Intra-individual variation in cognitive abilities has been widely reported in animals.Recent studies have found that individual cognitive performance varies with personality traits in a wide range of animal taxa,with ...Intra-individual variation in cognitive abilities has been widely reported in animals.Recent studies have found that individual cognitive performance varies with personality traits in a wide range of animal taxa,with a speed-accuracy trade-off between cognition and personality traits.Few studies investigated whether these relationships change depending on different contexts.Here we investigate whether the personality trait(as measured by exploratory behavior in a novel environment)is associated with cognition(novel skill learning and spatial memory)in wild male chestnut thrushes Turdus rubrocanus.Using an experimental novel skill-learning task set-up,we found that fast-exploring individuals explored the experimental device(a cardboard with 8 opaque cups)sooner than slow-exploring individuals.Exploratory behavior was not associated with individual spatial memory performances or an individual’s capacity to learn the novel skill.Learning speed was positively associated with the difficulty of learning phases,and fast-exploring individuals used less trials to meet the learning criterion.In addition,fast-exploring individuals took less time to complete the 24-h spatial memory test,but the accuracy of the test was not significantly different between individuals who were more or less exploratory.We suggest that variation in personality traits associates with individual learning speed in cognitive tasks and that this relationship is context-dependent.展开更多
Tracking the fast-moving object in occlusion situations is an important research topic in computer vision. Despite numerous notable contributions have been made in this field,few of them simultaneously incorporate bot...Tracking the fast-moving object in occlusion situations is an important research topic in computer vision. Despite numerous notable contributions have been made in this field,few of them simultaneously incorporate both object's extrinsic features and intrinsic motion patterns into their methodologies,thereby restricting the potential for tracking accuracy improvement. In this paper, on the basis of efficient convolution operators(ECO) model, a speed-accuracy-balanced model is put forward. This model uses the simple correlation filter to track the object in real-time, and adopts the sophisticated deep-learning neural network to extract high-level features to train a more complex filter correcting the tracking mistakes, when the tracking state is judged to be poor. Furthermore, in the context of scenarios involving regular fast-moving, a motion model based on Kalman filter is designed which greatly promotes the tracking stability, because this motion model could predict the object's future location from its previous movement pattern. Additionally,instead of periodically updating our tracking model and training samples, a constrained condition for updating is proposed,which effectively mitigates contamination to the tracker from the background and undesirable samples avoiding model degradation when occlusion happens. From comprehensive experiments, our tracking model obtains better performance than ECO on object tracking benchmark 2015(OTB100), and improves the area under curve(AUC) by about 8% and 32% compared with ECO, in the scenarios of fast-moving and occlusion on our own collected dataset.展开更多
Cognitive behaviors are determined by underlying neural networks. Many brain functions, such as learning and memory, have been successfully described by attractor dynamics. For decision making in the brain, a quantita...Cognitive behaviors are determined by underlying neural networks. Many brain functions, such as learning and memory, have been successfully described by attractor dynamics. For decision making in the brain, a quantitative description of global attractor landscapes has not yet been completely given. Here, we developed a theoretical framework to quantify the landscape associated with the steady state probability distributions and associated steady state curl flux, measuring the degree of non-equilibrium through the degree of detailed balance breaking for decision making. We quantified the decision-making processes with optimal paths from the undecided attractor states to the decided attractor states, which are identified as basins of attractions, on the landscape. Both landscape and flux determine the kinetic paths and speed. The kinetics and global stability of decision making are explored by quantifying the landscape topography through the barrier heights and the mean first passage time. Our theoretical predictions are in agreement with experimental observations： more errors occur under time pressure. We quantitatively explored two mechanisms of the speed-accuracy tradeoff with speed emphasis and further uncovered the tradeoffs among speed, accuracy, and energy cost. Our results imply that there is an optimal balance among speed, accuracy, and the energy cost in decision making. We uncovered the possible mechanisms of changes of mind and how mind changes improve performance in decision processes. Our landscape approach can help facilitate an understanding of the underlying physical mechanisms of cognitive processes and identify the key factors in the corresponding neural networks.展开更多
Information sharing is a critical task for group-living animals. The pattern of sharing can be modeled as a network whose structure can affect the decision-making performance of individual members as well as that of t...Information sharing is a critical task for group-living animals. The pattern of sharing can be modeled as a network whose structure can affect the decision-making performance of individual members as well as that of the group as a whole. A fully connected network, in which each member can directly transfer information to all other members, ensures rapid sharing of important information, such as a promising foraging location. However, it can also impose costs by amplifying the spread of inaccur- ate information （if, for example the foraging location is actually not profitable）. Thus, an optimal net- work structure should balance effective sharing of current knowledge with opportunities to discover new information. We used a computer simulation to measure how well groups characterized by dif- ferent network structures （fully connected, small world, lattice, and random） find and exploit resource peaks in a variable environment. We found that a fully connected network outperformed other struc- tures when resource quality was predictable. When resource quality showed random variation, however, the small world network was better than the fully connected one at avoiding extremely poor outcomes. These results suggest that animal groups may benefit by adjusting their informa- tion-sharing network structures depending on the noisiness of their environment.展开更多
The brain size of vertebrates represents a trade-off between natural selection for enhanced cognitive abilities and the energetic constraints of brain tissue production.Processing information efficiently can confer be...The brain size of vertebrates represents a trade-off between natural selection for enhanced cognitive abilities and the energetic constraints of brain tissue production.Processing information efficiently can confer benefits,but it also entails time costs.Breeding strategies,encompassing timing of breeding onset and nest-site selection,may be related to brain size.In this study,we aim to elucidate the relationship between brain size,breeding timing,nest-site choice,and breeding success in the red-backed shrike Lanius collurio.Our findings revealed that the timing of the first egg-laying date was associated with female head size,with larger-headed females tending to lay eggs later in the breeding season.Additionally,we observed that breeding success was positively correlated with increased nest concealment.However,this relationship was stronger in males with smaller heads.In turn,nest concealment was not related to head size but primarily influenced breeding onset.These results suggest that the choice of breeding strategy may be moderated by brain size,with differences between sexes.Larger-headed females may invest more time in selecting nesting sites,leading to delayed breeding onset,while larger-headed males may compensate for suboptimal nest concealment.Our study sheds light on the intricate interplay between brain size,breeding timing,nest-site preferences,and breeding success in passerine birds,underscoring the potential role of cognitive capacity in shaping individual decision-making processes.展开更多
基金supported by the National Natural Science Foundation of China(32070452).
文摘Intra-individual variation in cognitive abilities has been widely reported in animals.Recent studies have found that individual cognitive performance varies with personality traits in a wide range of animal taxa,with a speed-accuracy trade-off between cognition and personality traits.Few studies investigated whether these relationships change depending on different contexts.Here we investigate whether the personality trait(as measured by exploratory behavior in a novel environment)is associated with cognition(novel skill learning and spatial memory)in wild male chestnut thrushes Turdus rubrocanus.Using an experimental novel skill-learning task set-up,we found that fast-exploring individuals explored the experimental device(a cardboard with 8 opaque cups)sooner than slow-exploring individuals.Exploratory behavior was not associated with individual spatial memory performances or an individual’s capacity to learn the novel skill.Learning speed was positively associated with the difficulty of learning phases,and fast-exploring individuals used less trials to meet the learning criterion.In addition,fast-exploring individuals took less time to complete the 24-h spatial memory test,but the accuracy of the test was not significantly different between individuals who were more or less exploratory.We suggest that variation in personality traits associates with individual learning speed in cognitive tasks and that this relationship is context-dependent.
基金supported by the National Nature Science Foundation of China (62373246,62203299)the Oceanic Interdisciplinary Program of Shanghai Jiao Tong University (SL2022MS008,SL2020ZD206,SL2022MS010)。
文摘Tracking the fast-moving object in occlusion situations is an important research topic in computer vision. Despite numerous notable contributions have been made in this field,few of them simultaneously incorporate both object's extrinsic features and intrinsic motion patterns into their methodologies,thereby restricting the potential for tracking accuracy improvement. In this paper, on the basis of efficient convolution operators(ECO) model, a speed-accuracy-balanced model is put forward. This model uses the simple correlation filter to track the object in real-time, and adopts the sophisticated deep-learning neural network to extract high-level features to train a more complex filter correcting the tracking mistakes, when the tracking state is judged to be poor. Furthermore, in the context of scenarios involving regular fast-moving, a motion model based on Kalman filter is designed which greatly promotes the tracking stability, because this motion model could predict the object's future location from its previous movement pattern. Additionally,instead of periodically updating our tracking model and training samples, a constrained condition for updating is proposed,which effectively mitigates contamination to the tracker from the background and undesirable samples avoiding model degradation when occlusion happens. From comprehensive experiments, our tracking model obtains better performance than ECO on object tracking benchmark 2015(OTB100), and improves the area under curve(AUC) by about 8% and 32% compared with ECO, in the scenarios of fast-moving and occlusion on our own collected dataset.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.21190040,91430217,and 11305176)
文摘Cognitive behaviors are determined by underlying neural networks. Many brain functions, such as learning and memory, have been successfully described by attractor dynamics. For decision making in the brain, a quantitative description of global attractor landscapes has not yet been completely given. Here, we developed a theoretical framework to quantify the landscape associated with the steady state probability distributions and associated steady state curl flux, measuring the degree of non-equilibrium through the degree of detailed balance breaking for decision making. We quantified the decision-making processes with optimal paths from the undecided attractor states to the decided attractor states, which are identified as basins of attractions, on the landscape. Both landscape and flux determine the kinetic paths and speed. The kinetics and global stability of decision making are explored by quantifying the landscape topography through the barrier heights and the mean first passage time. Our theoretical predictions are in agreement with experimental observations： more errors occur under time pressure. We quantitatively explored two mechanisms of the speed-accuracy tradeoff with speed emphasis and further uncovered the tradeoffs among speed, accuracy, and energy cost. Our results imply that there is an optimal balance among speed, accuracy, and the energy cost in decision making. We uncovered the possible mechanisms of changes of mind and how mind changes improve performance in decision processes. Our landscape approach can help facilitate an understanding of the underlying physical mechanisms of cognitive processes and identify the key factors in the corresponding neural networks.
文摘Information sharing is a critical task for group-living animals. The pattern of sharing can be modeled as a network whose structure can affect the decision-making performance of individual members as well as that of the group as a whole. A fully connected network, in which each member can directly transfer information to all other members, ensures rapid sharing of important information, such as a promising foraging location. However, it can also impose costs by amplifying the spread of inaccur- ate information （if, for example the foraging location is actually not profitable）. Thus, an optimal net- work structure should balance effective sharing of current knowledge with opportunities to discover new information. We used a computer simulation to measure how well groups characterized by dif- ferent network structures （fully connected, small world, lattice, and random） find and exploit resource peaks in a variable environment. We found that a fully connected network outperformed other struc- tures when resource quality was predictable. When resource quality showed random variation, however, the small world network was better than the fully connected one at avoiding extremely poor outcomes. These results suggest that animal groups may benefit by adjusting their informa- tion-sharing network structures depending on the noisiness of their environment.
基金supported by the National Science Centre,Poland,under Grant 2017/25/N/NZ8/00822.
文摘The brain size of vertebrates represents a trade-off between natural selection for enhanced cognitive abilities and the energetic constraints of brain tissue production.Processing information efficiently can confer benefits,but it also entails time costs.Breeding strategies,encompassing timing of breeding onset and nest-site selection,may be related to brain size.In this study,we aim to elucidate the relationship between brain size,breeding timing,nest-site choice,and breeding success in the red-backed shrike Lanius collurio.Our findings revealed that the timing of the first egg-laying date was associated with female head size,with larger-headed females tending to lay eggs later in the breeding season.Additionally,we observed that breeding success was positively correlated with increased nest concealment.However,this relationship was stronger in males with smaller heads.In turn,nest concealment was not related to head size but primarily influenced breeding onset.These results suggest that the choice of breeding strategy may be moderated by brain size,with differences between sexes.Larger-headed females may invest more time in selecting nesting sites,leading to delayed breeding onset,while larger-headed males may compensate for suboptimal nest concealment.Our study sheds light on the intricate interplay between brain size,breeding timing,nest-site preferences,and breeding success in passerine birds,underscoring the potential role of cognitive capacity in shaping individual decision-making processes.
