Biology subjects

Güntürkün, O.

Publications and source records attributed to Güntürkün, O..

6 recordsLinked to original sources

bioRxiv2026

Continuous foraging behavior shapes patch-leaving decisions in pigeons: A 3D tracking study

Optimal foraging behavior is a key component of successful adaptations to natural environments. Understanding how animals decide to stay near food or to leave it for another food patch gives us insights into the underlying cognitive mechanisms that govern adaptive behaviors. 3D pose tracking was used to determine how pigeons exploit a 4 square meter arena with two separate platforms (i.e. food patches) whose absolute and relative elevations were manipulated. Detailed kinematic features of foraging and traveling behaviors were quantified using automated video tracking, without a need for manual coding. Our computational approach captured continuous, high-dimensional movement patterns and enabled precise quantification of travel costs between patches. Combined with mixed-effects survival analysis, our fine-grained behavioral tracking provided detailed insight into the moment-by-moment dynamics of patch-leaving decisions of pigeons. As expected from behavior optimization models, our results showed a preference to visit a ground food platform first, and longer latencies to leave an elevated platform. Foraging activity significantly decreased throughout the session, with shorter visits, less pecks per visit, and a decrease in inter-peck variability. However, a mixed-effects Cox regression modeled pigeons patch-leaving probability, demonstrating that current and cumulative foraging parameters between patches significantly enhanced the models predictive power beyond patch accessibility (i.e., beyond travel costs). This suggests that pigeons integrate both current environmental cues and their individual foraging history when making patch-leaving decisions. Our findings are discussed in relation to the marginal value theorem and optimal foraging theory.

animal behavior and cognition↗

bioRxiv2023

Investigating robust associations between functional connectivity based on graph theory and general intelligence

Previous research investigating relations between general intelligence and graph-theoretical properties of the brains intrinsic functional network has yielded contradictory results. A promising approach to tackle such mixed findings is multi-center analysis. For this study, we analyzed data from four independent data sets (total N > 2000) to identify robust associations amongst samples between g factor scores and global as well as node-specific graph metrics. On the global level, g showed no significant associations with global efficiency in any sample, but significant positive associations with global clustering coefficient and small-world propensity in two samples. On the node-specific level, elastic-net regressions for nodal efficiency and local clustering yielded no brain areas that exhibited consistent associations amongst data sets. Using the areas identified via elastic-net regression in one sample to predict g in other samples was not successful for nodal efficiency and only led to significant predictions between two data sets for local clustering. Thus, using conventional graph theoretical measures based on resting-state imaging did not result in replicable associations between functional connectivity and general intelligence.

neuroscience↗

bioRxiv2022

Robust associations between white matter microstructure and general intelligence

Early research on the neural correlates of human intelligence was almost exclusively focused on gray matter properties. The advent of diffusion-weighted imaging led to an exponential growth of white matter brain imaging studies. However, this line of research has yielded mixed observations, especially about the relations between general intelligence and white matter microstructure. We used a multi-center approach to identify white matter regions that show replicable structure-function associations, employing data from four independent samples comprising over 2000 healthy participants. We used tract-based spatial statistics to examine associations between g factor scores and white matter microstructure and identified 188 voxels which exhibited positive associations between g factor scores and fractional anisotropy in all four data sets. Replicable voxels formed three clusters: one located around the forceps minor, crossing with extensions of the anterior thalamic radiation, the cingulum-cingulate gyrus, and the inferior fronto-occipital fasciculus in the left hemisphere, one located around the left-hemispheric superior longitudinal fasciculus, and one located around the left-hemispheric cingulum-cingulate gyrus, crossing with extensions of the anterior thalamic radiation and the inferior fronto-occipital fasciculus. Our results indicate that individual differences in general intelligence are robustly associated with white matter organization in specific fiber bundles.

neuroscience↗

bioRxiv2022

Structural architecture and brain network efficiency links polygenic scores to intelligence

Intelligence is highly heritable. Genome-wide association studies (GWAS) have shown that thousands of alleles contribute to variation in intelligence with small effect sizes. Polygenic scores (PGS), which combine these effects into one genetic summary measure, are increasingly used to investigate polygenic effects in independent samples. Whereas PGS explain a considerable amount of variance in intelligence, it is largely unknown how brain structure and function mediate this relationship. Here we show that individuals with higher PGS for educational attainment and intelligence had higher scores on cognitive tests, larger surface area, and more efficient fiber connectivity derived by graph theory. Fiber network efficiency as well as surface of brain areas partly located in parieto-frontal regions were found to mediate the relationship between PGS and cognitive performance. These findings are a crucial step forward in decoding the neurogenetic underpinnings of intelligence, as they identify specific regional networks that link polygenic predisposition to intelligence.

genetics↗

bioRxiv2020

Polygenic Scores for Cognitive Abilities and their Association with Different Aspects of General Intelligence - a Deep Phenotyping Approach

Intelligence is a highly polygenic trait and genome-wide association studies (GWAS) have identified thousands of DNA variants contributing with small effects. Polygenic scores (PGS) can aggregate those effects for trait prediction in independent samples. As large-scale light-phenotyping GWAS operationalized intelligence as performance in rather superficial tests, the question arises which intelligence facets are actually captured. We used deep-phenotyping to investigate the molecular determinants of individual differences in cognitive ability. We therefore studied the association between PGS of intelligence (IQ-PGS), cognitive performance (CP-PGS) and educational attainment (EA-PGS) with a wide range of intelligence facets in a sample of 557 healthy adults. IQ-PGS, CP-PGS and EA-PGS had the highest incremental R2s for general (2.71%; 4.27%; 2.06%), verbal (3.30%; 4.64%; 1.61%) and numerical intelligence (3.06%; 3.24%; 1.26%) and the weakest for non-verbal intelligence (0.89%; 1.47%; 0.70%) and memory (0.80%; 1.06%; 0.67%). These results indicate that PGS derived from light-phenotyping GWAS do not reflect different facets of intelligence equally well, and thus should not be interpreted as genetic indicators of intelligence per se. The findings refine our understanding of how PGS are related to other traits or life outcomes.

genetics↗

bioRxiv2020

Complex dynamics of choice during operant extinction and a simple associative learning account

Extinction learning, the process of ceasing an acquired behavior in response to altered reinforcement contingencies, is not only essential for survival in a changing environment, but also plays a fundamental role in the treatment of pathological behaviors. During therapy and other forms of training involving extinction, subjects are typically exposed to several sessions with a similar structure. The effects of this repeated exposure are not well understood. Here, we studied the behavior of pigeons across several sessions of a discrimination learning task in context A, extinction in context B, and a return to context A to test the context-dependent return of the learned responses (ABA renewal). By focusing on individual learning curves across animals, we uncovered a session-dependent variability of behavior: (1) During extinction, pigeons preferred the unrewarded alternative choice in one-third of the sessions, predominantly during the first one. (2) In later sessions, abrupt transitions of behavior at the onset of context B emerged, and (3) the renewal effect decayed as sessions progressed. We show that the observed results can be parsimoniously accounted for by a computational model based only on associative learning between stimuli and actions. Our work thus demonstrates the critical importance of studying the trial-by-trial dynamics of learning in individual sessions, and the power of "simple" associative learning processes.

animal behavior and cognition↗