AIMC Topic: Cues

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Learning mechanisms in cue reweighting.

Cognition
Feedback has been shown to be effective in shifting attention across perceptual cues to a phonological contrast in speech perception (Francis, Baldwin & Nusbaum, 2000). However, the learning mechanisms behind this process remain obscure. We compare t...

On the Relative Contribution of Deep Convolutional Neural Networks for SSVEP-Based Bio-Signal Decoding in BCI Speller Applications.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Brain-computer interfaces (BCI) harnessing steady state visual evoked potentials (SSVEPs) manipulate the frequency and phase of visual stimuli to generate predictable oscillations in neural activity. For BCI spellers, oscillations are matched with al...

Magnetic body alignment in migratory songbirds: a computer vision approach.

The Journal of experimental biology
Several invertebrate and vertebrate species have been shown to align their body relative to the geomagnetic field. Many hypotheses have been proposed to explain the adaptive significance of magnetic body alignment outside the context of navigation. H...

How to make nonhumanoid mobile robots more likable: Employing kinesic courtesy cues to promote appreciation.

Applied ergonomics
Service robots that mimic human social behavior can appear polite. We tested the social and behavioral efficacy and legibility of two kinesic courtesy cues on people's approval of a service robot. In a repeated-measures design, 29 volunteers were ran...

(Not) hearing happiness: Predicting fluctuations in happy mood from acoustic cues using machine learning.

Emotion (Washington, D.C.)
Recent popular claims surrounding virtual assistants suggest that computers will soon be able to hear our emotions. Supporting this possibility, promising work has harnessed big data and emergent technologies to automatically predict stable levels of...

Who is a better teacher for children with autism? Comparison of learning outcomes between robot-based and human-based interventions in gestural production and recognition.

Research in developmental disabilities
BACKGROUND: Individuals with autism spectrum disorder (ASD) tend to show deficits in engaging with humans. Previous findings have shown that robot-based training improves the gestural recognition and production of children with ASD. It is not known w...

Social-cue perception and mentalizing ability following traumatic brain injury: A human-robot interaction study.

Brain injury
PRIMARY OBJECTIVE: Research studies and clinical observations of individuals with traumatic brain injury (TBI) indicate marked deficits in mentalizing-perceiving social information and integrating it into judgements about the affective and mental sta...

The computerized scoring algorithm for the autobiographical memory test: updates and extensions for analyzing memories of English-speaking adults.

Memory (Hove, England)
The Autobiographical Memory Test (AMT) has been central in psychopathological studies of memory dysfunctions, as reduced memory specificity or overgeneralised autobiographical memory has been recognised as a hallmark vulnerability for depression. In ...

Decoding of Human Movements Based on Deep Brain Local Field Potentials Using Ensemble Neural Networks.

Computational intelligence and neuroscience
Decoding neural activities related to voluntary and involuntary movements is fundamental to understanding human brain motor circuits and neuromotor disorders and can lead to the development of neuromotor prosthetic devices for neurorehabilitation. Th...

Humans, but Not Deep Neural Networks, Often Miss Giant Targets in Scenes.

Current biology : CB
Even with great advances in machine vision, animals are still unmatched in their ability to visually search complex scenes. Animals from bees [1, 2] to birds [3] to humans [4-12] learn about the statistical relations in visual environments to guide a...