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Mismatch Detection

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lightbulbAbout this topic
Mismatch detection refers to the cognitive and perceptual processes involved in identifying discrepancies between expected and actual stimuli or outcomes. It plays a crucial role in various fields, including psychology, neuroscience, and machine learning, as it influences decision-making, learning, and adaptive behavior.
lightbulbAbout this topic
Mismatch detection refers to the cognitive and perceptual processes involved in identifying discrepancies between expected and actual stimuli or outcomes. It plays a crucial role in various fields, including psychology, neuroscience, and machine learning, as it influences decision-making, learning, and adaptive behavior.

Key research themes

1. How can pattern matching algorithms efficiently detect and correct mismatches in strings, including handling wild cards and thresholds on mismatch counts?

This research area investigates algorithmic solutions to the string pattern matching problem with mismatches, focusing on efficient detection of approximate matches allowing a certain number of mismatched characters. The challenges involve minimizing computational complexity despite the combinatorial explosion in possible mismatches, extending approaches to handle wild cards (characters matching any symbol), and providing both exact and approximate matching capabilities. These methods underpin applications in text retrieval, computational biology for DNA/protein sequence analysis, and error-resilient data processing.

Key finding: Introduced novel deterministic, randomized, and approximation algorithms addressing the problem of pattern matching with mismatches measured by Hamming distance, including variants with wild cards in pattern/text; presented... Read more
Key finding: Presented simplified and efficient algorithms for pattern matching allowing up to k mismatches, achieving O(n√k log k) worst-case runtime by integrating filtering and convolution techniques; provided extensions to handle wild... Read more
Key finding: Offered comprehensive algorithms for three related problems: detecting all pattern occurrences with at most k mismatches, approximate counting of mismatches, and reporting exact mismatch counts at each alignment; enhanced... Read more
Key finding: Proposed an algorithm for pattern matching with k mismatches using a novel text analysis framework based on mismatch tracking arrays enabling O(n) amortized processing; introduced procedures for merging mismatch information... Read more
Key finding: Explored foundational error detection and correction mechanisms such as parity bits and Hamming codes, detailing how overlapping parity checks enable automated detection and correction of single-bit errors and identification... Read more

2. What are the computational frameworks and mathematical foundations for detecting and quantifying inconsistencies and mismatches in pairwise comparisons and model designs?

This theme focuses on theoretical and practical frameworks for formally quantifying inconsistency and mismatch in matrices representing pairwise comparisons, as well as in software models. The motivation is to detect violations of consistency constraints that can propagate errors in decision-making, software engineering, and data integration. The area explores algebraic structures (such as abelian groups), inconsistency indicators, and algorithmic frameworks that provide flexible, extensible, and precise mismatch detection applicable to both abstract mathematical objects and practical software models.

Key finding: Developed a novel abelian group based mathematical framework to define and analyze inconsistency indicator maps for pairwise comparison matrices, including metrics and generalized metrics in abelian linearly ordered groups;... Read more
Key finding: Proposed an extensible real-time software design inconsistency checker (XRTSDIC) framework supporting multiple modeling tools, rule languages, and visualization mechanisms to flexibly detect UML model inconsistencies;... Read more
Key finding: Introduced the concept of correspondence functions mapping points between images to robustly identify and reject mismatches in point correspondences; developed an iterative estimation algorithm (IECF) combining diagnostic... Read more
Key finding: Presented an approach for deterministic testing of mismatch in analog circuits (the Test of Mismatch, TOM) that yields compact, low-cost test vector sets specialized to detect parameter variations causing performance... Read more

3. How do neurocognitive and behavioral studies elucidate the mechanisms and effects of mismatch detection in auditory and language processing?

This theme explores empirical research examining the brain's pre-attentive and attentional responses to auditory mismatches and language-related mismatches, such as phonological or lexical deviations. These studies employ electrophysiological methods like event-related potentials (ERPs) and pupillometry to understand automatic mismatch negativity (MMN), attention switching, and associated cognitive processes across different populations including adults and young children. Insights inform theories of sensory processing, attention, language acquisition, and cognitive load measurement.

Key finding: Demonstrated that early-occurring auditory deviances trigger mismatch negativity (MMN) responses of similar amplitude to timely changes but provoke faster and stronger P3a-related involuntary attention switching and... Read more
Key finding: Confirmed and expanded findings on MMN, P3a, and RON components in response to auditory deviants occurring earlier than usual, indicating that although MMN amplitude remains unaffected by stimulus timing, neural signatures... Read more
Key finding: Using pupillometry, found increased pupil dilations in both 30-month-old children and adults when processing mispronounced words compared to correctly pronounced ones, indicating higher processing effort; additionally, adults... Read more

All papers in Mismatch Detection

component around 250 ms of the ERP to deviant stimulus; R EP , ERP to early pitch deviant stimulus; R EO , ERP to standard stimulus with early onset; R TP , ERP to timely pitch deviant stimulus; R S , ERP to timely standard stimulus; RON,... more
Ungan et al. Responses to Early Auditory Changes that the temporal, frontal, and parietal MMN components are simultaneously present rather than emerging sequentially in time, supporting the MMN models based on parallel deviance processing... more
We propose and test the keyhole hypothesis-that measurements from low dimensional EEG, such as ear-EEG reflect a broadly distributed set of neural processes. We formulate the keyhole hypothesis in information theoretical terms. The... more
Ungan et al. Responses to Early Auditory Changes that the temporal, frontal, and parietal MMN components are simultaneously present rather than emerging sequentially in time, supporting the MMN models based on parallel deviance processing... more
This study shows for the first time that mispronunciation detection in 30-month-old children and adults can be measured using pupillometry. Compared to correctly pronounced words we found that mispronounced ones result in larger pupil... more
An acoustic stimulus elicits an electroencephalographic response called auditory event-related potential (ERP). When some members of a stream of standard auditory stimuli are replaced randomly by a deviant stimulus and this stream is... more
This study shows for the first time that mispronunciation detection in 30-month-old children and adults can be measured using pupillometry. Compared to correctly pronounced words we found that mispronounced ones result in larger pupil... more
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