Article Figures & Data
Figures
- Figure 1.
A, Stimuli-specific semantic space. The trees contain the actual stimuli presented to the participants for three target words (unambiguous “dreams,” polyseme “flies,” and homonym “dates”) organized into a hierarchy of representations. B, RSA models of different organizational hypotheses of the semantic space shown in A. Models are hierarchically organized based on the level of representation in A that the model represents. This figure only contains models that formulated hypotheses about the organization of semantic space. C, Experiment design. The table contains the discourse sentence and stimuli for three example words from each ambiguity type.
- Figure 2.
Discourse-sentence bias ratings. Results from a stimulus norming experiment assessing the sense disambiguating force of our discourse sentences. Subjects rated the noun–verb disambiguating impact of each discourse sentence on a Likert scale (1–7). The violin plot shows the distances between the subjects’ ratings and the intended category membership of each target. Since unambiguous targets had noun and verb senses that were extremely close to each other, their distance scores were expected to be higher, which is what is observed here. Polysemes and homonyms were effectively disambiguated by the discourse sentences, as evidenced by the low distance scores.
- Figure 3.
Sense-norming experiment. Results from a sense-norming experiment which normed the ambiguity of the target words. Subjects characterized the sense relatedness of each word using both categorical yes–no judgments and by rating them on a Likert scale (1–7). We observed a categorical distinction between ambiguity types for both categorical and continuous judgements. A, Violin plot of results of an ANOVA on the Likert scores for each target word. The violin plot shows the mean Likert score for each Ambiguity Type. Significant differences between types which were evaluated with a post hoc Tukey HSD test are provided as asterisks above the violin plots. B, Predicted probabilities and logistic regression plot of categorical relatedness judgements. Error bars represent the 95% confidence interval for the predicted probabilities per category estimated using the standard error.
- Figure 4.
Trial structure and task. This is a sample trial for the target word flies. The target word which here has been indicated in bold was always stimulus final. For the embedded contexts, the four preceding words were presented on a single screen as a chunk to reduce experiment length. Screens displaying the target stimulus are indicated by thicker squares. The task was a forced choice semantic relatedness task and was randomly presented 30% of the time.
- Figure 5.
Univariate results for fixed effects. Significant clusters were determined through a nonparametric spatiotemporal permutation test (10,000 permutations) on F values from a mixed-measures ANOVA (whole brain, 0–650 ms) with Ambiguity Type, Syntactic Category, and Syntactic Context as fixed factors and Session and Subject as random factors. Each cluster is color-coded, and the colors correspond to the colored rectangles on the time course in between the right and left hemisphere Syntactic context effects. Barplots show further pairwise testing on the mean activity of each condition within the cluster. The clusters are in chronological order.
- Figure 6.
Summary for selected Representational Dissimilarity Models for three target words in the following order: dreams, flies, dates. Additional models are explained in Extended Data Table 6-1. A, Visual representation of RDMs with explanations of how they are calculated. B, Significant clusters showing the mean spatial distributions of model correlations with MEG activity. Cortical annotations of the Desikan–Killiany atlas are in white. Significance was determined through a nonparametric spatiotemporal permutation test (10,000 permutations) on a map of t statistics resulting from a one-sample t test against a mean of 0. A replication of the Syntactic Context results controlling for discourse length is presented in Extended Data Figure 6-2.
- Figure 7.
Spatiotemporal progression of significant RSA clusters. Significance of each cluster is presented below each model. The cortical annotations of the Desikan–Killiany atlas are in white. A color-coded time course of the temporal evolution of each cluster is located at the bottom of the figure.
- Figure 8.
A, Spearman correlations between RSA models. B, Significant Spearman correlations between RSA models (r > 0.4). Spearman correlations were thresholded based on if the correlation was significant (p < 0.05) and if it was high (r > 0.4). Models that have significant RSA results are underlined in bold.
- Figure 9.
Time course of the mean variance explained by significant context-sensitive and contextless RSA models across participants. All models were significant and made hypotheses about the neural representation of individual words. The total variance explained across the compared models is plotted in gray.
Extended Data
Table 6-1
Explanations of additional Representational Dissimilarity Models. Download Table 5-1, DOCX file.
Figure 6-2
Significant clusters replicating the mean spatial distributions of the partial correlation with the Syntactic Context controlling for discourse length with MEG activity. Cortical annotations of the Desikan-Killiany atlas are in white. Significance was determined through a nonparametric spatiotemporal permutation test (10,000 permutations) on a map of t-statistics resulting from a one-sample t-test against a mean of 0. Download Figure 6-2, TIF file.
