Human implicit learning can be investigated with implicit artificial grammar learning, a simple m... more Human implicit learning can be investigated with implicit artificial grammar learning, a simple model for aspects of natural language acquisition. In this paper we investigate the remaining effect of modality transfer in syntactic classification of an acquired grammatical ...
Naming speed deficits are well documented in developmental dyslexia, expressed by slower naming t... more Naming speed deficits are well documented in developmental dyslexia, expressed by slower naming times and more errors in response to familiar items. Here we used event-related potentials (ERPs) to examine at what processing level the deficits in dyslexia emerge during a discrete-naming task. Dyslexic and skilled adult control readers performed a primed object-naming task, in which the relationship between the prime and the target was manipulated along perceptual, semantic and phonological dimensions. A 3 Â 2 design that crossed Relationship Type (Visual, Phonemic Onset, and Semantic) with Relatedness (Related and Unrelated) was used. An attenuated N/P190 – indexing early visual processing – and N300 – which index late visual processing – was observed to pictures preceded by perceptually related (vs. unrelated) primes in the control but not in the dyslexic group. These findings suggest sub-optimal processing in early stages of object processing in dyslexia, when integration and mapping of perceptual information to a more form-specific percept in memory take place. On the other hand, both groups showed an N400 effect associated with semantically related pictures (vs. unrelated), taken to reflect intact integration of semantic similarities in both dyslexic and control readers. We also found an electrophysiological effect of phonological priming in the N400 range – that is, an attenuated N400 to objects preceded by phonemic related primes vs. unrelated – while it showed a more widespread distributed and more pronounced over the right hemisphere in the dyslexics. Topographic differences between groups might have originated from a word form encoding process with different characteristics in dyslexics compared to control readers.
This ERP study investigated the cognitive nature of the P1–N1 components during orthographic proc... more This ERP study investigated the cognitive nature of the P1–N1 components during orthographic processing. We used an implicit reading task with various types of stimuli involving different amounts of sublexical or lexical orthographic processing (words, pseudohomophones, pseudowords, nonwords, and symbols), and tested average and dyslexic readers. An orthographic regularity effect (pseudo-words–nonwords contrast) was observed in the average but not in the dyslexic group. This suggests an early sensitivity to the dependencies among letters in word-forms that reflect orthographic structure, while the dyslexic brain apparently fails to be appropriately sensitive to these complex features. Moreover, in the adults the N1-response may already reflect lexical access: (i) the N1 was sensitive to the familiar vs. less familiar orthographic sequence contrast; (ii) and early effects of the phonological form (words-pseudohomophones contrast) were also found. Finally, the later N320 component was attenuated in the dyslexics, suggesting suboptimal processing in later stages of phonological analysis.
During oral reading, the eyes tend to be ahead of the voice (eye-voice span, EVS). It has been hy... more During oral reading, the eyes tend to be ahead of the voice (eye-voice span, EVS). It has been hypothesized that the extent to which this happens depends on the automaticity of reading processes, namely on the speed of print-to-sound conversion. We tested whether EVS is affected by another automaticity component – immunity from interference. To that end, we manipulated word familiarity (high-frequency, low-frequency, and pseudowords, PW) and word length as proxies of immunity from interference, and we used linear mixed effects models to measure the effects of both variables on the time interval at which readers do parallel processing by gazing at word N + 1 while not having articulated word N yet (offset EVS). Parallel processing was enhanced by automaticity, as shown by familiarity × length interactions on offset EVS, and it was impeded by lack of automaticity, as shown by the transformation of offset EVS into voice-eye span (voice ahead of the offset of the eyes) in PWs. The relation between parallel processing and automaticity was strengthened by the fact that offset EVS predicted reading velocity. Our findings contribute to understand how the offset EVS, an index that is obtained in oral reading, may tap into different components of automaticity that underlie reading ability, oral or silent. In addition, we compared the duration of the offset EVS with the average reference duration of stages in word production, and we saw that the offset EVS may accommodate for more than the articulatory programming stage of word N.
... To show this, we observe that the Reber grammar yields exactly the same strings as RM2 (Figur... more ... To show this, we observe that the Reber grammar yields exactly the same strings as RM2 (Figure 2). Assume that we know RM1 and that we know the latest two symbols in an output string from RM2. This determines the internal state of RM1 and RM2. ...
... A well-known example of this is the Chomsky hierarchy of phrase structure grammars: right-lin... more ... A well-known example of this is the Chomsky hierarchy of phrase structure grammars: right-linear context-free context-sensitive general production grammars, where denotes strict inclusion (cf., [4]). The different levels of expressivity in the Chomsky hierarchy correspond exactly ...
In this paper we explore the feasibility of artificial (formal) grammar recognition (AGR) using s... more In this paper we explore the feasibility of artificial (formal) grammar recognition (AGR) using spiking neural networks. A biologically inspired minicolumn architecture is designed as the basic computational unit. A network topography is defined based on the minicolumn architecture, here referred to as nodes, connected with excitatory and inhibitory connections. Nodes in the network represent unique internal states of the
Language acquisition in both natural and artificial language learning settings crucially depends ... more Language acquisition in both natural and artificial language learning settings crucially depends on extracting information from ordered sequences. A shared sequence learning mechanism is thus assumed to underlie both natural and artificial language learning. A growing body of empirical evidence is consistent with this hypothesis. By means of artificial language learning experiments, we may therefore gain more insight in this shared mechanism. In this paper, we review empirical evidence from artificial language ...
Human implicit learning can be investigated with implicit artificial grammar learning, a simple m... more Human implicit learning can be investigated with implicit artificial grammar learning, a simple model for aspects of natural language acquisition. In this paper we investigate the remaining effect of modality transfer in syntactic classification of an acquired grammatical ...
Naming speed deficits are well documented in developmental dyslexia, expressed by slower naming t... more Naming speed deficits are well documented in developmental dyslexia, expressed by slower naming times and more errors in response to familiar items. Here we used event-related potentials (ERPs) to examine at what processing level the deficits in dyslexia emerge during a discrete-naming task. Dyslexic and skilled adult control readers performed a primed object-naming task, in which the relationship between the prime and the target was manipulated along perceptual, semantic and phonological dimensions. A 3 Â 2 design that crossed Relationship Type (Visual, Phonemic Onset, and Semantic) with Relatedness (Related and Unrelated) was used. An attenuated N/P190 – indexing early visual processing – and N300 – which index late visual processing – was observed to pictures preceded by perceptually related (vs. unrelated) primes in the control but not in the dyslexic group. These findings suggest sub-optimal processing in early stages of object processing in dyslexia, when integration and mapping of perceptual information to a more form-specific percept in memory take place. On the other hand, both groups showed an N400 effect associated with semantically related pictures (vs. unrelated), taken to reflect intact integration of semantic similarities in both dyslexic and control readers. We also found an electrophysiological effect of phonological priming in the N400 range – that is, an attenuated N400 to objects preceded by phonemic related primes vs. unrelated – while it showed a more widespread distributed and more pronounced over the right hemisphere in the dyslexics. Topographic differences between groups might have originated from a word form encoding process with different characteristics in dyslexics compared to control readers.
This ERP study investigated the cognitive nature of the P1–N1 components during orthographic proc... more This ERP study investigated the cognitive nature of the P1–N1 components during orthographic processing. We used an implicit reading task with various types of stimuli involving different amounts of sublexical or lexical orthographic processing (words, pseudohomophones, pseudowords, nonwords, and symbols), and tested average and dyslexic readers. An orthographic regularity effect (pseudo-words–nonwords contrast) was observed in the average but not in the dyslexic group. This suggests an early sensitivity to the dependencies among letters in word-forms that reflect orthographic structure, while the dyslexic brain apparently fails to be appropriately sensitive to these complex features. Moreover, in the adults the N1-response may already reflect lexical access: (i) the N1 was sensitive to the familiar vs. less familiar orthographic sequence contrast; (ii) and early effects of the phonological form (words-pseudohomophones contrast) were also found. Finally, the later N320 component was attenuated in the dyslexics, suggesting suboptimal processing in later stages of phonological analysis.
During oral reading, the eyes tend to be ahead of the voice (eye-voice span, EVS). It has been hy... more During oral reading, the eyes tend to be ahead of the voice (eye-voice span, EVS). It has been hypothesized that the extent to which this happens depends on the automaticity of reading processes, namely on the speed of print-to-sound conversion. We tested whether EVS is affected by another automaticity component – immunity from interference. To that end, we manipulated word familiarity (high-frequency, low-frequency, and pseudowords, PW) and word length as proxies of immunity from interference, and we used linear mixed effects models to measure the effects of both variables on the time interval at which readers do parallel processing by gazing at word N + 1 while not having articulated word N yet (offset EVS). Parallel processing was enhanced by automaticity, as shown by familiarity × length interactions on offset EVS, and it was impeded by lack of automaticity, as shown by the transformation of offset EVS into voice-eye span (voice ahead of the offset of the eyes) in PWs. The relation between parallel processing and automaticity was strengthened by the fact that offset EVS predicted reading velocity. Our findings contribute to understand how the offset EVS, an index that is obtained in oral reading, may tap into different components of automaticity that underlie reading ability, oral or silent. In addition, we compared the duration of the offset EVS with the average reference duration of stages in word production, and we saw that the offset EVS may accommodate for more than the articulatory programming stage of word N.
... To show this, we observe that the Reber grammar yields exactly the same strings as RM2 (Figur... more ... To show this, we observe that the Reber grammar yields exactly the same strings as RM2 (Figure 2). Assume that we know RM1 and that we know the latest two symbols in an output string from RM2. This determines the internal state of RM1 and RM2. ...
... A well-known example of this is the Chomsky hierarchy of phrase structure grammars: right-lin... more ... A well-known example of this is the Chomsky hierarchy of phrase structure grammars: right-linear context-free context-sensitive general production grammars, where denotes strict inclusion (cf., [4]). The different levels of expressivity in the Chomsky hierarchy correspond exactly ...
In this paper we explore the feasibility of artificial (formal) grammar recognition (AGR) using s... more In this paper we explore the feasibility of artificial (formal) grammar recognition (AGR) using spiking neural networks. A biologically inspired minicolumn architecture is designed as the basic computational unit. A network topography is defined based on the minicolumn architecture, here referred to as nodes, connected with excitatory and inhibitory connections. Nodes in the network represent unique internal states of the
Language acquisition in both natural and artificial language learning settings crucially depends ... more Language acquisition in both natural and artificial language learning settings crucially depends on extracting information from ordered sequences. A shared sequence learning mechanism is thus assumed to underlie both natural and artificial language learning. A growing body of empirical evidence is consistent with this hypothesis. By means of artificial language learning experiments, we may therefore gain more insight in this shared mechanism. In this paper, we review empirical evidence from artificial language ...
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