Limitations and future directions

The design of Study 2 was influenced by the hybrid approach to conceptual processing, which pays attention to both linguistic and sensorimotor information in words (Barsalou et al., 2008; Louwerse et al., 2015; Louwerse & Connell, 2011). Therefore, in each of the three studies composing Study 2, language and sensorimotor information were each represented by a variable. Insofar as both aspects were present in the model, the results suggest that both were important, although language was far more important than vision. Vision was more strictly constrained by other parameters, such as participants’ vocabulary size and the degree of semantic depth of the tasks.

Furthermore, it is necessary to acknowledge a longstanding caveat in the present topic, which also affects the present study. That is, it is extremely difficult to ascertain whether our variables encode what we intend for them to encode. Specifically, it is possible that the variables for language-based information encode some sensorimotor information, and vice versa. In the discussion of Study 2, we suggested two methods to address this caveat by measuring the specificity of the variables. One method consisted of examining the instantiation of language-based and vision-based information in the brain, whereas the other method consisted of crossvalidating several variables measuring each of the two constructs of interest. We can now consider a third possibility to approach this question in a novel way. The idea would be to investigate the role of language and sensorimotor information during language development—e.g., at 3, 5, 7 and 9 years of age—under the hypothesis of an increasing importance of language over time.20 Notwithstanding the existing research (Ponari, Norbury, & Vigliocco, 2018; Ponari, Norbury, Rotaru, et al., 2018; Ponari et al., 2020; Vigliocco et al., 2018), there is room for more developmental research in conceptual processing, as the vast majority of this topic is based on adult participants.

We are currently preparing a study that will follow up on Study 2.1 by incorporating several enhancements. First, the study will incorporate a measure of participants’ visual ability as an effect of interest. Alongside this measure, there will be reading ability as another effect of interest and working memory as a covariate. At the word level, the effects of interest will be the same as those of Study 2.1—namely, language-based similarity and vision-based difference. This measure will be alongside one of reading ability and will encompass several improvements. an additional encompass the three main variables of interest from Study 2.1, namely, language and vision at the word level and language at the participant level. along with incorporate visual ability as an individual difference. Thus, compare the effects of language and vision both at the level of an individual difference variable measuring visual ability. In a recent study, Muraki and Pexman (2021) investigated how individual differences in motor imagery predicted motor simulation. In contrast to Muraki et al.’s study, the study we are preparing will measure individual differences in vision. Furthermore, at the word level, whereas Muraki et al. focussed on motor semantic features, our next study will encompass language-based and vision-based information. Indeed, our study will comprise language and vision at the levels of participants and words.

In addition, our next study will address the time courses of language-based and vision-based processing. In Study 2.1, language and vision were both more important with the short SOA than with the long SOA. To address this comparison more reliably in our next study, we will implement a semantic priming paradigm with a semantic decision task, which elicits a deeper semantic processing than lexical decision, and is thus better suited for capturing the time course of linguistic processing and that of perceptual simulation (see Petilli et al., 2021). The concrete implementation of this task will be as follows. Participants will see a prime word and a target word on each trial, and they will assess whether the target word is abstract or concrete. Semantic decision also has the practical advantage that it involves fewer invalid trials than lexical decision, as semantic decision does not require any pseudowords.

Last, we will implement a recent recommendation from Petilli et al. (2021) to operationalise SOA as a continuous variable, rather than the typical categorical form. The continuous SOA measure will enable a more precise insight on the time course of language-based and vision-based information (for current materials and forthcoming updates on this study, see https://osf.io/gwh7x).

References

Barsalou, L. W., Santos, A., Simmons, W. K., & Wilson, C. D. (2008). Language and simulation in conceptual processing. In Symbols and Embodiment. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199217274.003.0013
Louwerse, M. M., & Connell, L. (2011). A taste of words: Linguistic context and perceptual simulation predict the modality of words. Cognitive Science, 35(2), 381–398. https://doi.org/10.1111/j.1551-6709.2010.01157.x
Louwerse, M. M., Hutchinson, S., Tillman, R., & Recchia, G. (2015). Effect size matters: The role of language statistics and perceptual simulation in conceptual processing. Language, Cognition and Neuroscience, 30(4), 430–447. https://doi.org/10.1080/23273798.2014.981552
Muraki, E. J., & Pexman, P. M. (2021). Simulating semantics: Are individual differences in motor imagery related to sensorimotor effects in language processing? Journal of Experimental Psychology: Learning, Memory, and Cognition, 47(12), 1939–1957. https://doi.org/10.1037/xlm0001039
Petilli, M. A., Günther, F., Vergallito, A., Ciapparelli, M., & Marelli, M. (2021). Data-driven computational models reveal perceptual simulation in word processing. Journal of Memory and Language, 117, 104194. https://doi.org/10.1016/j.jml.2020.104194
Ponari, M., Norbury, C. F., Rotaru, A., Lenci, A., & Vigliocco, G. (2018). Learning abstract words and concepts: Insights from developmental language disorder. Philosophical Transactions of the Royal Society B: Biological Sciences, 373, 20170140. https://doi.org/10.1098/rstb.2017.0140
Ponari, M., Norbury, C. F., & Vigliocco, G. (2018). Acquisition of abstract concepts is influenced by emotional valence. Developmental Science, 21(2), 12549. https://doi.org/10.1111/desc.12549
Ponari, M., Norbury, C. F., & Vigliocco, G. (2020). The role of emotional valence in learning novel abstract concepts. Developmental Psychology, 56(10), 1855–1865. https://doi.org/10.1037/dev0001091
Vigliocco, G., Ponari, M., & Norbury, C. (2018). Learning and processing abstract words and concepts: Insights from typical and atypical development. Topics in Cognitive Science, 10, 533–549. https://doi.org/10.1111/tops.12347

  1. Thank you to Dr. Margriet Groen for suggesting this idea.↩︎


Pablo Bernabeu, 2022. Licence: CC BY 4.0.
Thesis: https://doi.org/10.17635/lancaster/thesis/1795.

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