It has been recently argued that human bodies are processed by a specialized processing mechanism. Central evidence was that body inversion reduces recognition abilities (body inversion effect; BIE) as much as it does for faces, but more than for other objects. Here we showed that the BIE is markedly reduced for headless bodies and examined the reason for this unexpected finding. Two alternative hypotheses were examined. Either the BIE is reduced for any type of incomplete body, or the head plays a special role in discrimination of body posture. Results show that omission of other body parts (leg or arms) did not influence the magnitude of the BIE relative to complete bodies. Analogous manipulations with faces did not influence the magnitude of the face inversion effect. Importantly, similar to effects we found for headless bodies, discrimination abilities for upright bodies and the BIE were markedly reduced for complete bodies that did not differ in head posture. We conclude that intact discrimination of body posture relies heavily on the head position. Our findings also imply that the BIE and the face inversion effect may be generated by different mechanisms.
Rodents are optimal real-world foragers that regulate internal states maintaining a dynamic stability with their surroundings. How these internal drive based behaviors are regulated remains unclear. Based on the physiological notion of allostasis, we investigate 377 378 M. Sanchez-Fibla et al. a minimal control system able to approximate their behavior. Allostasis is the process of achieving stability with the environment through change, opposed to homeostasis which achieves it through constancy. Following this principle, the so-called allostatic control system orchestrates the interaction of the homeostatic modules by changing their desired values in order to achieve stability. We use a minimal number of subsystems and estimate the model parameters from rat behavioral data in three experimental setups: free exploration, presence of reward, delivery of cues with reward predictive value. From this analysis, we show that a rat is influenced by the shape of the arena in terms of its openness. We then use the estimated model configurations to control a simulated and real robot which captures essential properties of the observed rat behavior. The allostatic reactive control model is proposed as an augmentation of the Distributed Adaptive Control architecture and provides a further contribution towards the realization of an artificial rodent.
Factors leading humans to shift attention away from danger cues remain poorly understood. Two laboratory experiments reported here show that context interacts with learning experiences to shape attention avoidance of mild danger cues. The first experiment exposed 18 participants to contextual threat of electric shock. Attention allocation to mild danger cues was then assessed with the dot-probe task. Results showed that contextual threat caused subjects to avert attention from danger cues. In the second experiment, 36 participants were conditioned to the same contextual threat used in Experiment 1. These subjects then were randomly assigned to either an experimental group, trained to shift attention toward danger cues, or a placebo group exposed to the same stimuli without the training component. As in Experiment 1, contextual threat again caused attention allocation away from danger in the control group. However, this did not occur in the experimental group. These experiments show that acute contextual threat and learning experiences interact to shape the deployment of attention away from danger cues.
A large body of evidence shows that the hippocampus is necessary for successful spatial navigation. Various studies have shown anatomical and functional differences between the dorsal (DHC) and ventral (VHC) portions of this structure. The DHC is primarily involved in spatial navigation and contains cells with small place fields. The VHC is primarily involved in context and emotional encoding contains cells with large place fields and receives major projections from the medial prefrontal cortex. In the past, spatial navigation experiments have used relatively simple tasks that may not have required a strong coordination along the dorsoventral hippocampal axis. In this study, we tested the hypothesis that the DHC and VHC may be critical for goal-directed navigation in obstacle-rich environments. We used a learning task in which animals memorize the location of a set of rewarded feeders, and recall these locations in the presence of small or large obstacles. We report that bilateral DHC or VHC inactivation impaired spatial navigation in both large and small obstacle conditions. Importantly, this impairment did not result from a deficit in the spatial memory for the set of feeders (i.e., recognition of the goal locations) because DHC or VHC inactivation did not affect recall performance when there was no obstacle on the maze. We also show that the behavioral performance of the animals was correlated with several measures of maze complexity and that these correlations were significantly affected by inactivation only in the large object condition. These results suggest that as the complexity of the environment increases, both DHC and VHC are required for spatial navigation.
As rats learn to search for multiple sources of food or water in a complex environment, they generate increasingly efficient trajectories between reward sites. Such spatial navigation capacity involves the replay of hippocampal place-cells during awake states, generating small sequences of spatially related place-cell activity that we call “snippets”. These snippets occur primarily during sharp-wave-ripples (SWRs). Here we focus on the role of such replay events, as the animal is learning a traveling salesperson task (TSP) across multiple trials. We hypothesize that snippet replay generates synthetic data that can substantially expand and restructure the experience available and make learning more optimal. We developed a model of snippet generation that is modulated by reward, propagated in the forward and reverse directions. This implements a form of spatial credit assignment for reinforcement learning. We use a biologically motivated computational framework known as ‘reservoir computing’ to model prefrontal cortex (PFC) in sequence learning, in which large pools of prewired neural elements process information dynamically through reverberations. This PFC model consolidates snippets into larger spatial sequences that may be later recalled by subsets of the original sequences. Our simulation experiments provide neurophysiological explanations for two pertinent observations related to navigation. Reward modulation allows the system to reject non-optimal segments of experienced trajectories, and reverse replay allows the system to “learn” trajectories that it has not physically experienced, both of which significantly contribute to the TSP behavior.
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