scholarly journals How Optimal is Word-Referent Identification Under Multimodal Uncertainty?

2018 ◽  
Author(s):  
Abdellah Fourtassi ◽  
Michael C. Frank
Keyword(s):  
Visual Cues ◽  
Word Meaning ◽  
Spoken Word ◽  
Ideal Observer ◽  
Optimal Model ◽  
Sources Of Uncertainty ◽  
Multimodal Information ◽  
Model Predictions ◽  
Observer Model ◽  
Novel Words

Identifying a spoken word in a referential context requires both the ability to integrate multimodal input and the ability to reason under uncertainty. How do these tasks interact with one another? We study how adults identify novel words under joint uncertainty in the auditory and visual modalities and we propose an ideal observer model of how cues in these modalities are combined optimally. Model predictions are tested in four experiments where recognition is made under various sources of uncertainty. We found that participants use both auditory and visual cues to recognize novel words. When the signal is not distorted with environmental noise, participants weight the auditory and visual cues optimally, that is, according to the relative reliability of each modality. In contrast, when one modality has noise added to it, human perceivers systematically prefer the unperturbed modality to a greater extent than the optimal model does. This work extends the literature on perceptual cue combination to the case of word recognition in a referential context. In addition, this context offers a link to the study of multimodal information in word meaning learning.

Object Detection Using an Ideal Observer Model

2020 ◽  
Vol 2020 (16) ◽  
pp. 41-1-41-7
Author(s):  
Orit Skorka ◽  
Paul J. Kane
Keyword(s):  
Object Detection ◽  
Matched Filter ◽  
Image Sensors ◽  
Ideal Observer ◽  
Noise Power ◽  
Relative Evaluation ◽  
Detection And Identification ◽  
Cross Correlation Analysis ◽  
Observer Model ◽  
The Ideal

Many of the metrics developed for informational imaging are useful in automotive imaging, since many of the tasks – for example, object detection and identification – are similar. This work discusses sensor characterization parameters for the Ideal Observer SNR model, and elaborates on the noise power spectrum. It presents cross-correlation analysis results for matched-filter detection of a tribar pattern in sets of resolution target images that were captured with three image sensors over a range of illumination levels. Lastly, the work compares the crosscorrelation data to predictions made by the Ideal Observer Model and demonstrates good agreement between the two methods on relative evaluation of detection capabilities.

scholarly journals Evidence accumulation and change rate inference in dynamic environments

2016 ◽  
Author(s):  
Adrian E Radillo ◽  
Alan Veliz-Cuba ◽  
Kresimir Josic ◽  
Zachary Kilpatrick
Keyword(s):  
Rate Of Change ◽  
Ideal Observer ◽  
Moment Closure ◽  
Dimensional System ◽  
Change Rate ◽  
Observer Model ◽  
Low Dimensional ◽  
State Of The Environment ◽  
Rate Correlation ◽  
Moment Closure Approximation

In a constantly changing world, animals must account for environmental volatility when making decisions. To appropriately discount older, irrelevant information, they need to learn the rate at which the environment changes. We develop an ideal observer model capable of inferring the present state of the environment along with its rate of change. Key to this computation is updating the posterior probability of all possible changepoint counts. This computation can be challenging, as the number of possibilities grows rapidly with time. However, we show how the computations can be simplified in the continuum limit by a moment closure approximation. The resulting low-dimensional system can be used to infer the environmental state and change rate with accuracy comparable to the ideal observer. The approximate computations can be performed by a neural network model via a rate-correlation based plasticity rule. We thus show how optimal observers accumulates evidence in changing environments, and map this computation to reduced models which perform inference using plausible neural mechanisms.

Prediction error boosts retention of novel words in adults but not in children

2021 ◽  
Author(s):  
Chiara Gambi ◽  
Martin John Pickering ◽  
Hugh Rabagliati
Keyword(s):  
Prediction Error ◽  
Word Meaning ◽  
Linguistic Knowledge ◽  
Novel Object ◽  
Memory Traces ◽  
Prior Expectation ◽  
Adults And Children ◽  
Novel Words

How do we update our linguistic knowledge? In seven experiments, we asked whether error-driven learning can explain under what circumstances adults and children are more likely to store and retain a new word meaning. Participants were exposed to novel object labels in the context of more or less constraining sentences or visual contexts. Both two-to-four-year-olds (Mage = 38 months) and adults were strongly affected by expectations based on sentence constraint when choosing the referent of a new label. In addition, adults formed stronger memory traces for novel words that violated a stronger prior expectation. However, preschoolers’ memory was unaffected by the strength of their prior expectations. We conclude that the encoding of new word-object associations in memory is affected by prediction error in adults, but not in preschoolers.

Orthographic Overlap and Category Size in Unconscious Category Priming

2008 ◽  
Vol 55 (5) ◽  
pp. 350-357 ◽  
Author(s):  
Richard L. Abrams
Keyword(s):  
Semantic Analysis ◽  
Word Meaning ◽  
Category Size ◽  
Category Priming ◽  
Whole Word ◽  
Comparable Magnitude ◽  
Novel Words

Van den Bussche and Reynvoet (2007, Experiment 1 ) report unconscious priming of comparable magnitude from novel words belonging to small and large categories, evidence that they interpret as demonstrating independence from category size of priming that involves semantic analysis. Three experiments raise the possibility that the findings in Experiment 1c of Van den Bussche and Reynvoet reflect subword processing, not semantic analysis. In Experiments 1 and 2, priming was obtained from primes and targets that shared approximately the same degree of subword features as in Experiment 1c of Van den Bussche and Reynvoet, but no priming occurred when sharing of features was minimized. Experiment 3 demonstrated priming driven by subword features when those features were set in opposition to whole-word meaning. These results indicate that orthographic overlap must be considered a potentially important confound in findings that ostensibly support priming mediated by semantic analysis.

BAYESIAN SPECTRUM DECONVOLUTION INCLUDING UNCERTAINTIES AND MODEL SELECTION: APPLICATION TO X-RAY EMISSION DATA USING WINBUGS

2019 ◽  
Vol 185 (2) ◽  
pp. 157-167
Author(s):  
R Behrens ◽  
M Reginatto
Keyword(s):  
Parameter Estimation ◽  
Pulse Height ◽  
Measured Data ◽  
Measuring Instruments ◽  
Optimal Model ◽  
Emission Data ◽  
X Ray ◽  
Sources Of Uncertainty ◽  
Spectrum Deconvolution ◽  
Radiation Measurements

AbstractSpectrum deconvolution is an important task in ionizing radiation measurements, as the pulse height spectra, or, in general, the measured data from spectrometers or other measuring instruments are usually determined by the convolution of the response function with the fluence spectra. The method presented here for obtaining fluence spectra from the measurements is an application of Bayesian parameter estimation to the deconvolution of X-ray emission data. The problem of choosing the optimal model among several possible models is also considered, as well as an approach to include contributions from various sources of uncertainty, both correlated and uncorrelated. The application is carried out using the Bayesian software WinBUGS.

Mr. Chips: An ideal-observer model of reading.

1997 ◽  
Vol 104 (3) ◽  
pp. 524-553 ◽  
Author(s):  
Gordon E. Legge ◽  
Timothy S. Klitz ◽  
Bosco S. Tjan
Keyword(s):  
Ideal Observer ◽  
Observer Model

The Type, but Not the Amount, of Information Available Influences Toddlers' Fast Mapping and Retention of New Words

2014 ◽  
Vol 23 (2) ◽  
pp. 120-133 ◽  
Author(s):  
Kathryn W. Brady ◽  
Judith C. Goodman
Keyword(s):  
Word Learning ◽  
Word Meaning ◽  
The Novel ◽  
Fast Mapping ◽  
New Words ◽  
Amount Of Information ◽  
Significant Rate ◽  
Mapping Task ◽  
Novel Words

Purpose The authors of this study examined whether the type and number of word-learning cues affect how children infer and retain word-meaning mappings and whether the use of these cues changes with age. Method Forty-eight 18- to 36-month-old children with typical language participated in a fast-mapping task in which 6 novel words were presented with 3 types of cues to the words' referents, either singly or in pairs. One day later, children were tested for retention of the novel words. Results By 24 months of age, children correctly inferred the referents of the novel words at a significant level. Children retained the meanings of words at a significant rate by 30 months of age. Children retained the first 3 of the 6 word-meaning mappings by 24 months of age. For both fast mapping and retention, the efficacy of different cue types changed with development, but children were equally successful whether the novel words were presented with 1 or 2 cues. Conclusion The type of information available to children at fast mapping affects their ability to both form and retain word-meaning associations. Providing children with more information in the form of paired cues had no effect on either fast mapping or retention.

scholarly journals Surprise About Sensory Event Timing Drives Cortical Transients in the Beta Frequency Band

2018 ◽  
Author(s):  
T. Meindertsma ◽  
N.A. Kloosterman ◽  
A.K. Engel ◽  
E.J. Wagenmakers ◽  
T.H. Donner
Keyword(s):  
Frequency Band ◽  
Ideal Observer ◽  
Beta Band ◽  
Statistical Structure ◽  
Population Activity ◽  
Sensory Event ◽  
Event Timing ◽  
Beta Frequency Band ◽  
Observer Model ◽  
Beta Frequency

AbstractLearning the statistical structure of the environment is crucial for adaptive behavior. Humans and non-human decision-makers seem to track such structure through a process of probabilistic inference, which enables predictions about behaviorally relevant events. Deviations from such predictions cause surprise, which in turn helps improve inference. Surprise about the timing of behaviorally relevant sensory events drives phasic responses of neuromodulatory brainstem systems, which project to the cerebral cortex. Here, we developed a computational model-based magnetoencephalography (MEG) approach for mapping the resulting cortical transients across space, time, and frequency, in the human brain (N=28, 17 female). We used a Bayesian ideal observer model to learn the statistics of the timing of changes in a simple visual detection task. This model yielded quantitative trial-by-trial estimates of temporal surprise. The model-based surprise variable predicted trial-by trial variations in reaction time more strongly than the externally observable interval timings alone. Trial-by-trial variations in surprise were negatively correlated with the power of cortical population activity measured with MEG. This surprise-related power suppression occurred transiently around the behavioral response, specifically in the beta frequency band. It peaked in parietal and prefrontal cortices, remote from the motor cortical suppression of beta power related to overt report (button press) of change detection. Our results indicate that surprise about sensory event timing transiently suppresses ongoing beta-band oscillations in association cortex. This transient suppression of frontal beta-band oscillations might reflect an active reset triggered by surprise, and is in line with the idea that beta-oscillations help maintain cognitive sets.Significance statementThe brain continuously tracks the statistical structure of the environment to anticipate behaviorally relevant events. Deviations from such predictions cause surprise, which in turn drives neural activity in subcortical brain regions that project to the cerebral cortex. We used magnetoencephalography in humans to map out surprise-related modulations of cortical population activity across space, time, and frequency. Surprise was elicited by variable timing of visual stimulus changes requiring a behavioral response. Surprise was quantified by means of an ideal observer model. Surprise predicted behavior as well as a transient suppression of beta frequency band oscillations in frontal cortical regions. Our results are in line with conceptual accounts that have linked neural oscillations in the beta-band to the maintenance of cognitive sets.

scholarly journals Touch as an auxiliary proprioceptive cue for movement control

2019 ◽  
Vol 5 (6) ◽  
pp. eaaw3121 ◽  
Author(s):  
A. Moscatelli ◽  
M. Bianchi ◽  
S. Ciotti ◽  
G. C. Bettelani ◽  
C. V. Parise ◽  
...  
Keyword(s):  
Visual Feedback ◽  
Movement Control ◽  
External World ◽  
Ideal Observer ◽  
Proprioceptive Feedback ◽  
Systematic Deviation ◽  
Classical View ◽  
Observer Model ◽  
Perceptual Phenomenon

Recent studies extended the classical view that touch is mainly devoted to the perception of the external world. Perceptual tasks where the hand was stationary demonstrated that cutaneous stimuli from contact with objects provide the illusion of hand displacement. Here, we tested the hypothesis that touch provides auxiliary proprioceptive feedback for guiding actions. We used a well-established perceptual phenomenon to dissociate the estimates of reaching direction from touch and musculoskeletal proprioception. Participants slid their fingertip on a ridged plate to move toward a target without any visual feedback on hand location. Tactile motion estimates were biased by ridge orientation, inducing a systematic deviation in hand trajectories in accordance with our hypothesis. Results are in agreement with an ideal observer model, where motion estimates from different somatosensory cues are optimally integrated for the control of movement. These outcomes shed new light on the interplay between proprioception and touch in active tasks.

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