Sentence examples for affective inputs from inspiring English sources

Kober et al. (2008) identify several possible roles for medial prefrontal sites in emotion perception, including the attribution of mental states in oneself or others, and in metacognitive processing of affective inputs (e.g., to generate or regulate emotion; Mitchell and Greening 2012; Phillips et al. 2003).

Mechanisms by which affective input may influence sensory experience and clinical implications of the present study are discussed.

High dietary intake of omega-3 PUFAs has been directly associated with increased gray matter volume in corticolimbic circuitry that represents the affective input for memory formation and cortical arousal in the brain.

Instead of framing educational and occupational interruptions as defeats or accidents, they talk about them in terms of opportunities, allowing affective and rational inputs to guide behavior (Zinn 2004).

Users can access affective content by inputting their preferred affective intensity level (high, medium, and low) or detailed affective types.

Krashen (1985) argued that comprehensible input and a state of the affective filter that allows the input lead to L2 acquisition.

In pursuing our studies of the affective states generated by visual inputs, we concentrate in this work on the sentiment of hate.

These sensory inputs served as affective cues which could directly trigger patients' emotional and somatic reaction.

The model allowed the fMRI task (i.e., combination of the FACE and OBJECT regressors) to act on the investigated system as a driving input via an affective (amygdala) and a cognitive (DLPFC) brain region.

The affect-as-information model (Clore et al. 2001) indicates that affective feelings as a component of input in information processing can provide information and serve as crucial cues to guide judgment and decision making; therefore, affective feelings can lead to a higher or lower evaluation of a certain object.

Using signal processing, linguistic analysis, and psycho-physiological techniques, automated emotion recognition is feasible by aggregating affective cues from multi-modal user input such as facial expression, speech, body gesture, and neuroimaging [25 27].