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

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

3. 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

4. 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)

5. 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

6. 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

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

8. 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)

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

10. Hoyniak et al. (2018)
Results

11. Hoyniak et al. (2018)
Results

12. 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

13. 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/

14. 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

15. 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?

16. 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.

17. 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

18. 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.