Purpose:Human navigation skills are essential for everyday life and rely on several cognitive abilities, among which visual-spatial competences that are impaired in subjects with cerebral palsy. In this work, we proposed navigation tasks in immersive virtual reality to 15 children with CP and 13 typically developing peers in order to assess the individual navigation strategies and their modifiability in a situation resembling real life.Methods:We developed and adapted to IVR an application based on a 5-way maze in a playground (...) that was to be navigated to find a reward. The learning process, navigation strategies, and adaptation to changes were compared between participants with CP and their TD peers and correlated with visual-spatial abilities and cognitive competences.Results:Most participants with CP needed more attempts than TD participants to become proficient in navigation. Furthermore, the learning phase was correlated to visual-spatial memory but not with cognitive competences. Interestingly, navigation skills were comparable between groups after stabilization. While TD participants mainly relied on allocentric strategies based on environmental cues, egocentric strategies based on body motion prevailed in participants with CP. Furthermore, participants with CP had more difficulties in modifying their navigation strategies, caused by difficulties in executive processes beyond the visual-perceptual impairment, with an inefficient shift between implicit and explicit competences.Conclusions:The navigation abilities in participants with CP seem to be different from their TD peers in terms of learning and adaptation to new conditions; this could deeply affect their everyday life and ultimately participation and inclusion. A regular assessing and focused rehabilitative plans could help to better navigate the environment and affect self-perception. (shrink)

The aim of this study was to test whether an online video game can be used to investigate planning ability and whether fluid intelligence, objective numeracy, and subjective numeracy are predictive of game performance. Our results demonstrate that fluid intelligence is particularly important, which is in line with previous non-game-based studies that show a relationship between classical planning tests and fluid intelligence. Video games have been previously used for research into cognitive processes and taking them online facilitates data collection on (...) a larger scale. Online video games also afford data collection without the expense and stress of a laboratory environment. For these reasons, using online video games to investigate human cognition is a promising alternative to the classic cognitive paradigms used in laboratories. (shrink)

Can dreams reveal insight into our cognitive abilities and aptitudes (i.e., “human intelligence”)? The relationship between dream production and trait-like cognitive abilities is the foundation of several long-standing theories on the neurocognitive and cognitive-psychological basis of dreaming. However, direct experimental evidence is sparse and remains contentious. On the other hand, recent research has provided compelling evidence demonstrating a link between dream content and new learning, suggesting that dreams reflect memory processing during sleep. It remains to be investigated whether (...) the extent of learning-related dream incorporation (i.e., the semantic similarity between waking experiences and dream content) is related to inter-individual differences in cognitive abilities. The relationship between pre–post sleep memory performance improvements and learning-related dream incorporation was investigated ( N = 24) to determine if this relationship could be explained by inter-individual differences in intellectual abilities (e.g., reasoning, short term memory (STM), and verbal abilities). The extent of dream incorporation using a novel and objective method of dream content analysis, employed a computational linguistic approach to measure the semantic relatedness between verbal reports describing the experience on a spatial (e.g., maze navigation) or a motor memory task (e.g., tennis simulator) with subsequent hypnagogic reverie dream reports and waking “daydream” reports, obtained during a daytime nap opportunity. Consistent with previous studies, the extent to which something new was learned was related ( r = 0.47) to how richly these novel experiences were incorporated into the content of dreams. This was significant for early (the first 4 dream reports) but not late dreams (the last 4 dream reports). Notably, here, we show for the first time that the extent of this incorporation for early dreams was related ( r = 0.41) to inter-individual differences in reasoning abilities. On the other hand, late dream incorporation was related ( r = 0.46) to inter-individual differences in verbal abilities. There was no relationship between performance improvements and intellectual abilities, and thus, inter-individual differences in cognitive abilities did not mediate the relationship between performance improvements and dream incorporation; suggesting a direct relationship between reasoning abilities and dream incorporation. This study provides the first evidence that learning-related dream production is related to inter-individual differences in cognitive abilities. (shrink)

Reinforcement learning (RL) models of decision-making cannot account for human decisions in the absence of prior reward or punishment. We propose a mechanism for choosing among available options based on goal-option association strengths, where association strengths between objects represent previously experienced object proximity. The proposed mechanism, Goal-Proximity Decision-making (GPD), is implemented within the ACT-R cognitive framework. GPD is found to be more efficient than RL in three maze-navigation simulations. GPD advantages over RL seem to grow as task (...) difficulty is increased. An experiment is presented where participants are asked to make choices in the absence of prior reward. GPD captures human performance in this experiment better than RL. (shrink)

In this article, we expand the analytical and theoretical foundations of the study of knowledge commons in the context of more classical agrarian commons, such as seed commons. We show that it is possible to overcome a number of criticisms of earlier work by Ostrom (Governing the commons. The evolution of institutions for collective action, Cambridge University Press, Cambridge, 1990) on natural commons and its excludability/rivalry matrix in addressing the inclusive social practices of “commoning”, defined as a way of living (...) and acting for the preservation of the commons. Our empirical analysis emphasizes, using the most recent advances in the IAD/SES framework, the distributed and collaborative knowledge governance in a French peasant seed network as a key driver for reintroducing cultivated agrobiodiversity and on-farm seed conservation of ancient and landrace varieties. These inclusive peasant seed groups developed alternative peer-to-peer models of collaborative peasant-led community-based breeding and grassroots innovations in the search for more resilient population varieties. Our results highlight the various models of collective action within the network and discuss the organizational tradeoffs of opting out of peasant seed activities and recreating a shared collective knowledge base on the benefits of restoring cultivated agrobiodiversity. It helps us better understand how modern peasant seed groups function as epistemic communities which contributes to envisioning alternative agricultural systems. (shrink)

Multivariate pattern analysis can reveal new information from neuroimaging data to illuminate human cognition and its disturbances. Here, we develop a methodological approach, based on multivariate statistical/machine learning and time series analysis, to discern cognitive processing stages from functional magnetic resonance imaging (fMRI) blood oxygenation level dependent (BOLD) time series. We apply this method to data recorded from a group of healthy adults whilst performing a virtual reality version of the delayed win-shift radial arm maze (RAM) task. (...) This task has been frequently used to study working memory and decision making in rodents. Using linear classifiers and multivariate test statistics in conjunction with time series bootstraps, we show that different cognitive stages of the task, as defined by the experimenter, namely, the encoding/retrieval, choice, reward and delay stages, can be statistically discriminated from the BOLD time series in brain areas relevant for decision making and working memory. Discrimination of these task stages was significantly reduced during poor behavioral performance in dorsolateral prefrontal cortex (DLPFC), but not in the primary visual cortex (V1). Experimenter-defined dissection of time series into class labels based on task structure was confirmed by an unsupervised, bottom-up approach based on Hidden Markov Models. Furthermore, we show that different groupings of recorded time points into cognitive event classes can be used to test hypotheses about the specific cognitive role of a given brain region during task execution. We found that whilst the DLPFC strongly differentiated between task stages associated with different memory loads, but not between different visual-spatial aspects, the reverse was true for V1. Our methodology illustrates how different aspects of cognitive information processing during one and the same task can be separated and attributed to specific brain regions based on information contained in multivariate patterns of voxel activity. (shrink)

Learning from demonstration is one of the promising approaches for fast robot programming. Most learning systems learn both movements and stiffness profiles from human demonstrations. However, they rarely consider the unknown environment interaction. In this paper, a robot human-like learning framework is proposed, where it can learn human skills through demonstration and complete the interaction task with an unknown environment. Firstly, the desired trajectory was generated by dynamic movement primitive based on human demonstration. (...) Then, an adaptive optimal admittance control scheme was employed to interact with environments with the reference adaptation method. Finally, the experimental study was conducted, and the effectiveness of the framework proposed in this paper was verified via a group of curved surface wiping experiments on a balloon with unknown model parameters. (shrink)