Intro to EEG studies BCS204 Week 2 1/23/2019

What EEG can measure Auditory response Visual response Cognitive development Response to emotions Language processing Perceptual mismatch Task demand Timing of processing Cognitive aging

Some possible topics Processing of emotions (visual or auditory) in clinical and typical population Processing of emotions in preschool-aged and school-aged children Attention shift in demanding listening environment Processing of semantic or syntactic anomaly by non-native speakers Face recognition in clinical and atypical populations Timing of processing in young and old populations

Some basic terminology Naming convention of ERPs A letter: P or N A number: latency or the order of peaks Plot: time (ms); amplitude (microvolt)

Hoyniak et al. (2018) Emotion processing in atypically developed children Processing of facial emotion by children with callous-unemotional (CU) behaviors Biomarkers of CU behaviors

Hoyniak et al. (2018) CU traits: Deficit in guilt and empathy, shallow affect Difficulties in recognizing facial expression with distress High risk of conduct problems in childhood Smaller activation in amygdala when viewing emotional facial stimuli

Hoyniak et al. (2018) ERP N170 Negative-going waveform Peaked between 130-200ms post-stimulus Face recognition Emotion processing: larger amplitude to emotional expressions than neutral stimuli

Hoyniak et al. (2018) Predictions: high-level of CU traits  smaller N170 to distress  indicator of poorer emotion processing Task: passive viewing task – just see facial stimuli without making any responses Stimuli: happy face, fearful face, happy upside-down face, fearful upside-down face (48 each; 192 total)

Hoyniak et al. (2018) Subjects: 40 preschool children (3-5 yo) Monolinguals CU traits Excluded subjects receiving social services

Hoyniak et al. (2018) Results

Hoyniak et al. (2018) Results

Hoyniak et al. (2018): Main findings Correlation of CU traits and N170 to fearful emotion was found. N170 as a biomarker of emotion processing difficulties

Yang et al. (under revision) Goal: to investigate the representation of Mandarin diphthongs in working memory Task: sound monitoring task Press a button when hearing the syllable /ai/ Four types of stimuli: /a/, /u/, /i/, /ai/

Yang et al. (under revision) Conditions: complete mismatch (/u/), partial match (/a/ /i/), and complete match (/ai/) Predictions: Amplitude and latency of N200 modulated by perceptual mismatch with the target  small N200 elicited with complete match but large N200 elicited with complete mismatch Subjects: 22 Mandarin speakers

Lab activities: Processing behavioral data Download sample data from Blackboard (Course materials > Sample behavioral data) Dealing with RT data: Recode the accuracy into 1 (for Y) and 0 (for N) by using “search and replace” Plot a histogram to visualize the raw RT data. What patterns do you find? Create a new column named “logRT” and log-transform the raw RT data Plot a histogram of logRT data. Now what does the data look like?

Lab activities: Processing behavioral data Download sample data from Blackboard (Course materials > Sample behavioral data) Data trimming by subjective criteria: Create a duplicate copy of the whole worksheet Sort the data by raw RT (by ascending order) Delete rows with RTs < 300 ms Sort the data again by descending order Delete rows with RTs > 1500 ms Plot a histogram on the raw RT data again. Does the data show the same pattern before trimming? If yes, then log-transform the data again.

Lab activities: Processing behavioral data Download sample data from Blackboard (Course materials > Sample behavioral data) Basic averaging of RT data (by using Pivot table function): Average logRT (trimmed data) by subjects Average logRT by ItemTypes Average logRT by Groups Create a bar graph for logRT by Groups

Lab activities: Processing behavioral data Download sample data from Blackboard (Course materials > Sample behavioral data) Dealing with accuracy data Recode the accuracy data into 1 (for “Y”) and 0 (for “N”) Average ACC by ItemTypes Average ACC by Groups Average ACC by subjects Create a bar graph for ACC by Groups Create another dataset with “correct only” data.