Serena Planera’s research while affiliated with Osnabrück University and other places

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Publications (3)


Sensory augmentation device and rotating platform. (A) Shows the tactile sensory augmentation device with its main components. (B) Illustrates the experimental setup. A participant is sitting on the chair fixed on the rotating platform and is wearing the tactile belt. He is additionally provided with an eye mask and headphones for noise cancelation. The participant is holding the response box in his hands.
Example logistic fit (native condition). The figure demonstrates the performance of one participant in the native condition as an example. The abscissa illustrates the difference between the two angular rotations (reference—comparison angle) in degrees. The ordinate indicates the probability to choose the reference angle. The green circles show the recorded behavioral data, the solid red curve shows the logistic fit, and the dashed red lines indicate the uncertainty of the fit. The magenta line depicts the Point of subjective Equality, while the blue line depicts the sensory threshold, at one standard deviation (84%) of the psychometric function. The distance between the PSE and the intersection of the blue line with the abscissa represents the JND.
Investigation of learning effects. The abscissa divides the data of the three different sessions and the data of each session between the first half and second half of the block, separated by condition. The ordinate indicates the performance as a percentage. The error bars illustrate the average performance with the error bars representing the standard error of the mean.
Comparing conditions. (A) Shows the PSE (on the ordinate) separately for the three different conditions on the abscissa as a mean over subjects. The asterisks below indicate the significance level for the difference of the SPE to zero. The asterisks above show the significance level for the comparisons between conditions. (B) Shows the JND again separately for the three different conditions on the abscissa and as a mean over subjects. The asterisks illustrate the level of significant differences between conditions.
Model comparison. The abscissa shows the predicted squared JND; the ordinate shows the observed squared JND in the bimodal condition. Each black dot shows the predicted vs. the observed value for one subject. The error bars around the black dots illustrate the uncertainty of the observed bimodal values. The gray dashed diagonal line represents the ideal prediction. The resulting χred² is plotted for each model. (A) Bayesian integration. (B) Winner takes all. (C) Bayesian alteration (objective weights). (D) Bayesian alteration (subjective weights).

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Bayesian Alternation During Tactile Augmentation
  • Article
  • Full-text available

October 2016

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119 Reads

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16 Citations

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Serena Planera

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A large number of studies suggest that the integration of multisensory signals by humans is well-described by Bayesian principles. However, there are very few reports about cue combination between a native and an augmented sense. In particular, we asked the question whether adult participants are able to integrate an augmented sensory cue with existing native sensory information. Hence for the purpose of this study, we build a tactile augmentation device. Consequently, we compared different hypotheses of how untrained adult participants combine information from a native and an augmented sense. In a two-interval forced choice (2 IFC) task, while subjects were blindfolded and seated on a rotating platform, our sensory augmentation device translated information on whole body yaw rotation to tactile stimulation. Three conditions were realized: tactile stimulation only (augmented condition), rotation only (native condition), and both augmented and native information (bimodal condition). Participants had to choose one out of two consecutive rotations with higher angular rotation. For the analysis, we fitted the participants' responses with a probit model and calculated the just notable difference (JND). Then, we compared several models for predicting bimodal from unimodal responses. An objective Bayesian alternation model yielded a better prediction (χred2 = 1.67) than the Bayesian integration model (χred2 = 4.34). Slightly higher accuracy showed a non-Bayesian winner takes all (WTA) model (χred2 = 1.64), which either used only native or only augmented values per subject for prediction. However, the performance of the Bayesian alternation model could be substantially improved (χred2 = 1.09) utilizing subjective weights obtained by a questionnaire. As a result, the subjective Bayesian alternation model predicted bimodal performance most accurately among all tested models. These results suggest that information from augmented and existing sensory modalities in untrained humans is combined via a subjective Bayesian alternation process. Therefore, we conclude that behavior in our bimodal condition is explained better by top down-subjective weighting than by bottom-up weighting based upon objective cue reliability.

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A bayesian approach to multimodal integration between augmented and innate sensory modalities

Background / Purpose: We developed a sensory augmentation device (feelSpace belt), which transfers heading direction into tactile stimulation onto the waist of the participants. Therefore we aim to investigate the psychophysics underpinning multimodal integration between an augmented sense and an innate sensory modality, the vestibular one, for heading direction in humans. Main conclusion: Our preliminary results show that participants in both unimodal conditions, reliably used the information from vestibular and belt cues for the estimation of heading orientation. However, the same pattern did not show up in the bimodal condition. Nevertheless, further investigations with a bigger sample size need to be done in order to truly test our hypothesis of Bayesian integration. In summary, we hereby demonstrate that we are able to test and analyse multimodal cue integration in the domain of sensory augmentation.

Citations (1)


... Combinations of distance estimates can be calculated by either multiplying or summing the probabilities derived by sampling the functions for each of the landmarks. Here, a multiplication produces an integration model ( n i=1 g i (d)), which assumes each cue to be used in a given trial weighted according to its reliability (Zhao and Warren, 2015b;Chen et al., 2017), while a summation produces a cue alternation model ( n i=1 g i (d)), which assumes individual objects are picked at random from trial to trial with selection probabilities based on their reliability (Nardini et al., 2008;Goeke et al., 2016;Chen et al., 2017). For our study, the reliability of all landmarks was assumed to be equal, with the weight derived from the observed reliability in the one object condition (Jetzschke et al., 2017). ...

Reference:

Not seeing the forest for the trees: combination of path integration and landmark cues in human virtual navigation
Bayesian Alternation During Tactile Augmentation