6 research outputs found
Updating predictions in a complex repertoire of actions and its neural representation
No full textEven though actions we observe in everyday life seem to unfold in a continuous manner, they are automatically divided into meaningful chunks, that are single actions or segments, which provide information for the formation and updating of internal predictive models. Specifically, boundaries between actions constitute a hub for predictive processing since the prediction of the current action comes to an end and calls for updating of predictions for the next action. In the current study, we investigated neural processes which characterize such boundaries using a repertoire of complex action sequences with a predefined probabilistic structure. Action sequences consisted of actions that started with the hand touching an object (T) and ended with the hand releasing the object (U). These action boundaries were determined using an automatic computer vision algorithm. Participants trained all action sequences by imitating demo videos. Subsequently, they returned for an fMRI session during which the original action sequences were presented in addition to slightly modified versions thereof. Participants completed a post-fMRI memory test to assess the retention of original action sequences. The exchange of individual actions, and thus a violation of action prediction, resulted in increased activation of the action observation network and the anterior insula. At U events, marking the end of an action, increased brain activation in supplementary motor area, striatum, and lingual gyrus was indicative of the retrieval of the previously encoded action repertoire. As expected, brain activation at U events also reflected the predefined probabilistic branching structure of the action repertoire. At T events, marking the beginning of the next action, midline and hippocampal regions were recruited, reflecting the selected prediction of the unfolding action segment. In conclusion, our findings contribute to a better understanding of the various cerebral processes characterizing prediction during the observation of complex action repertoires
Agency and Perspective in Episodic Memory
No full textInvestigating the neuronal correlates of agency and perspective in episodic memory using a novel paradigm for episodic modificatio
Agency and Perspective in Episodic Memory
No full textInvestigating the neuronal correlates of agency and perspective in episodic memory using a novel paradigm for episodic modificatio
Seeing what I did (not): Cerebral and behavioral effects of agency and perspective on episodic memory re-activation
No full textIntuitively, we assume that we remember episodes better when we actively participated in them and were not mere observers. Independently of this, we can recall episodes from either the first-person perspective (1pp) or the third-person perspective (3pp). In this functional magnetic resonance imaging (fMRI) study, we tested whether agency and perspective modulate neural activity during memory retrieval and subsequently enhance memory performance. Subjects encoded a set of different episodes by either imitating or only observing videos that showed short toy stories. A week later, we conducted fMRI and cued episodic retrieval by presenting the original videos, or slightly modified versions thereof, from 1pp or from 3pp. The hippocampal formation responded more strongly to episodic mismatches in previously self-performed vs. only observed actions. In a post-fMRI memory test a history of self-performance did not improve behavioral memory performance. However, modified videos were often (falsely) accepted as showing truly experienced episodes when: (i) they were already presented in this modified version during fMRI or (ii) they were presented in their original form during fMRI but from 3pp. Together, our findings demonstrate that self-performance and self-perspective modulate the strength of a memory trace in different ways. Even when memory performance remains the same for different agentive states, the brain is capable of detecting mismatching information. Re-experiencing the latter impairs memory performance as well as retrieving encoded episodes from 3pp
Table_1_Seeing What I Did (Not): Cerebral and Behavioral Effects of Agency and Perspective on Episodic Memory Re-activation.DOCX
No full textIntuitively, we assume that we remember episodes better when we actively participated in them and were not mere observers. Independently of this, we can recall episodes from either the first-person perspective (1pp) or the third-person perspective (3pp). In this functional magnetic resonance imaging (fMRI) study, we tested whether agency and perspective modulate neural activity during memory retrieval and subsequently enhance memory performance. Subjects encoded a set of different episodes by either imitating or only observing videos that showed short toy stories. A week later, we conducted fMRI and cued episodic retrieval by presenting the original videos, or slightly modified versions thereof, from 1pp or from 3pp. The hippocampal formation was sensitive to self-performed vs. only observed actions only when there was an episodic mismatch. In a post-fMRI memory test a history of self-performance did not improve behavioral memory performance. However, modified videos were often (falsely) accepted as showing truly experienced episodes when: (i) they were already presented in this modified version during fMRI or (ii) they were presented in their original form during fMRI but from 3pp. While the overall effect of modification was strong, the effects of perspective and agency were more subtle. Together, our findings demonstrate that self-performance and self-perspective modulate the strength of a memory trace in different ways. Even when memory performance remains the same for different agentive states, the brain is capable of detecting mismatching information. Re-experiencing the latter impairs memory performance as well as retrieving encoded episodes from 3pp.</p
A Simulation Method for Design and Development of Magnetic Shape Memory Actuators
No full textThe systems/products and their design processes have become more and more complicated due to the fact that their requirements in terms of function, durability, reliability and energy efficiency have been increased significantly and that their leading time has to be short and their materials cost has to be low. To meet these requirements, individual parts and subsystems have to offer increased functionality and efficiency themselves. It has been found that smart materials, such as piezo ceramics or various shape memory alloys as well as less known dielectric elastomers or magnetic shape memory alloys, offer ideal preconditions to fulfil such requirements. Among the various shape memory alloys, the Magnetic Shape Memory (MSM) alloy is a kind of smart material that can elongate and contract in a magnetic field. Based on the MSM alloy a new type of smart electromagnetic actuators have been designed and developed. This kind of actuator exhibits the features above. Typically, the MSM material is a monocrystalline Ni-Mn-Ga alloy, which has the ability to change its size or shape very fast and many million times repeatedly. State-of-the-art alloys are able to achieve a magnetic field induced strain of up to 12%. The magneto-mechanical characteristic of MSM alloys is being constantly improved. However, as far as the author is aware, there are no efficient and commercially available tools for engineers to design MSM-based actuators. To achieve this, simulation tools for design are indispensable. This thesis is dedicated to this task.
In this PhD thesis, new design and simulation techniques for MSM-based actuators have been studied. In particular, three simulation methods have been proposed. These three methods extend standard magneto-static FEM simulation techniques by taking into account the magneto-mechanical coupling and the magnetic anisotropy of the MSM materials. They differ in terms of the necessary a priori alloy characterisation (i.e., measurement effort), computational complexity and consequent computing time. The magneto-mechanical characteristics of the MSM material are a necessary and fundamental ingredient for this type of simulation. However, the characterisation of the MSM materials is a very challenging task and requires specific modifications to standard measurement approaches. So, in this thesis, some specific measurement methods of the magneto-mechanical characteristics of the MSM materials have been proposed, designed and developed. It is described how existing measurement instruments can be modified to measure the unique magneto-mechanical characteristics of MSM, so they are applicable and with practical values. Various tests have been carried out to validate the new methods and the necessary characterisations of the properties of MSM materials have been performed, such as the measurement of the permeability of MSM under a defined stress during elongation. The new measurement results have been analysed and the findings have been used to design and develop the simulation methods. The three simulation methods can be used to predict and optimise the current-elongation behaviour of an MSM element under the load of a mechanical stress while excited by a magnetic field. Extensive experiments have been carried out to validate these three simulation methods. The results show that the three methods are relatively simple but, at the same time, very effective means to model, predict and optimise the properties of an MSM actuator using finite element tools. In addition, the experiment results have also shown that the simulation methods can be used to gain some deep insights into the magneto-mechanical interaction between the MSM element and the electromagnetic actuator. In this thesis an evolutionary algorithm which works together with the simulation methods has been developed to achieve individual optimised solutions in very short times.
In summary, from the experiment results, it has been found that the measurements and simulation methods proposed and developed in this thesis; enable designers to perform simulations for a high-quality actuator design based on the magneto-mechanical properties of MSM alloys. This is the first time that a MSM can be characterised for simulation purposes in a fast and precise way to predict MSM and electromagnetic actuator interactions and identify and optimise the design parameters of such actuators.
However, these simulation methods are strongly dependent on the measurement of the magneto-mechanical characteristics of magnetic shape memory alloys, whose precision can be further improved. To reach commercial success as well higher precision in the simulation prediction, further achievements in the field of material science (e.g. smoothness of mechanical curves) are also necessary
