Correlating neural and symbolic representations of language

Analysis methods which enable us to better understand the representations and functioning of neural models of language are increasingly needed as deep learning becomes the dominant approach in NLP. Here we present two methods based on Representational Similarity Analysis (RSA) and Tree Kernels (TK) which allow us to directly quantify how strongly the information encoded in neural activation patterns corresponds to information represented by symbolic structures such as syntax trees. We first validate our methods on the case of a simple synthetic language for arithmetic expressions with clearly defined syntax and semantics, and show that they exhibit the expected pattern of results. We then apply our methods to correlate neural representations of English sentences with their constituency parse trees.Comment: ACL 201

Neural correlates of processing valence and arousal in affective words

Psychological frameworks conceptualize emotion along 2 dimensions, "valence" and "arousal." Arousal invokes a single axis of intensity increasing from neutral to maximally arousing. Valence can be described variously as a bipolar continuum, as independent positive and negative dimensions, or as hedonic value (distance from neutral). In this study, we used functional magnetic resonance imaging to characterize neural activity correlating with arousal and with distinct models of valence during presentation of affective word stimuli. Our results extend observations in the chemosensory domain suggesting a double dissociation in which subregions of orbitofrontal cortex process valence, whereas amygdala preferentially processes arousal. In addition, our data support the physiological validity of descriptions of valence along independent axes or as absolute distance from neutral but fail to support the validity of descriptions of valence along a bipolar continuum

The role of right and left parietal lobes in the conceptual processing of numbers

Neuropsychological and functional imaging studies have associated the conceptual processing of numbers with bilateral parietal regions (including intraparietal sulcus). However, the processes driving these effects remain unclear because both left and right posterior parietal regions are activated by many other conceptual, perceptual, attention, and response-selection processes. To dissociate parietal activation that is number-selective from parietal activation related to other stimulus or response-selection processes, we used fMRI to compare numbers and object names during exactly the same conceptual and perceptual tasks while factoring out activations correlating with response times. We found that right parietal activation was higher for conceptual decisions on numbers relative to the same tasks on object names, even when response time effects were fully factored out. In contrast, left parietal activation for numbers was equally involved in conceptual processing of object names. We suggest that left parietal activation for numbers reflects a range of processes, including the retrieval of learnt facts that are also involved in conceptual decisions on object names. In contrast, number selectivity in right parietal cortex reflects processes that are more involved in conceptual decisions on numbers than object names. Our results generate a new set of hypotheses that have implications for the design of future behavioral and functional imaging studies of patients with left and right parietal damage

A broad-coverage distributed connectionist model of visual word recognition

In this study we describe a distributed connectionist model of morphological processing, covering a realistically sized sample of the English language. The purpose of this model is to explore how effects of discrete, hierarchically structured morphological paradigms, can arise as a result of the statistical sub-regularities in the mapping between word forms and word meanings. We present a model that learns to produce at its output a realistic semantic representation of a word, on presentation of a distributed representation of its orthography. After training, in three experiments, we compare the outputs of the model with the lexical decision latencies for large sets of English nouns and verbs. We show that the model has developed detailed representations of morphological structure, giving rise to effects analogous to those observed in visual lexical decision experiments. In addition, we show how the association between word form and word meaning also give rise to recently reported differences between regular and irregular verbs, even in their completely regular present-tense forms. We interpret these results as underlining the key importance for lexical processing of the statistical regularities in the mappings between form and meaning