Running Head: SHARED MEANING IN ART

artworks. However, the data show that this was not the case. As just stated, the means for computational measures capturing shared meaning were much lower for the interpretations of artworks than for the calibration with picture naming data, while the content-based analysis clearly showed metaphoric alongside perceptual (and other) interpretative elements for both art types. Further, it might be considered that viewers may have simply named the visual features of abstract artworks, such as color and form, which may have led to an above baseline level of shared meaning for abstract art. However, the analysis of the contents shows that this cannot be an explanation for the shared core meaning, because viewers provided many interpretive elements in the responses to abstract art. In addition, such a situation would have yielded a very high baseline mean for abstract artworks, because each artwork would have been given similar verbal responses, but this was not seen in the data. It is possible to consider the brief responses that our participants provided not to be representative of ordinary art interpretation. First of all, it may be that viewers do not routinely generate meanings when viewing artworks. This would mean that the results can only generalize to situations in which this occurs spontaneously or as a result of some request. Such a request may therefore trigger more meaning than would otherwise arise. The findings of Kuipers et al. (2018) suggest that meanings may arise spontaneously, but we Running Head: SHARED MEANING IN ART 32 acknowledge that this may not always be the case. Secondly, it may be that viewers felt restricted in their time to generate meaning compared to the longer meaning-making experience described by e.g. White (2011) and Specker, Tinio, and van Elk (2018). However, the duration of the experiment overall allowed for a similar length of viewing and interpretation time as the average time taken by naïve viewers visiting art galleries, estimated as 27 seconds (Smith & Smith, 2001) or 33 seconds on first pass (Carbon, 2017, note 17 seconds longer with re-visits factored in). This is much more time than more time-restricted experimental approaches that infer the presence of semantic interpretations, e.g. 1 second displays in Vessel and Rubin (2010), and 6.3 seconds for the trial sequence in Kuipers et al, (2018). Thus, it seems that the time available to our participants was sufficient to generate a meaningful semantic response. It may also be thought that our instruction to provide a word, phrase or short sentence restricted the participants’ freedom of expression. However, if there had been an urge to express more text than the level provided, we would have seen a preponderance of sentence-length responses, but we often saw relatively short responses consisting of a few words, suggesting that there was no artificial ceiling on participants’ freedom to express perceived meanings. Instead, it seems that participants performed naturally within the response length restrictions. It is important to consider the validity of the content-based analysis. Human coders showed a good level of correspondence in their interpretations prior to agreement. The agreed human coding statements corresponded well with the word frequency analysis. The two formed useful complementary techniques with balanced strengths and weaknesses. The word frequency technique was fully objective but lacked any form of interpretation or integration, while the human coding technique was more interpretative, which had the disadvantage of being potentially more subjective (despite the protections afforded by using two independent coders), but benefited from human interpretation during the summarization process. Latent Running Head: SHARED MEANING IN ART 33 semantic analysis using nearest neighbors most often further corroborated general themes identified by the other two methods. The fact that the results from these three fully independent analysis processes showed a strong correspondence suggests that they provided useful data triangulation. Thus, the insights provided by these combined methods can be relied upon as being valid. A shortcoming of our research, shared with the research of many others, is that the paintings were of reduced size, lacked any three-dimensionality that may come from textures, or contexts that may come from e.g. gallery environments. Two aspects are important to consider. First, it is possible to think that this shortcoming may have affected one art type more than the other, making this a confounding factor. For example, some abstract artworks may be more likely to evoke psychological reactions in their full size, rather than as small visual objects, because only then, perhaps, does the artwork convey the experience fully. However, a similar argument could apply to representational art. Examples for artworks of both types, in authentic settings and full size, triggering overwhelming emotions are documented in Elkins (2001), suggesting that overwhelming emotions do not only occur in response to either abstract or representational artwork, but in response to both. Thus, it seems difficult to sustain an argument that the small size of the images had a confounding effect by affecting one art type more than the other. Second, it may be that findings based on small images do not generalize to images in their original size. The issue is discussed extensively in Pelowski, Forster, Tinio, Scholl, and Leder (2017), who argue that lab studies are valuable, but that psychological processes in response to artwork are likely to differ in museum or gallery settings. Specker et al. (2017) support this argument with data. This is a possibility that we cannot address based on the current data, but an issue shared with many lab studies. Future research on this issue would be very interesting. Running Head: SHARED MEANING IN ART 34 Using our methods, meaning becomes a reliably extractable dimension of art experience that may play an important role in larger multidimensional models of art appreciation (e.g. Leder et al., 2004; Pelowski, Markey, Forster, Gerger, & Leder, 2017; Pelowski, Markey, Lauring, & Leder, 2016). In the related field of computer vision, it has proven fruitful to model semantic information into complex neural nets, where this has served to enhance the accuracy of machine-generated aesthetic evaluations (Kao, He, & Huang, 2017). It may be that models of human art evaluation could be enriched by including this important dimension with more precision and detail than has thus far been possible. Having rigorous methods to analyse and extract data, such as the ones used in the current paper, may help build and test these multidimensional theoretical models. We have addressed the constrained questions we set out to answer, but this research generates many questions that would benefit from further research. For example, it would be very interesting to explore the mapping between visual elements, color, form and different elements of semantic content. In addition, it would be valuable to probe more deeply into the processes that lead to shared meaning. Exploring the links between shared meaning and shared liking would also be valuable.