Scandinavian Journal of Psychology, 2014
DOI: 10.1111/sjop.12122
Cognition and Neurosciences Event-related potentials reveal linguistic suppression effect but not enhancement effect on categorical perception of color AITAO LU,1,2,3 LING YANG,1,2,3 YANPING YU,1,2,3 MEICHAO ZHANG,1,2,3 YULAN SHAO1,2,3 and HONGHONG ZHANG1,2,3 1
Center for Studies of Psychological Application & School of Psychology, South China Normal University, China Guangdong Key Laboratory of Mental Health and Cognitive Science, China 3 Guangdong Center of Mental Assistance and Contingency Technique for Emergency, China 2
Lu, A., Yang, L., Yu, Y., Zhang, M., Shao, Y. & Zhang, H. (2014). Event-related potentials reveal linguistic suppression effect but not enhancement effect on categorical perception of color. Scandinavian Journal of Psychology. The present study used the event-related potential technique to investigate the nature of linguistic effect on color perception. Four types of stimuli based on hue differences between a target color and a preceding color were used: zero hue step within-category color (0-WC); one hue step within-category color (1-WC); one hue step between-category color (1-BC); and two hue step between-category color (2-BC). The ERP results showed no significant effect of stimulus type in the 100-200 ms time window. However, in the 200–350 ms time window, ERP responses to 1-WC target color overlapped with that to 0-WC target color for right visual field (RVF) but not left visual field (LVF) presentation. For the 1-BC condition, ERP amplitudes were comparable in the two visual fields, both being significantly different from the 0-WC condition. The 2-BC condition showed the same pattern as the 1-BC condition. These results suggest that the categorical perception of color in RVF is due to linguistic suppression on within-category color discrimination but not between-category color enhancement, and that the effect is independent of early perceptual processes. Key words: Categorical perception, Color, Language suppression, ERP. Aitao Lu, School of Psychology, South China Normal University, Guangzhou, China. E-mail: [email protected]
INTRODUCTION The relationship between language and cognition has long been the subject of philosophical inquiry and psychological experimentation (e.g., Lu, Hodges, Zhang & Wang, 2012; Mo, Xu, Kay & € Tan, 2011; Pilling, Wiggett, Ozgen & Davies, 2003; Roberson, Davidoff & Shapiro, 2002; Roberson, Davies & Davidoff, 2000; Roberson, Pak & Hanley, 2008; Thierry, Athanasopoulos, Wiggett, Dering & Kuipers, 2009). There is no doubt that language and cognition are intimately linked in human mind, as demonstrated in many areas (e.g., number: Gordon, 2004; spatial relations: Majid, Bowerman, Kita, Haun & Levinson, 2004; shape: Roberson et al., 2002). The most compelling demonstration of such relationship comes from studies of categorical perception (CP) on color perception, which refers to the observation of faster or more accurate discrimination of two items (i.e., colors) between categories (e.g., comparing blue to green) than within a category (e.g., comparing two different shades of blue) (e.g., Liu, Li, Campos et al., 2010; Roberson, Davidoff, Davies & Shapiro, 2005; Pilling et al., 2003). Recently, this linguistic effect on color processing has been shown to be lateralized, that is, larger for stimuli presented in the right visual field (RVF) (projecting directly to the left hemisphere specialized for language) than in the left visual field (LVF) (Drivonikou, Kay, Regier et al., 2007; Gilbert, Regier, Kay & Ivry, 2006; Liu, Li, Campos et al., 2009). Such lateralization of the categorical effects was also found to disappear in the presence of a concurrent demand on verbal memory (Gilbert et al., 2006; Winawer, Witthoft, Frank, Wu, Wade & Boroditsky, 2007). One important question that remains unresolved is how language results in the categorical perception of color. The difference between between-category and within-category color discrimination could be possibly attributed to that (1) the linguistic © 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd
category facilitates the discrimination of between-category colors, (2) the linguistic category suppresses the discrimination of withincategory colors, or (3) the joint effect of enhancement and suppression of linguistic category. There has been evidence for each of these possibilities. For example, Thierry et al. (2009) found that relative to green colors an “enhanced” perceptual discrimination occurred in blue colors that are distinguished in Greek (ghalazio and ble for light and dark blue) but not in English. Recently, Mo et al. (2011) found that for between-category pairs, the visual mismatch negativity (vMMN) effect from the RVF deviants was significantly larger than that from the LVF deviants, but there was no significant difference between LVF and RVF for the within-category pair. These results suggest a facilitation effect. However, Liu et al. (2010) found evidence that categorical effects occurred for RVF more than for LVF. Specifically, withincategory color pairs elicited more negativity when presented in RVF than in LVF, but between-category color pairs remained comparable across the two visual fields. Their results suggest a suppression effect. Drivonikou et al. (2007) found that RVF targets were detected more quickly than LVF targets for crosscategory colors, but the pattern was reversed for within-category colors. Goldstone and Hendrickson (2009) showed that when an observer looked at colors that fell into two or more categories, differences among colors that fell into different categories were exaggerated, and differences among colors that fell into the same category were minimized. These results seem to support the existence of both linguistic suppression and enhancement effects. So far, few studies have addressed this issue directly. In addition, the linguistic effect on color perception in the above studies has been criticized to be an artifact open to other explanations. Liu et al. (2010) was to our knowledge the only study that focused on investigating the mechanism of linguistic effect on
2 A. Lu et al. color perception. Based on the results that within-category color pairs elicited more negativity when presented in RVF than in LVF, they argued that categorical perception effects may be more due to the difficulty of within-category discriminations than to the facilitation of between-category discriminations. If the same linguistic label shared by within-category colors make them similar and thus become more difficult to discriminate from each other, one should expect less negativity of within-category color discrimination in RVF than in LVF at particular stage of color processing. However, no such results were found in Liu et al. (2010). Moreover, Liu et al. (2010) also noted that their effect could be explained with a high-level conflict monitoring mechanism. When the color of the square and the surrounding frame came from the same category (i.e., within-category color condition), the congruent name would conflict with the incongruent physical properties. In other words, the greater ERP negativity found in within-category colors in RVF per se is not due to the language suppression effect on color perception making the colors difficult to discriminate, but only an effect of language-related processes that suppress the discrimination between two different shades of within-category colors (Liu et al., 2010). Therefore, the specific role language plays in color perception is still unclear. Another issue to be addressed is the processing stage in which the categorical perception effect occurs. Previous studies using tasks such as visual search, color oddball detection, same-different judgment or X-AB tasks (assumed to reflect perceptual sensitivity) showed that the color perception could occur at an early stage of processing (usually before 200 ms). For example, Holmes, Franklin, Clifford, and Davies (2009) compared English-speaking participants’ brain responses to same- versus cross-category color differences. ERPs showed shorter latencies for early components (P1-window: 80–120 ms and N1-window: 130–190) to betweencategory differences than for within-category differences, providing evidence for an early effect for categorical differences in color perception. In a follow-up study, Clifford, Holmes, Davies, and Franklin (2010) found a vMMN (100–250 ms) as well as a postperceptual effect (250–350 ms) greater for between-category than within-category change detection when the stimuli appeared in the lower visual field. The result was taken to be a clear piece of evidence for an automatic and pre-attentive categorical code for color. Mo et al. (2011) found that the category effect in the time window of 130–190 ms was lateralized to the right visual field, indicating a lateralized category effect on ERPs. However, Clifford, Franklin, € Holmes, Drivonikou, Ozgen, and Davies (2012) found that category effects only occurred during post-perceptual stages (350–600 cm) of newly trained colors. They argued that category effects can exist independent of early perceptual processes. They also claimed that early perceptual category effects could be acquired following category training sufficiently long or intensive. Thus, it is still unclear whether color perception can be independent of early perceptual processes and occur in post-perceptual processes. The present study was intended to investigate the two aforementioned issues: whether language plays a role in the postperceptual stage of CP and if so how. First, as stated, the comparison between within and between- category colors could not tell whether the linguistic effect on CP is enhancement or suppression. The lateralization of the category effect connects the effect directly to language (Mo et al., 2011). The comparison © 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd
Scand J Psychol (2014)
between LVF and RVF will shed light on the specific mechanism of the categorical effect. Second, the present study used ERP technique with high temporal resolution to discriminate perceptual and post-perceptual stage of color processing. Third, different from Clifford et al. (2012), we used color categories which had been acquired at early age and reinforced in daily experience to avoid the confounding effect of familiarity. Finally, we presented colors successively. In the simultaneous presentation paradigm, the comparison between colors is instant and primarily happens at the early perceptual stage. In the successive presentation paradigm, the comparison relies on the current color patch and the memory of the preceding one. Given that the stage in which CP occurs is affected by task demand and stimuli, we hypothesized that the involvement of memory would delay the categorical effect to the post-perceptual stage. As noted in Liu et al. (2010), the speeded response may confound the main effect of interest. Delayed responses were therefore adopted here, which would also reduce effects from motor responses on the ERP waveforms. Additionally, previous ERP studies suggest that the early ERP effects like vMMN appearing before 200 ms are good indexes of automatic and preattentive change detection (Mo et al., 2011; Thierry et al., 2009), and effects after 200 ms seem to reflect post-perceptual processing, or possibly extended time course of early effects. Thus, we mainly focused on the effects around 200 ms to examine the linguistic effect on color perception in perceptual and post-perceptual stages. We presented Chinese participants with four types of color pairs: zero-step-within-category color (0-WC; i.e., G2: G2 and B1: B1), one-step-within-category color (1-WC; i.e., G2: G1 and B1: B2), one-step-between-category color (1-BC; i.e., G2: B1 and B1: G2), and two-steps-between-category color (2-BC; i.e., G2: B2 and B1: G1) in a lateralized paradigm. If the categorical effect is independent of perceptual processes and influences the post-perceptual processes as proposed by Clifford et al. (2012), we would expect it to be only found in the time window after 200 ms but not earlier. If language suppression occurs in color perception, we would expect the ERP amplitude evoked by 1-WC color to be smaller for RVF than LVF presentation, and similar to the 0-WC condition for RVF presentation but different in the LVF. If language enhancement occurs, we would expect the 1-BC to show a larger effect in RVF than in LVF presentation, and a similar effect as 1-WC color in LVF presentation but a different effect in RVF presentation.
METHODS Materials and procedure Participants. Eighteen undergraduate students from South China Normal University (mean age = 20.1 years, SD = 2 years; 9 males) participated in the experiment. All were strongly right-handed native Mandarin Chinese speakers as assessed by the Edinburgh Handedness Inventory. All had normal or corrected-to-normal vision, and normal color vision as assessed by the City University color vision test (Fletcher, 1980) and Ishihara Color Test. None had any history of neurological impairment or psychoactive medication use. All provided informed consent and were paid for participation. Materials. The stimuli were displayed on a 17-inch cathode-ray tube monitor with a resolution of 1024 9 768 pixels and a refresh rate of 75 Hz. The screen was positioned 70 cm away from the viewer’s eyes.
Categorical perception of color 3
Scand J Psychol (2014) The four colors used in the current study were adopted from those used in Mo et al.’s (2011) study. They were labeled “G1 (green 1)”, “G2 (green 2)”, “B1 (blue 1)”, and “B2 (blue 2)” (see Fig. 1). The International Commission on Illumination (CIE) xyY values measured by a spectrometer were as follows: G1 = 0 .256, 0.374, 73.5; G2 = 0.242,0 .342, 72.5; B1 = 0.228, 0.308, 76.8; B2 = 0.215,0 .275, 62.9. The xyY values of the gray background were 0.321, 0.347, 34.70. The delta E separations of the stimuli were as follows: G1–G2, 13; G2–B1, 14.83; B1–B2, 17.18. Each color patch was 2 9 2 cm in size. There were four types of color conditions based on the hue step size from the prime color (G2 and B1), namely zero-step-within-category color (0-WC; i.e., G2: G2 and B1: B1), one-step-within-category color (1-WC; i.e., G2: G1 and B1: B2), one-step-between-category color (1-BC; i.e., G2: B1 and B1: G2), and two-steps-between-category color (2-BC; i.e., G2: B2 and B1: G1). Procedure. Participants were tested in a dimly lit, sound attenuated room, and seated in a comfortable chair. Following task instructions and 10 practice trials, they completed eight test blocks, each with 74 trials (i.e., 72 test trials with 2 filler trials). Each trial started with a fixation cross at screen center with a duration varying randomly from 400 to 600 ms in 100-ms steps. A prime color (G2 or B1) was presented for 1000 ms, about 4 cm either to the left or the right of the cross (within 3°–5°of visual angle) with the cross remaining onscreen, followed by another cross with a duration varying randomly from 400 to 600 ms with a step of 100 ms. Then a color (G1, G2, B1, or B2) appeared about 4 cm either to the left or the right of the cross for 1000 ms with the cross remaining visible, followed by a central red “X,” 0.94 cm in width and 0.91 cm in height. Participants determined whether or not the two successive colors were the same by using the index finger of each hand to press either “yes” key or “no” key designated by two keys on the left and right of the keyboard (counterbalanced across participants), whenever an “X” was displayed upon the offset of the second color patch. Participants were reminded to fixate on the cross to ensure the color was located in their left/right visual field. Note the task involved delayed responses. Therefore, only response accuracy was emphasized for the participants. The fixation for the next trial started 800 ms after response in the present trial. The prime color patch and the subsequent color patch were always presented on the same side to reduce eye movements. The first two trials of each test block were fillers. There was a one-minute break between blocks. After the ERP session, participants were asked to write down the name of each color stimulus. Over 96% participants named G1 and G2 as color green and B1 and B2 as color blue. Given the category boundaries strongly vary across observers (Lindsey & Brown, 2009), performance in this task may not reflect the actual individual boundaries (Witzel & Gegenfurtner, 2011).
amplifiers. Bipolar horizontal and vertical electro-oculograms were recorded simultaneously to monitor eye movements. The impedance of the electrodes was maintained below 5 kΩ throughout the recording session. The brain Vision Analyzer software package was used to analyze data. Eye movement artifacts were corrected using regression-based weighting coefficients. Epochs were from –200 to 800 ms time-locked to the onset of the second color patch in each trial, with artifact rejection threshold set at 30 lV. ERP amplitude was measured with respect to the average baseline voltage over the interval from –200 to 0 ms. Average waveforms were calculated off-line and separated by trial condition. Mean ERP amplitudes were computed for each participant in the time windows from 100 to 200 ms as well as from 200 to 350 ms. For each visual field, three-way repeated-measures ANOVA were conducted with three factors, trial type (0-WC, 1-WC, 1-BC, and 2-BC), laterality (left hemisphere, midline, and right hemisphere), and brain region (frontal region: F3, Fz, F4; central region: C3, Cz, C4; and parietal region: P3, Pz, P4).
RESULTS The behavioral results are presented in Fig. 2. The mean accuracies were above 90% in all conditions. The results indicate that participants were attentive to all stimuli and could clearly distinguish the two colors. The RT results showed no significant effect of trial type (Fs < 1). Figure 3 shows the grand-averaged ERP waveforms time-locked to the target color patch onset at representative electrodes. Figure 4 shows a summary of the results.
EEG recording and analysis EEGs were recorded from the scalp with a 64-channel Ag-AgCl electrode cap (10–20 system) with 1000 Hz sampling rate and a 0.05–250 Hz band-pass. The data were refiltered offline with a 0.05– 30 Hz band pass zero-phase shift digital filter (slope 48 db/Oct). All electrodes were re-referenced off-line to the mean of the two mastoids. EEG and EOG data were amplified with two 32-channel BrainAmp MR Plus
Fig. 1. Four color stimuli used in the current study (adopted from Mo et al.’s (2011) study). © 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd
Fig. 2. Mean latency (upper panel) and accuracy (lower panel) in all conditions. Error bars for standard error to the mean.
4 A. Lu et al.
Scand J Psychol (2014)
Fig. 3. Grand-averaged ERP waveforms time locked to target color onset at selected electrodes for all conditions.
Figure 5 shows the scalp topographic map for the 0-WC, 1-WC, 1-BC, and 2-BC conditions in LVF (upper) and RVF (lower) in the time window of 200–350 ms.
100–200 ms time window A 2 (visual field: LVF versus RVF) 9 4 (trial type: 0-WC, 1-WC, 1-BC, and 2-BC) within-subject ANOVA on mean © 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd
signal amplitudes in the time window of 100-200 ms was conducted. There was a significant main effect for visual field (F(1,17) = 5.19, p = 0.036), more positive-going in RVF (0.16) than in LVF (–0.25), which may reflect a stronger language-related response in the left hemisphere. The main effect of trial type was not significant (F(3,51) = 30, p = 0.83), nor was its interaction with visual field (F(3,51) = 1.16, p = 0.34).
Categorical perception of color 5
Scand J Psychol (2014)
Fig. 4. Mean amplitudes in all conditions. Error bars for standard error to the mean.
200–350 ms time window A 2 (visual field: LVF versus RVF) 9 4 (trial type: 0-WC, 1-WC, 1-BC, and 2-BC) within-subject ANOVA on mean signal amplitudes in the time window of 200–350 ms was conducted. There was no significant main effect for visual field (F < 1). However, there was a significant main effect of trial type (F(3,51) = 7.51, p < 0.001) as well as a significant interaction between these two variables (F(3,51) = 4.74, p = 0 .005). LVF analysis. The three-way ANOVA on mean signal amplitudes in the time window of 200–350 ms showed a main effect of brain region (F(2,34) = 7.01, p = 0.003) and an interaction effect between brain region and laterality (F(4,68) = 5.38, p = 0.001).
The amplitude became more positive from anterior to posterior (frontal: 0.79 lV, central: 2.38 lV, and parietal: 3.25 lV). There was a larger positivity in the right hemisphere compared with the left hemisphere over the central and parietal areas (central: 2.45 lV vs. 1.98 lV; parietal: 3.67 lV vs. 2.98 lV), but a reverse pattern in frontal area (0.48 lV vs. 0.93 lV). There was a main effect of trial type (F(3,51) = 4.89, p = 0.005). Compared with the 0-WC condition (2.86 lV), the amplitude was less positive in the 1-WC (1.7 lV, F(1,17) = 17.94, p = 0.001), 1-BC (2 lV, F(1,17) = 6.88, p < 0.02), and 2-BC conditions (2.01 lV, F(1,17) = 7.48, p < 0.015), but the latter three were comparable (Fs < 1). There was no significant interaction between trial type and the other two factors (Fs < 1). These results suggested that the 1-WC color was perceived as similar to 1-BC color and even to 2-BC color, but differently from 0-WC color when presented in left visual field. RVF analysis. ANOVA showed significant main effects for brain region (F(2,34) = 9.28, p = 0.001) and laterality (F(2,34) = 5.82, p = 0.007). Similar to the LVF, the amplitude became more positive from anterior to posterior (frontal: 0.78 lV, central: 2.43 lV, and parietal: 3.31 lV). Different from the LVF, there was a larger positivity in the left hemisphere compared with the right hemisphere over all the three regions (frontal: 0.93 lV vs. 0.3 lV; central: 2.67 lV vs. 1.8 lV; parietal: 3.7 lV vs. 2.95 lV). There was also a main effect of trial type (F(3,51) = 8.54, p < 0.001). Similar to LVF, compared with the 0-WC (2.72 lV), the amplitude was less positive in the 1-BC (1.64 lV, F(1,17) = 14.33, p = 0.001) and 2-BC conditions
(a)
(b)
Fig. 5. Scalp topographic maps displaying the topographic distributions of the mean amplitude in the time windows of 200–350 ms in the LVF (a) and the RVF (b). © 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd
6 A. Lu et al. (1.75 lV, F(1,17) = 9.14, p = 0.008). There was no significant difference between 0-WC and 1-WC (2.72 lV vs. 2.58 lV, F(1,17) = 0.19, p = 0.68), nor between 1-BC and 2-BC (1.64 lV vs. 1.75 lV, F(1,17) = 0.24, p = 0.63). The difference between 1-WC and 2-BC (2.58 lV vs.1.75 lV) and that between 1-WC and 1-BC (2.58 lV vs. 1.64 lV) was significant (F(1,17) = 18.82, p < 0.001; F(1,17) = 12.55, p = 0.002). This indicates that the 1-WC color was perceived as similar to 0-WC color, but different from 1-BC and 2-BC colors when presented in right visual field. There was also a significant interaction between trial type and laterality (F(6,102) = 3.12, p = 0.008). There were significant differences between 0-WC and 1-BC (left hemisphere: 2.92 vs. 1.95 lV, F(1,17) = 12.15, p = 0.003; midline: 3.04 vs. 1.86 lV, F(1,17) = 13.71, p = 0.002; right hemisphere: 2.2 vs. 1.12 lV, F(1,17) = 12.99, p = 0.002), between 0-WC and 2-BC (left hemisphere: 2.92 vs. 2.18 lV, F(1,17) = 5.97, p = 0.026; midline: 3.04 vs. 1.76 lV, F(1,17) = 12.84, p = 0.002; right hemisphere: 2.2 vs. 1.3 lV, F(1,17) = 6.34, p = 0.022), between 1-WC and 1-BC (left hemisphere: 2.67 vs. 1.95 lV, F(1,17) = 8.67, p = 0.009; midline: 2.97 vs. 1.86 lV, F(1,17) = 13.13, p = 0.002; right hemisphere: 2.11 vs. 1.12 lV, F(1,17) = 10.64, p = 0.005), and between 1-WC and 2-BC (left hemisphere: 2.67 vs. 2.18 lV, F(1,17) = 7.79, p = 0.013; midline: 2.97 vs. 1.76 lV, F(1,17) = 28.51, p < 0.001; right hemisphere: 2.11 vs. 1.3 lV, F(1,17) = 12.12, p = 0.003) in left hemisphere, midline, and right hemisphere. To further confirm the suppression effect in within-category colors, we conducted four paired-sample comparisons. The results showed significantly larger amplitude for 1-WC when presented in LVF than in RVF (1.7 vs. 2.58 lV, t(17) = –2.69, p < 0.02), suggesting that suppression occurred in within-category colors presented in RVF. However, the amplitude was comparable in two visual fields for 0-WC (2.86 vs. 2.72 lV, t(17) = 0.56, p > 0.1), 1-BC (2 vs. 1.64 lV, t(17) = 1.04, p > 0.1) and 2-BC (2.01 vs. 1.75 lV, t(17) = 0.99, p > 0.1), indicating that there was no enhancement occurring in between-category colors. If the effects were reliable, the same result pattern should occur in both categories (green and blue) separately. Thus, we conducted the analyses when G2 was the prime and when B1 was the prime. When G2 was the prime, a 2 (visual field: LVF versus RVF) 9 4 (trial type: 0-WC, 1-WC, 1-BC, and 2-BC) within-subject ANOVA showed that there was no effect for visual field (F < 1). However, there was an effect of trial type (F(3,51) = 5.37, p = 0.003) as well as a significant interaction between visual field and trial type (F(3,51) = 2.89, p = 0.044). In LVF, compared with the 0-WC condition (2.81 lV), the amplitude was less positive in the 1-WC (1.72 lV, F(1,17) = 8.69, p = 0.009), 1-BC (1.89 lV, F(1,17) = 4.60, p = 0.047), and 2-BC conditions (1.72 lV, F(1,17) = 4.80, p = 0.043), but the latter three were comparable (Fs < 1). In RVF, compared with the 0-WC (2.80 lV), the amplitude was less positive in the 1-BC (1.76 lV, F(1,17) = 9.47, p = 0.007), and 2-BC conditions (1.78 lV, F(1,17) = 7.57, p = 0.014). There was no significant difference between 0-WC and 1-WC conditions (2.80 lV vs. 2.76 lV, F(1,17) = 0.012, p = 0.92), nor between 1-BC and 2-BC conditions (1.76 lV vs. 1.78 lV, F(1,17) = 0.012, p = 0.91). The difference between 1-WC and 2-BC (2.76 lV vs.1.78 lV) and that between 1-WC and 1-BC (2.76 lV © 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd
Scand J Psychol (2014)
vs. 1.76 lV) was significant (F(1,17) = 6.45, p = 0.021; F(1,17) = 10.17, p = 0.005). We conducted four paired-sample comparisons. The results showed that there was significantly larger amplitude for 1-WC when presented in LVF than in RVF (1.72 vs. 2.76 lV, t(17) = -2.47, p = 0.024), suggesting that suppression occurred in within-category colors presented in RVF. However, the amplitude was comparable in two visual fields for 0-WC (2.81 vs. 2.80 lV, t(17) = 0.26, p > 0.1), 1-BC (1.89 vs. 1.76 lV, t(17) = 0.35, p > 0.1) and 2-BC (1.72 vs. 1.78 lV, t(17) = -0.18, p > 0.1), indicating that there was no enhancement occurring in between-category colors. For B1 as the prime, a 2 (visual field: LVF versus RVF) 9 4 (trial type: upward versus downward) within-subject ANOVA showed that there was no significant main effect for visual field (F < 1). However, there was a significant main effect of trial type (F(3,51) = 10.62, p < 0.001) and a significant interaction between visual field and trial type (F(3,51) = 4.59, p = 0.006). In LVF, compared with the 0-WC condition (2.86 lV), the amplitude was less positive in the 1-WC (1.71 lV, F(1,17) = 18.12, p = 0.001), 1-BC (1.95 lV, F(1,17) = 12.95, p = 0.002), and 2-BC conditions (2.1 lV, F(1,17) = 9.92, p = 0.006), but the latter three were comparable (Fs less than or near 1). In RVF, compared with the 0-WC (2.65 lV), the amplitude was less positive in the 1-BC (1.62 lV, F(1,17) = 9.38, p = 0.007), and 2-BC conditions (1.64 lV, F(1,17) = 7.86, p = 0.012). There was no significant difference between 0-WC and 1-WC conditions (2.65 lV vs. 2.53 lV, F(1,17) = 0.18, p = 0.68), nor between 1-BC and 2-BC conditions (1.62 lV vs. 1.64 lV, F(1,17) = 0.012, p = 0.92). The difference between 1-WC and 2-BC (2.67 lV vs.1.64 lV) and that between 1-WC and 1-BC (2.67 lV vs. 1.78 lV) was significant (F(1,17) = 13.29, p = 0.002; F(1,17) = 14.22, p = 0.002). We conducted four paired-sample comparisons. The results showed that there was significantly larger amplitude for 1-WC when presented in LVF than in RVF (1.71 vs. 2.53 lV, t(17) = –2.45, p = 0.026), suggesting that suppression occurred in within-category colors presented in RVF. However, the amplitude was comparable in two visual fields for 0-WC (2.86 vs. 2.65 lV, t(17) = 0.75, p > 0.1), 1-BC (1.95 vs. 1.64 lV, t(17) = 1.27, p > 0.1) and 2-BC (2.1 vs. 1.64 lV, t(17) = 1.47, p > 0.1), indicating that there was no enhancement occurring in between-category colors.
DISCUSSION Many recent studies have examined the linguistic effect on shaping and affecting the automatic, low-level, and unconscious color perception. The present study examined the specific role of language in post-perceptual processing by addressing whether language facilitates or inhibits the perception of color independent of perceptual processing. The results showed that the categorical effect only occurred in the time window of 200-350 ms, suggesting that the categorical effect can occur in post-perceptual processes independent of perceptual processes. Moreover, the 1-WC condition elicited highly similar ERP responses as that of the 0-WC condition when presented in the RVF, but significantly different from the 0-WC condition in LVF. And the 1-WC condition was more positive-going in RVF than in LVF. These results suggest that linguistic category suppressed the
Scand J Psychol (2014)
discrimination of within-category color when they were presented in RVF and projected to left hemisphere. However, the 1-BC vs. 0-WC difference was comparable in RVF and LVF, suggesting that language did not facilitate the discrimination of between-category color. This finding was also confirmed by the results in the 2-BC condition with a much larger hue difference than the 1-BC condition.1 However, there are alternative interpretations of our results. First, the results may be confounded by the unequal chromaticity spaces across four types of stimuli as the assumption that stimuli are separated by a constant distance in some presumably uniform metric color space is unwarranted (Brown, Lindsey & Guckes, 2011; Witzel & Gegenfurtner, 2011). For example, Munsell colors were regarded as being perceptually uniform, but may not necessarily be uniform for sensory color discrimination. Indeed, stimuli that are separated by constant distance in Munsell space are not generally equally discriminable from one another (e.g., Kuehni, 1999). Though colors used in the current study are roughly equated in CIE, yet color category effects here could not be accounted for by inequalities in CIE color metrics. First, our critical results were based on the comparisons of 1-WC and 1-BC between RVF and LVF. Since the lateralized category effect was an interaction between the category effect and the visual field, the fact that stimuli within each pair were not perfectly equidistant would not undermine the authenticity of this effect (Witzel & Gegenfurtner, 2011). Second, inequalities in color metrics between within-category and between-category color pairs predicted that there should be difference among the four types of color pairs. However, in neither LVF nor RVF, could the pattern of results be explained by the inequality of color metric among trial types. Therefore, it is unlikely that the categorical effect in the current study was confounded by the unequal color space among four types of stimuli. Additionally, Goldstone and Hendrickson (2009) proposed that the perceptual systems transform relatively linear sensory signals into relatively nonlinear internal representations. Specifically, this kind of nonlinear transformation is a step function by which increases to a sensory signal have no effect on perception until the signal reaches a certain threshold. At that threshold, perception changes qualitatively and abruptly. During the flat portion of the staircase function, different input signals have equivalent effects. In other words, the inequalities in color metrics among stimuli do not necessarily result in perception difference among them. This explains why the ERP amplitude of 1-BC and 2-BC overlapped with each other in two visual fields, though there was hue difference between them. Second, the lateralized categorical effect found in the present study is inconsistent with previous studies that failed to replicate the lateralized category effects with behavioral task (Brown et al., 2011; Witzel & Gegenfurtner, 2011). For example, Brown et al. (2011) found no categorical effect at the Green-Blue color boundary for either RVF or LVF presentation. Witzel and Gegenfurtner (2011) showed categorical effect in both visual fields, not restricted to RVF. It should be noted that though these two studies did not demonstrate a lateralized categorical effect, their results were in conflict with each other, which indicate that RT may not be a sensitive index to categorical effect. Moreover, as Witzel and Gegenfurtner (2011) suggested, the naming patterns for the © 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd
Categorical perception of color 7 stimulus colors were less clear-cut and complex, with the category membership of the colors varying considerably between individual observers. That is, task demand and stimuli may have an impact on the effect. Different from these studies, we used the ERP technique and different shades of blue/green colors, which may account for the inconsistency between the present study and the previous ones. Third, in the present study, the two colors around the boundary (B1 & G2) were presented more frequently than the other stimuli, because they were used as “primes”. The different frequency of presentation should not affect the results. The 0-WC condition involves G2-G2 and B1-B1 pairs, the 1-WC condition involves G2-G1 and B1-B2 pairs, the 1-BC condition involves G2-B1 and B1-G2 pairs; and 2-BC condition involves G2-B2 and B1-G1 pairs. We only found a neural response elicited by the second color patch. Given frequency was the same between 0-WC and 1-BC, and between 1-WC and 2-BC, one would expect the neural responses be the same between 0-WC and 1-BC, and between 1-WC and 2-BC. However, this is not what we found. In summary, frequency of stimulus presentation would not account for the results obtained here. Finally, though there was a lateralized categorical effect, the strongest activity was along the midline, possibly reflecting advanced integrative processing. For LVF presentation, there were no hemispheric differences, but for RVF presentation, there were hemispheric differences. Further, there was a larger positivity in the left hemisphere than the right hemisphere over all three regions. This may reflect language-related processing due to RVF presentation, as we expected. Our result supported the suppression account of the role of language in the categorical effect. Moreover, it is consistent with Clifford et al. (2012) in that a categorical effect only occurs in post-perceptual stage, independent of the early perceptual processes. But the categorical effect in the present study occurs earlier than that in Clifford et al. (2012). The present study makes several important contributions to the literature. First, it helps to clarify the nature of color category effects. On the one hand, similar to Clifford et al. (2012), the present study ruled out the possible influence of cone-excitation or inequalities in the color metric between within and between category color pairs in categorical effect (Brown et al., 2011; Witzel & Gegenfurtner, 2011). The stimuli were the same in LVF and RVF, and therefore the category effect can clearly be attributed to linguistic category. On the other hand, different from Mo et al.’s (2011) finding that a linguistic enhancement effect occurs in the early perceptual stage of between- category color processing in a perceptual sensitivity task, the present study showed a linguistic suppression effect in the post-perceptual stage of within- category color processing in a task involving memory. The results suggest that this kind of categorical effect can be attributed mainly to the linguistic suppression in within-category color but not enhancement in between-category color. Second, the present study confirmed the conclusion in Clifford et al. (2012) that the influence of color categories on the detection of color differences is not necessarily due to changes in early perceptual processes, and that categories can affect higherorder processes directly. However, different from Clifford et al. (2012), our categorical effect in post-perceptual process cannot
8 A. Lu et al. be confounded by the insufficiently long/intensive training as we used the color category acquired at early age and reinforced in daily life. Thus, the present study provides clearer evidence that the categorical effect can be independent of early perceptual processing. Third, the absence of perceptual difference in the present study may be explained by the effectiveness of categorical codes in the successive presentation paradigm. According to Roberson and Hanley (2009) and Roberson, Hanley & Pak (2010), participants could perform the same-different judgment task using either categorical or perceptual information about the target item. The categorical code contains less information but may be easier to retain than the perceptual code except when subject to verbal interference during retention interval. That is, categorical color has a priority over perceptual code. This priority can also explain Roberson and Davidoff’s (2000) finding that when verbal interference was interposed between presentation of the target and the test pair, CP was abolished both for colors and for facial expressions. Presumably, verbal interference disrupts participants’ ability to retain the category label, and performance for both within and cross-category targets had to rely on the retention of visual representation. In the present study, the presentation of prime color was 1 second long. That is, at the offset of the prime color, its categorical code and perceptual code must have been activated. Due to the priority of categorical code over perceptual code, participants made the same-different judgment based on the categorical codes of prime and target colors first. And they used perceptual information to make judgment unless the categorical code cannot distinguish prime and target colors. However, relative to the perceptual information, the categorical code of the target color was activated much slower. This explains the absence of categorical effect in early perceptual process when the task involves memory. Finally, though Witzel and Gegenfurtner (2013) showed that the discriminability was higher for between than within stimuli in their particular stimulus set, as the discriminability of our stimuli were higher for within than between stimuli (larger delta E for within stimuli), our results could not be explained by the difference of discriminability between conditions, nor by the color channels proposed by Lindsey et al. (2010). Moreover, the current study could partially contribute to the question of whether there are any reliable lateralization effects depending on discriminability. After excluding the linguistic effect (i.e., 1-WC condition), our results support the symmetry of visual fields in chromatic discrimination found in Danilova and Mollon (2009), subject to further research. In summary, with ERPs, the present study provides evidence for the suppression role of language in the post-perceptual stage of color processing. It also shows that the categorical effect in post-perceptual processes can be dissociated from the same effect in perceptual processes. This work was supported by the National Natural Science Foundation of China (No. 31200762), Guangdong NSF, China (S2011040001823), General research for Humanities and Social Sciences Project, Chinese Ministry of Education (11YJC740071), and by the National Training Fund for Basic Research (J1030729 and J1210024).
© 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd
Scand J Psychol (2014)
NOTE 1 The behavioral results showed no significant effects. This may be because the responses were delayed and/or the task was very easy.
REFERENCES Brown, A. M., Lindsey, D. T. & Guckes, K. M. (2011). Color names, color categories, and color-cued visual search: sometimes, color perception is not categorical. Journal of Vision, 11, 1–21. € Clifford, A., Franklin, A., Holmes, A., Drivonikou, V. G., Ozgen, E. & Davies, I. R. L. (2012). Neural correlates of acquired colour category effects. Brain and Cognition, 80, 126–143. Clifford, A., Holmes, A., Davies, I. R. & Franklin, A. (2010). Color categories affect pre-attentive color perception. Biological Psychology, 85, 275–282. Danilova, M. V. & Mollon, J. D. (2009). The symmetry of visual fields in chromatic discrimination. Brain and Cognition, 69, 39–46. Drivonikou, G. V., Kay, P., Regier, T., Ivry, R. B., Gilbert, A. L., Franklin, A. & Davies, I. R. L. (2007). Further evidence that Whorfian effects are stronger in the right visual field than the left. Proceedings of the National Academy of Sciences USA, 104, 1097–1102. Fletcher, R. (1980). The City University Colour Vision Test. Windsor: Keeler. Gilbert, A. L., Regier, T., Kay, P. & Ivry, R. B. (2006). Whorf hypothesis is supported in the right visual field but not the left. Proceedings of the National Academy of Sciences USA, 103, 489–494. Goldstone, R. L. & Hendrickson, A. T. (2009). Categorical perception. Interdisciplinary Reviews. Cognitive Science, 1, 65–78. Gordon, P. (2004). Numerical cognition without words. Evidence from Amazonia. Science, 306, 496–499. Holmes, A., Franklin, A., Clifford, A. & Davies, I. (2009). Neurophysiological evidence for categorical perception of color. Brain and Cognition, 69, 426–434. Kuehni, R. G. (1999). Hue scale adjustment derived from the Munsell system. Color Research & Application, 24, 33–37. Lindsey, D. T. & Brown, A. M. (2009). World Color Survey color naming reveals universal motifs and their within-language diversity. Proceedings of the National Academy of Sciences USA, 106, 19785–19790. Lindsey, D. T., Brown, A. M., Reijnen, E., Rich, A. N., Kuzmova, Y. I. & Wolfe, J. M. (2010). Color channels, not color appearance or color categories, guide visual search for desaturated color targets. Psychological Science, 21, 1208–1214. Liu, Q., Li, H., Campos, J. L., Teeter, C., Tao, W., Zhang, Q. L. & Sun, H.-J. (2010). Language suppression effects on the categorical perception of colour as evidenced through ERPs. Biological Psychology, 85, 45–52. Liu, Q., Li, H., Campos, J. L., Wang, Q., Zhang, Y., Qiu, J., Zhang, Q. & Sun, H.-J. (2009). The N2pc component in ERP and the lateralization effect of language on color perception. Neuroscience Letters, 454, 58–61. Lu, A., Hodges, B. H., Zhang, J. & Wang, X. (2012). A Whorfian speed bump? Effects of Chinese color names on recognition across hemispheres. Language Science, 34, 591–603. Majid, A., Bowerman, M., Kita, S., Haun, D. B. M. & Levinson, S. C. (2004). Can language restructure cognition? The case for space. Trends in Cognitive Science, 8, 108–114. Mo, L., Xu, G., Kay, P. & Tan, L. H. (2011). Electrophysiological evidence for the left-lateralized effect of language on preattentive categorical perception of color. Proceedings of the National Academy of Sciences USA, 108, 14026–14030. € Pilling, M., Wiggett, A., Ozgen, E. & Davies, I. R. L. (2003). Is color “categorical perception” really perceptual? Memory and Cognition, 31, 538–551. Roberson, D. & Davidoff, J. (2000). The categorical perception of colors and facial expressions: The effect of verbal interference. Memory and Cognition, 28, 977–986.
Scand J Psychol (2014) Roberson, D., Davidoff, J., Davies, I. R. L. & Shapiro, L. R. (2005). Color categories: Evidence for the cultural relativity hypothesis. Cognitive Psychology, 50, 378–411. Roberson, D., Davidoff, J. & Shapiro, L. (2002). Squaring the circle: The cultural relativity of good shape. Journal of Cognition and Culture, 2, 29–52. Roberson, D., Davies, I. & Davidoff, J. (2000). Color categories are not universal: Replications and new evidence from a Stone-age culture. Journal of Experimental Psychology: General, 129, 369–398. Roberson, D. & Hanley, J. R. (2010). Relatively speaking; an account of the relationship between language and thought in the color domain. In B. Malt, & P. Wolff (Eds.), Words and the world: How words capture human experience (pp. 183–198). New York: Oxford University Press. Roberson, D., Hanley, J. R. & Pak, H. (2009). Thresholds for color discrimination in English and Korean speakers. Cognition, 112, 482–487.
© 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd
Categorical perception of color 9 Roberson, D., Pak, H. S. & Hanley, J. R. (2008). Categorical perception of color in the left and right visual field is verbally mediated: Evidence from Korean. Cognition, 107, 752–762. Thierry, G., Athanasopoulos, P., Wiggett, A., Dering, B. & Kuipers, J. P. (2009). Unconscious effects of language-specific terminology on preattentive color perception. Proceedings of the National Academy of Sciences USA, 106, 4567–4570. Winawer, J., Witthoft, N., Frank, M. C., Wu, L., Wade, A. R. & Boroditsky, L. (2007). Russian blues reveal effects of language on colour discrimination. PNAS, 104(19), 7780–7785. Witzel, C. & Gegenfurtner, K. R. (2011). Is there a lateralized category effect for color? Journal of Vision, 11(16), 1–25. Witzel, C. & Gegenfurtner, K. R. (2013). Categorical sensitivity to color differences. Journal of vision, 13, 1–33. Received 23 May 2013, accepted 3 February 2014
DOI: 10.1111/sjop.12122
Cognition and Neurosciences Event-related potentials reveal linguistic suppression effect but not enhancement effect on categorical perception of color AITAO LU,1,2,3 LING YANG,1,2,3 YANPING YU,1,2,3 MEICHAO ZHANG,1,2,3 YULAN SHAO1,2,3 and HONGHONG ZHANG1,2,3 1
Center for Studies of Psychological Application & School of Psychology, South China Normal University, China Guangdong Key Laboratory of Mental Health and Cognitive Science, China 3 Guangdong Center of Mental Assistance and Contingency Technique for Emergency, China 2
Lu, A., Yang, L., Yu, Y., Zhang, M., Shao, Y. & Zhang, H. (2014). Event-related potentials reveal linguistic suppression effect but not enhancement effect on categorical perception of color. Scandinavian Journal of Psychology. The present study used the event-related potential technique to investigate the nature of linguistic effect on color perception. Four types of stimuli based on hue differences between a target color and a preceding color were used: zero hue step within-category color (0-WC); one hue step within-category color (1-WC); one hue step between-category color (1-BC); and two hue step between-category color (2-BC). The ERP results showed no significant effect of stimulus type in the 100-200 ms time window. However, in the 200–350 ms time window, ERP responses to 1-WC target color overlapped with that to 0-WC target color for right visual field (RVF) but not left visual field (LVF) presentation. For the 1-BC condition, ERP amplitudes were comparable in the two visual fields, both being significantly different from the 0-WC condition. The 2-BC condition showed the same pattern as the 1-BC condition. These results suggest that the categorical perception of color in RVF is due to linguistic suppression on within-category color discrimination but not between-category color enhancement, and that the effect is independent of early perceptual processes. Key words: Categorical perception, Color, Language suppression, ERP. Aitao Lu, School of Psychology, South China Normal University, Guangzhou, China. E-mail: [email protected]
INTRODUCTION The relationship between language and cognition has long been the subject of philosophical inquiry and psychological experimentation (e.g., Lu, Hodges, Zhang & Wang, 2012; Mo, Xu, Kay & € Tan, 2011; Pilling, Wiggett, Ozgen & Davies, 2003; Roberson, Davidoff & Shapiro, 2002; Roberson, Davies & Davidoff, 2000; Roberson, Pak & Hanley, 2008; Thierry, Athanasopoulos, Wiggett, Dering & Kuipers, 2009). There is no doubt that language and cognition are intimately linked in human mind, as demonstrated in many areas (e.g., number: Gordon, 2004; spatial relations: Majid, Bowerman, Kita, Haun & Levinson, 2004; shape: Roberson et al., 2002). The most compelling demonstration of such relationship comes from studies of categorical perception (CP) on color perception, which refers to the observation of faster or more accurate discrimination of two items (i.e., colors) between categories (e.g., comparing blue to green) than within a category (e.g., comparing two different shades of blue) (e.g., Liu, Li, Campos et al., 2010; Roberson, Davidoff, Davies & Shapiro, 2005; Pilling et al., 2003). Recently, this linguistic effect on color processing has been shown to be lateralized, that is, larger for stimuli presented in the right visual field (RVF) (projecting directly to the left hemisphere specialized for language) than in the left visual field (LVF) (Drivonikou, Kay, Regier et al., 2007; Gilbert, Regier, Kay & Ivry, 2006; Liu, Li, Campos et al., 2009). Such lateralization of the categorical effects was also found to disappear in the presence of a concurrent demand on verbal memory (Gilbert et al., 2006; Winawer, Witthoft, Frank, Wu, Wade & Boroditsky, 2007). One important question that remains unresolved is how language results in the categorical perception of color. The difference between between-category and within-category color discrimination could be possibly attributed to that (1) the linguistic © 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd
category facilitates the discrimination of between-category colors, (2) the linguistic category suppresses the discrimination of withincategory colors, or (3) the joint effect of enhancement and suppression of linguistic category. There has been evidence for each of these possibilities. For example, Thierry et al. (2009) found that relative to green colors an “enhanced” perceptual discrimination occurred in blue colors that are distinguished in Greek (ghalazio and ble for light and dark blue) but not in English. Recently, Mo et al. (2011) found that for between-category pairs, the visual mismatch negativity (vMMN) effect from the RVF deviants was significantly larger than that from the LVF deviants, but there was no significant difference between LVF and RVF for the within-category pair. These results suggest a facilitation effect. However, Liu et al. (2010) found evidence that categorical effects occurred for RVF more than for LVF. Specifically, withincategory color pairs elicited more negativity when presented in RVF than in LVF, but between-category color pairs remained comparable across the two visual fields. Their results suggest a suppression effect. Drivonikou et al. (2007) found that RVF targets were detected more quickly than LVF targets for crosscategory colors, but the pattern was reversed for within-category colors. Goldstone and Hendrickson (2009) showed that when an observer looked at colors that fell into two or more categories, differences among colors that fell into different categories were exaggerated, and differences among colors that fell into the same category were minimized. These results seem to support the existence of both linguistic suppression and enhancement effects. So far, few studies have addressed this issue directly. In addition, the linguistic effect on color perception in the above studies has been criticized to be an artifact open to other explanations. Liu et al. (2010) was to our knowledge the only study that focused on investigating the mechanism of linguistic effect on
2 A. Lu et al. color perception. Based on the results that within-category color pairs elicited more negativity when presented in RVF than in LVF, they argued that categorical perception effects may be more due to the difficulty of within-category discriminations than to the facilitation of between-category discriminations. If the same linguistic label shared by within-category colors make them similar and thus become more difficult to discriminate from each other, one should expect less negativity of within-category color discrimination in RVF than in LVF at particular stage of color processing. However, no such results were found in Liu et al. (2010). Moreover, Liu et al. (2010) also noted that their effect could be explained with a high-level conflict monitoring mechanism. When the color of the square and the surrounding frame came from the same category (i.e., within-category color condition), the congruent name would conflict with the incongruent physical properties. In other words, the greater ERP negativity found in within-category colors in RVF per se is not due to the language suppression effect on color perception making the colors difficult to discriminate, but only an effect of language-related processes that suppress the discrimination between two different shades of within-category colors (Liu et al., 2010). Therefore, the specific role language plays in color perception is still unclear. Another issue to be addressed is the processing stage in which the categorical perception effect occurs. Previous studies using tasks such as visual search, color oddball detection, same-different judgment or X-AB tasks (assumed to reflect perceptual sensitivity) showed that the color perception could occur at an early stage of processing (usually before 200 ms). For example, Holmes, Franklin, Clifford, and Davies (2009) compared English-speaking participants’ brain responses to same- versus cross-category color differences. ERPs showed shorter latencies for early components (P1-window: 80–120 ms and N1-window: 130–190) to betweencategory differences than for within-category differences, providing evidence for an early effect for categorical differences in color perception. In a follow-up study, Clifford, Holmes, Davies, and Franklin (2010) found a vMMN (100–250 ms) as well as a postperceptual effect (250–350 ms) greater for between-category than within-category change detection when the stimuli appeared in the lower visual field. The result was taken to be a clear piece of evidence for an automatic and pre-attentive categorical code for color. Mo et al. (2011) found that the category effect in the time window of 130–190 ms was lateralized to the right visual field, indicating a lateralized category effect on ERPs. However, Clifford, Franklin, € Holmes, Drivonikou, Ozgen, and Davies (2012) found that category effects only occurred during post-perceptual stages (350–600 cm) of newly trained colors. They argued that category effects can exist independent of early perceptual processes. They also claimed that early perceptual category effects could be acquired following category training sufficiently long or intensive. Thus, it is still unclear whether color perception can be independent of early perceptual processes and occur in post-perceptual processes. The present study was intended to investigate the two aforementioned issues: whether language plays a role in the postperceptual stage of CP and if so how. First, as stated, the comparison between within and between- category colors could not tell whether the linguistic effect on CP is enhancement or suppression. The lateralization of the category effect connects the effect directly to language (Mo et al., 2011). The comparison © 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd
Scand J Psychol (2014)
between LVF and RVF will shed light on the specific mechanism of the categorical effect. Second, the present study used ERP technique with high temporal resolution to discriminate perceptual and post-perceptual stage of color processing. Third, different from Clifford et al. (2012), we used color categories which had been acquired at early age and reinforced in daily experience to avoid the confounding effect of familiarity. Finally, we presented colors successively. In the simultaneous presentation paradigm, the comparison between colors is instant and primarily happens at the early perceptual stage. In the successive presentation paradigm, the comparison relies on the current color patch and the memory of the preceding one. Given that the stage in which CP occurs is affected by task demand and stimuli, we hypothesized that the involvement of memory would delay the categorical effect to the post-perceptual stage. As noted in Liu et al. (2010), the speeded response may confound the main effect of interest. Delayed responses were therefore adopted here, which would also reduce effects from motor responses on the ERP waveforms. Additionally, previous ERP studies suggest that the early ERP effects like vMMN appearing before 200 ms are good indexes of automatic and preattentive change detection (Mo et al., 2011; Thierry et al., 2009), and effects after 200 ms seem to reflect post-perceptual processing, or possibly extended time course of early effects. Thus, we mainly focused on the effects around 200 ms to examine the linguistic effect on color perception in perceptual and post-perceptual stages. We presented Chinese participants with four types of color pairs: zero-step-within-category color (0-WC; i.e., G2: G2 and B1: B1), one-step-within-category color (1-WC; i.e., G2: G1 and B1: B2), one-step-between-category color (1-BC; i.e., G2: B1 and B1: G2), and two-steps-between-category color (2-BC; i.e., G2: B2 and B1: G1) in a lateralized paradigm. If the categorical effect is independent of perceptual processes and influences the post-perceptual processes as proposed by Clifford et al. (2012), we would expect it to be only found in the time window after 200 ms but not earlier. If language suppression occurs in color perception, we would expect the ERP amplitude evoked by 1-WC color to be smaller for RVF than LVF presentation, and similar to the 0-WC condition for RVF presentation but different in the LVF. If language enhancement occurs, we would expect the 1-BC to show a larger effect in RVF than in LVF presentation, and a similar effect as 1-WC color in LVF presentation but a different effect in RVF presentation.
METHODS Materials and procedure Participants. Eighteen undergraduate students from South China Normal University (mean age = 20.1 years, SD = 2 years; 9 males) participated in the experiment. All were strongly right-handed native Mandarin Chinese speakers as assessed by the Edinburgh Handedness Inventory. All had normal or corrected-to-normal vision, and normal color vision as assessed by the City University color vision test (Fletcher, 1980) and Ishihara Color Test. None had any history of neurological impairment or psychoactive medication use. All provided informed consent and were paid for participation. Materials. The stimuli were displayed on a 17-inch cathode-ray tube monitor with a resolution of 1024 9 768 pixels and a refresh rate of 75 Hz. The screen was positioned 70 cm away from the viewer’s eyes.
Categorical perception of color 3
Scand J Psychol (2014) The four colors used in the current study were adopted from those used in Mo et al.’s (2011) study. They were labeled “G1 (green 1)”, “G2 (green 2)”, “B1 (blue 1)”, and “B2 (blue 2)” (see Fig. 1). The International Commission on Illumination (CIE) xyY values measured by a spectrometer were as follows: G1 = 0 .256, 0.374, 73.5; G2 = 0.242,0 .342, 72.5; B1 = 0.228, 0.308, 76.8; B2 = 0.215,0 .275, 62.9. The xyY values of the gray background were 0.321, 0.347, 34.70. The delta E separations of the stimuli were as follows: G1–G2, 13; G2–B1, 14.83; B1–B2, 17.18. Each color patch was 2 9 2 cm in size. There were four types of color conditions based on the hue step size from the prime color (G2 and B1), namely zero-step-within-category color (0-WC; i.e., G2: G2 and B1: B1), one-step-within-category color (1-WC; i.e., G2: G1 and B1: B2), one-step-between-category color (1-BC; i.e., G2: B1 and B1: G2), and two-steps-between-category color (2-BC; i.e., G2: B2 and B1: G1). Procedure. Participants were tested in a dimly lit, sound attenuated room, and seated in a comfortable chair. Following task instructions and 10 practice trials, they completed eight test blocks, each with 74 trials (i.e., 72 test trials with 2 filler trials). Each trial started with a fixation cross at screen center with a duration varying randomly from 400 to 600 ms in 100-ms steps. A prime color (G2 or B1) was presented for 1000 ms, about 4 cm either to the left or the right of the cross (within 3°–5°of visual angle) with the cross remaining onscreen, followed by another cross with a duration varying randomly from 400 to 600 ms with a step of 100 ms. Then a color (G1, G2, B1, or B2) appeared about 4 cm either to the left or the right of the cross for 1000 ms with the cross remaining visible, followed by a central red “X,” 0.94 cm in width and 0.91 cm in height. Participants determined whether or not the two successive colors were the same by using the index finger of each hand to press either “yes” key or “no” key designated by two keys on the left and right of the keyboard (counterbalanced across participants), whenever an “X” was displayed upon the offset of the second color patch. Participants were reminded to fixate on the cross to ensure the color was located in their left/right visual field. Note the task involved delayed responses. Therefore, only response accuracy was emphasized for the participants. The fixation for the next trial started 800 ms after response in the present trial. The prime color patch and the subsequent color patch were always presented on the same side to reduce eye movements. The first two trials of each test block were fillers. There was a one-minute break between blocks. After the ERP session, participants were asked to write down the name of each color stimulus. Over 96% participants named G1 and G2 as color green and B1 and B2 as color blue. Given the category boundaries strongly vary across observers (Lindsey & Brown, 2009), performance in this task may not reflect the actual individual boundaries (Witzel & Gegenfurtner, 2011).
amplifiers. Bipolar horizontal and vertical electro-oculograms were recorded simultaneously to monitor eye movements. The impedance of the electrodes was maintained below 5 kΩ throughout the recording session. The brain Vision Analyzer software package was used to analyze data. Eye movement artifacts were corrected using regression-based weighting coefficients. Epochs were from –200 to 800 ms time-locked to the onset of the second color patch in each trial, with artifact rejection threshold set at 30 lV. ERP amplitude was measured with respect to the average baseline voltage over the interval from –200 to 0 ms. Average waveforms were calculated off-line and separated by trial condition. Mean ERP amplitudes were computed for each participant in the time windows from 100 to 200 ms as well as from 200 to 350 ms. For each visual field, three-way repeated-measures ANOVA were conducted with three factors, trial type (0-WC, 1-WC, 1-BC, and 2-BC), laterality (left hemisphere, midline, and right hemisphere), and brain region (frontal region: F3, Fz, F4; central region: C3, Cz, C4; and parietal region: P3, Pz, P4).
RESULTS The behavioral results are presented in Fig. 2. The mean accuracies were above 90% in all conditions. The results indicate that participants were attentive to all stimuli and could clearly distinguish the two colors. The RT results showed no significant effect of trial type (Fs < 1). Figure 3 shows the grand-averaged ERP waveforms time-locked to the target color patch onset at representative electrodes. Figure 4 shows a summary of the results.
EEG recording and analysis EEGs were recorded from the scalp with a 64-channel Ag-AgCl electrode cap (10–20 system) with 1000 Hz sampling rate and a 0.05–250 Hz band-pass. The data were refiltered offline with a 0.05– 30 Hz band pass zero-phase shift digital filter (slope 48 db/Oct). All electrodes were re-referenced off-line to the mean of the two mastoids. EEG and EOG data were amplified with two 32-channel BrainAmp MR Plus
Fig. 1. Four color stimuli used in the current study (adopted from Mo et al.’s (2011) study). © 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd
Fig. 2. Mean latency (upper panel) and accuracy (lower panel) in all conditions. Error bars for standard error to the mean.
4 A. Lu et al.
Scand J Psychol (2014)
Fig. 3. Grand-averaged ERP waveforms time locked to target color onset at selected electrodes for all conditions.
Figure 5 shows the scalp topographic map for the 0-WC, 1-WC, 1-BC, and 2-BC conditions in LVF (upper) and RVF (lower) in the time window of 200–350 ms.
100–200 ms time window A 2 (visual field: LVF versus RVF) 9 4 (trial type: 0-WC, 1-WC, 1-BC, and 2-BC) within-subject ANOVA on mean © 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd
signal amplitudes in the time window of 100-200 ms was conducted. There was a significant main effect for visual field (F(1,17) = 5.19, p = 0.036), more positive-going in RVF (0.16) than in LVF (–0.25), which may reflect a stronger language-related response in the left hemisphere. The main effect of trial type was not significant (F(3,51) = 30, p = 0.83), nor was its interaction with visual field (F(3,51) = 1.16, p = 0.34).
Categorical perception of color 5
Scand J Psychol (2014)
Fig. 4. Mean amplitudes in all conditions. Error bars for standard error to the mean.
200–350 ms time window A 2 (visual field: LVF versus RVF) 9 4 (trial type: 0-WC, 1-WC, 1-BC, and 2-BC) within-subject ANOVA on mean signal amplitudes in the time window of 200–350 ms was conducted. There was no significant main effect for visual field (F < 1). However, there was a significant main effect of trial type (F(3,51) = 7.51, p < 0.001) as well as a significant interaction between these two variables (F(3,51) = 4.74, p = 0 .005). LVF analysis. The three-way ANOVA on mean signal amplitudes in the time window of 200–350 ms showed a main effect of brain region (F(2,34) = 7.01, p = 0.003) and an interaction effect between brain region and laterality (F(4,68) = 5.38, p = 0.001).
The amplitude became more positive from anterior to posterior (frontal: 0.79 lV, central: 2.38 lV, and parietal: 3.25 lV). There was a larger positivity in the right hemisphere compared with the left hemisphere over the central and parietal areas (central: 2.45 lV vs. 1.98 lV; parietal: 3.67 lV vs. 2.98 lV), but a reverse pattern in frontal area (0.48 lV vs. 0.93 lV). There was a main effect of trial type (F(3,51) = 4.89, p = 0.005). Compared with the 0-WC condition (2.86 lV), the amplitude was less positive in the 1-WC (1.7 lV, F(1,17) = 17.94, p = 0.001), 1-BC (2 lV, F(1,17) = 6.88, p < 0.02), and 2-BC conditions (2.01 lV, F(1,17) = 7.48, p < 0.015), but the latter three were comparable (Fs < 1). There was no significant interaction between trial type and the other two factors (Fs < 1). These results suggested that the 1-WC color was perceived as similar to 1-BC color and even to 2-BC color, but differently from 0-WC color when presented in left visual field. RVF analysis. ANOVA showed significant main effects for brain region (F(2,34) = 9.28, p = 0.001) and laterality (F(2,34) = 5.82, p = 0.007). Similar to the LVF, the amplitude became more positive from anterior to posterior (frontal: 0.78 lV, central: 2.43 lV, and parietal: 3.31 lV). Different from the LVF, there was a larger positivity in the left hemisphere compared with the right hemisphere over all the three regions (frontal: 0.93 lV vs. 0.3 lV; central: 2.67 lV vs. 1.8 lV; parietal: 3.7 lV vs. 2.95 lV). There was also a main effect of trial type (F(3,51) = 8.54, p < 0.001). Similar to LVF, compared with the 0-WC (2.72 lV), the amplitude was less positive in the 1-BC (1.64 lV, F(1,17) = 14.33, p = 0.001) and 2-BC conditions
(a)
(b)
Fig. 5. Scalp topographic maps displaying the topographic distributions of the mean amplitude in the time windows of 200–350 ms in the LVF (a) and the RVF (b). © 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd
6 A. Lu et al. (1.75 lV, F(1,17) = 9.14, p = 0.008). There was no significant difference between 0-WC and 1-WC (2.72 lV vs. 2.58 lV, F(1,17) = 0.19, p = 0.68), nor between 1-BC and 2-BC (1.64 lV vs. 1.75 lV, F(1,17) = 0.24, p = 0.63). The difference between 1-WC and 2-BC (2.58 lV vs.1.75 lV) and that between 1-WC and 1-BC (2.58 lV vs. 1.64 lV) was significant (F(1,17) = 18.82, p < 0.001; F(1,17) = 12.55, p = 0.002). This indicates that the 1-WC color was perceived as similar to 0-WC color, but different from 1-BC and 2-BC colors when presented in right visual field. There was also a significant interaction between trial type and laterality (F(6,102) = 3.12, p = 0.008). There were significant differences between 0-WC and 1-BC (left hemisphere: 2.92 vs. 1.95 lV, F(1,17) = 12.15, p = 0.003; midline: 3.04 vs. 1.86 lV, F(1,17) = 13.71, p = 0.002; right hemisphere: 2.2 vs. 1.12 lV, F(1,17) = 12.99, p = 0.002), between 0-WC and 2-BC (left hemisphere: 2.92 vs. 2.18 lV, F(1,17) = 5.97, p = 0.026; midline: 3.04 vs. 1.76 lV, F(1,17) = 12.84, p = 0.002; right hemisphere: 2.2 vs. 1.3 lV, F(1,17) = 6.34, p = 0.022), between 1-WC and 1-BC (left hemisphere: 2.67 vs. 1.95 lV, F(1,17) = 8.67, p = 0.009; midline: 2.97 vs. 1.86 lV, F(1,17) = 13.13, p = 0.002; right hemisphere: 2.11 vs. 1.12 lV, F(1,17) = 10.64, p = 0.005), and between 1-WC and 2-BC (left hemisphere: 2.67 vs. 2.18 lV, F(1,17) = 7.79, p = 0.013; midline: 2.97 vs. 1.76 lV, F(1,17) = 28.51, p < 0.001; right hemisphere: 2.11 vs. 1.3 lV, F(1,17) = 12.12, p = 0.003) in left hemisphere, midline, and right hemisphere. To further confirm the suppression effect in within-category colors, we conducted four paired-sample comparisons. The results showed significantly larger amplitude for 1-WC when presented in LVF than in RVF (1.7 vs. 2.58 lV, t(17) = –2.69, p < 0.02), suggesting that suppression occurred in within-category colors presented in RVF. However, the amplitude was comparable in two visual fields for 0-WC (2.86 vs. 2.72 lV, t(17) = 0.56, p > 0.1), 1-BC (2 vs. 1.64 lV, t(17) = 1.04, p > 0.1) and 2-BC (2.01 vs. 1.75 lV, t(17) = 0.99, p > 0.1), indicating that there was no enhancement occurring in between-category colors. If the effects were reliable, the same result pattern should occur in both categories (green and blue) separately. Thus, we conducted the analyses when G2 was the prime and when B1 was the prime. When G2 was the prime, a 2 (visual field: LVF versus RVF) 9 4 (trial type: 0-WC, 1-WC, 1-BC, and 2-BC) within-subject ANOVA showed that there was no effect for visual field (F < 1). However, there was an effect of trial type (F(3,51) = 5.37, p = 0.003) as well as a significant interaction between visual field and trial type (F(3,51) = 2.89, p = 0.044). In LVF, compared with the 0-WC condition (2.81 lV), the amplitude was less positive in the 1-WC (1.72 lV, F(1,17) = 8.69, p = 0.009), 1-BC (1.89 lV, F(1,17) = 4.60, p = 0.047), and 2-BC conditions (1.72 lV, F(1,17) = 4.80, p = 0.043), but the latter three were comparable (Fs < 1). In RVF, compared with the 0-WC (2.80 lV), the amplitude was less positive in the 1-BC (1.76 lV, F(1,17) = 9.47, p = 0.007), and 2-BC conditions (1.78 lV, F(1,17) = 7.57, p = 0.014). There was no significant difference between 0-WC and 1-WC conditions (2.80 lV vs. 2.76 lV, F(1,17) = 0.012, p = 0.92), nor between 1-BC and 2-BC conditions (1.76 lV vs. 1.78 lV, F(1,17) = 0.012, p = 0.91). The difference between 1-WC and 2-BC (2.76 lV vs.1.78 lV) and that between 1-WC and 1-BC (2.76 lV © 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd
Scand J Psychol (2014)
vs. 1.76 lV) was significant (F(1,17) = 6.45, p = 0.021; F(1,17) = 10.17, p = 0.005). We conducted four paired-sample comparisons. The results showed that there was significantly larger amplitude for 1-WC when presented in LVF than in RVF (1.72 vs. 2.76 lV, t(17) = -2.47, p = 0.024), suggesting that suppression occurred in within-category colors presented in RVF. However, the amplitude was comparable in two visual fields for 0-WC (2.81 vs. 2.80 lV, t(17) = 0.26, p > 0.1), 1-BC (1.89 vs. 1.76 lV, t(17) = 0.35, p > 0.1) and 2-BC (1.72 vs. 1.78 lV, t(17) = -0.18, p > 0.1), indicating that there was no enhancement occurring in between-category colors. For B1 as the prime, a 2 (visual field: LVF versus RVF) 9 4 (trial type: upward versus downward) within-subject ANOVA showed that there was no significant main effect for visual field (F < 1). However, there was a significant main effect of trial type (F(3,51) = 10.62, p < 0.001) and a significant interaction between visual field and trial type (F(3,51) = 4.59, p = 0.006). In LVF, compared with the 0-WC condition (2.86 lV), the amplitude was less positive in the 1-WC (1.71 lV, F(1,17) = 18.12, p = 0.001), 1-BC (1.95 lV, F(1,17) = 12.95, p = 0.002), and 2-BC conditions (2.1 lV, F(1,17) = 9.92, p = 0.006), but the latter three were comparable (Fs less than or near 1). In RVF, compared with the 0-WC (2.65 lV), the amplitude was less positive in the 1-BC (1.62 lV, F(1,17) = 9.38, p = 0.007), and 2-BC conditions (1.64 lV, F(1,17) = 7.86, p = 0.012). There was no significant difference between 0-WC and 1-WC conditions (2.65 lV vs. 2.53 lV, F(1,17) = 0.18, p = 0.68), nor between 1-BC and 2-BC conditions (1.62 lV vs. 1.64 lV, F(1,17) = 0.012, p = 0.92). The difference between 1-WC and 2-BC (2.67 lV vs.1.64 lV) and that between 1-WC and 1-BC (2.67 lV vs. 1.78 lV) was significant (F(1,17) = 13.29, p = 0.002; F(1,17) = 14.22, p = 0.002). We conducted four paired-sample comparisons. The results showed that there was significantly larger amplitude for 1-WC when presented in LVF than in RVF (1.71 vs. 2.53 lV, t(17) = –2.45, p = 0.026), suggesting that suppression occurred in within-category colors presented in RVF. However, the amplitude was comparable in two visual fields for 0-WC (2.86 vs. 2.65 lV, t(17) = 0.75, p > 0.1), 1-BC (1.95 vs. 1.64 lV, t(17) = 1.27, p > 0.1) and 2-BC (2.1 vs. 1.64 lV, t(17) = 1.47, p > 0.1), indicating that there was no enhancement occurring in between-category colors.
DISCUSSION Many recent studies have examined the linguistic effect on shaping and affecting the automatic, low-level, and unconscious color perception. The present study examined the specific role of language in post-perceptual processing by addressing whether language facilitates or inhibits the perception of color independent of perceptual processing. The results showed that the categorical effect only occurred in the time window of 200-350 ms, suggesting that the categorical effect can occur in post-perceptual processes independent of perceptual processes. Moreover, the 1-WC condition elicited highly similar ERP responses as that of the 0-WC condition when presented in the RVF, but significantly different from the 0-WC condition in LVF. And the 1-WC condition was more positive-going in RVF than in LVF. These results suggest that linguistic category suppressed the
Scand J Psychol (2014)
discrimination of within-category color when they were presented in RVF and projected to left hemisphere. However, the 1-BC vs. 0-WC difference was comparable in RVF and LVF, suggesting that language did not facilitate the discrimination of between-category color. This finding was also confirmed by the results in the 2-BC condition with a much larger hue difference than the 1-BC condition.1 However, there are alternative interpretations of our results. First, the results may be confounded by the unequal chromaticity spaces across four types of stimuli as the assumption that stimuli are separated by a constant distance in some presumably uniform metric color space is unwarranted (Brown, Lindsey & Guckes, 2011; Witzel & Gegenfurtner, 2011). For example, Munsell colors were regarded as being perceptually uniform, but may not necessarily be uniform for sensory color discrimination. Indeed, stimuli that are separated by constant distance in Munsell space are not generally equally discriminable from one another (e.g., Kuehni, 1999). Though colors used in the current study are roughly equated in CIE, yet color category effects here could not be accounted for by inequalities in CIE color metrics. First, our critical results were based on the comparisons of 1-WC and 1-BC between RVF and LVF. Since the lateralized category effect was an interaction between the category effect and the visual field, the fact that stimuli within each pair were not perfectly equidistant would not undermine the authenticity of this effect (Witzel & Gegenfurtner, 2011). Second, inequalities in color metrics between within-category and between-category color pairs predicted that there should be difference among the four types of color pairs. However, in neither LVF nor RVF, could the pattern of results be explained by the inequality of color metric among trial types. Therefore, it is unlikely that the categorical effect in the current study was confounded by the unequal color space among four types of stimuli. Additionally, Goldstone and Hendrickson (2009) proposed that the perceptual systems transform relatively linear sensory signals into relatively nonlinear internal representations. Specifically, this kind of nonlinear transformation is a step function by which increases to a sensory signal have no effect on perception until the signal reaches a certain threshold. At that threshold, perception changes qualitatively and abruptly. During the flat portion of the staircase function, different input signals have equivalent effects. In other words, the inequalities in color metrics among stimuli do not necessarily result in perception difference among them. This explains why the ERP amplitude of 1-BC and 2-BC overlapped with each other in two visual fields, though there was hue difference between them. Second, the lateralized categorical effect found in the present study is inconsistent with previous studies that failed to replicate the lateralized category effects with behavioral task (Brown et al., 2011; Witzel & Gegenfurtner, 2011). For example, Brown et al. (2011) found no categorical effect at the Green-Blue color boundary for either RVF or LVF presentation. Witzel and Gegenfurtner (2011) showed categorical effect in both visual fields, not restricted to RVF. It should be noted that though these two studies did not demonstrate a lateralized categorical effect, their results were in conflict with each other, which indicate that RT may not be a sensitive index to categorical effect. Moreover, as Witzel and Gegenfurtner (2011) suggested, the naming patterns for the © 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd
Categorical perception of color 7 stimulus colors were less clear-cut and complex, with the category membership of the colors varying considerably between individual observers. That is, task demand and stimuli may have an impact on the effect. Different from these studies, we used the ERP technique and different shades of blue/green colors, which may account for the inconsistency between the present study and the previous ones. Third, in the present study, the two colors around the boundary (B1 & G2) were presented more frequently than the other stimuli, because they were used as “primes”. The different frequency of presentation should not affect the results. The 0-WC condition involves G2-G2 and B1-B1 pairs, the 1-WC condition involves G2-G1 and B1-B2 pairs, the 1-BC condition involves G2-B1 and B1-G2 pairs; and 2-BC condition involves G2-B2 and B1-G1 pairs. We only found a neural response elicited by the second color patch. Given frequency was the same between 0-WC and 1-BC, and between 1-WC and 2-BC, one would expect the neural responses be the same between 0-WC and 1-BC, and between 1-WC and 2-BC. However, this is not what we found. In summary, frequency of stimulus presentation would not account for the results obtained here. Finally, though there was a lateralized categorical effect, the strongest activity was along the midline, possibly reflecting advanced integrative processing. For LVF presentation, there were no hemispheric differences, but for RVF presentation, there were hemispheric differences. Further, there was a larger positivity in the left hemisphere than the right hemisphere over all three regions. This may reflect language-related processing due to RVF presentation, as we expected. Our result supported the suppression account of the role of language in the categorical effect. Moreover, it is consistent with Clifford et al. (2012) in that a categorical effect only occurs in post-perceptual stage, independent of the early perceptual processes. But the categorical effect in the present study occurs earlier than that in Clifford et al. (2012). The present study makes several important contributions to the literature. First, it helps to clarify the nature of color category effects. On the one hand, similar to Clifford et al. (2012), the present study ruled out the possible influence of cone-excitation or inequalities in the color metric between within and between category color pairs in categorical effect (Brown et al., 2011; Witzel & Gegenfurtner, 2011). The stimuli were the same in LVF and RVF, and therefore the category effect can clearly be attributed to linguistic category. On the other hand, different from Mo et al.’s (2011) finding that a linguistic enhancement effect occurs in the early perceptual stage of between- category color processing in a perceptual sensitivity task, the present study showed a linguistic suppression effect in the post-perceptual stage of within- category color processing in a task involving memory. The results suggest that this kind of categorical effect can be attributed mainly to the linguistic suppression in within-category color but not enhancement in between-category color. Second, the present study confirmed the conclusion in Clifford et al. (2012) that the influence of color categories on the detection of color differences is not necessarily due to changes in early perceptual processes, and that categories can affect higherorder processes directly. However, different from Clifford et al. (2012), our categorical effect in post-perceptual process cannot
8 A. Lu et al. be confounded by the insufficiently long/intensive training as we used the color category acquired at early age and reinforced in daily life. Thus, the present study provides clearer evidence that the categorical effect can be independent of early perceptual processing. Third, the absence of perceptual difference in the present study may be explained by the effectiveness of categorical codes in the successive presentation paradigm. According to Roberson and Hanley (2009) and Roberson, Hanley & Pak (2010), participants could perform the same-different judgment task using either categorical or perceptual information about the target item. The categorical code contains less information but may be easier to retain than the perceptual code except when subject to verbal interference during retention interval. That is, categorical color has a priority over perceptual code. This priority can also explain Roberson and Davidoff’s (2000) finding that when verbal interference was interposed between presentation of the target and the test pair, CP was abolished both for colors and for facial expressions. Presumably, verbal interference disrupts participants’ ability to retain the category label, and performance for both within and cross-category targets had to rely on the retention of visual representation. In the present study, the presentation of prime color was 1 second long. That is, at the offset of the prime color, its categorical code and perceptual code must have been activated. Due to the priority of categorical code over perceptual code, participants made the same-different judgment based on the categorical codes of prime and target colors first. And they used perceptual information to make judgment unless the categorical code cannot distinguish prime and target colors. However, relative to the perceptual information, the categorical code of the target color was activated much slower. This explains the absence of categorical effect in early perceptual process when the task involves memory. Finally, though Witzel and Gegenfurtner (2013) showed that the discriminability was higher for between than within stimuli in their particular stimulus set, as the discriminability of our stimuli were higher for within than between stimuli (larger delta E for within stimuli), our results could not be explained by the difference of discriminability between conditions, nor by the color channels proposed by Lindsey et al. (2010). Moreover, the current study could partially contribute to the question of whether there are any reliable lateralization effects depending on discriminability. After excluding the linguistic effect (i.e., 1-WC condition), our results support the symmetry of visual fields in chromatic discrimination found in Danilova and Mollon (2009), subject to further research. In summary, with ERPs, the present study provides evidence for the suppression role of language in the post-perceptual stage of color processing. It also shows that the categorical effect in post-perceptual processes can be dissociated from the same effect in perceptual processes. This work was supported by the National Natural Science Foundation of China (No. 31200762), Guangdong NSF, China (S2011040001823), General research for Humanities and Social Sciences Project, Chinese Ministry of Education (11YJC740071), and by the National Training Fund for Basic Research (J1030729 and J1210024).
© 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd
Scand J Psychol (2014)
NOTE 1 The behavioral results showed no significant effects. This may be because the responses were delayed and/or the task was very easy.
REFERENCES Brown, A. M., Lindsey, D. T. & Guckes, K. M. (2011). Color names, color categories, and color-cued visual search: sometimes, color perception is not categorical. Journal of Vision, 11, 1–21. € Clifford, A., Franklin, A., Holmes, A., Drivonikou, V. G., Ozgen, E. & Davies, I. R. L. (2012). Neural correlates of acquired colour category effects. Brain and Cognition, 80, 126–143. Clifford, A., Holmes, A., Davies, I. R. & Franklin, A. (2010). Color categories affect pre-attentive color perception. Biological Psychology, 85, 275–282. Danilova, M. V. & Mollon, J. D. (2009). The symmetry of visual fields in chromatic discrimination. Brain and Cognition, 69, 39–46. Drivonikou, G. V., Kay, P., Regier, T., Ivry, R. B., Gilbert, A. L., Franklin, A. & Davies, I. R. L. (2007). Further evidence that Whorfian effects are stronger in the right visual field than the left. Proceedings of the National Academy of Sciences USA, 104, 1097–1102. Fletcher, R. (1980). The City University Colour Vision Test. Windsor: Keeler. Gilbert, A. L., Regier, T., Kay, P. & Ivry, R. B. (2006). Whorf hypothesis is supported in the right visual field but not the left. Proceedings of the National Academy of Sciences USA, 103, 489–494. Goldstone, R. L. & Hendrickson, A. T. (2009). Categorical perception. Interdisciplinary Reviews. Cognitive Science, 1, 65–78. Gordon, P. (2004). Numerical cognition without words. Evidence from Amazonia. Science, 306, 496–499. Holmes, A., Franklin, A., Clifford, A. & Davies, I. (2009). Neurophysiological evidence for categorical perception of color. Brain and Cognition, 69, 426–434. Kuehni, R. G. (1999). Hue scale adjustment derived from the Munsell system. Color Research & Application, 24, 33–37. Lindsey, D. T. & Brown, A. M. (2009). World Color Survey color naming reveals universal motifs and their within-language diversity. Proceedings of the National Academy of Sciences USA, 106, 19785–19790. Lindsey, D. T., Brown, A. M., Reijnen, E., Rich, A. N., Kuzmova, Y. I. & Wolfe, J. M. (2010). Color channels, not color appearance or color categories, guide visual search for desaturated color targets. Psychological Science, 21, 1208–1214. Liu, Q., Li, H., Campos, J. L., Teeter, C., Tao, W., Zhang, Q. L. & Sun, H.-J. (2010). Language suppression effects on the categorical perception of colour as evidenced through ERPs. Biological Psychology, 85, 45–52. Liu, Q., Li, H., Campos, J. L., Wang, Q., Zhang, Y., Qiu, J., Zhang, Q. & Sun, H.-J. (2009). The N2pc component in ERP and the lateralization effect of language on color perception. Neuroscience Letters, 454, 58–61. Lu, A., Hodges, B. H., Zhang, J. & Wang, X. (2012). A Whorfian speed bump? Effects of Chinese color names on recognition across hemispheres. Language Science, 34, 591–603. Majid, A., Bowerman, M., Kita, S., Haun, D. B. M. & Levinson, S. C. (2004). Can language restructure cognition? The case for space. Trends in Cognitive Science, 8, 108–114. Mo, L., Xu, G., Kay, P. & Tan, L. H. (2011). Electrophysiological evidence for the left-lateralized effect of language on preattentive categorical perception of color. Proceedings of the National Academy of Sciences USA, 108, 14026–14030. € Pilling, M., Wiggett, A., Ozgen, E. & Davies, I. R. L. (2003). Is color “categorical perception” really perceptual? Memory and Cognition, 31, 538–551. Roberson, D. & Davidoff, J. (2000). The categorical perception of colors and facial expressions: The effect of verbal interference. Memory and Cognition, 28, 977–986.
Scand J Psychol (2014) Roberson, D., Davidoff, J., Davies, I. R. L. & Shapiro, L. R. (2005). Color categories: Evidence for the cultural relativity hypothesis. Cognitive Psychology, 50, 378–411. Roberson, D., Davidoff, J. & Shapiro, L. (2002). Squaring the circle: The cultural relativity of good shape. Journal of Cognition and Culture, 2, 29–52. Roberson, D., Davies, I. & Davidoff, J. (2000). Color categories are not universal: Replications and new evidence from a Stone-age culture. Journal of Experimental Psychology: General, 129, 369–398. Roberson, D. & Hanley, J. R. (2010). Relatively speaking; an account of the relationship between language and thought in the color domain. In B. Malt, & P. Wolff (Eds.), Words and the world: How words capture human experience (pp. 183–198). New York: Oxford University Press. Roberson, D., Hanley, J. R. & Pak, H. (2009). Thresholds for color discrimination in English and Korean speakers. Cognition, 112, 482–487.
© 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd
Categorical perception of color 9 Roberson, D., Pak, H. S. & Hanley, J. R. (2008). Categorical perception of color in the left and right visual field is verbally mediated: Evidence from Korean. Cognition, 107, 752–762. Thierry, G., Athanasopoulos, P., Wiggett, A., Dering, B. & Kuipers, J. P. (2009). Unconscious effects of language-specific terminology on preattentive color perception. Proceedings of the National Academy of Sciences USA, 106, 4567–4570. Winawer, J., Witthoft, N., Frank, M. C., Wu, L., Wade, A. R. & Boroditsky, L. (2007). Russian blues reveal effects of language on colour discrimination. PNAS, 104(19), 7780–7785. Witzel, C. & Gegenfurtner, K. R. (2011). Is there a lateralized category effect for color? Journal of Vision, 11(16), 1–25. Witzel, C. & Gegenfurtner, K. R. (2013). Categorical sensitivity to color differences. Journal of vision, 13, 1–33. Received 23 May 2013, accepted 3 February 2014
