon of homonymy in which a single lexical item, presenting the same form, is related to different meanings. Our study aims to test the hypothesis that weak association of meaning characterizing homonyms may be especially prone to brain damage. To verify this hypothesis a test of attribution of the meaning of homonymous words, the Humpty Dumpty (HD) test, was applied to 50 patients with Alzheimer's disease (AD) and 50 healthy subjects. Results show that AD patients are impaired in the HD test in an early phase of disease and that performance correlates with naming ability and phonological fluency. The data are in keeping with a growing body of literature that supports dual impairment to the semantic system in AD, i.e., to semantic knowledge and active processing and access to the semantic field. The evaluation of the ability to resolve homonymous ambiguity, using the HD test, may provide a useful and quick clinical tool to detect the anomalies of the semantic network linked to either a loss of the core system where meaning of words is stored or an impairment of the access to an intact semantic representation.

Examining the N400 semantic context effect item-by-item: relationship to corpus-based measures of word co-occurrence

With increasing availability of digital text, there has been an explosion of computational methods designed to turn patterns of word co-occurrence in large text corpora into numerical scores expressing the "semantic distance" between any two words. The success of such methods is typically evaluated by how well they predict human judgments of similarity. Here, I examine how well corpus-based methods predict amplitude of the N400 component of the event-related potential (ERP), an online measure of lexical processing in brain electrical activity. ERPs elicited by the second words of 303 word pairs were analyzed at the level of individual items. Three corpus-based measures (mutual information, distributional similarity, and latent semantic analysis) were compared to a traditional measure of free association strength. In a regression analysis, corpus-based and free association measures each explained some of the variance in N400 amplitude, suggesting that these may tap distinct aspects of word relationships. Lexical factors of concreteness of word meaning, word frequency, number of semantic associates, and orthographic similarity also explained variance in N400 amplitude at the single-item level.

Word-to-text integration: Message level and lexical level influences in ERPs

Although the reading of connected text proceeds in a largely incremental fashion, the relative degree to which message level and lexical level factors contribute to integration processes across sentences remains an open question. We examined the influence of both factors on single words using event-related potentials (ERPs). Word pairs with either strong or weak forward association strength were critical items: embedded as coreferential words within two-sentence passages in a text comprehension task, and as isolated word pairs in a word meaning judgment task. While the N400 ERP component reflected an effect of forward association strength on lexico-semantic processing in the word task (i.e., reduced N400 amplitudes were seen for strongly associated pairs relative to weakly associated pairs), in the comprehension task, passages embedded with any associated word pairs elicited reduced N400 amplitudes relative to coherent baseline passages lacking one of the critical words. These comprehension effects reflect responses from the highest skilled comprehenders. The results demonstrate the effects of message level factors, and reading abilities, on the processing of single words.

Inter-synaptic learning of combination rules in a cortical network model

Selecting responses in working memory while processing combinations of stimuli depends strongly on their relations stored in long-term memory. However, the learning of XOR-like combinations of stimuli and responses according to complex rules raises the issue of the non-linear separability of the responses within the space of stimuli. One proposed solution is to add neurons that perform a stage of non-linear processing between the stimuli and responses, at the cost of increasing the network size. Based on the non-linear integration of synaptic inputs within dendritic compartments, we propose here an inter-synaptic (IS) learning algorithm that determines the probability of potentiating/depressing each synapse as a function of the co-activity of the other synapses within the same dendrite. The IS learning is effective with random connectivity and without either a priori wiring or additional neurons. Our results show that IS learning generates efficacy values that are sufficient for the processing of XOR-like combinations, on the basis of the sole correlational structure of the stimuli and responses. We analyze the types of dendrites involved in terms of the number of synapses from pre-synaptic neurons coding for the stimuli and responses. The synaptic efficacy values obtained show that different dendrites specialize in the detection of different combinations of stimuli. The resulting behavior of the cortical network model is analyzed as a function of inter-synaptic vs. Hebbian learning. Combinatorial priming effects show that the retrospective activity of neurons coding for the stimuli trigger XOR-like combination-selective prospective activity of neurons coding for the expected response. The synergistic effects of inter-synaptic learning and of mixed-coding neurons are simulated. The results show that, although each mechanism is sufficient by itself, their combined effects improve the performance of the network.

Prospective and retrospective semantic processing: prediction, time, and relationship strength in event-related potentials

Semantic context effects have variously been attributed to prospective processing - predictions about upcoming words - or to retrospective appreciation of relationships after reading both context and target. In two experiments, we altered the core variable distinguishing prospective from retrospective processing, namely time. Word pairs varying in strength of relationship were presented sequentially, to allow time for anticipation of the second word, or simultaneously. For both sorts of presentation, the amplitude of the N400 component of the event-related potential was graded from Unrelated to Moderate/Weak to Strong associates. Strong associates showed a temporal advantage over weaker associates - an earlier context effect - only during sequential presentation. Spatial distributions of the N400 context effects also differed for simultaneous versus sequential presentation.

Studies on semantic priming effects in right hemisphere stroke: A systematic review

The role of the right cerebral hemisphere (RH) associated with semantic priming effects (SPEs) must be better understood, since the consequences of RH damage on SPE are not yet well established.

Early dynamics of the semantic priming shift

Semantic processing of sequences of words requires the cognitive system to keep several word meanings simultaneously activated in working memory with limited capacity. The real- time updating of the sequence of word meanings relies on dynamic changes in the associates to the words that are activated. Protocols involving two sequential primes report a semantic priming shift from larger priming of associates to the first prime to larger priming of associates to the second prime, in a range of long SOAs (stimulus-onset asynchronies) between the second prime and the target. However, the possibility for an early semantic priming shift is still to be tested, and its dynamics as a function of association strength remain unknown. Three multiple priming experiments are proposed that cross-manipulate association strength between each of two successive primes and a target, for different values of short SOAs and prime durations. Results show an early priming shift ranging from priming of associates to the first prime only to priming of strong associates to the first prime and all of the associates to the second prime. We investigated the neural basis of the early priming shift by using a network model of spike frequency adaptive cortical neurons (e.g., Deco & Rolls, 2005), able to code different association strengths between the primes and the target. The cortical network model provides a description of the early dynamics of the priming shift in terms of pro-active and retro-active interferences within populations of excitatory neurons regulated by fast and unselective inhibitory feedback.

Dynamics of the semantic priming shift: behavioral experiments and cortical network model

Multiple semantic priming processes between several related and/or unrelated words are at work during the processing of sequences of words. Multiple priming generates rich dynamics of effects depending on the relationship between the target word and the first and/or second prime previously presented. The experimental literature suggests that during the on-line processing of the primes, the activation can shift from associates to the first prime to associates to the second prime. Though the semantic priming shift is central to the on-line and rapid updating of word meanings in the working memory, its precise dynamics are still poorly understood and it is still a challenge to model how it functions in the cerebral cortex. Four multiple priming experiments are proposed that cross-manipulate delays and association strength between the primes and the target. Results show for the first time that association strength determines complex dynamics of the semantic priming shift, ranging from an absence of a shift to a complete shift. A cortical network model of spike frequency adaptive neuron populations is proposed to account for the non-continuous evolution of the priming shift over time. It allows linking the dynamics of the priming shift assessed at the behavioral level to the non-linear dynamics of the firing rates of neurons populations.

High arousal words influence subsequent processing of neutral information: evidence from event-related potentials

Recent data suggest that word valence modulates subsequent cognitive processing. However, the contribution of word arousal is less understood. In this study, behavioral and electrophysiological measures to neutral nouns and pseudowords that were preceded by either a high-arousal or a low-arousal word were recorded during a lexical decision task. Effects were found at an electrophysiological level. Target words and pseudowords elicited enhanced N100 amplitudes when they were preceded by high- compared to low-arousing words. This effect may reflect perceptual potentiation during the allocation of attentional resources when the new stimulus is processed. Enhanced amplitudes in a late positivity when target words and pseudowords followed high-arousal primes were also observed, which could be related to sustained attention during supplementary analyses at a post-lexical level.

Using ontology databases for scalable query answering, inconsistency detection, and data integration

An ontology database is a basic relational database management system that models an ontology plus its instances. To reason over the transitive closure of instances in the subsumption hierarchy, for example, an ontology database can either unfold views at query time or propagate assertions using triggers at load time. In this paper, we use existing benchmarks to evaluate our method-using triggers-and we demonstrate that by forward computing inferences, we not only improve query time, but the improvement appears to cost only more space (not time). However, we go on to show that the true penalties were simply opaque to the benchmark, i.e., the benchmark inadequately captures load-time costs. We have applied our methods to two case studies in biomedicine, using ontologies and data from genetics and neuroscience to illustrate two important applications: first, ontology databases answer ontology-based queries effectively; second, using triggers, ontology databases detect instance-based inconsistencies-something not possible using views. Finally, we demonstrate how to extend our methods to perform data integration across multiple, distributed ontology databases.

A framework to support automated classification and labeling of brain electromagnetic patterns

This paper describes a framework for automated classification and labeling of patterns in electroencephalographic (EEG) and magnetoencephalographic (MEG) data. We describe recent progress on four goals: 1) specification of rules and concepts that capture expert knowledge of event-related potentials (ERP) patterns in visual word recognition; 2) implementation of rules in an automated data processing and labeling stream; 3) data mining techniques that lead to refinement of rules; and 4) iterative steps towards system evaluation and optimization. This process combines top-down, or knowledge-driven, methods with bottom-up, or data-driven, methods. As illustrated here, these methods are complementary and can lead to development of tools for pattern classification and labeling that are robust and conceptually transparent to researchers. The present application focuses on patterns in averaged EEG (ERP) data. We also describe efforts to extend our methods to represent patterns in MEG data, as well as EM patterns in source (anatomical) space. The broader aim of this work is to design an ontology-based system to support cross-laboratory, cross-paradigm, and cross-modal integration of brain functional data. Tools developed for this project are implemented in MATLAB and are freely available on request.

Determinants of multiple semantic priming: a meta-analysis and spike frequency adaptive model of a cortical network

Recall and language comprehension while processing sequences of words involves multiple semantic priming between several related and/or unrelated words. Accounting for multiple and interacting priming effects in terms of underlying neuronal structure and dynamics is a challenge for current models of semantic priming. Further elaboration of current models requires a quantifiable and reliable account of the simplest case of multiple priming resulting from two primes on a target. The meta-analytic approach offers a better understanding of the experimental data from studies on multiple priming regarding the additivity pattern of priming. The meta-analysis points to the effects of prime-target stimuli onset asynchronies on the pattern of underadditivity, overadditivity, or strict additivity of converging activation from multiple primes. The modeling approach is then constrained by results of the meta-analysis. We propose a model of a cortical network embedding spike frequency adaptation, which allows frequency and time-dependent modulation of neural activity. Model results give a comprehensive understanding of the meta-analysis results in terms of dynamics of neuron populations. They also give predictions regarding how stimuli intensities, association strength, and spike frequency adaptation influence multiple priming effects.

Automatic semantic priming abnormalities in schizophrenia

Abnormal activation of semantic networks characterizes schizophrenia and can be studied using the N400 event-related potential (ERP). N400 is elicited by words that are not primed by the preceding context and provides a direct measure of the neural mechanisms underlying semantic priming. Semantic priming refers to facilitated semantic processing gained through pre-exposure to semantic context, which can happen automatically if the interval between the prime and target is very short. We predicted that (1) schizophrenia patients have overly inclusive semantic networks, reflected in a less negative than expected N400 to relatively unprimed words, and (2) schizophrenia patients are deficient in their use of semantic context, responding to primed words as if they were unprimed, reflected in a more negative than expected N400 to primed words. N400s were acquired from patients with DSM-IV schizophrenia (n=26) and age-matched healthy comparison subjects (n=29) performing a picture-word verification (match vs. non-match) task. Word targets were presented 325ms after a picture prime, which either matched (CAMEL-->"camel"), or did not match (In Category: CAMEL-->"cow"; Out Category: CAMEL-->"candle") the prime. N400 data suggest that both patients and controls are sensitive to the difference between primed and unprimed words, but patients are less sensitive than controls. Similarly, N400 data suggest that both groups were sensitive to the subtler difference between classes of unprimed words (In Category versus Out Category picture-word non-matches), but patients are less sensitive, especially those with prominent negative symptoms.

Semantic priming in a cortical network model

Contextual recall in humans relies on the semantic relationships between items stored in memory. These relationships can be probed by priming experiments. Such experiments have revealed a rich phenomenology on how reaction times depend on various factors such as strength and nature of associations, time intervals between stimulus presentations, and so forth. Experimental protocols on humans present striking similarities with pair association task experiments in monkeys. Electrophysiological recordings of cortical neurons in such tasks have found two types of task-related activity, "retrospective" (related to a previously shown stimulus), and "prospective" (related to a stimulus that the monkey expects to appear, due to learned association between both stimuli). Mathematical models of cortical networks allow theorists to understand the link between the physiology of single neurons and synapses, and network behavior giving rise to retrospective and/or prospective activity. Here, we show that this type of network model can account for a large variety of priming effects. Furthermore, the model allows us to interpret semantic priming differences between the two hemispheres as depending on a single association strength parameter.

Lexical quality in the brain: ERP evidence for robust word learning from context

We examined event-related potentials (ERPs) before and after word learning, using training contexts that differed in their level of contextual support for meaning acquisition. Novel words appeared either in contexts that were semantically constraining, providing strong cues to meaning, or in contexts that were weakly constraining, that is, uninformative. After each sentence, participants were shown the word in isolation and were asked to generate a close synonym. Immediately after training, words trained in high-constraint contexts elicited a smaller left temporal negativity (N300(FT7)) compared with words trained in low-constraint contexts, and both types of trained words elicited a stronger medial frontal negativity (N350(Fz)) relative to familiar words. Two days after training the N300(FT7) disappeared and was replaced by a later, left parietal (P600(Pz)) effect. To examine robust learning, we administered a semantic priming test two days after training. Familiar words and words trained in high-constraint contexts elicited strong N400 effects. By contrast, words trained in low-constraint contexts elicited a weak N400 effect, and novel (untrained rare) words elicited no semantic priming. These findings suggest that supportive contexts and the use of an active meaning-generation task may lead to robust word learning. The effects of this training can be observed as changes in an early left frontal component, as well as the classical N400 effect. We discuss implications for theories of "partial" semantic knowledge and for robust word learning and instruction.

Electrophysiological evidence of interaction between contextual expectation and semantic integration during the processing of collocations

Despite the potentially infinite creativity of language, many words are patterned in ordered strings called collocations. Final words of these clusters are highly predictable; in addition, their overall meaning can vary on the literality dimension, ranging from (figurative) idiomatic strings to literal strings. These structures thus offer a natural linguistic scenario to contrast ERP correlates of contextual expectation and semantic integration processes during comprehension. In this study, expected endings elicited a positive peak around 300ms compared to less expected synonyms, suggesting that the earlier recognition of the string leads to the specific pre-activation of the lexical items that conclude the expression. On the other hand, meaning variations of these fixed strings (either a literal or a figurative whole meaning) affected ERPs only around 400ms, i.e. in the frontal portion of the N400. These findings are discussed within a more general cognitive framework as outlined in Kok's (2001) dual categorization model.

ERP measures of partial semantic knowledge: left temporal indices of skill differences and lexical quality

This study examines the sensitivity of early event-related potentials (ERPs) to degrees of word semantic knowledge. Participants with strong, average, or weak vocabulary skills made speeded lexical decisions to letter strings. To represent the full spectrum of word knowledge among adult native-English speakers, we used rare words that were orthographically matched with more familiar words and with pseudowords. Since the lexical decision could not reliably be made on the basis of word form, subjects were obliged to use semantic knowledge to perform the task. A d' analysis suggested that high-skilled subjects adopted a more conservative strategy in response to rare versus more familiar words. Moreover, the high-skilled participants showed a trend towards an enhanced "N2c" to rare words, and a similar posterior temporal effect reached significance approximately 650 ms. Generators for these effects were localized to left temporal cortex. We discuss implications of these results for word learning and for theories of lexical semantic access.

Activation of the posterior cingulate by semantic priming: a co-registered ERP/fMRI study

Although the N400 is the best understood semantically sensitive component of the event-related potential (ERP), others have been observed as well. In an earlier lexical decision study, an N300 ERP was found to be enhanced to unprimed targets, although the effect could also be characterized as a prolonged P2 to primed targets as described in other reports. Because its scalp topography suggested its neural source might be of interest, a source localization was conducted that suggested that this component emanated from the dorsal posterior cingulate cortex (dPCC). In order to confirm this word N300 localization, a functional magnetic resonance imaging (fMRI) study was conducted to replicate the ERP study with a separate sample of 17 participants in an event-related design, using a 3-T scanner. A significant activation in the right dPCC was found corresponding to the N300 localization. The activation was greater on the related prime trials, supporting the characterization of the ERP component as being a P2 rather than an N300. A review is provided which suggests that a number of separate lines of ERP research regarding the word N300, the picture N300, the word P2, the phonological mismatch negativity, and the word midline frontal negativity may be most parsimoniously regarded as dealing with the same ERP component and that they all therefore emanate from the dPCC. It is suggested that this region plays a role in stimulus-response mapping in polymodal fashion. It is also suggested that the ERP component be termed a P2-dPCC.