Skip to main content
SpringerLink
Log in

No need to integrate action information during coarse semantic processing of man-made tools

  • Brief Report
  • Published:
Psychonomic Bulletin & Review Aims and scope Submit manuscript

Abstract

Action representation of man-made tools consists of two subtypes: structural action representation concerning how to grasp an object, and functional action representation concerning the skilled use of an object. Compared to structural action representation, functional action representation plays the dominant role in fine-grained (i.e., basic level) object recognition. However, it remains unclear whether the two types of action representation are involved differently in the coarse semantic processing in which the object is recognized at a superordinate level (i.e., living/non-living). Here we conducted three experiments using the priming paradigm, in which video clips displaying structural and functional action hand gestures were used as prime stimuli and grayscale photos of man-made tools were used as target stimuli. Participants recognized the target objects at the basic level in Experiment 1 (i.e., naming task) and at the superordinate level in Experiments 2 and 3 (i.e., categorization task). We observed a significant priming effect for functional action prime-target pairs only in the naming task. In contrast, no priming effect was found in either the naming or the categorization task for the structural action prime-target pairs (Experiment 2), even when the categorization task was preceded by a preliminary action imitation of the prime gestures (Experiment 3). Our results suggest that only functional action information is retrieved during fine-grained object processing. In contrast, coarse semantic processing does not require the integration of either structural or functional action information.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

The materials used in the current study and the preprocessed data generated during the current study are available via the Open Science Framework at https://osf.io/tvns3/.

References

  • Alam, M. M., Vilankar, K. P., Field, D. J., & Chandler, D. M. (2014). Local masking in natural images: A database and analysis. Journal of Vision, 14(8), 22.

    Article  PubMed  Google Scholar 

  • Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of Memory and Language, 59(4), 390–412.

    Article  Google Scholar 

  • Binkofski, F., & Buxbaum, L. J. (2013). Two action systems in the human brain. Brain and Language, 127(2), 222–229.

    Article  PubMed  Google Scholar 

  • Borghi, A. M., Bonfiglioli, C., Lugli, L., Ricciardelli, P., Rubichi, S., & Nicoletti, R. (2007). Are visual stimuli sufficient to evoke motor information? Studies with hand primes. Neuroscience Letters, 411(1), 17–21.

    Article  PubMed  Google Scholar 

  • Bub, D. N., & Masson, M. E. J. (2012). On the Dynamics of Action Representations Evoked by Names of Manipulable Objects. Journal of Experimental Psychology-General, 141(3), 502–517.

    Article  PubMed  Google Scholar 

  • Bub, D. N., Masson, M. E. J., & Cree, G. S. (2008). Evocation of functional and volumetric gestural knowledge by objects and words. Cognition, 106(1), 27–58.

    Article  PubMed  Google Scholar 

  • Buxbaum, L. J., & Kalénine, S. (2010). Action knowledge, visuomotor activation, and embodiment in the two action systems. Annals of the New York Academy of Sciences, 1191(1), 201–218.

    Article  PubMed  PubMed Central  Google Scholar 

  • Buxbaum, L. J., Kyle, K. M., Tang, K., & Detre, J. A. (2006). Neural substrates of knowledge of hand postures for object grasping and functional object use: Evidence from fMRI. Brain Research, 1117, 175–185.

    Article  PubMed  Google Scholar 

  • Campanella, F., & Shallice, T. (2011). Manipulability and object recognition: is manipulability a semantic feature? Experimental Brain Research, 208(3), 369–383.

    Article  PubMed  Google Scholar 

  • Chong, I., & Proctor, R. W. (2020). On the Evolution of a Radical Concept: Affordances According to Gibson and Their Subsequent Use and Development. Perspectives on Psychological Science, 15(1), 117–132.

    Article  PubMed  Google Scholar 

  • Clarke, A. (2015). Dynamic information processing states revealed through neurocognitive models of object semantics. Language Cognition and Neuroscience, 30(4), 409–419.

    Article  PubMed  Google Scholar 

  • Clarke, A., Taylor, K. I., Devereux, B., Randall, B., & Tyler, L. K. (2013). From Perception to Conception: How Meaningful Objects Are Processed over Time. Cerebral Cortex, 23(1), 187–197.

    Article  PubMed  Google Scholar 

  • Clarke, A., Devereux, B. J., Randall, B., & Tyler, L. K. (2015). Predicting the Time Course of Individual Objects with MEG. Cerebral Cortex, 25(10), 3602–3612.

    Article  PubMed  Google Scholar 

  • Gibson, J. J. (1979). The Ecological Approach to Visual Perception: Classic Edition. Boston, MA: Houghton Mifflin.

  • Godard, M., Wamain, Y., & Kalenine, S. (2019). Do manufactured and natural objects evoke similar motor information? The case of action priming. Quarterly Journal of Experimental Psychology, 72(12), 2801–2806.

    Article  Google Scholar 

  • Helbig, H. B., Graf, M., & Kiefer, M. (2006). The role of action representations in visual object recognition. Experimental Brain Research, 174(2), 221–228.

    Article  PubMed  Google Scholar 

  • Kalenine, S., Bonthoux, F., & Borghi, A. M. (2009). How action and context priming influence categorization: A developmental study. British Journal of Developmental Psychology, 27, 717–730.

    Article  PubMed  Google Scholar 

  • Kiefer, M., Sim, E.-J., Helbig, H., & Graf, M. (2011). Tracking the time course of action priming on object recognition: Evidence for fast and slow influences of action on perception. Journal of Cognitive Neuroscience, 23(8), 1864–1874.

    Article  PubMed  Google Scholar 

  • Kumle, L., Vo, M. L. H., & Draschkow, D. (2021). Estimating power in (generalized) linear mixed models: An open introduction and tutorial in R. Behavior Research Methods, 53(6), 2528–2543.

    Article  PubMed  PubMed Central  Google Scholar 

  • Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. (2017). lmerTest package: Tests in linear mixed effects models. Journal of statistical software, 82, 1–26.

    Article  Google Scholar 

  • Lee, C.-L., Middleton, E., Mirman, D., Kalenine, S., & Buxbaum, L. J. (2013). Incidental and context-responsive activation of structure- and function-based action features during object identification. Journal of Experimental Psychology-Human Perception and Performance, 39(1), 257–270.

    Article  PubMed  Google Scholar 

  • Lee, C.-L., Huang, H.-W., Federmeier, K. D., & Buxbaum, L. J. (2018). Sensory and semantic activations evoked by action attributes of manipulable objects: Evidence from ERPs. Neuroimage, 167, 331–341.

    Article  PubMed  Google Scholar 

  • Lenth, R., Singmann, H., Love, J., Buerkner, P., & Herve, M. (2019). Package ‘emmeans’ (Version 1.1). Retrieved Nov 5, 2021, from https://github.com/rvlenth/emmeans

  • Martin, A. (2016). GRAPES-Grounding representations in action, perception, and emotion systems: How object properties and categories are represented in the human brain. Psychonomic Bulletin & Review, 23(4), 979–990.

    Article  Google Scholar 

  • Masson, M. E. J., Bub, D. N., & Newton-Taylor, M. (2008). Language-based access to gestural components of conceptual knowledge. Quarterly Journal of Experimental Psychology, 61(6), 869–882.

    Article  Google Scholar 

  • Masson, M. E. J., Bub, D. N., & Lavelle, H. (2013). Dynamic evocation of hand action representations during sentence comprehension. Journal of Experimental Psychology-General, 142(3), 742–762.

    Article  PubMed  Google Scholar 

  • McRae, K., Cree, G. S., Seidenberg, M. S., & McNorgan, C. (2005). Semantic feature production norms for a large set of living and nonliving things. Behavior Research Methods, 37(4), 547–559.

    Article  PubMed  Google Scholar 

  • Mounoud, P., Duscherer, K., Moy, G., & Perraudin, S. (2007). The influence of action perception on object recognition: A developmental study. Developmental Science, 10(6), 836–852.

    Article  PubMed  Google Scholar 

  • Ni, L., Liu, Y., & Yu, W. (2019a). The dominant role of functional action representation in object recognition. Experimental Brain Research, 237(2), 363–375.

    Article  PubMed  Google Scholar 

  • Ni, L., Liu, Y., Yu, W., & Fu, X. (2019b). The China Image Set (CIS): A new set of 551 colored photos with Chinese norms for 12 psycholinguistic variables. Frontiers in Psychology, 10, 2631.

    Article  PubMed  PubMed Central  Google Scholar 

  • Osiurak, F., Rossetti, Y., & Badets, A. (2017). What is an affordance? 40 years later. Neuroscience and Biobehavioral Reviews, 77, 403–417.

    Article  PubMed  Google Scholar 

  • Patterson, K., Nestor, P. J., & Rogers, T. T. (2007). Where do you know what you know? The representation of semantic knowledge in the human brain. Nature Reviews Neuroscience, 8(12), 976–987.

    Article  PubMed  Google Scholar 

  • Pulvermuller, F. (2013). How neurons make meaning: Brain mechanisms for embodied and abstract-symbolic semantics. Trends in Cognitive Sciences, 17(9), 458–470.

    Article  PubMed  Google Scholar 

  • R Core Team. (2021). R: A language and environment for statistical computing (Version 4.1.2). Vienna, Austria: R Foundation for Statistical Computing. Retrieved Nov 5, 2021, from https://www.R-project.org/

  • Sakreida, K., Effnert, I., Thill, S., Menz, M. M., Jirak, D., Eickhoff, C. R., … Binkofski, F. (2016). Affordance processing in segregated parieto-frontal dorsal stream sub-pathways. Neuroscience and Biobehavioral Reviews, 69, 89–112.

  • Symes, E., Tucker, M., Ellis, R., Vainio, L., & Ottoboni, G. (2008). Grasp preparation improves change detection for congruent objects. Journal of Experimental Psychology-Human Perception and Performance, 34(4), 854–871.

    Article  PubMed  Google Scholar 

  • Taylor, K. I., Stamatakis, E. A., & Tyler, L. K. (2009). Crossmodal integration of object features: Voxel-based correlations in brain-damaged patients. Brain, 132, 671–683.

    Article  PubMed  Google Scholar 

  • Taylor, K. I., Devereux, B. J., & Tyler, L. K. (2011). Conceptual structure: Towards an integrated neurocognitive account. Language and Cognitive Processes, 26(9), 1368–1401.

    Article  PubMed  PubMed Central  Google Scholar 

  • Taylor, K. I., Devereux, B. J., Acres, K., Randall, B., & Tyler, L. K. (2012). Contrasting effects of feature-based statistics on the categorisation and basic-level identification of visual objects. Cognition, 122(3), 363–374.

    Article  PubMed  Google Scholar 

  • Tyler, L. K., Chiu, S., Zhuang, J., Randall, B., Devereux, B. J., Wright, P., … Taylor, K. I. (2013). Objects and categories: Feature statistics and object processing in the ventral stream. Journal of Cognitive Neuroscience, 25(10), 1723–1735.

  • Vainio, L., Symes, E., Ellis, R., Tucker, M., & Ottoboni, G. (2008). On the relations between action planning, object identification, and motor representations of observed actions and objects. Cognition, 108(2), 444–465.

    Article  PubMed  Google Scholar 

Download references

Funding

This research was supported by the National Natural Science Foundation of China, and the German Research Foundation (NSFC62061136001/DFGTRR-169).

Author information

Author notes

  1. Long Ni

    Present address: Department of Psychology, University of Pennsylvania, Philadelphia, PA, 19106, USA

Authors and Affiliations

  1. State Key Laboratory of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Chaoyang District, Beijing, 100101, People’s Republic of China

    Wenyuan Yu, Long Ni, Zijian Zhang & Ye Liu

  2. Department of Psychology, University of Chinese Academy of Sciences, Beijing, 101408, People’s Republic of China

    Wenyuan Yu, Zijian Zhang & Ye Liu

  3. Research Center for Applied Mathematics and Machine Intelligence, Research Institute of Basic Theories, Zhejiang Lab, Hangzhou, 311121, People’s Republic of China

    Wenyuan Yu

  4. School of Psychology, Beijing Sport University, Beijing, 100084, People’s Republic of China

    Weiqi Zheng

Authors
  1. Wenyuan Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Long Ni
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zijian Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Weiqi Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ye Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Wenyuan Yu: Conceptualization, methodology, investigation, software, formal analysis, writing – original draft.

Ni Long: Software, formal analysis, writing – original draft

Zijiang Zhang: Investigation, validation, software

Weiqi Zheng: Investigation, validation; writing – original draft

Ye Liu: Conceptualization, resources, writing – review and editing.

Corresponding author

Correspondence to Ye Liu.

Ethics declarations

Ethics approval

The study adhered to the tenets of the Declaration of Helsinki and was approved by the Institutional Review Board of the Institute of Psychology, Chinese Academy of Sciences.

Consent to participate

Informed consent was obtained from all individual participants included in the study.

Consent for publication

No identifying information of any participant is included in this article.

Conflict of interest

None.

Additional information

Open practices statement

None of the experiments was preregistered.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 17 KB)

Supplementary file2 (RAR 35101 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yu, W., Ni, L., Zhang, Z. et al. No need to integrate action information during coarse semantic processing of man-made tools. Psychon Bull Rev 30, 2230–2239 (2023). https://doi.org/10.3758/s13423-023-02301-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3758/s13423-023-02301-6

Keywords

Search

Navigation