It is unclear how sensory signals are routed to motor behaviour in the experiment. With this approach, the later negative cluster (Fig. Perception is facilitated by a hierarchy of expectations generated from context and prior knowledge. The observation that dAG was activated during perceptual as well as semantic decisions is unlikely to be related to visual attention or eye movements because dAG activation did not differ for perceptual decisions (that require a visual comparison of three stimuli) and saying “1,2,3” (that involves minimal attention to the stimuli). In vAG, males showed higher semantic activation than females (t = 3.2, p = 0.002). However, the later effect behaved in an apredictive manner, i.e., was most extreme for fulfilled expectations, rather than violations. Meanwhile, responding to stimuli with purely spatial correlations, pairs of ganglion cells showed increased correlations consistent with a static, non-adapting receptive field and nonlinearity. There seems to be something on the left that is not mentioned. Therefore, having this goal will speed RTs by preparing the link to the appropriate motor plan. Expectations are sent down from higher levels of the hierarchy and any subsequent unexplained sensory input is sent back up the hierarchy as prediction error (Rao and Ballard, 1999; Heilbron and Chait, 2018; Friston and Kiebel, 2009; Bubic et al., 2010). It was also interesting to note that the semantic system did not overlap with the default network in any area other than left AG (Fig. The p values that we obtained were corrected for multiple comparisons across AAL regions using false discovery rate (FDR; Yekutieli and Benjamini, 1999). Next, for each participant individually, we extracted the average source estimation values of all VEs (from prior source estimation; cf. We considered participants to have used strategic expectation (i.e., those referred to as the strategy group) if they correctly identified the color that was assigned for the high validity condition (question 1), answered YES in questions 2 and 3, and described a strategy in question 4. In contrast, higher mAG activation for words than pictures reflects the fact that conceptual identification of words can occur indirectly after accessing phonology. 3C,D) in time windows selected to be entirely within the significant dipolar sensor level clusters (early: 232–280 ms; late: 316–350 ms; Fig. (2016) reported a larger MEG signal for auditory stimuli that become predictable, relative to stimuli that are entirely unpredictable, i.e., an apredictive pattern, that they linked to up-weighting of the expected stimuli by precision (Heilbron and Chait, 2018). Consequently, for a qualitative visualization of the source estimates, here we plot the whole-brain thresholded t values (p < 0.05) of the source estimate contrasts, uncorrected for multiple comparisons. In addition, the perceptual decision task required attention to the visual stimulus whereas saying “1,2,3” was unrelated to the visual stimulus. (2016) reported a relatedness proportion interaction in these regions. We chose to use a pronunciation task as our aim was to observe the behavioral effect produced by the manipulation of both the local (relatedness) and global context (prime validity) as implemented by Hutchison (2007). Priming is a phenomenon whereby exposure to one stimulus influences a response to a subsequent stimulus, without conscious guidance or intention. Finally, we converted the power estimates to decibels relative to the mean of the baseline window (−200–0 ms). Next, we interpolated the data of any previously removed channels via the spherical interpolation method of EEGLAB and re-referenced the data to the average of the whole head. Yet some studies indicate that semantic processing of non-linguistic stimuli is not impaired, suggesting a language-specific deficit in semantic processing. Functionally, left AG has shown huge variability across functional neuroimaging studies. To minimize artefacts from additional preparatory muscular activity before vocal onset, in our preregistered methods, we planned to choose the latest time point for analysis post-target by identifying when the mouth EMG signal began to significantly differ between prime validity conditions in a temporal cluster mass randomization test, as implemented in FieldTrip (Oostenveld et al., 2011). Furthermore, to test for evidence for the null hypothesis, we calculated Bayes Factors using the Bayes equivalent t test, according to Rouder et al. Having a goal, i.e., having the intention to anticipate and pronounce a target word, will form a task-set that prepares the motor representation of the target word for execution when sufficient evidence about the target identity has been accumulated. Moreover, as α-β frequency bands include a wide range of frequencies we analyzed them separately. Within the realm of more ecologically valid stimulus processing, speech comprehension is similarly influenced by expectations at multiple levels of a hierarchy (Hutchison, 2007; Lau et al., 2013a; Lewis and Bastiaansen, 2015; Kuperberg and Jaeger, 2016; Ylinen et al., 2016). After removal of these individual trials, we conducted an additional check for bad channels, and removed them, by interrogating the average of the channels across all trials (i.e., the ERP, averaged across all conditions). Introduction. Thus, to ensure that we included activity in the entire left AG, we reported all significant foci that were located within a mask that extended from −20 mm to −60 mm in the x-direction, from −54 mm to −82 mm in the y-direction, and from +16 mm to +54 mm in the z-direction (Rushworth et al., 2006). Consistent with prior reports (Binder et al., 1999, 2009), we found that semantic activation and the default network overlapped in left AG (Fig. Our findings are in line with previous literature that highlighted the need for a better characterization of the spatial heterogeneity in left AG. Specifically, from the pool of participants of the strategy group, we selected with replacement N participants and conducted the same two-way repeated measures ANOVA 1000 times to test for the RT interaction effect. This threshold is lowered to describe any effects of task and stimuli in each AG region at p < 0.001 uncorrected for the group analysis. A two-way repeated measures ANOVA analysis showed a significant interaction between prime validity and relatedness of the target (F(1,21) = 9.071, p = 0.007, ηp2 = 0.302), while the Bayesian repeated measures ANOVA analysis showed anecdotal evidence for the interaction (BFinclusion = 2.519). NOTE: We request your email address only to inform the recipient that it was you who recommended this article, and that it is not junk mail. Reviewing Editor: Satu Palva, University of Helsinki. The N400, a negative deflection peaking around 400 ms poststimulus (Kutas and Federmeier, 2011), is a potential marker of errors of such semantic expectations (Rabovsky and McRae, 2014). Stimuli. A Bayes factor between 1 and 3 is considered to be weak/anecdotal evidence in support of the hypothesis being tested; from 3 to 10 is substantial evidence and 10 to 100 is strong evidence (Jeffreys, 1961). Thank you for sharing this Journal of Neuroscience article. In our preregistered EEG analyses, we observed a “local” prediction error ERP effect (i.e., semantic priming) ∼250 ms post-target, which, in exploratory analyses, was followed 100 ms later by a signal that interacted with the global context. However, neither of these two regions were part of our preregistered hypotheses. Specifically, we considered a channel to be bad in this step if its absolute z score across channels exceeds three on any of the following metrics: (1) the variance of voltages across time within the ERP, (2) the median gradient of the signal across time within the ERP, and (3) the range of voltages across time within the ERP. The main effects of interest (semantic system and default network) are reported at p < 0.05 FWE-corrected for multiple comparisons across the whole brain in the group analysis. The absence of (expected) CNV should be discussed more thoroughly. Critically, we also compared each condition to fixation to dissociate voxels that were activated or deactivated (see Box 2 in the work of Gusnard and Raichle, 2001). Specifically, we conducted two-tailed dependent samples t tests at each spatiotemporal data point within our time window of interest. A total of 28 stimuli were included in the study paradigm. Furthermore, participants generated a more conceptually complex expectation based on the global context (i.e., prime validity) to exhibit greater behavioral facilitation in the high prime validity context than the low prime validity context (Boudewyn et al., 2015). This study was preregistered in the Open Science Framework website, details and all codes described in the paper can be found under the following link: https://osf.io/v35te/. The sensor covariance matrix was estimated for all these sets of data in the time window −382–382 ms relative to target onset. Two out of four sets of three non-semantic stimuli were used for each participant, and the order of presentation was counterbalanced. On a local level, the N400 is larger to words that have not been primed relative to those that have (e.g., larger for DOG when preceded by Lamp than by Cat; Koivisto and Revonsuo, 2001; Lau et al., 2013a; Cruse et al., 2014), and at a more global level, the N400 is larger to words that are unexpected within a sentential context (Berkum et al., 1999; Thornhill and Van Petten, 2012; Boudewyn et al., 2015; Brothers et al., 2017). A functional MRI study, Where is the semantic system? Instead, using a pronunciation task allows for a purer measure of expectation, with the caveat of limiting the time window of artifact-free EEG for analysis. Within predictive coding, attention is one specific mechanism that is thought to increase precision (Hohwy, 2012). The comparison of semantic processing in response to pictures and written words (Vandenberghe et al., 1996; Van Doren et al., 2010) also allows us to make inferences concerning possible access to a common semantic system (Binder et al., 2009). The clusters in our data occurred in two distinct periods within the time window as shown in Figure 3. But there are certain cases when semantic memory is lost. 2). Moreover, we found that, within anatomically defined left AG, the default network only intersected one part of the extensive dorsal-mesial to ventral-lateral AG activation observed for semantic relative to perceptual decisions (Figs. (responses: YES/NO); (3) did you engage in any strategy to speed up your responses using the color cue? semantic priming / semantic satiation / schizophrenia INTROdUCTION Dereism can be defined as a sense of unreal physical and social world, or changed and un-real perception of the reality. We found that the intersection of the semantic system with the default network defines a functional landmark that segregates three different functional subdivisions in AG that we refer to as mAG (at the point of overlap), vAG (ventral to the overlap) and dAG (dorsal to the overlap). For example, “nurse” and “butter” (Refer to appendix A). This default or resting-state network has frequently been described as the reduction of activity in specific brain regions when subjects are engaged in effortful tasks (Shulman et al., 1997; Gusnard and Raichle, 2001; Mazoyer et al., 2001; Raichle et al., 2001), or engage in self-relevant internal thoughts about past and future events (Buckner et al., 2008; Andrews-Hanna et al., 2010a,b). Half of the participants received the following written instructions: “If the uppercase word is Blue, it is highly likely that the meaning of the lower case word will be related; and if the uppercase word is Yellow, it is highly likely that the meaning of the lower case word will be unrelated” (as per Hutchison, 2007). Semantic Feature Training: The treatment is designed to stimulate the semantic feature network so that it may serve as not only a mechanism for improving disrupted lexical semantic processing, but also as a compensatory strategy during word retrieval failures. The local-global paradigm (Bekinschtein et al., 2009) elegantly pits local expectation within each trial (i.e., standard vs deviant pitch tones) against a global expectation built from the context across blocks of trials. By examining the effects of task and stimuli in each AG subdivision, we propose that mAG is involved in semantic associations regardless of the presence or absence of a stimulus; dAG is involved in searching for semantics in all visual stimuli, and vAG is involved in the conceptual identification of visual inputs. (5) “Name” indicated that the participant should name each of the 3 objects in the pictures aloud. (2006) assessed an average coordinates over 27 foci at [−45 −68 + 26] with a large SD of ∼14.1 mm (Vigneau et al., 2006, their Table 4). Thus, these results support the notion of early ERPs as prediction errors and later ERPs reflecting conscious access and strategic use of context. Therefore, the relative levels of attention across conditions could interact to generate this apredictive effect. For each subdivision and condition, we investigated whether semantic activation correlated, across our 94 subjects, with age, gender, handedness and/or the in-scanner semantic decision times. Time courses (ERPs/time frequency) within the time window of interest (0–1240 ms for primes; 0–382 ms for targets) were compared with the cluster mass method of the open-source MATLAB toolbox FieldTrip (Oostenveld et al., 2011). Hence, each participant saw all words within the full set of 352 word-pairs exactly once, composed of 176 related word-pairs and 176 unrelated word-pairs. NOTE: We request your email address only to inform the recipient that it was you who recommended this article, and that it is not junk mail. All participants gave written informed consent before participation in this study, which was approved by the STEM Ethical Review Committee of the University of Birmingham. First, vAG and dAG activation was higher for semantic decisions than fixation which suggests these regions are more involved in stimulus driven semantics than amodal semantic associations. Red, Semantic more than perceptual matching; Green, similar amplitudes during both matching tasks; Blue, semantic less than perceptual matching. Conceptual roles are functional roles minus such non-semantic causes and effects. It is also unlikely to reflect visual or perceptual processing per se because dAG activation was not differentially activated by (a) perceptual decisions and saying “1,2,3” or (b) non-objects and Greek letters. Specifically, we expected these differential expectations to be reflected in the ERPs, including the slope of a putative slow wave, or contingent negative variation (CNV; Chennu et al., 2013), and/or in the power of the EEG in the α/β bands, as these have been previously associated with the precision of expectations (Bauer et al., 2014). 3C). Within mAG, activation was proportional to the level of semantic associations with most activation when semantic associations were allowed to occur continuously and randomly during fixation; high when the task focused on semantic associations between 3 concepts; and least during perceptual decisions and naming that focused attention on perceptual processing and name retrieval respectively. Next, bad channels were identified and removed from the data. Chosen Bayesian result terminology (anectodical, etc.. based on BF thresholds) should be specified. In contrast, the data from the younger participants (<18 years) will also be used in a study of developmental dyslexia who do not have hemiparesis. To test for significant differences between conditions we conducted five contrasts as mentioned above; first, an interaction between prime validity (high/low) and relatedness of the target (related/unrelated) in a time window from 316 to 350 ms; next, we tested the early and late main effects of relatedness of the target (related/unrelated) as observed in the sensor analyses results (four main effects), in their respective time windows for the early effect (226–280 and 232–290 ms); and the late effect (306–382 and 316–350 ms). A common spatial filter was then computed on the dataset containing all trials using a linear constraint minimum variance (LCMV) beamformer (Van Drongelen et al., 1996; Van Veen et al., 1997; Robinson and Vrba, 1999). In particular, we note that higher mAG activation for reading relative to picture naming might reflect unconstrained semantic associations that occur in parallel to the direct links from orthography and phonology (Strain et al., 1995) or post articulation because reading is faster than picture naming (Fraise, 1969; Potter and Falconer, 1975) and our interstimulus onset was held constant. However, we limited our analyses to the 0- to 382-ms time window post-target so as to avoid muscle artifact created by the pronunciation responses. However, as noted above, appeal to precision-weighting problematically allows for post hoc explanations of all possible ERP patterns (Bowman et al., 2013). Rote stimulus–response learning was possible in our experiment because only eight stimuli (the numbers 1, 4, 6 and 9 in Arabic or in Specifically, RTs that were more than X SDs from the mean were considered to be outliers and were removed, where the value of X decreases with decreasing sample size (i.e., number of trials in each condition for that participant) and is anchored at X = 2.5 for a sample size of 100. All functional volumes were spatially realigned, un-warped, normalized to MNI space using the unified normalization-segmentation procedure of SPM5, and smoothed with an isotropic 6 mm full-width at half-maximum Gaussian kernel, with resulting voxels size of 2 × 2 × 2 mm3. Indeed, we powered our study to detect the post-target behavioral effect specifically. 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