Sage Journals: Discover world-class research

Abstract

Visual textures are critical in how individuals form sensory expectations about objects, which include somatosensory properties such as temperature. This study aimed to uncover crossmodal associations between visual textures and temperature concepts. In Experiment 1 (N = 193), we evaluated crossmodal associations between 43 visual texture categories and different temperature concepts (via temperature words such as cold and hot) using an explicit forced-choice test. The results revealed associations between striped, cracked, matted, and waffled visual textures and high temperatures and between crystalline and flecked visual textures and low temperatures. In Experiment 2 (N = 247), we conducted six implicit association tests (IATs) pairing the two visual textures most strongly associated with low (crystalline and flecked) and high (striped and cracked) temperatures with the words cold and hot as per the results of Experiment 1. When pairing the crystalline and striped visual textures, the results revealed that crystalline was matched to the word cold, and striped was matched to the word hot. However, some associations found in the explicit test were not found in the IATs. In Experiment 3 (N = 124), we investigated how mappings between visual textures and concrete entities may influence crossmodal associations with temperature and these visual textures. Altogether, we found a range of association strengths and automaticity levels. Importantly, we found evidence of relative effects. Furthermore, some of these crossmodal associations are partly influenced by indirect mappings to concrete entities.

Keywords

Crossmodal correspondences visual textures temperature automaticity

Get full access to this article

View all access options for this article.

References

Adelson

E. H.

(2001). On seeing stuff: The perception of materials by humans and machines. In Rogowitz

B. E.

Pappas

T. N.

(Eds.), Human vision and electronic imaging VI (Vol. 4299, pp. 1–12). SPIE. https://doi.org/10.1117/12.429489

Agrawal

Lee

Calcetas

Clarke

Lin

Schachner

(2020). Hearing water temperature: Characterizing the development of nuanced perception of auditory events. In Denison

Mack

Armstrong

B. C.

(Eds.), 42nd annual meeting of the Cognitive Science Society (CogSci 2020) (pp. 494–499). Cognitive Science Society.

American Psychological Association. (n.d.a). Automaticity. In APA dictionary of psychology. https://dictionary.apa.org/automaticity

American Psychological Association. (n.d.b). Visual texture. In APA dictionary of psychology. https://dictionary.apa.org/visual-texture

Anwyl-Irvine

Dalmaijer

E. S.

Hodges

Evershed

J. K.

(2020). Realistic precision and accuracy of online experiment platforms, web browsers, and devices. Behavior Research Methods, 53, 1407–1425. https://doi.org/10.3758/s13428-020-01501-5

Badde

Navarro

Landy

(2018). Vision and touch are not automatically integrated. Journal of Vision, 18(10), Article 95. https://doi.org/10.1167/18.10.95

Ball

(2017). Material witness: How does it feel? Nature Materials, 17(1), Article 12. https://doi.org/10.1038/nmat5062

Barbosa Escobar

Velasco

Motoki

Byrne

D. V.

Wang

Q. J

. (2021). The temperature of emotions. PLOS ONE, 16(6), Article e0252408. https://doi.org/10.1371/journal.pone.0252408

Barenholtz

Lewkowicz

D. J.

Davidson

Mavica

(2014). Categorical congruence facilitates multisensory associative learning. Psychonomic Bulletin and Review, 21(5), 1346–1352. https://doi.org/10.3758/s13423-014-0612-7

10.

Bates

Mächler

Bolker

B. M.

Walker

S. C.

(2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01

11.

Baumgartner

Wiebel

C. B.

Gegenfurtner

K. R.

(2013). Visual and haptic representations of material properties. Multisensory Research, 26(5), 429–455. https://doi.org/10.1163/22134808-00002429

12.

Bergmann Tiest

W. M.

Kappers

A. M. L

. (2009). Tactile perception of thermal diffusivity. Attention, Perception, & Psychophysics, 71(3), 481–489. https://doi.org/10.3758/APP.71.3.481

13.

Blaine

R. L.

(2018). In search of thermal effusivity reference materials. Journal of Thermal Analysis and Calorimetry, 132(2), 1419–1422. https://doi.org/10.1007/s10973-018-7020-6

14.

Bridges

Pitiot

MacAskill

M. R.

Peirce

J. W.

(2020). The timing mega-study: Comparing a range of experiment generators, both lab-based and online. PeerJ, 8, 1–29. https://doi.org/10.7717/peerj.9414

15.

Buckingham

Cant

J. S.

Goodale

M. A.

(2009). Living in a material world: How visual cues to material properties affect the way that we lift objects and perceive their weight. Journal of Neurophysiology, 102(6), 3111–3118. https://doi.org/10.1152/jn.00515.2009

16.

Cavanaugh

L. A.

Macinnis

D. J.

Weiss

A. M.

(2016). Perceptual dimensions differentiate emotions. Cognition and Emotion, 30(8), 1430–1445. https://doi.org/10.1080/02699931.2015.1070119

17.

Charrad

Ghazzali

Boiteau

Niknafs

(2014). NbClust: An R package for determining the relevant number of clusters in a data set. Journal of Statistical Software, 61(6), 1–36. https://doi.org/10.18637/jss.v061.i06

18.

Chen

Tanaka

Namatame

Watanabe

(2016). Color-shape associations in deaf and hearing people. Frontiers in Psychology, 7, Article 355. https://doi.org/10.3389/fpsyg.2016.00355

19.

Chen

Tanaka

Watanabe

(2015). Color-shape associations revealed with implicit association tests. PLOS ONE, 10(1), Article e0116954. https://doi.org/10.1371/journal.pone.0116954

20.

Chen

Shao

Barnes

Childs

Henson

(2009). Exploring relationships between touch perception and surface physical properties. International Journal of Design, 3(2), 67–76. https://www.ijdesign.org/index.php/IJDesign/article/view/596/261

21.

Chen

Y.-C.

Spence

(2010). When hearing the bark helps to identify the dog: Semantically-congruent sounds modulate the identification of masked pictures. Cognition, 114(3), 389–404. https://doi.org/10.1016/j.cognition.2009.10.012

22.

Cimpoi

Maji

Kokkinos

Mohamed

Vedaldi

(2014). Describing textures in the wild. In Proceedings of the IEEE Computer Society conference on computer vision and pattern recognition (pp. 3606–3613). https://doi.org/10.1109/CVPR.2014.461

23.

Connolly

(2014). Multisensory perception as an associative learning process. Frontiers in Psychology, 5, Article 1095. https://doi.org/10.3389/fpsyg.2014.01095

24.

Cottrill

A. L.

Liu

A. T.

Kunai

Koman

V. B.

Kaplan

Mahajan

S. G.

Liu

Toland

A. R.

Strano

M. S.

(2018). Ultra-high thermal effusivity materials for resonant ambient thermal energy harvesting. Nature Communications, 9(1), 1–11. https://doi.org/10.1038/s41467-018-03029-x

25.

Crisinel

A.-S.

Spence

(2009). Implicit association between basic tastes and pitch. Neuroscience Letters, 464(1), 39–42. https://doi.org/10.1016/j.neulet.2009.08.016

26.

Crisinel

A.-S.

Spence

(2010). A sweet sound? Food names reveal implicit associations between taste and pitch. Perception, 39(3), 417–425. https://doi.org/10.1068/p6574

27.

Demattè

M. L.

Sanabria

Sugarman

Spence

(2006). Cross-modal interactions between olfaction and touch. Chemical Senses, 31(4), 291–300. https://doi.org/10.1093/chemse/bjj031

28.

Deroy

Crisinel

A.-S.

Spence

(2013). Crossmodal correspondences between odors and contingent features: Odors, musical notes, and geometrical shapes. Psychonomic Bulletin and Review, 20(5), 878–896. https://doi.org/10.3758/s13423-013-0397-0

29.

Erceg-Hurn

D. M.

Mirosevich

V. M.

(2008). Modern robust statistical methods: An easy way to maximize the accuracy and power of your research. American Psychologist, 63(7), 591–601. https://doi.org/10.1037/0003-066X.63.7.591

30.

Ernst

M. O.

(2007). Learning to integrate arbitrary signals from vision and touch. Journal of Vision, 7(5), 1–14. https://doi.org/10.1167/7.5.7

31.

Fenko

Schifferstein

H. N. J.

Hekkert

(2010). Looking hot or feeling hot: What determines the product experience of warmth? Materials and Design, 31(3), 1325–1331. https://doi.org/10.1016/j.matdes.2009.09.008

32.

Fields

Verhave

Fath

(1984). Stimulus equivalence and transitive associations: A methodological analysis. Journal of Experimental Behavior, 42(1), 143–157. https://doi.org/10.1901/jeab.1984.42-143

33.

Filingeri

Ackerley

(2021). The biology of skin wetness perception and its implications in manual function and for reproducing complex somatosensory signals in neuroprosthetics. Journal of Neurophysiology, 117(4), 1761–1775. https://doi.org/10.1152/jn.00883.2016

34.

Fleming

R. W.

Wiebel

Gegenfurtner

(2013). Perceptual qualities and material classes. Journal of Vision, 13(8), 1–20. https://doi.org/10.1167/13.8.9

35.

Fujisaki

Tokita

Kariya

(2015). Perception of the material properties of wood based on vision, audition, and touch. Vision Research, 109, 185–200. https://doi.org/10.1016/j.visres.2014.11.020

36.

Getz

L. M.

Kubovy

(2018). Questioning the automaticity of audiovisual correspondences. Cognition, 175, 101–108. https://doi.org/10.1016/j.cognition.2018.02.015

37.

Getz

L. M.

Toscano

J. C.

(2021). Rethinking the McGurk effect as a perceptual illusion. Attention, Perception, & Psychophysics, 83(6), 2583–2598. https://doi.org/10.3758/s13414-021-02265-6

38.

The GIMP Development Team. (2020). GIMP (Version 2.10.18) [Computer software]. https://www.gimp.org

39.

Greenwald

A. G.

Nosek

B. A.

Banaji

M. R.

(2003). Understanding and using the implicit association test: I—An improved scoring algorithm. Journal of Personality and Social Psychology, 85(2), 197–216. https://doi.org/10.1037/0022-3514.85.2.197

40.

Groissboeck

Lughofer

Thumfart

(2010). Associating visual textures with human perceptions using genetic algorithms. Information Sciences, 180(11), 2065–2084. https://doi.org/10.1016/j.ins.2010.01.035

41.

Guest

Spence

(2003). What role does multisensory integration play in the visuotactile perception of texture? International Journal of Psychophysiology, 50(1–2), 63–80. https://doi.org/10.1016/S0167-8760(03)00125-9

42.

Gutmann

Simmons

L. M.

(1952). The temperature dependence of the viscosity of liquids. Journal of Applied Physics, 23(9), 977–978. https://doi.org/10.1063/1.1702361

43.

Halekoh

Højsgaard

(2014). A Kenward-Roger approximation and parametric bootstrap methods for tests in linear mixed models: The R package pbkrtest. Journal of Statistical Software, 59(9), 1–32. https://doi.org/10.18637/jss.v059.i09

44.

Hanada

(2016). Using Japanese onomatopoeias to explore perceptual dimensions in visual material perception. Perception, 45(5), 568–587. https://doi.org/10.1177/0301006616629028

45.

Hekkert

Karana

(2014). Designing material experience. In Karana

Pedgley

Rognoli

(Eds.), Materials experience (pp. 3-13). Butterworth-Heinemann. https://doi.org/10.1016/B978-0-08-099359-1.00001-1

46.

Heschong

(1979). Thermal delight in architecture. MIT Press.

47.

H.-N.

Iwai

Yoshikawa

Watanabe

Nishida

(2014). Combining colour and temperature: A blue object is more likely to be judged as warm than a red object. Scientific Reports, 4, Article 5527. https://doi.org/10.1038/srep05527

48.

H.-N.

Van Doorn

G. H.

Kawabe

Watanabe

Spence

(2014). Colour-temperature correspondences: When reactions to thermal stimuli are influenced by colour. PLOS ONE, 9(3), e91854. https://doi.org/10.1371/journal.pone.0091854

49.

Y. X.

Landy

M. S.

Maloney

L. T.

(2006). How direction of illumination affects visually perceived surface roughness. Journal of Vision, 6(5), 634–648. https://doi.org/10.1167/6.5.8

50.

Hothorn

Bretz

Westfall

(2008). Simultaneous inference in general parametric models. Biometric Journal, 50(3), 346–363. https://doi.org/10.1002/bimj.200810425

51.

Houwer

De Teige-Mocigemba

(2009). Implicit measures: A normative analysis and review. Psychological Bulletin, 135(3), 347–368. https://doi.org/10.1037/a0014211

52.

Imram

(1999). Visual texture perception in formulated chilled dairy desserts. British Food Journal, 101(1), 22–31. https://doi.org/10.1108/00070709910251441

53.

Iosifyan

Korolkova

(2019). Emotions associated with different textures during touch. Consciousness and Cognition, 71, 79–85. https://doi.org/10.1016/j.concog.2019.03.012

54.

Jones

O’Neil

(1985). Combining vision and touch in texture perception. Perception & Psychophysics, 37(1), 66–72. https://doi.org/10.3758/BF03207140

55.

Kahn

B. E.

(2017). Using visual design to improve customer perceptions of online assortments. Journal of Retailing, 93(1), 29–42. https://doi.org/10.1016/j.jretai.2016.11.004

56.

Karana

Hekkert

Kandachar

(2009). Meanings of materials through sensorial properties and manufacturing processes. Materials and Design, 30(7), 2778–2784. https://doi.org/10.1016/j.matdes.2008.09.028

57.

Katkov

Harris

Sagi

(2015). Visual perception of order-disorder transition. Frontiers in Psychology, 6, Article 734. https://doi.org/10.3389/fpsyg.2015.00734

58.

Komatsu

Goda

(2018). Neural mechanisms of material perception: Quest on shitsukan. Neuroscience, 392, 329–347. https://doi.org/10.1016/j.neuroscience.2018.09.001

59.

Kurdi

Ratliff

K. A.

Cunningham

W. A.

(2021). Can the implicit association test serve as a valid measure of automatic cognition? A response to Schimmack (2021). Perspectives on Psychological Science, 16(2), 422–434. https://doi.org/10.1177/1745691620904080

60.

Kuznetsova

(2017). lmerTest package: Tests in linear mixed effects models. Journal of Statistical Software, 82(13), 1–26. https://doi.org/10.18637/jss.v082.i13

61.

Lebel

E. P.

Paunonen

S. V.

(2011). Sexy but often unreliable: The impact of unreliability on the replicability of experimental findings with implicit measures. Personality and Social Psychology Bulletin, 37(4), 570–583. https://doi.org/10.1177/0146167211400619

62.

Lederman

S. J.

Abbott

S. G.

(1981). Texture perception: Studies of intersensory organization using a discrepancy paradigm, and visual versus tactual psychophysics. Journal of Experimental Psychology: Human Perception and Performance, 7(4), 902–915. https://doi.org/10.1037/0096-1523.7.4.902

63.

Lesch

M. F.

Chang

W. R.

Chang

C. C.

(2008). Visually based perceptions of slipperiness: Underlying cues, consistency and relationship to coefficient of friction. Ergonomics, 51(12), 1973–1983. https://doi.org/10.1080/00140130802558979

64.

Liu

Lughofer

Zeng

(2015). Aesthetic perception of visual textures: A holistic exploration using texture analysis, psychological experiment, and perception modeling. Frontiers in Computational Neuroscience, 9, Article 134. https://doi.org/10.3389/fncom.2015.00134

65.

Andrews

(2015). To transform or not to transform: Using generalized linear mixed models to analyse reaction time data. Frontiers in Psychology, 6, Article 1171. https://doi.org/10.3389/fpsyg.2015.01171

66.

Manfredi

L. R.

Saal

H. P.

Brown

K. J.

Zielinski

M. C.

Iii

J. F. D.

Polashock

V. S.

Bensmaia

S. J.

(2014). Natural scenes in tactile texture. Journal of Neurophysiology, 111(9), 1792–1802. https://doi.org/10.1152/jn.00680.2013

67.

Marks

L. E.

(1978). The unity of the senses. Academic Press.

68.

Marks

L. E.

(1996). On perceptual metaphors. Metaphor and Symbolic Activity, 11(1), 39–66. https://doi.org/10.1207/s15327868ms1101_3

69.

Martino

Marks

L. E.

(1999). Perceptual and linguistic interactions in speeded classification: Tests of the semantic coding hypothesis. Perception, 28(7), 903–923. https://doi.org/10.1068/p2866

70.

Martino

Marks

L. E.

(2001). Synesthesia: Strong and weak. Current Directions in Psychological Science, 10(2), 61–65. https://doi.org/10.1111/1467-8721.00116

71.

McGlone

Reilly

(2010). The cutaneous sensory system. Neuroscience and Biobehavioral Reviews, 34(2), 148–159. https://doi.org/10.1016/j.neubiorev.2009.08.004

72.

Mogensen

M. F.

English

H. B.

(1926). The apparent warmth of colors. The American Journal of Psychology, 37(3), 427–428. https://doi.org/10.2307/1413633

73.

Motoki

Saito

Nouchi

Kawashima

Sugiura

(2019). Light colors and comfortable warmth: Crossmodal correspondences between thermal sensations and color lightness influence consumer behavior. Food Quality and Preference, 72, 45–55. https://doi.org/10.1016/j.foodqual.2018.09.004

74.

Motoki

Saito

Nouchi

Sugiura

(2020). Cross-modal correspondences between temperature and taste attributes. Frontiers in Psychology, 11, Article 571852. https://doi.org/10.3389/fpsyg.2020.571852

75.

Motoki

Takahashi

Spence

(2021). Tasting atmospherics: Taste associations with colour parameters of coffee shop interiors. Food Quality and Preference, 94, Article 104315. https://doi.org/10.1016/j.foodqual.2021.104315

76.

Motoyoshi

Nishida

Sharan

Adelson

E. H.

(2007). Image statistics and the perception of surface qualities. Nature, 447(7141), 206–209. https://doi.org/10.1038/nature05724

77.

Noguchi

Gel

Y. R.

Brunner

Konietschke

(2012). nparLD: An R software package for the nonparametric analysis of longitudinal data in factorial experiments. Journal of Statistical Software, 50(12), 1–23. https://doi.org/10.18637/jss.v050.i12

78.

Norman

J. F.

Crabtree

C. E.

Norman

H. F.

Moncrief

B. K.

Herrmann

Kapley

(2006). Aging and the visual, haptic, and cross-modal perception of natural object shape. Perception, 35(10), 1383–1395. https://doi.org/10.1068/p5504

79.

Norman

J. F.

Norman

H. F.

Clayton

A. M.

Lianekhammy

Zielke

(2004). The visual and haptic perception of natural object shape. Perception & Psychophysics, 66(2), 342–351. https://doi.org/doi.org/10.3758/BF03194883

80.

Obata

Takeuchi

Furuta

Kanayama

(2005). Research on better use of wood for sustainable development: Quantitative evaluation of good tactile warmth of wood. Energy, 30(8), 1317–1328. https://doi.org/10.1016/j.energy.2004.02.001

81.

Okamoto

Nagano

Yamada

(2013). Psychophysical dimensions of tactile perception of textures. IEEE Transactions on Haptics, 6(1), 81–93. https://doi.org/10.1109/ToH.2012.32

82.

Parise

C. V.

(2016). Crossmodal correspondences: Standing issues and experimental guidelines. Multisensory Research, 29(1–3), 7–28. https://doi.org/10.1163/22134808-00002502

83.

Parise

C. V.

Knorre

Ernst

M. O.

(2014). Natural auditory scene statistics shapes human spatial hearing. Proceedings of the National Academy of Sciences of the United States of America, 111(16), 6104–6108. https://doi.org/10.1073/pnas.1322705111

84.

Parise

C. V.

Spence

(2012). Audiovisual crossmodal correspondences and sound symbolism: A study using the implicit association test. Experimental Brain Research, 220(3–4), 319–333. https://doi.org/10.1007/s00221-012-3140-6

85.

Parise

C. V.

Spence

(2013). Audiovisual cross-modal correspondences in the general population. In Simner

Hubbard

(Eds.), The Oxford handbook of synesthesia (pp. 790–815). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199603329.013.0039

86.

Parthasarathy

Chhapgar

A. F.

(1955). Sound absorption in liquids in relation to their physical properties viscosity and specific heats. Annalen der Physik, 451(5–8), 297–303. https://doi.org/10.1002/andp.19554510509

87.

Partos

T. R.

Cropper

S. J.

Rawlings

(2016). You don’t see what I see: Individual differences in the perception of meaning from visual stimuli. PLOS ONE, 11(3), Article e0150615. https://doi.org/10.1371/journal.pone.0150615

88.

Pellegrino

Jones

J. D.

Shupe

G. E.

Luckett

C. R.

(2019). Sensitivity to viscosity changes and subsequent estimates of satiety across different senses. Appetite, 133, 101–106. https://doi.org/10.1016/j.appet.2018.10.028

89.

Phillips

Egan

E. J. L.

Perry

B. N.

(2009). Perceptual equivalence between vision and touch is complexity dependent. Acta Psychologica, 132(3), 259–266. https://doi.org/10.1016/j.actpsy.2009.07.010

90.

Picard

(2006). Partial perceptual equivalence between vision and touch for texture information. Acta Psychologica, 121(3), 227–248. https://doi.org/10.1016/j.actpsy.2005.06.001

91.

Piqueras-Fiszman

Spence

(2011). Crossmodal correspondences in product packaging: Assessing color-flavor correspondences for potato chips (crisps). Appetite, 57(3), 753–757. https://doi.org/10.1016/j.appet.2011.07.012

92.

Piqueras-Fiszman

Spence

(2015). Sensory expectations based on product-extrinsic food cues: An interdisciplinary review of the empirical evidence and theoretical accounts. Food Quality and Preference, 40, 165–179. https://doi.org/10.1016/j.foodqual.2014.09.013

93.

R Core Team. (2021). R: A language and environment for statistical computing. https://www.r-project.org/

94.

Rempel

A. R.

Rempel

A. W.

(2013). Rocks, clays, water, and salts: Highly durable, infinitely rechargeable, eminently controllable thermal batteries for buildings. Geosciences, 3(1), 63–101. https://doi.org/10.3390/geosciences3010063

95.

Richetin

Costantini

Perugini

Schönbrodt

(2015). Should we stop looking for a better scoring algorithm for handling implicit association test data? Test of the role of errors, extreme latencies treatment, scoring formula, and practice trials on reliability and validity. PLOS ONE, 10(6), Article e0129601. https://doi.org/10.1371/journal.pone.0129601

96.

Rousselet

G. A.

Pernet

C. R.

Wilcox

R. R.

(2021). The percentile bootstrap: A primer with step-by-step instructions in R. Advances in Methods and Practices in Psychological Science, 4(1), 1–10. https://doi.org/10.1177/2515245920911881

97.

Sakamoto

Watanabe

(2017). Exploring tactile perceptual dimensions using materials associated with sensory vocabulary. Frontiers in Psychology, 8, Article 569. https://doi.org/10.3389/fpsyg.2017.00569

98.

Saluja

Stevenson

R. J.

(2018). Cross-modal associations between real tastes and colors. Chemical Senses, 43, 475–480. https://doi.org/10.1093/chemse/bjy033

99.

Schifferstein

H. N. J.

Wastiels

(2014). Sensing materials: Exploring the building blocks for experiential design. In Karana

Pedgley

Rognoli

(Eds.), Materials experience (pp. 15–26). Butterworth-Heinemann. https://doi.org/10.1016/B978-0-08-099359-1.00002-3

100.

Schwartz

Nishino

(2020). Recognizing material properties from images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(8), 1981–1995. https://doi.org/10.1109/TPAMI.2019.2907850

101.

Scrucca

Fop

Murphy

T. B.

Raftery

A. E.

(2016). mclust 5: Clustering, classification and density estimation using Gaussian finite mixture models. The R Journal, 8(1), 289–317. https://doi.org/10.32614/RJ-2016-021

102.

Shams

Seitz

A. R.

(2008). Benefits of multisensory learning. Trends in Cognitive Sciences, 12(11), 411–417. https://doi.org/10.1016/j.tics.2008.07.006

103.

Siegelman

(2019). Statistical learning abilities and their relation to language. Language and Linguistic Compass, 14, Article e12365. https://doi.org/10.1111/lnc3.12365

104.

Speed

L. J.

Croijmans

Dolscheid

Majid

(2021). Crossmodal associations with olfactory, auditory, and tactile stimuli in children and adults. I-Perception, 12(6), 1–34. https://doi.org/10.1177/20416695211048513

105.

Spence

(2011). Crossmodal correspondences: A tutorial review. Attention, Perception, & Psychophysics, 73(4), 971–995. https://doi.org/10.3758/s13414-010-0073-7

106.

Spence

(2020a). Olfactory-colour crossmodal correspondences in art, science, and design. Cognitive Research: Principles and Implications, 5, Article 52. https://doi.org/10.1186/s41235-020-00246-1

107.

Spence

(2020b). Shitsukan: The multisensory perception of quality. Multisensory Research, 33(7), 737–775. https://doi.org/10.1163/22134808-bja10003

108.

Spence

(2020c). Temperature-based crossmodal correspondences: Causes and consequences. Multisensory Research, 33(6), 1–38. https://doi.org/10.1163/22134808-20191494

109.

Spence

Deroy

(2013). How automatic are crossmodal correspondences? Consciousness and Cognition, 22(1), 245–260. https://doi.org/10.1016/j.concog.2012.12.006

110.

Sun

H. C.

Welchman

A. E.

Chang

D. H. F.

Di Luca

(2016). Look but don’t touch: Visual cues to surface structure drive somatosensory cortex. NeuroImage, 128, 353–361. https://doi.org/10.1016/j.neuroimage.2015.12.054

111.

Sun

Han

Wang

Liu

Chen

Lou

(2018). An extended research of crossmodal correspondence between color and sound in psychology and cognitive ergonomics. PeerJ, 6, Article e4443. https://doi.org/10.7717/peerj.4443

112.

Thumfart

Jacobs

R. H. A. H.

Lughofer

Eitzinger

Cornelissen

F. W.

Groissboeck

Richter

(2011). Modeling human aesthetic perception of visual textures. ACM Transactions on Applied Perception, 8(4), Article 27. https://doi.org/10.1145/2043603.2043609

113.

Ueda

Spence

Okajima

(2020). Effects of varying the standard deviation of the luminance on the appearance of food, flavour expectations, and taste/flavour perception. Scientific Reports, 10(1), 1–12. https://doi.org/10.1038/s41598-020-73189-8

114.

Ulusoy

Olguntürk

(2018). Effects of material pairs on warmth perception in interiors. Journal of Imaging Science and Technology, 62(5), 1–9. https://doi.org/10.2352/J.ImagingSci.Technol.2018.62.5.050408

115.

van Rompay

T. J. L.

Kramer

L. M.

Saakes

. (2018). The sweetest punch: Effects of 3D-printed surface textures and graphic design on ice-cream evaluation. Food Quality and Preference, 68, 198–204. https://doi.org/10.1016/j.foodqual.2018.02.015

116.

Velasco

Jones

King

Spence

(2013). The sound of temperature: What information do pouring sounds convey concerning the temperature of a beverage. Journal of Sensory Studies, 28(5), 335–345. https://doi.org/10.1111/joss.12052

117.

Velasco

Wan

Knoeferle

Zhou

Salgado-Montejo

Spence

(2015). Searching for flavor labels in food products: The influence of color-flavor congruence and association strength. Frontiers in Psychology, 6, Article 301. https://doi.org/10.3389/fpsyg.2015.00301

118.

Velasco

Woods

A. T.

Deroy

Spence

(2015). Hedonic mediation of the crossmodal correspondence between taste and shape. Food Quality and Preference, 41, 151–158. https://doi.org/10.1016/j.foodqual.2014.11.010

119.

Vianello

Bar-Anan

(2021). Can the Implicit Association Test measure automatic judgment? The validation continues. Perspectives on Psychological Science, 16(2), 415–421. https://doi.org/10.1177/1745691619897960

120.

Wang

Q. J.

Spence

(2017). The role of pitch and tempo in sound-temperature crossmodal correspondences. Multisensory Research, 30(3–5), 307–320. https://doi.org/10.1163/22134808-00002564

121.

Wastiels

Schifferstein

H. N. J.

Heylighen

Wouters

(2012a). Red or rough, what makes materials warmer? Materials and Design, 42, 441–449. https://doi.org/10.1016/j.matdes.2012.06.028

122.

Wastiels

Schifferstein

H. N. J.

Heylighen

Wouters

(2012b). Relating material experience to technical parameters: A case study on visual and tactile warmth perception of indoor wall materials. Building and Environment, 49(1), 359–367. https://doi.org/10.1016/j.buildenv.2011.08.009

123.

Whitaker

T. A.

Simões-Franklin

Newell

F. N.

(2008). Vision and touch: Independent or integrated systems for the perception of texture? Brain Research, 1242, 59–72. https://doi.org/10.1016/j.brainres.2008.05.037

124.

Wilkes

Miodownik

(2018). Materials library collections as tools for interdisciplinary research. Interdisciplinary Science Reviews, 43(1), 3–23. https://doi.org/10.1080/03080188.2018.1435450

125.

Wilkes

Wongsriruksa

Howes

Gamester

Witchel

Conreen

Laughlin

Miodownik

(2014). Design tools for interdisciplinary translation of material experiences. Materials and Design, 90, 1228–1237. https://doi.org/10.1016/j.matdes.2015.04.013

126.

Zettersten

Wojcik

Benitez

V. L.

Saffran

(2018). The company objects keep: Linking referents together during cross-situational word learning. Journal of Memory and Language, 99, 62–73. https://doi.org/10.1016/j.jml.2017.11.001

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.61 MB

Crossmodal associations between visual textures and temperature concepts

Abstract

Keywords

Get full access to this article

References

Supplementary Material