64 research outputs found

    Summary of model variables and paramters.

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    <p>Summary of model variables and paramters.</p

    Effect of on speed-accuracy tradeoff.

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    <p>(a) Model predictions of psychometric and chronometric functions for different values of . (b) Comparison of model predictions and experimental data for different speed-accuracy regimes. The black dots represent the response time and accuracy of a human subject in the direction discrimination task under normal speed conditions, while the red crosses represent data with a slower speed instruction. The model predictions are plotted as black solid curves (with ) and red dashed lines (), respectively. The per-step duration and non-decision residual time are fixed to be the same for both conditions: ms/step, and ms. Human data are from human subject LH in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0053344#pone.0053344-Hanks1" target="_blank">[33]</a>.</p

    Optimal Value and Policy for the Random Dots Task.

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    <p>(a) Optimal value as a joint function of and the number of POMDP steps . (b) Optimal Policy as a function of and the number of POMDP steps . The boundaries and divide the belief space into three areas: (red), (green), and (blue), each of which represents belief states whose optimal actions are and respectively. Model parameters: , , and . (c) <i>Left:</i> The rightward decision boundary for different values of . <i>Right:</i> The half time of for different values of , where .</p

    Comparison of Model and Neural Responses.

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    <p>(a) Model response to coherence motion is shown in red. Blue curve depicts a fit using a hyperbolic function where ms, which is comparable to the value of ms estimated from neural data <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0053344#pone.0053344-Churchland1" target="_blank">[30]</a>. (b) The first ms of decision time was used to compute the buildup rate from the model response following the procedure in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0053344#pone.0053344-Churchland1" target="_blank">[30]</a>. The red points show model buildup rates estimated for each coherence value. The effect of a unit change in the coherence on buildup rate can be estimated from the slope of the blue fitted line: this value, spike s coh, is similar to the corresponding value spike s coh estimated from neural data <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0053344#pone.0053344-Churchland1" target="_blank">[30]</a>.</p

    Comparison of Performance of the Model and Monkey.

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    <p>Black dots with error bars represent a monkey's decision accuracy and reaction time for correct trials. Blue solid curves are model predictions ( and in the text) for parameter values , and . Monkey data from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0053344#pone.0053344-Roitman1" target="_blank">[31]</a>.</p

    POMP Framework for Decision Making.

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    <p><i>Left:</i> The graphical model representing the probabilistic relationship between random variables , , and . In the POMDP model, the hidden state corresponds to coherence and direction jointly. The observation corresponds to MT response . The relations between these variables are summarized in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0053344#pone-0053344-t001" target="_blank">table 1</a>. <i>Right:</i> In order to solve a POMDP problem, the animal maintains a belief , which is a posterior probability distribution over hidden states of the world given observations . At a current belief state , an action is selected according to the learned policy , which maps belief states to actions.</p

    Spontaneous Decoding of the Timing and Content of Human Object Perception from Cortical Surface Recordings Reveals Complementary Information in the Event-Related Potential and Broadband Spectral Change

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    <div><p>The link between object perception and neural activity in visual cortical areas is a problem of fundamental importance in neuroscience. Here we show that electrical potentials from the ventral temporal cortical surface in humans contain sufficient information for spontaneous and near-instantaneous identification of a subject’s perceptual state. Electrocorticographic (ECoG) arrays were placed on the subtemporal cortical surface of seven epilepsy patients. Grayscale images of faces and houses were displayed rapidly in random sequence. We developed a template projection approach to decode the continuous ECoG data stream spontaneously, predicting the occurrence, timing and type of visual stimulus. In this setting, we evaluated the independent and joint use of two well-studied features of brain signals, broadband changes in the frequency power spectrum of the potential and deflections in the raw potential trace (event-related potential; ERP). Our ability to predict both the timing of stimulus onset and the type of image was best when we used a combination of both the broadband response and ERP, suggesting that they capture different and complementary aspects of the subject’s perceptual state. Specifically, we were able to predict the timing and type of 96% of all stimuli, with less than 5% false positive rate and a ~20ms error in timing.</p></div

    Classification accuracy for decoding stimulus class and onset in a continuous data stream.

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    <p>When both features were used (red bars), approximately 96% of all stimuli were captured correctly in every subject, with 15–20 ms error. An average of 4% of predictions using both features were incorrect (i.e., predicted stimuli at the wrong time, or as the wrong class). One should not confuse the fraction of guesses incorrect with the fraction of stimuli that were not captured (the bars on the top and bottom axes do not sum to 1)–it is a coincidence that also 4% of stimuli were missed.</p

    Robotic tabletop organization task setup.

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    <p>(a) The robot is located on the left side of the work area and the Kinect looks down from the left side from the robot perspective. The three predefined areas that distinguish object states are notated. (b) Toy tabletop objects.</p

    Text length distributions in the different corpora used in the analysis.

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    <p>The raw corpus (M77) contains four instances of outliers, texts of length <i>n</i> = 2 and <i>n</i> = 3 which occur in unusually large numbers. Keeping only single occurrences of these removes the sharp maximum around <i>n</i> = 2 in the raw corpus. The corpus free of the outliers is then reduced again to keep only unique occurrences of the texts. This gives the M77-unique corpus. Finally, damaged, illegible and multi-line texts are removed to give the EBUDS corpus. Texts of length <i>n</i> = 3 and <i>n</i> = 5 are most frequent in this corpus.</p
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