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The clip below from PBS demonstrates the McGurk Effect: When you hear a sound (like “ba”) that conflicts with how you’re seeing someone “produce” it (like “ga”), your mind tries to reconcile them by making you think you’re hearing something more consistent with your visual input (like “da”).
If you want to prove to yourself that both versions in the video above are the same sound, try rewatching the first part of the video with your eyes closed. Sure enough, you’ll hear “ba ba ba” instead of “da da da.”
For another example of the McGurk Effect, check out this clip from the BBC, which shows how “ba ba ba” can sound like “fa fa fa” when accompanied by the appropriate visuals, and includes a brief interview with the psychologist Lawrence Rosenblum, who’s been studying the McGurk Effect for decades.
The McGurk Effect is surprisingly robust: According to this list from Rosenblum, it persists with babies as young as 4-5 months, with speakers of every language that’s been tested, when the audio and video are from people of different genders, when viewers don’t realize they’re looking at a face, when viewers touch a face instead of looking at it, and when the audio and video aren’t precisely synched. It does work better with certain consonant pairs than others, and less well with vowels or non-speech sounds, such as plucking versus bowing sounds on a cello. But it even happens when the viewer knows perfectly well to be expecting the McGurk Effect, such as Rosenblum himself!
(Image: © Ollyy/Shutterstock)
It’s pretty easy to spot a badly dubbed foreign film: The sounds that you hear coming out of the actors’ mouths don’t seem to match up with the movements of their lips that you see.
In other words, even when our vision and hearing are being stimulated at the same time during the film, our brains do a really good job of picking up on which lip movements go with which speech sounds.
But the brain can also be fooled. In an intriguing illusion known as the McGurk effect, watching the movements of a person’s lips can trick the brain into hearing the wrong sound. [ 10 Things You Didn’t Know About the Brain]
The McGurk effect occurs when there is a conflict between visual speech, meaning the movements of someone’s mouth and lips, and auditory speech, which are the sounds a person hears. And it can result in the perception of an entirely different message.
Now, in a new study, neuroscientists at the Baylor College of Medicine in Houston attempted to offer a quantitative explanation for why the McGurk effect occurs. They developed a computer model that was able to accurately predict when the McGurk effect should or should not occur in people, according to the findings, published (Feb. 16) in the journal PLOS Computational Biology. (Here is one demonstration, and another; neither of these examples were the actual video used in the study.)
In the demonstration of the McGurk effect used in the study, the participant is asked to keep his or her eyes closed while listening to a video that shows a person making the sounds “ba ba ba.” Then that individual is asked to open their eyes and watch the mouth of the person in the video closely, but with the sound off. Now, the visuals look like the person is saying “ga ga ga.” In the final step of the experiment, the exact same video is replayed, but this time the sound is on, and the participant is asked to keep his or her eyes open. People who are sensitive to the McGurk effect will report hearing “da da da” – a sound that doesn’t match up with either the auditory or visual cues previously seen.
That’s because the brain is attempting to resolve what it thinks it’s hearing with a sound closer to what it visually sees. If the person closes their eyes again, and the video’s sound is replayed, he or she will once again hear the original sound of “ba ba ba.”
The effect was first described in an experiment done in 1976 by psychologists Harry McGurk and John MacDonald, which showed that visual information provided by mouth movements can influence and override what a person thinks he or she is hearing.
Predicting an illusion
The McGurk effect is a powerful, multisensory illusion, said study co-author John Magnotti, a postdoctoral fellow in the department of neurosurgery at Baylor. “The brain is taking auditory speech and visual speech and putting them together to form something new,” he said. [ 6 Foods That Are Good For Your Brain]
When people are having a face-to-face conversation, the brain is engaged in complicated activity as it tries to decide how to put lip movements together with the speech sounds that are heard, Magnotti said.
In the study, the researchers tried to understand why the brain was better able to put some syllables together to interpret the sound heard correctly but not others, Magnotti said.
To do this, their model relied on an idea known as causal inference, or a process in which a person’s brain decides whether the auditory and visual speech sounds were produced by the same source. What this means is that the sounds come from one person talking, or from multiple speakers, so you are hearing one person’s voice, but looking at another person who is also talking, at the same time.
Other researchers have developed models to help predict when the McGurk effect may occur, but this new study is the first one to include causal inference in its calculation, Magnotti told Live Science. Factoring in causal inference may have improved the new model’s accuracy, compared with previous prediction models of the illusion.
To test the accuracy of their prediction model, the researchers recruited 60 people and asked them to listen to pairs of auditory and visual speech from a single speaker. Then the participants were asked to decide if they thought they heard the sound “ba,” “da” or “ga.”
Their results showed that the model they developed could reliably predict when the majority of participants involved in the experiment would experience the McGurk effect. But as expected from their calculation, there were also some people who were not susceptible to it, Magnotti said. [ Eye Tricks: Gallery of Visual Illusions]
Interestingly, Magnotti said that when this same test has been done with students in China rather than people in the United States, the McGurk effect has been shown to work in other languages.
Magnotti said that he thinks the computer models developed for this study may also have some practical uses. For example, the model could be helpful to companies that build computers that assist in speech recognition, such as a product like Google Home or Amazon Echo, he said.
If these smart speakers had cameras on them, they could integrate people’s lip movement into what a person was saying to increase the accuracy of their speech-recognition systems, he said.
The model may also help children with cochlear implants, by improving researchers’ understanding of how visual speech affects what a person hears, Magnotti said.
Variability And Stability In The Mcgurk Effect: Contributions Of Participants, Stimuli, Time, And Response Type / 2023
In the McGurk effect, pairing incongruent auditory and visual syllables produces a percept different from the component syllables. Although it is a popular assay of audiovisual speech integration, little is known about the distribution of responses to the McGurk effect in the population. In our first experiment, we measured McGurk perception using 12 different McGurk stimuli in a sample of 165 English-speaking adults, 40 of whom were retested following a one-year interval. We observed dramatic differences both in how frequently different individuals perceived the illusion (from 0 % to 100 %) and in how frequently the illusion was perceived across different stimuli (17 % to 58 %). For individual stimuli, the distributions of response frequencies deviated strongly from normality, with 77 % of participants almost never or almost always perceiving the effect (≤10 % or ≥90 %). This deviation suggests that the mean response frequency, the most commonly reported measure of the McGurk effect, is a poor measure of individual participants’ responses, and that the assumptions made by parametric statistical tests are invalid. Despite the substantial variability across individuals and stimuli, there was little change in the frequency of the effect between initial testing and a one-year retest (mean change in frequency = 2 %; test-retest correlation, r = 0.91). In a second experiment, we replicated our findings of high variability using eight new McGurk stimuli and tested the effects of open-choice versus forced-choice responding. Forced-choice responding resulted in an estimated 18 % greater frequency of the McGurk effect but similar levels of interindividual variability. Our results highlight the importance of examining individual differences in McGurk perception instead of relying on summary statistics averaged across a population. However, individual variability in the McGurk effect does not preclude its use as a stable measure of audiovisual integration.
Keywords: Individual differences, Audiovisual integration, McGurk effect, Speech perception
Humans use information from both the auditory modality (the sound of the talker’s voice) and the visual modality (the sight of the talker’s face) to understand spoken language. The McGurk effect is an illusion that demonstrates the importance of the visual modality for speech perception: Pairing an auditory syllable with an incongruent visual syllable produces the percept of a third syllable, different from both the auditory and visual syllables ( McGurk & MacDonald, 1976). Because of its simplicity, the McGurk effect has been used as a measure of audiovisual integration in healthy children ( Nath, Fava, & Beauchamp, 2011; Tremblay, Champoux, Bacon, Lepore & Théoret, 2007a); in children and adults ( Erdener, Sekiyama & Burnham, 2010; Tremblay, Champoux, Voss, Bacon, Lepore & Théoret, 2007b); in clinical groups, such as individuals with autism spectrum disorder ( Irwin, Tornatore, Brancazio, & Whalen, 2011; Woynaroski et al., 2013); and to examine the neural substrates of speech perception ( Beauchamp, Nath, & Pasalar, 2010; Keil, Müller, Ihssen, & Weisz, 2012; McKenna Benoit, Raij, Lin, Jääskeläinen, & Stufflebeam, 2010; Nath & Beauchamp, 2012; Skipper, van Wassenhove, Nusbaum, & Small, 2007).
Fundamental questions about the McGurk effect remain unanswered. Most importantly, the distribution of responses to McGurk stimuli in the population and their stability over time is poorly understood. In the initial description of the effect ( McGurk & MacDonald, 1976), 98 % of adult participants reported an illusory “da” percept when an auditory “ba” was paired with a visual “ga,” whereas a follow-up study reported a frequency of only 64 % for the same combination ( MacDonald & McGurk, 1978). At least three obvious possibilities may explain such differing estimates of the frequency of the McGurk effect. First, because the original McGurk stimuli are no longer available, different studies use different McGurk stimuli, often created in the laboratory solely for that study. Second, there are substantial individual differences in the frequency of the McGurk effect, from 0 % to 100 % across different participants ( Keil et al., 2012; McKenna Benoit et al., 2010; Nath & Beauchamp, 2012; Sekiyama, Braida, Nishino, Hayashi, & Tuyo, 1995; Stevenson, Zemtsov, & Wallace, 2012; Tremblay et al., 2007a). Third, different studies use different experimental procedures, and a procedure that incorporates experimenter expectations (“did the stimulus sound like da?”) might give different results than one that does not (“what did the stimulus sound like?”) ( Colin, Radeau, & Deltenre, 2005; Orne, 1962). In order to assess the possible contributions of differences in the stimuli, participants, and procedures to the variation in the published estimates of McGurk frequency, we tested 360 individuals, 20 different McGurk stimuli, and open-choice and forced-choice experimental procedures. For the McGurk effect to be a useful measure of audiovisual integration, it must not vary greatly within individuals from day to day. To assess the stability of the effect within individuals, we tested 40 individuals across a one-year interval.
Undergraduate students from Rice University participated in the study for psychology course credit ( N = 165: 106 female, 59 male; mean age = 19 years). The participants gave written informed consent under an experimental protocol approved by the Rice University Institutional Review Board. After one year, participants were invited to return for an additional testing session; 40 participants (29 female, 11 male) did so. All of the participants had normal or corrected-to-normal vision and no hearing impairments.
The stimuli consisted of 12 McGurk syllable pairs that had been used in previously published studies or were freely available on the Internet (). Seven of the McGurk stimuli were tested with all N = 165 participants, one stimulus was tested with a subset of N = 88 participants, and four stimuli were tested with a subset of N = 66. For the one-year retest session, only stimuli used at both test and retest were compared. All stimuli were edited to have a duration of 1.5-2.0 s and were sized at 640 × 480 pixels. The control stimuli consisted of eight auditory-only syllables and eight audiovisual (non-McGurk) syllables. All stimuli were presented using the Psychophysics Toolbox ( Brainard, 1997; Pelli, 1997).
Second, participants were presented with audiovisual stimuli after receiving the following instructions: “You will see videos of different people talking. Please watch the screen. After each video, wait until the gray screen appears. As soon as it appears, repeat loudly and clearly what the person said. If you are not sure, take your best guess. There are no right or wrong answers.” The instructions were designed to be modality neutral, so as not to bias participants toward auditory or visual responses. Audiovisual stimuli were presented ten times each in random order.
Responses to the McGurk stimuli were divided into four mutually exclusive categories ( McGurk & MacDonald, 1976): fusion responses (“da” or “tha”), auditory responses (“ba”), visual responses (“ga”), and other (e.g., “va”). Stimuli 3, 4, and 9 consisted of two syllables (auditory “baba” paired with visual “gaga”). For these stimuli, a half-point was assigned to each syllable: For instance, the response “dada” was scored as 1.0 as a fusion response, whereas “daba” was scored as 0.5 fusion 0.5 auditory. All data were analyzed using R statistical software ( R Development Core Team, 2014).
Because the stimuli in Experiment 1 were selected from previous studies, they had been created in many different laboratories and varied along a number of dimensions, including auditory and visual quality and the size of the face within the video frame. To minimize the effect of these potential confounds, we created an additional stimulus set consisting of eight McGurk stimuli (labeled 2.1-2.8) recorded from four male and four female talkers; a different talker was used to record three audiovisual control stimuli (congruent “ba,” “da,” and “ga”). The stimuli in Experiment 2 are available for download at http://openwetware.org/wiki/Beauchamp:McGurkStimuli.
Experimental procedure and participants
To determine the accuracy of unisensory syllable perception, we extracted the auditory and visual components from the eight McGurk stimuli used in Experiment 2 and tested them using the same forced-choice procedure as in Experiment 2. Participants ( N = 50: 21 female, 29 male; seven had also participated in Experiment 2) were presented with each of auditory-only and visual-only “ba,” “da,” and “ga” from eight talkers (48 unique stimuli). Each stimulus was repeated twice (96 trials) and randomly interleaved.
Experiment 1, Session 1
Across 165 participants, there was a high degree of variability in the frequency of the McGurk percepts, ranging from 0 % (no illusory percepts reported for any presentation of any stimulus) to 100 % (illusory percept reported on every presentation of every stimulus), covering many values in between ().
Within participants, we did not observe consistent response frequencies across stimuli (). Although Participant 108 had a mean frequency of 50 %, only one stimulus had a frequency near this value (). The mean of 50 % resulted from six stimuli with low frequencies (≤20 %) and five stimuli with high frequencies (≥90 %).
To determine whether this all-or-none McGurk perception was typical, we examined the response distributions for individual stimuli. As with participants, we found a high degree of variability in McGurk frequencies for different stimuli, with the most effective stimulus eliciting over 3 times as many McGurk responses as the least effective one (range from 17 % for Stimulus 1 to 58 % for Stimulus 12; ).
No gender difference emerged in the frequency of the McGurk effect (mean across genders, 36.8 %; females: 36.7 %, SEM = 2.9 %; males: 37.2 %, SEM = 4.1 %; Kolmogorov-Smirnov D = 0.10, p = 0.83). When participants did not report the fusion percept (“da” or “tha”), they reported the auditory component of the stimulus (“ba,” 51 %), the visual component of the stimulus (“ga,” 1 %), or some other percept (10 %). The other percepts included “ah” (3 %), “fa” (3 %), “ta” (2 %), “pa” (1 %), “la” (0.6 %), “va” (0.5 %), and 13 other percepts (each reported less than 0.5 % of the time). Participants were at ceiling accuracy for control stimuli consisting of auditory-only syllables (mean = 98 %) and congruent audiovisual syllables (mean = 99 %).
Experiment 1, Session 2
A total of 40 participants returned for a second testing session at least one year after the first testing session. We found no change in the frequency of the McGurk effect within individuals between the two sessions [mean difference in each individual = 2 %; paired t-test, t(39) = 1.54, p = 0.13], resulting in a high correlation between test and retest (; r = 0.92, p = 10 −16; Cronbach’s α = 0.96 [ Cronbach, 1951]). There was no gender difference in the frequency of McGurk responses at retest (females: mean = 43.3 %, SEM = 6.0 %; males: mean = 47.4 %, SEM = 10.1 %; D = 0.19, p = 0.95).
Experiment 2: Effect of choice type and replication
In Experiment 2, we presented a set of eight new McGurk stimuli to two different groups of participants. The first group used the same open-choice design as in Experiment 1, but the second group made a three-alternative forced choice (corresponding to the auditory component of the stimulus, the visual component of the stimulus, or the illusory McGurk percept). The forced-choice group was much more likely to report the McGurk effect than the open-choice group (69 % vs. 42 %, Kolmogorov-Smirnov D = 0.36, p = 10 −7; see ). This increase was consistent across all eight stimuli ().
Replicating the results from Experiment 1, we found high variability across participants (range from 0 % to 100 %) in both the open-choice () and forced-choice () groups. Replicating Experiment 1, most participants were found in the extremes of the distribution, both averaged across stimuli () and for each individual stimulus (; data combined across choice groups). A Shapiro-Wilk (1965) test of normality rejected normality for each stimulus distribution (all ps < 10 −13), and a dip test ( Hartigan, & Hartigan, 1985) rejected the hypothesis that any of the distributions were unimodal (all ps < 10 −16).
Replicating the results from Experiment 1, we also found high variability across stimuli, ranging from 30 % to 52 % for open choice and 57 % to 80 % for forced choice. Many possible factors could contribute to the differences in McGurk frequencies that we observed across stimuli in Experiment 1, including the size of the face within the video frame, the auditory sound quality, the use of single versus double syllables, and talker gender. The McGurk stimulus set for Experiment 2 was created with four female and four male talkers, allowing us to measure the effects of talker gender and its interaction with the participant and task factors, while holding other stimulus factors constant. We fit a linear mixed-effects model to the behavioral data, with choice type (open vs. forced), talker gender, participant gender, and their interactions as fixed effects, and participant and stimulus as random effects. Using Satterthwaite approximations to test the significance of the model coefficients, the only large effect was the main effect of choice type [estimated 18.0 % higher for forced choice, SE = 3.2 %; t(1754) = 5.6, p = 10 −8]. There was no main effect of participant gender [8.1 % lower for males, SE = 5.1 %; t(292.3) = −1.6, p = .12] or talker gender [2.6 % higher for male talkers, SE = 6.0 %; t(7.8) = 0.43, p = 0.68], and only weak interactions between participant gender and choice type [5.8 % lower for male participants in forced choice, SE = 4.1 %; t(1753) = −1.4, p = .15], talker gender and choice type [6.8 % lower for male talkers in forced choice, SE = 3.0 %; t(1562) = −2.3, p = 0.02], and talker gender and participant gender [3.4 % lower for male participants viewing male talkers, SE = 2.9 %; t(1562) = −1.2, p = 0.24]. The three-way interaction was also weak [3.2 % lower for male participants viewing male talkers in forced choice, SE = 3.9 %; t(1562) = −0.08, p = 0.94]. Eliminating participant gender and stimulus gender from the model did not significantly change its predictive accuracy [full model, root mean squared error (RMSE) = 19.1 %; reduced model, RMSE = 19.3 %; mean difference = 0.05 %; paired t-test: t(1767) = −1.14, p = .25].
Because the stimuli in Experiment 1 were selected from previous studies, we did not have access to the talkers in each stimulus speaking other syllables. Therefore, while creating the McGurk stimuli for Experiment 2, we recorded the same eight talkers speaking “ba,” “ga,” and “da.” We presented the auditory and visual components of these stimuli in Experiment 3 using the three-alternative forced-choice design used in Experiment 2. Identification of the auditory-only syllables was at ceiling (mean accuracy = 97 %, SD = 4 %), whereas identification of the visual-only syllables was significantly worse (80 %, SD = 10 %) [paired t-test: t(49) = 13, p < 10 −16]. Accuracy for the “ga” visual-only syllable was especially low (58 %, as compared with 96 % for “ba” and 86 % for “da”), with high variability across talkers (range from 5 % to 77 %) and participants (range from 6 % to 88 %).
Across 360 individuals and 20 different McGurk stimuli, we observed an astonishing diversity of responses to the illusion. In Experiment 1, we found that some participants never perceived the illusion across more than a hundred presentations (frequency of 0 %), whereas others perceived it every single time (frequency of 100 %). Similarly large variability was observed for stimuli created from different talkers (range of 17 % to 58 %). Experiment 2 replicated these findings and showed that manipulating response type also significantly alters the frequency of the McGurk effect, with forced-choice responding increasing the frequency of McGurk perception by an estimated 18 %, as compared with open choice for identical stimuli, similar to the findings of Colin et al. (2005). Together, these results demonstrate that differences in participants, stimuli, and experimental paradigms all contribute to the wide range of published estimates of the frequency of the McGurk effect. The high variability in the effect suggests that caution is necessary when comparing McGurk frequencies across groups or across studies in which any of these factors vary.
Although the McGurk frequencies for individual participants were evenly distributed across the range from 0 % to 100 %, within each stimulus we found an all-or-none pattern of responding. For the individual stimuli in Experiment 1, 77 % of participants almost never or almost always perceived the McGurk effect (≤10 % or ≥90 %). Therefore, using the mean and standard deviation to characterize the frequency of the McGurk effect can lead to errors in inference, both conceptual and statistical ( Gravetter & Wallnau, 2006). For example, for Stimulus 9, the mean frequency of McGurk responses was 45 %, but only 7 % of participants had a McGurk frequency near the mean (frequencies of 25 % to 65 %).
The non-normality of the distribution of McGurk responses violates the assumptions of parametric statistics such as t tests and reduces their ability to detect differences between groups. For instance, Stimulus 10 had many more participants in the middle of the distribution than did Stimulus 9 (40 % vs. 10 %; ). Although a t test showed no difference between the stimuli (mean frequencies of 45 % ± 47 % vs. 48 % ± 42 %) [ t(164) = −1.31, p = 0.2], the Kolmogorov-Smirnov test, which does not assume any distribution, successfully detected the difference ( D = 0.22, p = 0.0004); similar problems could prevent the t test from detecting mean differences between two groups. This example shows that researchers employing the McGurk effect should be wary of using mean frequencies to examine group differences.
A more fundamental problem raised by our results is that raw measures of McGurk frequency confound individual differences and stimulus differences. To extract the individual differences (which are typically of greater interest), one solution would be to apply the noisy encoding of disparity (NED) model of the McGurk effect ( Magnotti & Beauchamp, 2014). The NED model separately estimates individual parameters (disparity threshold and sensory noise) that can be used to make participant and group comparisons that are unaffected by stimulus differences. The NED model also provides an explanation for the non-normality of the McGurk response data. Stimuli are characterized by a disparity between the auditory and visual speech cues. If the perceived stimulus disparity falls below an individual’s disparity threshold, the individual infers that the auditory and visual speech cues arise from the same talker and integrate them, resulting in the McGurk percept, whereas if a stimulus falls above the threshold, the individual infers that the cues arise from different talkers and does not integrate them, resulting in a percept of the auditory component of the stimulus. As perceived stimulus disparity decreases below an individual’s threshold, there is a rapid transition from never perceiving the illusion to always perceiving it. Only in the rare case that the stimulus disparity almost exactly matches the individual’s threshold will the illusion be perceived on some presentations but not others. The NED model does not specify the source of the disparity between the auditory and visual speech cues. Participants show remarkably high tolerances for temporal asynchrony between the auditory and visual components of speech ( Magnotti, Ma, & Beauchamp, 2013; Munhall, Gribble, Sacco, & Ward, 1996), and perceive the McGurk effect even if the voice and the face are of different genders ( Green, Kuhl, Meltzoff, & Stevens, 1991). A comprehensive study of many acoustic and visual properties of McGurk stimuli showed that they accounted for about half of the variability in the frequency of the effect across stimuli and participants ( Jiang & Bernstein, 2011).
Previous studies have characterized individuals as being strong or weak perceivers of the McGurk effect in order to show group differences in mental imagery ( Berger & Ehrsson, 2013) or brain activity in adults ( Nath & Beauchamp, 2012) and children ( Nath et al., 2011). Our data suggest that this is reasonable for any particular stimulus, since the responses to all stimuli were characterized by distributions in which most participants were found in the extremes (either almost always or almost never perceiving the illusion). However, the classification into strong and weak perceivers is entirely dependent on the stimulus chosen and the behavioral paradigm used. The weakest stimulus from Experiment 1 would classify 12 % of individuals as strong perceivers (using a classification threshold of 50 %), whereas the strongest stimulus from Experiment 2 using a forced-choice response would classify 84 % of individuals as strong perceivers (using the same classification threshold).
Our results confirm and extend a number of results from previous studies that have used smaller sample sizes. We did not find an effect of talker gender or of participant gender, consistent with previous reports for McGurk syllables ( Irwin, Whalen, & Fowler, 2006) and visual-only phonemes ( Strelnikov et al., 2009). Our visual-only results confirm that visual “da” and visual “ga” are easily confusable, whereas visual “ba” is distinct ( Binnie, Montgomery & Jackson, 1974; Erber, 1975; Fisher, 1968; Lucey, Martin, & Sriradharan, 2004). Although we found a large effect of response type, we did not examine the effect of the task instructions. Our instructions were designed to be modality neutral, so as not to bias participants toward any particular response. It is possible that different task instructions could change the frequency of the McGurk effect, adding an additional source of variability across studies.
Although we found high variability in our examination of the McGurk effect, individual participants’ McGurk frequencies were stable over a one-year period. We observed an r = 0.91 in 40 individuals over a 1 year interval, similar to the findings of Strand, Cooperman, Rowe and Simenstad (2014) ( r = 0.77 over a 2-month test-retest window in 58 individuals). This provides reassurance for longitudinal examinations of the McGurk effect and studies that correlate the frequency of the McGurk effect with brain activity (e.g., McKenna Benoit et al., 2010; Nath & Beauchamp, 2012; Nath et al., 2011), clinical status (e.g., Hamilton, Shenton, & Coslett, 2006; Pearl et al., 2009; Stevenson et al., 2014; Woynaroski et al., 2013), or other behavioral measures (e.g., Berger & Ehrsson, 2013; Stevenson et al., 2012; Tremblay et al., 2007a).
This research was supported by NIH R01NS065395. Haley Lindsay, Cara Miekka, Joanne Guidry, and Alexandra Hernandez assisted with data collection.
The authors report no conflicts of interest.
John F. Magnotti, Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, TX, USA.
Michael S. Beauchamp, Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, TX, USA.
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Thỉnh thoảng, một điếu xì gà xuất hiện độc đáo theo mọi cách pha trộn thú vị, hình dạng sáng tạo, câu chuyện hấp dẫn, và đồng thời, vẫn có thể duy trì các tiêu chuẩn xuất sắc cổ điển. Đây là trường hợp với Arturo Fuente Don Carlos Eye of the Shark, xì gà AficionadoXì gà của năm 2017. Sự pha trộn dựa trên điếu xì gà Don Carlos gốc (được phát hành năm 1976), nhưng đã thay đổi một chút cho phiên bản đặc biệt này. Nếu bạn muốn biết chính xác điều gì làm cho Eye of the Shark khác với dòng Don Carlos thông thường, bạn sẽ phải sử dụng trí tưởng tượng của mình. Các Fuentes khá được bảo vệ về các chi tiết của sự pha trộn. Điều đó đủ để biết rằng bao bì đến từ Cameroon và thuốc lá nội thất có tuổi và được trồng ở Cộng hòa Dominican, đây là công thức cơ bản cho hầu hết các loại xì gà Don Carlos.
Tuy nhiên,bạn sẽ biết rằng bạn đang hút một hỗn hợp nâng cao, một thứ gì đó thoải mái quen thuộc với nhiều người hâm mộ Fuente, nhưng với một chút thay đổi. Những người quen thuộc với chủ thương hiệu (và bây giờ, tộc trưởng công ty) Carlos Fuente Jr. biết rằng anh ấy pha trộn theo bản năng chứ không phải bằng công thức nghiêm ngặt, đặc biệt là khi tạo ra xì gà không chính thống như thế này. Hình dạng thêm vào âm mưu. Eye of the Shark không phải dạng hộp cũng không thông thường. Thay vào đó, nửa dưới được nhấn và nửa trên thon thành một hình chữ thường, tròn đều, một máy ép demi. Fuente đã sử dụng hình dạng trước đây, vì vậy có vẻ kỳ quặc, có vẻ như nó được tái phát ở các dòng khác, nhưng chỉ với số lượng hạn chế. Về lý do tại sao nó được gọi là Mắt cá mập, rất có thể là do hình dạng tổng thể. Nếu bạn nhìn vào điếu xì gà theo chiều ngang, Nó giống với cơ thể của cá mập trắng lớn. Khi bốn góc của chân hộp được thắp sáng đầy đủ, Cá mập bắt đầu thể hiện ngay lập tức.
Ngoài một loạt các loại gia vị nướng và ghi chú có trong hầu hết các loại xì gà Don Carlos, mỗi túi còn truyền thêm các lớp phức tạp, giống như một phần thưởng cho một album yêu thích hoặc một đoạn kết bất ngờ ở cuối tiểu thuyết cổ điển. Nhiều ngữ điệu của các loại hạt và da mở ra trước một kết thúc ấm áp của gỗ tuyết tùng và rượu táo. Nói một cách đơn giản: chúng tôi không muốn điếu xì gà này kết thúc. Năm 2016, cha của Fuente, Carlos Fuente Sr., đã qua đời. Anh ấy là Don Carlos, và hoạt động với công ty cho đến cuối cùng. Bạn sẽ tìm thấy dòng chữ “Người đàn ông thứ 80” được in một cách yêu thương ở bên cạnh và trên nắp của mỗi hộp màu đỏ tươi. Eye of the Shark được phát hành vào năm 2016 như một sự tôn vinh dành cho Don Carlos. và những đóng góp của ông trong nhiều năm qua cho thế giới xì gà. Mắt cá mập là xì gà số 1 năm 2017. Đáng buồn thay, Don Carlos không ở đây để xem hay nhận giải thưởng, nhưng tinh thần của anh ta chắc chắn vẫn còn sống trong làn khói.
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