Decoding Modality-Independent Emotion Perception in Medial Prefrontal and Superior Temporal Cortex

Kanske, Philipp; Hasting, Anna S.

doi:10.1523/jneurosci.5078-10.2010

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…[35], [36]). An important question is whether the ability to recognise emotion from one such cue (e.g., faces) is correlated with the ability to recognise emotion from another cue (e.g., voices).…”

Section: Introductionmentioning

confidence: 99%

New Tests to Measure Individual Differences in Matching and Labelling Facial Expressions of Emotion, and Their Association with Ability to Recognise Vocal Emotions and Facial Identity

et al. 2013

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Although good tests are available for diagnosing clinical impairments in face expression processing, there is a lack of strong tests for assessing “individual differences” – that is, differences in ability between individuals within the typical, nonclinical, range. Here, we develop two new tests, one for expression perception (an odd-man-out matching task in which participants select which one of three faces displays a different expression) and one additionally requiring explicit identification of the emotion (a labelling task in which participants select one of six verbal labels). We demonstrate validity (careful check of individual items, large inversion effects, independence from nonverbal IQ, convergent validity with a previous labelling task), reliability (Cronbach’s alphas of.77 and.76 respectively), and wide individual differences across the typical population. We then demonstrate the usefulness of the tests by addressing theoretical questions regarding the structure of face processing, specifically the extent to which the following processes are common or distinct: (a) perceptual matching and explicit labelling of expression (modest correlation between matching and labelling supported partial independence); (b) judgement of expressions from faces and voices (results argued labelling tasks tap into a multi-modal system, while matching tasks tap distinct perceptual processes); and (c) expression and identity processing (results argued for a common first step of perceptual processing for expression and identity).

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Section: Introductionmentioning

confidence: 99%

New Tests to Measure Individual Differences in Matching and Labelling Facial Expressions of Emotion, and Their Association with Ability to Recognise Vocal Emotions and Facial Identity

et al. 2013

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“…Speech contains acoustic features that vary with the speaker's affective state, and the ability to interpret these communication signals (e.g., emotions) affects social interaction (Warren et al, 2006). Humans also perceive how emotional environmental cues such as fear or anger indicate danger (Kanske and Hasting, 2010) and keep them fit for survival.…”

Section: Introductionmentioning

confidence: 99%

Reward-based learning for virtual neurorobotics through emotional speech processing

Bray¹,

Ferneyhough²,

Barker³

et al. 2013

Front. Neurorobot.

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Reward-based learning can easily be applied to real life with a prevalence in children teaching methods. It also allows machines and software agents to automatically determine the ideal behavior from a simple reward feedback (e.g., encouragement) to maximize their performance. Advancements in affective computing, especially emotional speech processing (ESP) have allowed for more natural interaction between humans and robots. Our research focuses on integrating a novel ESP system in a relevant virtual neurorobotic (VNR) application. We created an emotional speech classifier that successfully distinguished happy and utterances. The accuracy of the system was 95.3 and 98.7% during the offline mode (using an emotional speech database) and the live mode (using live recordings), respectively. It was then integrated in a neurorobotic scenario, where a virtual neurorobot had to learn a simple exercise through reward-based learning. If the correct decision was made the robot received a spoken reward, which in turn stimulated synapses (in our simulated model) undergoing spike-timing dependent plasticity (STDP) and reinforced the corresponding neural pathways. Both our ESP and neurorobotic systems allowed our neurorobot to successfully and consistently learn the exercise. The integration of ESP in real-time computational neuroscience architecture is a first step toward the combination of human emotions and virtual neurorobotics.

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Decoding the neural representation of affective states

et al. 2012

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Decoding Modality-Independent Emotion Perception in Medial Prefrontal and Superior Temporal Cortex

Cited by 3 publications

References 9 publications

New Tests to Measure Individual Differences in Matching and Labelling Facial Expressions of Emotion, and Their Association with Ability to Recognise Vocal Emotions and Facial Identity

New Tests to Measure Individual Differences in Matching and Labelling Facial Expressions of Emotion, and Their Association with Ability to Recognise Vocal Emotions and Facial Identity

Reward-based learning for virtual neurorobotics through emotional speech processing

Decoding the neural representation of affective states

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