Over the past 15 years, there have been two increasingly popular approaches to the study of meaning in cognitive science. One, based on theories of embodied cognition, treats meaning as a simulation of perceptual and motor states. An alternative approach treats meaning as a consequence of the statistical distribution of words across spoken and written language. On the surface, these appear to be opposing scientific paradigms. In this review, we aim to show how recent cross-disciplinary developments have done much to (...) reconcile these two approaches. The foundation to these developments has been the recognition that intralinguistic distributional and sensory-motor data are interdependent. We describe recent work in philosophy, psychology, cognitive neuroscience, and computational modeling that are all based on or consistent with this conclusion. We conclude by considering some possible directions for future research that arise as a consequence of these developments. (shrink)

The Affective grounding hypothesis suggests that affective experiences play a crucial role in abstract concepts’ processing (Kousta et al. 2011). Vigliocco and colleagues test the role of affective experiences as well as the role of language in learning words denoting abstract concepts, comparing children with typical and atypical development. They conclude that besides the affective experiences also language plays a critical role in the processing of words referring to abstract concepts.

This study investigates how speed of motion is processed in language. In three eye-tracking experiments, participants were presented with visual scenes and spoken sentences describing fast or slow events (e.g., The lion ambled/dashed to the balloon). Results showed that looking time to relevant objects in the visual scene was affected by the speed of verb of the sentence, speaking rate, and configuration of a supporting visual scene. The results provide novel evidence for the mental simulation of speed in language and (...) show that internal dynamic simulations can be played out via eye movements toward a static visual scene. (shrink)

Successful face‐to‐face communication involves multiple channels, notably hand gestures in addition to speech for spoken language, and mouth patterns in addition to manual signs for sign language. In four experiments, we assess the extent to which comprehenders of British Sign Language (BSL) and English rely, respectively, on cues from the hands and the mouth in accessing meaning. We created congruent and incongruent combinations of BSL manual signs and mouthings and English speech and gesture by video manipulation and asked participants to (...) carry out a picture‐matching task. When participants were instructed to pay attention only to the primary channel, incongruent “secondary” cues still affected performance, showing that these are reliably used for comprehension. When both cues were relevant, the languages diverged: Hand gestures continued to be used in English, but mouth movements did not in BSL. Moreover, non‐fluent speakers and signers varied in the use of these cues: Gestures were found to be more important for non‐native than native speakers; mouth movements were found to be less important for non‐fluent signers. We discuss the results in terms of the information provided by different communicative channels, which combine to provide meaningful information. (shrink)

The contents and structure of semantic memory have been the focus of much recent research, with major advances in the development of distributional models, which use word co‐occurrence information as a window into the semantics of language. In parallel, connectionist modeling has extended our knowledge of the processes engaged in semantic activation. However, these two lines of investigation have rarely been brought together. Here, we describe a processing model based on distributional semantics in which activation spreads throughout a semantic network, (...) as dictated by the patterns of semantic similarity between words. We show that the activation profile of the network, measured at various time points, can successfully account for response times in lexical and semantic decision tasks, as well as for subjective concreteness and imageability ratings. We also show that the dynamics of the network is predictive of performance in relational semantic tasks, such as similarity/relatedness rating. Our results indicate that bringing together distributional semantic networks and spreading of activation provides a good fit to both automatic lexical processing (as indexed by lexical and semantic decisions) as well as more deliberate processing (as indexed by ratings), above and beyond what has been reported for previous models that take into account only similarity resulting from network structure. (shrink)

In this paper, we describe a model that learns semantic representations from the distributional statistics of language. This model, however, goes beyond the common bag‐of‐words paradigm, and infers semantic representations by taking into account the inherent sequential nature of linguistic data. The model we describe, which we refer to as a Hidden Markov Topics model, is a natural extension of the current state of the art in Bayesian bag‐of‐words models, that is, the Topics model of Griffiths, Steyvers, and Tenenbaum (2007), (...) preserving its strengths while extending its scope to incorporate more fine‐grained linguistic information. (shrink)

Iconicity refers to a resemblance between word form and meaning. Previous work has shown that iconic words are learned earlier and processed faster. Here, we examined whether iconic words are recognized better on a recognition memory task. We also manipulated the level at which items were encoded—with a focus on either their meaning or their form—in order to gain insight into the mechanism by which iconicity would affect memory. In comparison with non‐iconic words, iconic words were associated with a higher (...) false alarm rate, a lower d’ score, and a lower response criterion in Experiment 1. We did not observe any interaction between iconicity and encoding condition. To test the generalizability of these findings, we examined effects of iconicity in a recognition memory megastudy across 3880 items. After controlling for a variety of lexical and semantic variables, iconicity was predictive of more hits and false alarms, and a lower response criterion in this dataset. In Experiment 2, we examined whether these effects were due to increased feelings of familiarity for iconic items by including a familiar versus recollect decision. This experiment replicated the overall results of Experiment 1 and found that participants were more likely to categorize words that they had seen before as familiar (vs. recollected) if they were iconic. Together, these results demonstrate that iconicity has an effect on memory. We discuss implications for theories of iconicity. (shrink)

A number of recent models of semantics combine linguistic information, derived from text corpora, and visual information, derived from image collections, demonstrating that the resulting multimodal models are better than either of their unimodal counterparts, in accounting for behavioral data. Empirical work on semantic processing has shown that emotion also plays an important role especially in abstract concepts; however, models integrating emotion along with linguistic and visual information are lacking. Here, we first improve on visual and affective representations, derived from (...) state‐of‐the‐art existing models, by choosing models that best fit available human semantic data and extending the number of concepts they cover. Crucially then, we assess whether adding affective representations (obtained from a neural network model designed to predict emojis from co‐occurring text) improves the model's ability to fit semantic similarity/relatedness judgments from a purely linguistic and linguistic–visual model. We find that, given specific weights assigned to the models, adding both visual and affective representations improves performance, with visual representations providing an improvement especially for more concrete words, and affective representations improving especially the fit for more abstract words. (shrink)

Speakers retrieve words to use them in sentences. Errors in incorporating words into sentential frames are revealing with respect to the lexical units as well as the lexical retrieval mechanism; hence they constrain theories of lexical access. We present a reanalysis of a corpus of spontaneously occurring lexical exchange errors that highlights the contact points between lexical and sentential processes.

Previous studies show that reading sentences about actions leads to specific motor activity associated with actually performing those actions. We investigate how sign language input may modulate motor activation, using British Sign Language sentences, some of which explicitly encode direction of motion, versus written English, where motion is only implied. We find no evidence of action simulation in BSL comprehension, but we find effects of action simulation in comprehension of written English sentences by deaf native BSL signers. These results provide (...) constraints on the nature of mental simulations involved in comprehending action sentences referring to transfer events, suggesting that the richer contextual information provided by BSL sentences versus written or spoken English may reduce the need for action simulation in comprehension, at least when the event described does not map completely onto the signer's own body. (shrink)

We discuss two key assumptions of Levelt et al.'s model of lexical retrieval: (1) the nondecompositional character of concepts and (2) lemmas as purely syntactic representations. These assumptions fail to capture the broader role of lemmas, which we propose as that of lexical–semantic representations binding (compositional) semantics with phonology (or orthography).

Dual-mechanism models of language maintain a distinction between a lexicon and a computational system of linguistic rules. In his target article, Clahsen provides support for such a distinction, presenting evidence from German inflections. He argues for a structured lexicon, going beyond the strict lexicon versus rules dichotomy. We agree with the author in assuming a dual mechanism; however, we argue that a next step must be taken, going beyond the notion of the computational system as specific rules applying to a (...) linguistic domain. By assuming a richer lexicon, the computational system can be conceived as a more general binding process that applies to different linguistic levels: syntax, morphology, reading, and spelling. (shrink)