34 research outputs found
Konzeptionelle Verankerung digitaler Medien in schulisches Lernen und Lehren
No full textHeldt M, Labusch A, Eickelmann B, Port S. Konzeptionelle Verankerung digitaler Medien in schulisches Lernen und Lehren. In: Eickelmann B, Labusch A, Drossel K, Vennemann M, eds. ICILS 2018 #NRW. Vertiefende Analysen und Befunde für Nordrhein-Westfalen im internationalen Vergleich. Münster: Waxmann; 2020: 55-66
Wünsche zur schulischen Nutzung digitaler Medien, digitalisierungsbezogene Berufswahlneigung und Sichtweisen von Schülerinnen und Schülern auf die Relevanz digitaler Medien für die Gesellschaft
No full textEickelmann B, Drossel K, Labusch A, Heldt M. Wünsche zur schulischen Nutzung digitaler Medien, digitalisierungsbezogene Berufswahlneigung und Sichtweisen von Schülerinnen und Schülern auf die Relevanz digitaler Medien für die Gesellschaft. In: Eickelmann B, Labusch A, Drossel K, Vennemann M, eds. ICILS 2018 #NRW. Vertiefende Analysen und Befunde für Nordrhein-Westfalen im internationalen Vergleich. Münster: Waxmann; 2020: 259-270
Dyadic and triadic search: Benefits, costs, and predictors of group performance
No full textWahn B, Czeszumski A, Labusch M, Kingstone A, König P. Dyadic and triadic search: Benefits, costs, and predictors of group performance. Attention, Perception, & Psychophysics. 2020;82(5):2415-2433
Are goats chèvres, chévres, chēvres, and chevres? Unveiling the orthographic code of diacritical vowels.
No full textInternational audienceAn often overlooked but fundamental issue for any comprehensive model of visualword recognition is the representation of diacritical vowels: Do diacritical and nondiacritical vowels share their abstract letter representations? Recent research suggests that the answer is "yes" in languages where diacritics indicate suprasegmental information (e.g., lexical stress, as in cámara ['ka.ma.ɾa] camera; Spanish), but "no" in languages where diacritics indicate segmental information such as a different phoneme (e.g., the German vowels ä /ɛ/ and a /a/). Here we examined this issue in French, a language that contains a complex set of diacritical vowels (e.g., for the letter e: é, è, ê, and ë). In Experiment 1, using a semantic categorization task, we compared the word identification times to intact diacritical words (e.g., chèvre, goat in English) with a condition with omitted diacritics (chevre). Results showed that the two conditions behaved similarly. In Experiments 2-4, we compared the intact diacritical words with a condition containing a mismatching diacritic, either existing in French (e.g., chévre, chêvre) or not (the macron sign, as in chēvre). We only found a reading cost when replacing the diacritic with an existing one. In Experiments 5-6, we compared the semantic categorization times to intact non-diacritical words (e.g., cheval, horse in English) versus a condition with an added diacritic, either existing (chèval) or not (chēval). We found a reading cost for the words with the added diacritical mark in both cases. We discuss how models of visual-word recognition can be modified to represent diacritical vowels
Rëâdīńg wõrdš wîth ōrńåmêńtš: is there a cost?
No full textIntroduction: Recent research has reported that adding non-existent diacritical marks to a word produces a minimal reading cost compared to the intact word. Here we examined whether this minimal reading cost is due to: (1) the resilience of letter detectors to the perceptual noise (i.e., the cost should be small and comparable for words and nonwords) or (2) top-down lexical processes that normalize the percept for words (i.e., the cost would be larger for nonwords).
Methods: We designed a letter detection experiment in which a target stimulus (either a word or a nonword) was presented intact or with extra non-existent diacritics [e.g., amigo (friend) vs. ãmîgô; agimo vs. ãgîmô]. Participants had to decide which of two letters was in the stimulus (e.g., A vs. U).
Results: Although the task involved lexical processing, with responses being faster and more accurate for words compared to nonwords, we found only a minimal advantage in error rates for intact stimuli versus those with non-existent diacritics. This advantage was similar for both words and nonwords.
Discussion: The letter detectors in the word recognition system appear to be resilient to non-existent diacritics without the need for feedback from higher levels of processing
The importance of diacritical marks and inner letter lines in the process of letter identification: The case of semi-complex letters
No full textMost contemporary models of visual word recognition remain silent about the role of diacritical marks, being implemented only for languages like English (i.e., an orthography lacking in diacritics). For instance, Dehaene et al.’s (2005) Local Combinations Detector model (LCD model) assumes that visual features are mapped onto abstract letter units during visual word recognition (e.g., see Dehaene et al., 2005; Grainger et al., 2008, for neurally-inspired models). While there are visual letter similarity effects in the earliest stages of visual word recognition in masked priming experiments (e.g., obiect-OBJECT producing faster RTs than obaect-OBJECT; see Marcet & Perea, 2017), these effects vanish in later processing stages (see Gutierrez-Sigut et al., 2019, for ERP evidence).
Notably, recent research has found an asymmetric effect in masked priming experiments with accented vs non-accented letters. In masked priming experiments with an alphabetic decision task, Perea et al. (2019) found that when an accented target letter Á was primed with its non-accented counterpart (a-Á), it was similarly fast recognized as when it was primed with its identical letter (á-Á = a-Á), but slower when it was primed with an unrelated letter (á–Á = a–Á < e–Á; i.e., a visual similarity effect). Critically, for target letters without a diacritical mark, Perea et al. (2019) found slower target letter recognition times for the diacritical condition than the identity condition (a–A < á–A), together with faster response times than with an unrelated control condition (a–A < á–A< é–A; see also Chetail & Boursain, 2019 for similar results in French).
Hence, in masked priming, changing a letter from “á” to “a” is not the same as changing a letter from “a” to “á” (a á but á ↛ a).
Importantly, this dissociative pattern has also been replicated with diacritical consonant letters (n/ñ; Marcet et al., 2020) and with letters in other scripts (e.g., hiragana letters) (see Tversky, 1977, for a general model of perceptual asymmetries). In addition, Kinoshita et al. (2021) found a similar asymmetric pattern of letters like E-F: F would be a quite effective prime for E (note that E has an extra line than F), but E was not particularly effective at activating F.
To explain these effects, one could argue that these asymmetric effects in masked priming could reflect perceptual noise about letter identity in the first moments of word processing in a “noisy-channel” model (see Norris & Kinoshita, 2012). The idea is that “á” is less similar to “a” than vice versa because the extra diacritic in “á” makes the stimulus less similar to its non-diacritical counterpart. Similarly, the same pattern would occur with E-F: the letter F would be more effective as a prime of the target letter E than vice versa.
What is not yet known is whether the effects from the added (removed) diacritics (Ñ-N) are stronger than those from the added (removed) inner lines (E-F), and how these elements interplay (i.e., adding/removing both diacritics and inner lines). Keep in mind that diacritics are perceptually very salient (see Wiley et al., 2016; see also Perea et al., 2020).
Note that linguistic function may modulate the above effects (e.g., see Marcet et al., 2022, for evidence in Catalan, as opposed to Spanish). To minimize any impact of linguistic information, we chose to examine these effects with an artificial alphabet where we examined the prime-target relationship manipulating an added/removed inner feature of the “base letter” vs. an added/removed diacritic.
The main goal of the present experiment is to examine the processing of visual-orthographic information during letter recognition. We created an artificial alphabet to avoid possible confounds of linguistic associations of accent marks. To that end, we created two types of “base letters”:
(1) Plain letter with a diacritical mark. This letter could be preceded by one of the following primes:
(a) the plain letter (- -)
(b) the plain letter with an added diacritic (+ -)
(c) the plain letter with an added inner line (- +); and
(d) the plain letter with an added diacritic and inner line (+ +)
(2) Plain letter with an additional inner line. This letter could be preceded by one of the following primes:
(a) the plain letter with an additional inner line and added diacritic (+ +)
(b) the plain letter with an added diacritic (+ -)
(c) the plain letter with an additional inner line (- +) and
(d) the plain letter, with the diacritics and inner lines removed (- -) (see Table 1 and Figure 1).
In this way, it is possible to directly compare, on the one hand, the processing of letters with an added diacritic, and on the one hand, the processing of letters with an added inner feature in the base letter.
As in previous research (e.g., Kinoshita et al., 2021), we will use the masked priming letter match task. In this task, participants have to decide whether a target letter matched the previously presented referent letter. Each target letter was preceded by a prime that could be either (1) identical (2) the letter with an added/removed diacritical mark (3) the letter with an added/removed inner line to the letter (as in E for F); or (4) the letter with an added/removed inner line and diacritical mark. As usual, the focus will be on the “same” trials (i.e., trials in which referent and target are the same)—there will be the same number of “different” trials (i.e., trials in which referent and target are different; from these, half would be visually similar and the other half visually dissimilar) (see Figure 2)
Which role do diacritical marks and inner letter lines play in the process of letter identification?
No full textMost contemporary models of visual word recognition remain silent about the role of diacritical marks, being implemented only for languages like English (i.e., an orthography lacking in diacritics). For instance, Dehaene et al.’s (2005) Local Combinations Detector model (LCD model) assumes that visual features are mapped onto abstract letter units during visual word recognition (e.g., see Dehaene et al., 2005; Grainger et al., 2008, for neurally-inspired models). While there are visual letter similarity effects in the earliest stages of visual word recognition in masked priming experiments (e.g., obiect-OBJECT producing faster RTs than obaect-OBJECT; see Marcet & Perea, 2017), these effects vanish in later processing stages (see Gutierrez-Sigut et al., 2019, for ERP evidence).
Notably, recent research has found an asymmetric effect in masked priming experiments with accented vs non-accented letters. In masked priming experiments with an alphabetic decision task, Perea et al. (2019) found that when an accented target letter Á was primed with its non-accented counterpart (a-Á), it was similarly fast recognized as when it was primed with its identical letter (á-Á = a-Á), but slower when it was primed with an unrelated letter (á–Á = a–Á < e–Á; i.e., a visual similarity effect). Critically, for target letters without a diacritical mark, Perea et al. (2019) found slower target letter recognition times for the diacritical condition than the identity condition (a–A < á–A), together with faster response times than with an unrelated control condition (a–A < á–A< é–A; see also Chetail & Boursain, 2019 for similar results in French).
Hence, in masked priming, changing a letter from “á” to “a” is not the same as changing a letter from “a” to “á” (a á but á ↛ a).
Importantly, this dissociative pattern has also been replicated with diacritical consonant letters (n/ñ; Marcet et al., 2020) and with letters in other scripts (e.g., hiragana letters) (see Tversky, 1977, for a general model of perceptual asymmetries). In addition, Kinoshita et al. (2021) found a similar asymmetric pattern of letters like E-F: F would be a quite effective prime for E (note that E has an extra line than F), but E was not particularly effective at activating F.
To explain these effects, one could argue that these asymmetric effects in masked priming could reflect perceptual noise about letter identity in the first moments of word processing in a “noisy-channel” model (see Norris & Kinoshita, 2012). The idea is that “á” is less similar to “a” than vice versa because the extra diacritic in “á” makes the stimulus less similar to its non-diacritical counterpart. Similarly, the same pattern would occur with E-F: the letter F would be more effective as a prime of the target letter E than vice versa.
What is not yet known is whether the effects from the added (removed) diacritics (Ñ-N) are stronger than those from the added (removed) inner lines (E-F), and how these elements interplay (i.e., adding/removing both diacritics and inner lines). Keep in mind that diacritics are perceptually very salient (see Wiley et al., 2016; see also Perea et al., 2020).
Note that linguistic function may modulate the above effects (e.g., see Marcet et al., 2022, for evidence in Catalan, as opposed to Spanish). To minimize any impact of linguistic information, we chose to examine these effects with an artificial alphabet where we examined the prime-target relationship manipulating an added/removed inner feature of the “base letter” vs. an added/removed diacritic.
The main goal of the present experiment is to examine the processing of visual-orthographic information during letter recognition. We created an artificial alphabet to avoid possible confounds of linguistic associations of accent marks. To that end, we created two types of “base letters”:
(1) Plain letter. This letter could be preceded by one of the following primes: (1) the plain letter (- -) (2) the plain letter with an added diacritic (+ -) (3) the plain letter with an added inner line (- +); and (4) the plain letter with an added diacritic and inner line (+ +)
(1) Complex letter. This letter could be preceded by one of the following primes: (1) the complex letter (+ +) (2) the complex letter with a removed diacritic (- +) (3) the complex letter with a removed inner line (+ -) and (4) the complex letter with the diacritics and inner lines removed (- -) (see Table 1 and Figure 1)
In this way, it is possible to directly compare, on the one hand, the processing of letters with an added diacritic vs. letters with an added feature in the base letter, and on the one hand, the processing of letters with a removed diacritic vs. letters with a removed feature in the base letter.
As in previous research (e.g., Kinoshita et al., 2021), we will use the masked priming letter match task. In this task, participants have to decide whether a target letter matched the previously presented referent letter. Each target letter was preceded by a prime that could be either (1) identical (2) the letter with an added/removed diacritical mark (3) the letter with an added/removed inner line to the letter (as in E for F); or (4) the letter with an added/removed inner line and diacritical mark. As usual, the focus will be on the “same” trials (i.e., trials in which referent and target are the same)—there will be the same number of “different” trials (i.e., trials in which referent and target are different; from these, half would be visually similar and the other half visually dissimilar) (see Figure 2)
Just a mark: Diacritic function does not play a role in the early stages of visual word recognition
No full textThe impact of capitalized German words on lexical access
No full textLeading models of visual word recognition assume that the process of word identification is driven by abstract, case-invariant units (e.g., table and TABLE activate the same abstract representation). But do these models need to be modified to meet nuances of orthography as in German, where the first letter of common nouns is capitalized (e.g., Buch [book] and Hund [dog], but blau [blue])? To examine the role of initial capitalization of German words in lexical access, we chose a semantic categorization task ("is the word an animal name?"). In Experiment 1, we compared German words in all-lowercase vs. initial capitalization (hund, buch, blau vs. Hund, Buch, Blau). Results showed faster responses for animal nouns with initial capitalization (Hund < hund) and faster responses for lowercase non-nouns (blau < Blau). Surprisingly, we found faster responses for lowercase non-animal nouns (buch < Buch). As the latter difference could derive from task demands (i.e., buch does not follow German orthographic rules and requires a "no" response), we replaced the all-lowercase format with an orthographically legal all-uppercase format in Experiment 2. Results showed an advantage for all nouns with initial capitalization (Hund < HUND and Buch < BUCH). These findings clearly show that initial capitalization in German words constitutes an essential part of the words' representations and is used during lexical access. Thus, models of visual word recognition, primarily focused on English orthography, should be expanded to the idiosyncrasies of other Latin-based orthographies
Rëâdīńg wõrdš wîth ōrńåmêńtš: is there a cost?
No full textIntroductionRecent research has reported that adding non-existent diacritical marks to a word produces a minimal reading cost compared to the intact word. Here we examined whether this minimal reading cost is due to: (1) the resilience of letter detectors to the perceptual noise (i.e., the cost should be small and comparable for words and nonwords) or (2) top-down lexical processes that normalize the percept for words (i.e., the cost would be larger for nonwords).MethodsWe designed a letter detection experiment in which a target stimulus (either a word or a nonword) was presented intact or with extra non-existent diacritics [e.g., amigo (friend) vs. ãmîgô; agimo vs. ãgîmô]. Participants had to decide which of two letters was in the stimulus (e.g., A vs. U).ResultsAlthough the task involved lexical processing, with responses being faster and more accurate for words compared to nonwords, we found only a minimal advantage in error rates for intact stimuli versus those with non-existent diacritics. This advantage was similar for both words and nonwords.DiscussionThe letter detectors in the word recognition system appear to be resilient to non-existent diacritics without the need for feedback from higher levels of processing
