Global and Local Deviance Effects in the Processing of Temporal Patterns.

This study examined visual, auditory, and cross-modal temporal pattern processing at the nonlinguistic and sublexical linguistic levels, and the relationships between these abilities and decoding skill. The central question addressed whether dyslexic readers are impaired in their perception of timing, as assessed by sensitivity to rhythm. Participants were college-level adult dyslexic and normal readers. The dyslexic adults evidenced generalized impairment in temporal processing: they were less accurate and slower than normal readers when required to detect the temporal gap that differentiated pairs of patterns. Impairment was greatest when processing visual syllables. Temporal pattern processing correlated to decoding ability only among normal readers. It is suggested that high-functioning dyslexics may cope with temporal processing problems by adopting a predominantly holistic, orthographic strategy when decoding. It is proposed that there may be cumulative effects of processing demands from different sources including modality, stimulus complexity, and linguistic demands, and that combinations of these may interact to impact temporal processing ability. Moreover, there may be fundamentally distinct and dissociable temporal processing abilities, each of which may be differently linked developmental dyslexia.

Three normal‐hearing right‐handed patients, who underwent complete section of the corpus callosum because of medically uncontrollable seizures, were tested on auditory tone patterns. The pattern stimuli were composed of three 1000‐Hz tone bursts with two different intensities (ΔI = 7 dB) or three tone bursts of two different frequencies (high = 1222 Hz, low = 880 Hz). The duration of the intensity patterns was 250 ms while the duration of the frequency patterns was 150 ms. Interstimulus interval was 200 ms for all patterns. A total of 30 intensity and 30 frequency patterns were presented through earphones at a 40‐dB sensation level to each ear of the subjects. Only one of the patients was tested preoperatively while all three were tested postoperatively. The one patient preoperatively performed at a near normal level for pattern perception whereas the three patients tested postoperatively performed at a level of chance or slightly better on both auditory pattern tests. Based on these results, it is hypothesized that the verbal response to auditory pattern perception depends on interhemispheric processing. More specifically, the contour of the pattern is recognized in the right hemisphere and the verbal response sequenced in the speech‐dominant left hemisphere.

A modified leaky integrator, that is, a neuron model that generates continuous output according to its activation history, is proposed for temporal pattern processing. A modified leaky integrator network is described, and its applicability to temporal pattern processing is discussed theoretically. The learning algorithm for the modified leaky integrator network is presented, and the efficiency in temporal pattern processing is shown through simulations. >

Instruction differentiates the processing of temporal and spatial sequential patterns: evidence from slow wave activity in humans

Multistability of delayed neural networks with hard-limiter saturation nonlinearities

Behavioral experiments using the comodulation masking release (CMR) paradigm indicate that the auditory system is able to encode and compare the temporal characteristics of stimuli across multiple frequency regions. Such processing leads to improved signal detection under certain masking conditions. While there is growing electrophysiological evidence of the coding of temporal patterns at a single spectral region (e.g., the envelope-following response), few electrophysiological studies have demonstrated (directly or indirectly) across-frequency processing of temporal patterns. In the present study, behavioral and electrophysiological estimates of signal detection were obtained in masking conditions that typically lead to a CMR. Detection by normal-hearing human adults was estimated behaviorally using an adaptive staircase technique and electrophysiologically by recording late auditory-evoked potentials (N1-P2). Thresholds were estimated for a 1000-Hz sinusoidal signal (100-ms duration) in the presence of continuous, narrow-band noise maskers. Masker conditions included a single narrow-band noise masker centered at 1000 Hz and maskers composed of five noise bands (centered at 600, 800, 1000, 1200, and 1400 Hz) having either independent or identical (comodulated) temporal envelopes. Behavioral results indicated a masking release of about 8 to 12 dB while physiological estimates revealed somewhat smaller, but substantial, CMRs.

Objectives: To assess the processing of spectrotemporal sound patterns in multiple sclerosis by using auditory evoked potentials (AEPs) to complex harmonic tones. Methods: 22 patients with definite multiple sclerosis but...

Some scholars consider music to exemplify the classic criteria for a complex human adaptation, including universality, orderlying development, and special-purpose cortical processes. The present account focuses on processing predispositions for music. The early appearance of receptive musical skills, well before they have obvious utility, is consistent with their proposed status as predispositions. Infants' processing of musical or music-like patterns is much like that of adults. In the early months of life, infants engage in relational processing of pitch and temporal patterns. They recognize a melody when its pitch level is shifted upward or downward, provided the relations between tones are preserved. They also recognize a tone sequence when the tempo is altered so long as the relative durations remain unchanged. Melodic contour seems to be the most salient feature of melodies for infant listeners. However, infants can detect interval changes when the component tones are related by small-integer frequency ratios. They also show enhanced processing for scales with unequal steps and for metric rhythms. Mothers sing regularly to infants, doing so in a distinctive manner marked by high pitch, slow tempo, and emotional expressiveness. The pitch and tempo of mothers' songs are unusually stable over extended periods. Infant listeners prefer the maternal singing style to the usual style of singing, and they are more attentive to maternal singing than to maternal speech. Maternal singing also has a moderating effect on infant arousal. The implications of these findings for the origins of music are discussed.

The perception of an acoustic rhythm is invariant to the absolute temporal intervals constituting a sound sequence. It is unknown where in the brain temporal Gestalt, the percept emerging from the relative temporal proximity between acoustic events, is encoded. Two different relative temporal patterns, each induced by three experimental conditions with different absolute temporal patterns as sensory basis, were presented to participants. A linear support vector machine classifier was trained to differentiate activation patterns in functional magnetic resonance imaging data to the two different percepts. Across the sensory constituents the classifier decoded which percept was perceived. A searchlight analysis localized activation patterns specific to the temporal Gestalt bilaterally to the temporoparietal junction, including the planum temporale and supramarginal gyrus, and unilaterally to the right inferior frontal gyrus (pars opercularis). We show that auditory areas not only process absolute temporal intervals, but also integrate them into percepts of Gestalt and that encoding of these percepts persists in high-level associative areas. The findings complement existing knowledge regarding the processing of absolute temporal patterns to the processing of relative temporal patterns relevant to the sequential binding of perceptual elements into Gestalt.

Humans tune in to the native language prosody before they are even born. Prior findings with newborns reported language-specific processing of forward versus backward speech and intonational contours and also indicated language-specific processing of iambic versus trochaic patterns in non-linguistic tone stimuli. The present experiment tested newborns’ processing of temporal rhythm patterns in naturalistic native-language speech. Czech-learning newborns were played naturally recorded well-formed Czech utterances with native Czech rhythm (virtually lacking cues to word-level stress) and with non-native rhythm (prolonged foot-initial syllables), while their hemodynamic activity was recorded. The results showed larger hemodynamic responses to the non-native than to the native rhythm in a late analysis window, attributable to a double-peak response shape in the non-native condition. This finding is discussed in terms of suprisal-induced resonating activity after hearing familiar native speech paired with an unfamiliar rhythm pattern. Further, there was an overall attenuated response to the native rhythm localized in the right frontal region (comprising the Broca's area), evidencing right-lateralized processing of speech rhythm. Traditional language development theories claimed that only coarse between-class rhythm differences between languages are processed at birth. Having demonstrated that newborns differentially process non-native vs. native rhythmic patterns within natural native-langugae speech, even in a language outside of the traditional rhythm classes, the present findings disprove some of the early theories and substantially deepen our understanding of early speech development.

Contents: Part I:Neuroscience. T. Rebotier, J. Droulez, Sigma-Pi Properties of Spiking Neurons. H.S. Wan, D.S. Touretzky, A.D. Redish, Towards a Computational Theory of Rat Navigation. H.T. Blair, Evaluating Connectionist Models in Psychology and Neuroscience. Part II:Vision. J. Sirosh, R. Miikkulainen, Self-Organizing Feature Maps with Lateral Connections: Modeling Ocular Dominance. A.K. Bhattacharjya, B. Roysam, Joint Solution of Low, Intermediate, and High Level Vision Tasks by Global Optimization: Application to Computer Vision at Low SNR. T.B. Ghiselli-Crippa, P.W. Munro, Learning Global Spatial Structures from Local Associations. Part III:Cognitive Modeling. D. Ascher, A Connectionist Model of Auditory Morse Code Perception. V. Dragoi, J.E.R. Staddon, A Competitive Neural Network Model for the Process of Recurrent Choice. A.M. Lindemann, A Neural Network Simulation of Numerical Verbal-to-Arabic Transcoding. T. Lund, Combining Models of Single-Digit Arithmetic and Magnitude Comparison. I.E. Dror, Neural Network Models as Tools for Understanding High-Level Cognition: Developing Paradigms for Cognitive Interpretation of Neural Network Models. Part IV:Language. F.J. Eisenhart, Modeling Language as Sensorimotor Coordination. A. Govindjee, G. Dell, Structure and Content in Word Production: Why It's Hard to Say Dlorm. P. Gupta, Investigating Phonological Representations: A Modeling Agenda. H. Schutze, Y. Singer, Part-of-Speech Tagging Using a Variable Context Markov Model. M. Spivey-Knowlton, Quantitative Predictions from a Constraint-Based Theory of Syntactic Ambiguity Resolution. B.B. Tesar, Optimality Semantics. Part V:Symbolic Computation and Rules. K.G. Daugherty, M. Hare, What's in a Rule? The Past Tense by Some Other Name Might Be Called a Connectionist Net. A. Almor, M. Rindner, On the Proper Treatment of Symbolism -- A Lesson from Linguistics. L.F. Niklasson, Structure Sensitivity in Connectionist Models. M. Crucianu, Looking for Structured Representations in Recurrent Networks. I. Tchoumatchenko, Back Propagation with Understandable Results. M.W. Craven, J.W. Shavlik, Understanding Neural Networks via Rule Extraction and Pruning. A-H. Tan, Rule Learning and Extraction with Self-Organizing Neural Networks. Part VI:Recurrent Networks and Temporal Pattern Processing. J.F. Kolen, Recurrent Networks: State Machines or Iterated Function Systems? F. Cummins, R.F. Port, On the Treatment of Time in Recurrent Neural Networks. J.D. McAuley, Finding Metrical Structure in Time. C. Stevens, J. Wiles, Representations of Tonal Music: A Case Study in the Development of Temporal Relationships. M.A.S. Potts, D.S. Broomhead, J.P. Huke, Applications of Radial Basis Function Fitting to the Analysis of Dynamical Systems. M.E. Young, T.M. Bailey, Event Prediction: Faster Learning in a Layered Hebbian Network with Memory. Part VII:Control. S. Thrun, A. Schwartz, Issues in Using Function Approximation for Reinforcement Learning. P. Sabes, Approximating Q-Values with Basis Function Representations. K.L. Markey, Efficient Learning of Multiple Degree-of-Freedom Control Problems with Quasi-Independent Q-Agents. A.L. Tascillo, V.A. Skormin, Neural Adaptive Control of Systems with Drifting Parameters. Part VIII:Learning Algorithms and Architectures. R.C. O'Reilly, Temporally Local Unsupervised Learning: The MaxIn Algorithm for Maximizing Input Information. V.R. de Sa, Minimizing Disagreement for Self-Supervised Classification. S.N. Lindstaedt, Comparison of Two Unsupervised Neural Network Models for Redundancy Reduction. Z. Ghahramani, Solving Inverse Problems Using an EM Approach to Density Estimation. M. Finke, K-R. Muller, Estimating A-Posteriori Probabilities Using Stochastic Network Models. Part IX:Learning Theory. A.S. Weigend, On Overfitting and the Effective Number of Hidden Units. R. Dodier, Increase of Apparent Complexity Is Due to Decrease of Training Set Error. G.B. Orr, T.K. Leen, Momentum and Optimal Stochastic Search. R. Garces, Scheme to Improve the Generalization Error. M.P. Perrone, General Averaging Results for Convex Optimization. R.A. Caruana, Multitask Connectionist Learning. Z. Cataltepe, Y.S. Abu-Mostafa, Estimating Learning Performance Using Hints. Part X:Simulation Tools. A. Jagota, A Simulator for Asynchronous Hopfield Models. A. Linden, An Object-Oriented Dataflow Approach for Better Designs of Neural Net Architectures.

Pre-attentive processing of auditory patterns in dyslexic human subjects

Accurate risk prediction remains a critical challenge in reliability engineering and system safety, particularly in complex systems characterized by interdependent temporal progression and spatial co-occurrence patterns. While existing approaches predominantly focus on either temporal dynamics or co-occurrence relationships, this study introduces a novel spatio-temporal graph learning architecture. First, a dual-matrix graph construction mechanism simultaneously captures spatial risk correlations through co-occurrence frequency analysis and temporal progression patterns using transition probability modeling. Second, an adaptive subgraph extraction module generates system-specific topological representations that preserve both localized risk clusters and directed temporal pathways. Third, a dual-channel graph convolutional network with bilinear interaction fusion facilitates synergistic processing of spatial coexistence features and temporal progression patterns while preserving modality-specific characteristics. Empirical validation across medical diagnosis and vehicular risk domains demonstrates the model's effectiveness in handling multi-risk coexistence scenarios and long-term progression patterns, significantly outperforming conventional single-modality approaches. The proposed methodology offers a generalizable solution for cross-domain risk prediction tasks. The source code is available at https://github.com/FanghuaX/HS-TGN .

Chapter 7 Temporal information processing: A computational role for paired-pulse facilitation and slow inhibition

Recruitment of reciprocal inhibition upon initiation of voluntary movement in man