It has been argued that numbers are spatially organized along a "mental number line" that facilitates left-hand responses to small numbers, and right-hand responses to large numbers. We hypothesized that whenever the representations of visual and numerical space are concurrently activated, interactions can occur between them, before response selection. A spatial prime is processed faster than a numerical target, and consistent with our hypothesis, we found that such a spatial prime affects non-spatial, verbal responses more when the prime follows a (...) numerical target (backward priming) then when it precedes it (forward priming). This finding emerged both in a number-comparison and a parity judgment task, and cannot be ascribed to a "Spatial-Numerical Association of Response Codes" (SNARC). Contrary to some earlier claims, we therefore conclude that visuospatial-numerical interactions do occur, even before response selection. (shrink)

It is often assumed that graphemes are a crucial level of orthographic representation above letters. Current connectionist models of reading, however, do not address how the mapping from letters to graphemes is learned. One major challenge for computational modeling is therefore developing a model that learns this mapping and can assign the graphemes to linguistically meaningful categories such as the onset, vowel, and coda of a syllable. Here, we present a model that learns to do this in English for strings (...) of any letter length and any number of syllables. The model is evaluated on error rates and further validated on the results of a behavioral experiment designed to examine ambiguities in the processing of graphemes. The results show that the model (a) chooses graphemes from letter strings with a high level of accuracy, even when trained on only a small portion of the English lexicon; (b) chooses a similar set of graphemes as people do in situations where different graphemes can potentially be selected; (c) predicts orthographic effects on segmentation which are found in human data; and (d) can be readily integrated into a full-blown model of multi-syllabic reading aloud such as CDP++ (Perry, Ziegler, & Zorzi, 2010). Altogether, these results suggest that the model provides a plausible hypothesis for the kind of computations that underlie the use of graphemes in skilled reading. (shrink)

On the basis of neuropsychological evidence, it is clear that attention should be given a role in any model of consciousness. What is known about the many instances of dissociation between explicit and implicit knowledge after brain damage suggests that conscious experience might not be linked to a restricted area of the brain. Even if it were true that there is a single brain area devoted to consciousness, the subicular area would seem to be an unlikely possibility.

The mental representation of brief temporal durations, when assessed in standard laboratory conditions, is highly accurate. Here we show that adding or subtracting temporal durations systematically results in strong and opposite biases, namely over-estimation for addition and under-estimation for subtraction. The difference with respect to a baseline temporal reproduction task changed across durations in an operation-specific way and survived correcting for the effect due to operation sign alone, indexing a reliable signature of arithmetic processing on time representation. A second experiment (...) replicated these findings with a different set of stimuli. This novel behavioral marker conceptually mirrors in the time domain the representational momentum found with motion, whereby the estimated spatial position of a visual target is displaced in the direction of motion itself. This momentum effect in temporal arithmetic suggests a striking analogy between time processing and visuospatial processing, which might index the presence of common computational principles. (shrink)

The number of elements in a small set of items is appraised in a fast and exact manner, a phenomenon called subitizing. In contrast, humans provide imprecise responses when comparing larger numerosities, with decreasing precision as the number of elements increases. Estimation is thought to rely on a dedicated system for the approximate representation of numerosity. While previous behavioral and neuroimaging studies associate subitizing to a domain-general system related to object tracking and identification, the nature of small numerosity processing is (...) still debated. We investigated the neural processing of numerosity across subitizing and estimation ranges by examining electrophysiological activity during the memory retention period in a delayed numerical match-to-sample task. We also assessed potential differences in the neural signature of numerical magnitude in a fully non-symbolic or cross-format comparison. In line with behavioral performance, we observed modulation of parietal-occipital neural activity as a function of numerosity that differed in two ranges, with distinctive neural signatures of small numerosities showing clear similarities with those observed in visuospatial working memory tasks. We also found differences in neural activity related to numerical information in anticipation of single vs. cross-format comparison, suggesting a top-down modulation of numerical processing. Finally, behavioral results revealed enhanced performance in the mixed-format conditions and a significant correlation between task performance and symbolic mathematical skills. Overall, we provide evidence for distinct mechanisms related to small and large numerosity and differences in numerical encoding based on task demands. (shrink)

Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in connectionist modeling. Here, we investigated a sequential version of the restricted Boltzmann machine, a stochastic recurrent neural network that extracts high-order structure from sensory data through unsupervised generative learning and can encode contextual (...) information in the form of internal, distributed representations. We assessed whether this type of network can extract the orthographic structure of English monosyllables by learning a generative model of the letter sequences forming a word training corpus. We show that the network learned an accurate probabilistic model of English graphotactics, which can be used to make predictions about the letter following a given context as well as to autonomously generate high-quality pseudowords. The model was compared to an extended version of simple recurrent networks, augmented with a stochastic process that allows autonomous generation of sequences, and to non-connectionist probabilistic models. We conclude that sequential RBMs and stochastic simple recurrent networks are promising candidates for modeling cognition in the temporal domain. (shrink)

Dual-mechanism models of language maintain a distinction between a lexicon and a computational system of linguistic rules. In his target article, Clahsen provides support for such a distinction, presenting evidence from German inflections. He argues for a structured lexicon, going beyond the strict lexicon versus rules dichotomy. We agree with the author in assuming a dual mechanism; however, we argue that a next step must be taken, going beyond the notion of the computational system as specific rules applying to a (...) linguistic domain. By assuming a richer lexicon, the computational system can be conceived as a more general binding process that applies to different linguistic levels: syntax, morphology, reading, and spelling. (shrink)

O'Brien & Opie argue that (1) only explicit representations give rise to conscious experience, and (2) explicit representations depend on stable patterns of activation. In neglect patients, the stimuli presented to the neglected hemifield are not consciously experienced but exert causal effects on the processing of other stimuli presented to the intact hemifield. We argue that O'Brien & Opie cannot account for a nonconscious representation that is stable, as attested by the fact that it affects behavior, but is neither potentially (...) explicit nor tacit. (shrink)

The cognitive impairments shown by brain-damaged patients emphasize the role of task difficulty as a major determinant for performance. We discuss the proposal of Kurzban et al. in light of our findings on right-hemisphere–damaged patients, who show increasing awareness deficits for the contralesional hemispace when engaged with resource-consuming dual tasks. This phenomenon is readily explained by the assumption of unspecific depletable resources.

Speakers retrieve words to use them in sentences. Errors in incorporating words into sentential frames are revealing with respect to the lexical units as well as the lexical retrieval mechanism; hence they constrain theories of lexical access. We present a reanalysis of a corpus of spontaneously occurring lexical exchange errors that highlights the contact points between lexical and sentential processes.

We discuss two key assumptions of Levelt et al.'s model of lexical retrieval: (1) the nondecompositional character of concepts and (2) lemmas as purely syntactic representations. These assumptions fail to capture the broader role of lemmas, which we propose as that of lexical–semantic representations binding (compositional) semantics with phonology (or orthography).