150 Psychologie
Refine
Year of publication
- 2020 (11) (remove)
Has Fulltext
- yes (11)
Is part of the Bibliography
- no (11)
Keywords
- EEG (2)
- Brain size I (1)
- Computational modelling (1)
- Gray matter volume (1)
- Handwriting (1)
- Intelligence (1)
- MVPA (1)
- Machine learning (1)
- N2 (1)
- N400 (1)
Institute
To characterize the left-ventral occipito-temporal cortex (lvOT) role during reading in a quantitatively explicit and testable manner, we propose the lexical categorization model (LCM). The LCM assumes that lvOT optimizes linguistic processing by allowing fast meaning access when words are familiar and filter out orthographic strings without meaning. The LCM successfully simulates benchmark results from functional brain imaging. Empirically, using functional magnetic resonance imaging, we demonstrate that quantitative LCM simulations predict lvOT activation across three studies better than alternative models. Besides, we found that word-likeness, which is assumed as input to LCM, is represented posterior to lvOT. In contrast, a dichotomous word/non-word contrast, which is assumed as the LCM’s output, could be localized to upstream frontal brain regions. Finally, we found that training lexical categorization results in more efficient reading. Thus, we propose a ventral-visual-stream processing framework for reading involving word-likeness extraction followed by lexical categorization, before meaning extraction.
Most current models assume that the perceptual and cognitive processes of visual word recognition and reading operate upon neuronally coded domain-general low-level visual representations – typically oriented line representations. We here demonstrate, consistent with neurophysiological theories of Bayesian-like predictive neural computations, that prior visual knowledge of words may be utilized to ‘explain away’ redundant and highly expected parts of the visual percept. Subsequent processing stages, accordingly, operate upon an optimized representation of the visual input, the orthographic prediction error, highlighting only the visual information relevant for word identification. We show that this optimized representation is related to orthographic word characteristics, accounts for word recognition behavior, and is processed early in the visual processing stream, i.e., in V4 and before 200 ms after word-onset. Based on these findings, we propose that prior visual-orthographic knowledge is used to optimize the representation of visually presented words, which in turn allows for highly efficient reading processes.
Most current models assume that the perceptual and cognitive processes of visual word recognition and reading operate upon neuronally coded domain-general low-level visual representations – typically oriented line representations. We here demonstrate, consistent with neurophysiological theories of Bayesian-like predictive neural computations, that prior visual knowledge of words may be utilized to ‘explain away’ redundant and highly expected parts of the visual percept. Subsequent processing stages, accordingly, operate upon an optimized representation of the visual input, the orthographic prediction error, highlighting only the visual information relevant for word identification. We show that this optimized representation is related to orthographic word characteristics, accounts for word recognition behavior, and is processed early in the visual processing stream, i.e., in V4 and before 200 ms after word-onset. Based on these findings, we propose that prior visual-orthographic knowledge is used to optimize the representation of visually presented words, which in turn allows for highly efficient reading processes.
A positive association between brain size and intelligence is firmly established, but whether region-specific anatomical differences contribute to general intelligence remains an open question. Results from voxel-based morphometry (VBM) - one of the most widely used morphometric methods - have remained inconclusive so far. Here, we applied cross-validated machine learning-based predictive modeling to test whether out-of-sample prediction of individual intelligence scores is possible on the basis of voxel-wise gray matter volume. Features were derived from structural magnetic resonance imaging data (N = 308) using (a) a purely data-driven method (principal component analysis) and (b) a domain knowledge-based approach (atlas parcellation). When using relative gray matter (corrected for total brain size), only the atlas-based approach provided significant prediction, while absolute gray matter (uncorrected) allowed for above-chance prediction with both approaches. Importantly, in all significant predictions, the absolute error was relatively high, i.e., greater than ten IQ points, and in the atlas-based models, the predicted IQ scores varied closely around the sample mean. This renders the practical value even of statistically significant prediction results questionable. Analyses based on the gray matter of functional brain networks yielded significant predictions for the fronto-parietal network and the cerebellum. However, the mean absolute errors were not reduced in contrast to the global models, suggesting that general intelligence may be related more to global than region-specific differences in gray matter volume. More generally, our study highlights the importance of predictive statistical analysis approaches for clarifying the neurobiological bases of intelligence and provides important suggestions for future research using predictive modeling.
Children with attention-deficit/hyperactivity disorder (ADHD) are characterized by symptoms of inattention, impulsivity, and hyperactivity. Neurophysiological correlates of ADHD include changes in the P3 component of event-related brain potentials (ERPs). Motivated by recent advances towards a more dimensional understanding of ADHD, we investigate whether ADHD-related ERP markers relate to continuous variations in attention and executive functioning also in typically-developing children. ERPs were measured while 31 school children (9–11 years) completed an adapted version of the Continuous Performance Task that additionally to inhibitory processes also isolates effects of physical stimulus salience. Children with higher levels of parent-reported ADHD symptoms did not differ in task performance, but exhibited smaller P3 amplitudes related to stimulus salience. Furthermore, ADHD symptoms were associated with the variability of neural responses over time: Children with higher levels of ADHD symptoms demonstrated lower variability in inhibition- and salience-related P3 amplitudes. No effects were observed for ERP latencies and the salience-related N2. By demonstrating that ADHD-associated neurophysiological mechanisms of inhibition and salience processing covary with attention and executive functioning in a children community sample, our study provides neurophysiological support for dimensional models of ADHD. Also, temporal variability in event-related potentials is highlighted as additional indicator of ADHD requiring further investigation.
Cognitive flexibility – the ability to adjust one’s behavior to changing environmental demands – is crucial for controlled behavior. However, the term ‘cognitive flexibility’ is used heterogeneously, and associations between cognitive flexibility and other facets of flexible behavior have only rarely been studied systematically. To resolve some of these conceptual uncertainties, we directly compared cognitive flexibility (cue-instructed switching between two affectively neutral tasks), affective flexibility (switching between a neutral and an affective task using emotional stimuli), and feedback-based flexibility (non-cued, feedback-dependent switching between two neutral tasks). Three experimental paradigms were established that share as many procedural features (in terms of stimuli and/or task rules) as possible and administered in a pre-registered study plan (N = 100). Correlation analyses revealed significant associations between the efficiency of cognitive and affective task switching (response time switch costs). Feedback-based flexibility (measured as mean number of errors after rule reversals) did not correlate with task switching efficiency in the other paradigms, but selectively with the effectiveness of affective switching (error rate costs when switching from neutral to emotion task). While preregistered confirmatory factor analysis (CFA) provided no clear evidence for a shared factor underlying the efficiency of switching in all three domains of flexibility, an exploratory CFA suggested commonalities regarding switching effectiveness (accuracy-based switch costs). We propose shared mechanisms controlling the efficiency of cue-dependent task switching across domains, while the relationship to feedback-based flexibility may depend on mechanisms controlling switching effectiveness. Our results call for a more stringent conceptual differentiation between different variants of psychological flexibility.
A question of striking the right balance : how do digital media influence how we think and act?
(2020)
What influence do digital technologies have on human perception, thinking and action? Do computer games harm the development of young brains? And is there really such a thing as »digital dementia«, an increasing forgetfulness caused by the use of modern technologies? For some of these questions, answers are available that are empirically corroborated.
Auf das richtige Maß kommt es an : wie beeinflussen digitale Medien unser Denken und Handeln?
(2020)
Welchen Einfluss haben digitale Technologien auf das menschliche Wahrnehmen, Denken und Handeln? Schaden Computerspiele der Entwicklung junger Gehirne? Und gibt es tatsächlich so etwas wie eine »digitale Demenz«, eine durch die Nutzung moderner Technologien bedingte wachsende Vergesslichkeit? Auf einige dieser Fragen gibt es bereits Antworten, die empirisch belegt sind.
How is semantic information stored in the human mind and brain? Some philosophers and cognitive scientists argue for vectorial representations of concepts, where the meaning of a word is represented as its position in a high-dimensional neural state space. At the intersection of natural language processing and artificial intelligence, a class of very successful distributional word vector models has developed that can account for classic EEG findings of language, that is, the ease versus difficulty of integrating a word with its sentence context. However, models of semantics have to account not only for context-based word processing, but should also describe how word meaning is represented. Here, we investigate whether distributional vector representations of word meaning can model brain activity induced by words presented without context. Using EEG activity (event-related brain potentials) collected while participants in two experiments (English and German) read isolated words, we encoded and decoded word vectors taken from the family of prediction-based Word2vec algorithms. We found that, first, the position of a word in vector space allows the prediction of the pattern of corresponding neural activity over time, in particular during a time window of 300 to 500 ms after word onset. Second, distributional models perform better than a human-created taxonomic baseline model (WordNet), and this holds for several distinct vector-based models. Third, multiple latent semantic dimensions of word meaning can be decoded from brain activity. Combined, these results suggest that empiricist, prediction-based vectorial representations of meaning are a viable candidate for the representational architecture of human semantic knowledge.
Working memory (WM) performance varies substantially among individuals but the precise contribution of different WM component processes to these functional limits remains unclear. By analyzing different types of responses in a spatial WM task, we recently demonstrated a functional dissociation between confident and not-confident errors reflecting failures of WM encoding and maintenance, respectively. Here, we use event-related brain potentials to further explore this dissociation. Healthy participants performed a delayed orientation-discrimination task and rated their response confidence for each trial. The encoding-related N2pc component was significantly reduced for confident errors compared to confident correct responses, which is indicative of an encoding failure. In contrast, the maintenance-related contra-lateral delay activity was similar for these response types indicating that in confident error trials, WM representations – potentially the wrong ones – were maintained accurately and with stability throughout the delay interval. However, contra-lateral delay activity measured during the early part of the delay period was decreased for not-confident errors, potentially reflecting compromised maintenance processes. These electrophysiological findings contribute to a refined understanding of the encoding and maintenance processes that contribute to limitations in WM performance and capacity.