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Ramotowska S, Archambeau K, Augurzky P, Schlotterbeck F, Berberyan H, Van Maanen L, Szymanik J. Testing two-step models of negative quantification using a novel machine learning analysis of EEG. LANGUAGE, COGNITION AND NEUROSCIENCE 2024; 39:632-656. [PMID: 39040138 PMCID: PMC11261742 DOI: 10.1080/23273798.2024.2345302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 04/05/2024] [Indexed: 07/24/2024]
Abstract
The sentences "More than half of the students passed the exam" and "Fewer than half of the students failed the exam" describe the same set of situations, and yet the former results in shorter reaction times in verification tasks. The two-step model explains this result by postulating that negative quantifiers contain hidden negation, which involves an extra processing stage. To test this theory, we applied a novel EEG analysis technique focused on detecting cognitive stages (HsMM-MVPA) to data from a picture-sentence verification task. We estimated the number of processing stages during reading and verification of quantified sentences (e.g. "Fewer than half of the dots are blue") that followed the presentation of pictures containing coloured geometric shapes. We did not find evidence for an extra step during the verification of sentences with fewer than half. We provide an alternative interpretation of our results in line with an expectation-based pragmatic account.
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Affiliation(s)
- S. Ramotowska
- Institute for Logic, Language and Computation, University of Amsterdam, Amsterdam, The Netherlands
| | | | - P. Augurzky
- Department of Psychology, Universität Tübingen, Tübingen, Germany
| | - F. Schlotterbeck
- Institute of German Language and Literatures, Universität Tübingen, Tübingen, Germany
| | - H.S. Berberyan
- Bernoulli Institute, University of Groningen, Groningen, The Netherlands
| | - L. Van Maanen
- Department of Experimental Psychology & Helmholtz Institute, Utrecht University, Utrecht, The Netherlands
| | - J. Szymanik
- Center for Mind/Brain Sciences and Dept. of Information Engineering and Computer Science, University of Trento, Trento TN, Italy
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Heimisch L, Preuss K, Russwinkel N. Cognitive processing stages in mental rotation - How can cognitive modelling inform HsMM-EEG models? Neuropsychologia 2023; 188:108615. [PMID: 37423423 DOI: 10.1016/j.neuropsychologia.2023.108615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 06/02/2023] [Accepted: 06/06/2023] [Indexed: 07/11/2023]
Abstract
The aspiration for insight into human cognitive processing has traditionally driven research in cognitive science. With methods such as the Hidden semi-Markov Model-Electroencephalography (HsMM-EEG) method, new approaches have been developed that help to understand the temporal structure of cognition by identifying temporally discrete processing stages. However, it remains challenging to assign concrete functional contributions by specific processing stages to the overall cognitive process. In this paper, we address this challenge by linking HsMM-EEG3 with cognitive modelling, with the aim of further validating the HsMM-EEG3 method and demonstrating the potential of cognitive models to facilitate functional interpretation of processing stages. For this purpose, we applied HsMM-EEG3 to data from a mental rotation task and developed an ACT-R cognitive model that is able to closely replicate human performance in this task. Applying HsMM-EEG3 to the mental rotation experiment data revealed a strong likelihood for 6 distinct stages of cognitive processing during trials, with an additional stage for non-rotated conditions. The cognitive model predicted intra-trial mental activity patterns that project well onto the processing stages, while explaining the additional stage as a marker of non-spatial shortcut use. Thereby, this combined methodology provided substantially more information than either method by itself and suggests conclusions for cognitive processing in general.
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Affiliation(s)
- Linda Heimisch
- Technische Universität Berlin, Department of Psychology and Ergonomics, Marchstraße 23, 10587, Berlin, Germany.
| | - Kai Preuss
- Technische Universität Berlin, Department of Psychology and Ergonomics, Marchstraße 23, 10587, Berlin, Germany.
| | - Nele Russwinkel
- Universität zu Lübeck, Institut für Informationssysteme, Ratzeburger Allee 160, 23562, Lübeck, Germany.
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Behavioral Analysis of EEG Signals in Loss-Gain Decision-Making Experiments. Behav Neurol 2022; 2022:3070608. [PMID: 35874640 PMCID: PMC9307401 DOI: 10.1155/2022/3070608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2021] [Accepted: 06/17/2022] [Indexed: 11/18/2022] Open
Abstract
Extraction and analysis of the EEG (electroencephalograph) information features generated during behavioral decision-making can provide a better understanding of the state of mind. Previous studies have focused more on the brainwave features after behavioral decision-making. In fact, the EEG before decision-making is more worthy of our attention. In this study, we introduce a new index based on the reaction time of subjects before decision-making, called the Prestimulus Time (PT), which have important reference value for the study of cognitive function, neurological diseases, and other fields. In our experiments, we use a wearable EEG feature signal acquisition device and a systematic reward and punishment experiment to obtain the EEG features before and after behavioral decision-making. The experimental results show that the EEG generated after behavioral decision due to loss is more intense than that generated by gain in the medial frontal cortex (MFC). In addition, different characteristics of EEG signals are generated prior to behavioral decisions because people have different expectations of the outcome. It will produce more significant negative-polarity event-related potential (ERP) in the forebrain area when the humans are optimistic about the outcomes.
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Manning C, Hassall CD, Hunt LT, Norcia AM, Wagenmakers EJ, Evans NJ, Scerif G. Behavioural and neural indices of perceptual decision-making in autistic children during visual motion tasks. Sci Rep 2022; 12:6072. [PMID: 35414064 PMCID: PMC9005733 DOI: 10.1038/s41598-022-09885-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 03/14/2022] [Indexed: 11/30/2022] Open
Abstract
Many studies report atypical responses to sensory information in autistic individuals, yet it is not clear which stages of processing are affected, with little consideration given to decision-making processes. We combined diffusion modelling with high-density EEG to identify which processing stages differ between 50 autistic and 50 typically developing children aged 6-14 years during two visual motion tasks. Our pre-registered hypotheses were that autistic children would show task-dependent differences in sensory evidence accumulation, alongside a more cautious decision-making style and longer non-decision time across tasks. We tested these hypotheses using hierarchical Bayesian diffusion models with a rigorous blind modelling approach, finding no conclusive evidence for our hypotheses. Using a data-driven method, we identified a response-locked centro-parietal component previously linked to the decision-making process. The build-up in this component did not consistently relate to evidence accumulation in autistic children. This suggests that the relationship between the EEG measure and diffusion-modelling is not straightforward in autistic children. Compared to a related study of children with dyslexia, motion processing differences appear less pronounced in autistic children. Exploratory analyses also suggest weak evidence that ADHD symptoms moderate perceptual decision-making in autistic children.
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Affiliation(s)
- Catherine Manning
- Department of Experimental Psychology, University of Oxford, Oxford, UK.
- School of Psychology and Clinical Language Sciences, University of Reading, Reading, UK.
| | | | | | | | - Eric-Jan Wagenmakers
- Faculty of Social and Behavioural Sciences, University of Amsterdam, Amsterdam, The Netherlands
| | - Nathan J Evans
- School of Psychology, University of Queensland, Brisbane, Australia
| | - Gaia Scerif
- Department of Experimental Psychology, University of Oxford, Oxford, UK
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Desender K, Ridderinkhof KR, Murphy PR. Understanding neural signals of post-decisional performance monitoring: An integrative review. eLife 2021; 10:e67556. [PMID: 34414883 PMCID: PMC8378845 DOI: 10.7554/elife.67556] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 08/08/2021] [Indexed: 12/22/2022] Open
Abstract
Performance monitoring is a key cognitive function, allowing to detect mistakes and adapt future behavior. Post-decisional neural signals have been identified that are sensitive to decision accuracy, decision confidence and subsequent adaptation. Here, we review recent work that supports an understanding of late error/confidence signals in terms of the computational process of post-decisional evidence accumulation. We argue that the error positivity, a positive-going centro-parietal potential measured through scalp electrophysiology, reflects the post-decisional evidence accumulation process itself, which follows a boundary crossing event corresponding to initial decision commitment. This proposal provides a powerful explanation for both the morphological characteristics of the signal and its relation to various expressions of performance monitoring. Moreover, it suggests that the error positivity -a signal with thus far unique properties in cognitive neuroscience - can be leveraged to furnish key new insights into the inputs to, adaptation, and consequences of the post-decisional accumulation process.
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Affiliation(s)
- Kobe Desender
- Brain and Cognition, KU LeuvenLeuvenBelgium
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-EppendorfHamburgGermany
| | - K Richard Ridderinkhof
- Department of Psychology, University of AmsterdamAmsterdamNetherlands
- Amsterdam center for Brain and Cognition (ABC), University of AmsterdamAmsterdamNetherlands
| | - Peter R Murphy
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-EppendorfHamburgGermany
- Trinity College Institute of Neuroscience, Trinity College DublinDublinIreland
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Berberyan HS, van Rijn H, Borst JP. Discovering the brain stages of lexical decision: Behavioral effects originate from a single neural decision process. Brain Cogn 2021; 153:105786. [PMID: 34385085 DOI: 10.1016/j.bandc.2021.105786] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 07/29/2021] [Accepted: 08/01/2021] [Indexed: 11/30/2022]
Abstract
Lexical decision (LD) - judging whether a sequence of letters constitutes a word - has been widely investigated. In a typical lexical decision task (LDT), participants are asked to respond whether a sequence of letters is an actual word or a nonword. Although behavioral differences between types of words/nonwords have been robustly detected in LDT, there is an ongoing discussion about the exact cognitive processes that underlie the word identification process in this task. To obtain data-driven evidence on the underlying processes, we recorded electroencephalographic (EEG) data and applied a novel machine-learning method, hidden semi-Markov model multivariate pattern analysis (HsMM-MVPA). In the current study, participants performed an LDT in which we varied the frequency of words (high, low frequency) and "wordlikeness" of non-words (pseudowords, random non-words). The results revealed that models with six processing stages accounted best for the data in all conditions. While most stages were shared, Stage 5 differed between conditions. Together, these results indicate that the differences in word frequency and lexicality effects are driven by a single cognitive processing stage. Based on its latency and topology, we interpret this stage as a Decision process during which participants discriminate between words and nonwords using activated lexical information.
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Affiliation(s)
| | - Hedderik van Rijn
- Department of Experimental Psychology, University of Groningen, Groningen, the Netherlands
| | - Jelmer P Borst
- Bernoulli Institute, University of Groningen, Groningen, the Netherlands
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Gäbler M, Berberyan HS, Prieske O, Elferink-Gemser MT, Hortobágyi T, Warnke T, Granacher U. Strength Training Intensity and Volume Affect Performance of Young Kayakers/Canoeists. Front Physiol 2021; 12:686744. [PMID: 34248673 PMCID: PMC8264585 DOI: 10.3389/fphys.2021.686744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Accepted: 05/28/2021] [Indexed: 11/15/2022] Open
Abstract
Purpose The aim of this study was to compare the effects of moderate intensity, low volume (MILV) vs. low intensity, high volume (LIHV) strength training on sport-specific performance, measures of muscular fitness, and skeletal muscle mass in young kayakers and canoeists. Methods Semi-elite young kayakers and canoeists (N = 40, 13 ± 0.8 years, 11 girls) performed either MILV (70–80% 1-RM, 6–12 repetitions per set) or LIHV (30–40% 1-RM, 60–120 repetitions per set) strength training for one season. Linear mixed-effects models were used to compare effects of training condition on changes over time in 250 and 2,000 m time trials, handgrip strength, underhand shot throw, average bench pull power over 2 min, and skeletal muscle mass. Both between- and within-subject designs were used for analysis. An alpha of 0.05 was used to determine statistical significance. Results Between- and within-subject analyses showed that monthly changes were greater in LIHV vs. MILV for the 2,000 m time trial (between: 9.16 s, SE = 2.70, p < 0.01; within: 2,000 m: 13.90 s, SE = 5.02, p = 0.01) and bench pull average power (between: 0.021 W⋅kg–1, SE = 0.008, p = 0.02; within: 0.010 W⋅kg–1, SE = 0.009, p > 0.05). Training conditions did not affect other outcomes. Conclusion Young sprint kayakers and canoeists benefit from LIHV more than MILV strength training in terms of 2,000 m performance and muscular endurance (i.e., 2 min bench pull power).
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Affiliation(s)
- Martijn Gäbler
- Department of Human Movement Sciences, University Medical Center Groningen, University of Groningen, Groningen, Netherlands.,Division of Training and Movement Sciences, Research Focus Cognitive Sciences, University of Potsdam, Potsdam, Germany
| | - Hermine S Berberyan
- Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, Netherlands
| | - Olaf Prieske
- Division of Exercise and Movement, University of Applied Sciences for Sports and Management Potsdam, Potsdam, Germany
| | - Marije T Elferink-Gemser
- Department of Human Movement Sciences, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Tibor Hortobágyi
- Department of Human Movement Sciences, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Torsten Warnke
- Research Group Canoeing, Institute for Applied Training Science, Leipzig, Germany
| | - Urs Granacher
- Division of Training and Movement Sciences, Research Focus Cognitive Sciences, University of Potsdam, Potsdam, Germany
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Abstract
AbstractTo improve the understanding of cognitive processing stages, we combined two prominent traditions in cognitive science: evidence accumulation models and stage discovery methods. While evidence accumulation models have been applied to a wide variety of tasks, they are limited to tasks in which decision-making effects can be attributed to a single processing stage. Here, we propose a new method that first uses machine learning to discover processing stages in EEG data and then applies evidence accumulation models to characterize the duration effects in the identified stages. To evaluate this method, we applied it to a previously published associative recognition task (Application 1) and a previously published random dot motion task with a speed-accuracy trade-off manipulation (Application 2). In both applications, the evidence accumulation models accounted better for the data when we first applied the stage-discovery method, and the resulting parameter estimates where generally in line with psychological theories. In addition, in Application 1 the results shed new light on target-foil effects in associative recognition, while in Application 2 the stage discovery method identified an additional stage in the accuracy-focused condition — challenging standard evidence accumulation accounts. We conclude that the new framework provides a powerful new tool to investigate processing stages.
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