This paper describes a novel theoretical framework of how the position of a letter within a string is encoded, the SERIOL model (sequential encoding regulated by inputs to oscillations within letter units). Letter order is represented by a temporal activation pattern across letter units, as is consistent with current theories of information coding based on the precise timing of neural spikes. The framework specifies how this pattern is invoked via an activation gradient that interacts with subthreshold oscillations and how it is decoded via contextual units that activate word units. Using mathematical modeling, this theoretical framework is shown to account for the experimental data from a wide variety of string-processing studies, including hemispheric asymmetries, the optimal viewing position, and positional priming effects

We report four experiments investigating the effects of repeated and transposed letters in orthographic processing. Orthographically related primes were formed by removing one letter from the target word, by transposing two adjacent letters, or by replacing two adjacent letters with different letters. Robust masked priming in a lexical decision task was found for primes formed by removing a single letter (e.g., mircle-MIRACLE), and this was not influenced by whether or not the prime contained a letter repetition (e.g., balace vs. balnce as a prime for BALANCE). Target words containing a repeated letter tended to be harder to respond to than words without a letter repetition, but the nonwords formed by removing a repeated letter (e.g., BALNCE) were no harder to reject than nonwords formed by removing a non-repeated letter (e.g., MIRCLE, BALACE). Significant transposition priming effects were found for 7-letter words (e.g., sevrice-SERVICE), and these priming effects did not vary as a function of the position of the transposition (initial, final, or inner letter pair). Priming effects disappeared when primes were formed by replacing the two transposed letters with different letters (e.g., sedlice-SERVICE), and fiveletter words only showed priming effects with inner letter transpositions (e.g., Correspondence should be addressed to Jonathan Grainger, Laboratoire de Psychologie

similarity effects in masked associative priming One issue that all models of visual word recognition in alphabetic orthographies must ultimately take a position on is how the human processing system encodes letter positions when creating internal orthographic representations. Furthermore, although the choice of a coding scheme might seem to be a secondary aspect of these models, it can have a large impact on a model’s predictions (Andrews, 1996). For example, virtually all of the current models assume that the derived orthographic representation activates the lexical representations of formally similar words

Abstract not found

Letter position dyslexia (LPD) is a peripheral dyslexia that causes errors of letter order within words. So far, only cases of acquired LPD have been reported. This study presents selective LPD in its developmental form, via the testing of 11 Hebrew-speaking individuals with developmental dyslexia. The study explores the types of errors and effects on reading in this dyslexia, using a variety of tests: reading aloud, lexical decision, same-different decision, definition and letter naming. The findings indicate that individuals with developmental LPD have a deficit in the letter position encoding function of the orthographic-visual analyzer, which leads to underspecification of letter position within words. Letter position errors occur mainly in adjacent middle letters, when the error creates another existing word. The participants did not show an output deficit or phonemic awareness deficit. The selectivity of the deficit, causing letter position errors but no letter identity errors and no migrations between words, supports the existence of letter position encoding function as separate from letter identification and letter-to-word binding.

The relationships between repetition/form priming effects and neighborhood density were analyzed in two masked priming experiments with the lexical decision task. Given that form priming effects appear to be influenced by a word's orthographic neighborhood, it is theoretically important to find out whether or not repetition priming also differs as a function of the words orthographic neighborhood. Within an activation framework, repetition and form priming effects are just quantitatively different phenomena, whereas the two effects are qualitatively different in a serial-ordered model of lexical access (the entry-opening model). The results show that repetition and form priming effects were stronger for hermit words than for words with many neighbors. These results pose some problems for both activation and serial-ordered models. The implications of these results for determining how neighbors affect the identification of a word are discussed.

Recent research has shown that letter identity and letter position are not integral perceptual dimensions (e.g., jugde primes judge in word-recognition experiments). Most comprehensive computational models of visual word recognition (e.g., the interactive activation model, J. L. McClelland & D. E. Rumelhart, 1981, and its successors) assume that the position of each letter within a word is perfectly encoded. Thus, these models are unable to explain the presence of effects of letter transposition (trial–trail), letter migration (beard–bread), repeated letters (moose–mouse), or subset/superset effects (faulty–faculty). The authors extend R. Ratcliff’s (1981) theory of order relations for encoding of letter positions and show that the model can successfully deal with these effects. The basic assumption is that letters in the visual stimulus have distributions over positions so that the representation of one letter will extend into adjacent letter positions. To test the model, the authors conducted a series of forced-choice perceptual identification experiments. The overlap model produced very good fits to the empirical data, and even a simplified 2-parameter model was capable of producing fits for 104 observed data points with a correlation coefficient of.91.

How have connectionist models informed the study of development? This paper considers three contributions from specific models. First, connectionist models have proven useful for exploring nonlinear dynamics and emergent properties, and their role in nonlinear developmental trajectories, critical periods and developmental disorders. Second, connectionist models have informed the study of the representations that lead to behavioral dissociations. Third, connectionist models have provided insight into neural mechanisms, and why different brain regions are specialized for different functions. Connectionist and dynamic systems approaches to development have differed, with connectionist approaches focused on learning processes and representations in cognitive tasks, and dynamic systems approaches focused on mathematical characterizations of physical elements of the system and their interactions with the environment. The two approaches also share much in common, such as their emphasis on continuous, nonlinear processes and their broad application to a range of behaviors.

The current work proposes a neuronal-inspired solution to active visual search, that is, visual search for a given target in displays that are too large in spatial extent to be inspected in a single visual fixation. Recent experimental data [52, 53, 55] using behaving, fixating monkeys is used as a guide for the model; [43] and the current body of work are the first attempts to model this behaviour. The strategy presented here includes novel components such as a representation of saccade history and of peripheral targets that is computed in an entirely separate stream from foveal attention. Although this presentation describes the prototype of this model and much work remains, preliminary results obtained from its implementation seem consistent with the behaviour exhibited in humans and macaque monkeys as described by Motter.

A connectionist model of causal attribution is presented, emphasizing the use of domain-general principles of processing and learning previously employed in models of semantic cognition. The model categorizes objects dependent upon their observed “causal properties ” and is capable of making several types of inferences that four-year-old children have been shown to be capable of. The model gives rise to approximate conformity to normative models of causal inference and gives approximate estimates of the probability that an object presented in an ambiguous situation actually possesses a particular causal power, based on background knowledge and recent observations. It accounts for data from three sets of experimental studies of the causal inferencing abilities of young children. The model provides a base for further efforts to delineate the intuitive mechanisms of causal inference employed by children and adults, without appealing to inherent principles or mechanisms specialized for causal as opposed to other forms of reasoning.