Presentations

Abstract

Sentence processing requires the ability to establish thematic relations between constituents. Here we investigate the computational basis of this ability in a neurobiologically motivated comprehension model. The model has a tripartite architecture where input representations are supplied by the mental lexicon to a network that performs incremental thematic role assignment. Roles are combined into a representation of sentence-level meaning by a downstream system (semantic unification). Recurrent, sparsely connected, spiking networks were used which project a time-varying input signal (word sequences) into a high-dimensional, spatio-temporal pattern of activations. Local, adaptive linear read-out units were then calibrated to map the internal dynamics to desired output (thematic role sequences) [1]. Read-outs were adjusted on network dynamics driven by input sequences drawn from argument-structure templates with small variation in function words and larger variation in content words. Models were trained on sequences of 10K words for 200ms per word at a 1ms resolution, and tested on novel items generated from the language. We found that a static, random recurrent spiking network outperformed models that used only local word information without context. To improve performance, we explored various ways of increasing the model’s processing memory (e.g., network size, time constants, sparseness, input strength, etc.) and employed spiking neurons with more dynamic variables (leaky integrate-and-fire versus Izhikevich-neurons). The largest gain was observed when the model’s input history was extended to include previous words and/or roles. Model behavior was also compared for localist and distributed encodings of word sequences. The latter were obtained by compressing lexical co-occurrence statistics into continuous-valued vectors [2]. We found that performance for localist-input was superior even though distributed representations contained extra information about word context and semantic similarity. Finally, we compared models that received input enriched with combinations of semantic features, word-category, and verb sub-categorization labels. Counter-intuitively, we found that adding this information to the model’s lexical input did not further improve performance. Consistent with previous results, however, performance improved for increased variability in content words [3]. This indicates that the approach to comprehension taken here might scale to more diverse and naturalistic language input. Overall, the results suggest that active processing memory beyond pure state-dependent effects is important for sentence interpretation, and that memory in neurobiological systems might be actively computing [4]. Future work therefore needs to address how the structure of word representations interacts with enhanced processing memory in adaptive spiking networks. [1] Maass W., Natschläger T., & Markram H. (2002). Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Computation, 14: 2531-2560. [2] Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word represen-tations in vector space. Proceedings of the International Conference on Learning Represen-tations, Scottsdale/AZ. [3] Fitz, H. (2011). A liquid-state model of variability effects in learning nonadjacent dependencies. Proceedings of the 33rd Annual Conference of the Cognitive Science Society, Austin/TX. [4] Petersson, K.M., & Hagoort, P. (2012). The neurobiology of syntax: Beyond string-sets. Philo-sophical Transactions of the Royal Society B 367: 1971-1883.

Abstract

Hagoort [1] suggested that the posterior temporal cortex is involved in the retrieval of lexical frames that form building blocks for syntactic unification, supported by the inferior frontal gyrus (IFG). FMRI results support the role of the IFG in the unification operations that are performed at the structural/syntactic [2] and conceptual/ semantic levels [3]. While these studies tackle the unification operations within linguistic components, in the present event-related FMRI study we investigated the interplay between sentence-level semantics and syntax by adapting an EEG comprehension paradigm [4]. The ERP results showed typical P600 and N400 effects, while their combined effect revealed an interaction expressed in the N400 component ([CB-SE] - [SY-CR] > 0). Although the N400 component was similar in the correct and syntactic conditions (SY  CR), the combined effect was significantly larger than the effect of semantic anomaly alone. In contrast, the size of the P600 effect was not affected by an additional semantic violation, suggesting an asymmetry between semantic and syntactic processing. In the current FMRI study we characterize this asymmetry by means of a 2x2 experimental design included the conditions: correct (CR), syntactic (SY), semantic (SE), and combined (CB) anomalies. Standard SPM procedures were used for analysis and only clusters significant at P <.05 family-wise error corrected are reported. The main effect of semantic anomaly ([CB+SE] > [SY+CR]) yielded activation in the anterior IFG (BA 45/47). The opposite contrast revealed the theory-ofmind and default-mode network. The main effect of syntactically correct sentences ([SE+CR] > [CB+SY]), showed significant activation in the IFG (BA 44/45), including the mid-anterior insula extending into the superior temporal poles (BA 22/38). In addition, significant effects were observed in medial prefrontal/ anterior cingulate cortex, posterior middle and superior temporal regions (BA 21/22), and the basal ganglia. The reverse contrast yielded activations in the MFG (BA 9/46), the inferior parietal region (BA 39/40), precuneus and the posterior cingulate region. The only region that showed a significant interaction ([CBSE]  [SYCR] > 0) was the left temporo-parietal region (BA 22/39/40). In summary, the results show that the IFG is involved in unification during comprehension. The effect of semantic anomaly and its implied unification load engages the anterior IFG while the effect of syntactic anomaly and its implied unification failure engages MFG. Finally, the results suggest that the syntax of gender agreement interacts with sentence-level semantics in the left temporo-parietal region. [1] Hagoort, P. (2005). On Broca, brain, and binding: A new framework. TICS, 9, 416-423. [2] Snijders, T. M., Vosse, T., Kempen, G., Van Berkum, J. J. A., Petersson, K. M., Hagoort, P. (2009). Retrieval and unification of syntactic structure in sentence comprehension: An fMRI study using word-category ambiguity. Cerebral Cortex, 19, 1493-1503. doi:10.1093/ cercor/bhn187. [3] Hagoort, P., Hald, L., Baastiansen, M., Petersson, K.M. (2004). Integration of word meaning and world knowledge in language comprehension. Science 304, 438-441. [4] Hagoort, P. (2003). Interplay between syntax and semantics during sentence comprehension: ERP effects of combining syntactic and semantic violations. Journal of Cognitive Neuroscience, 15, 883- 899.