PhD Interview Questions Flashcards
Why have you applied to do a PhD in York? What is it about working with Professor Andrews that appeals to you? - (4)
I am very keen to do a neuroscience PhD at the University of York as it is renowned for being one of the top 10 universities in the UK and well-known for its world- leading research, housing cutting-edge neuroimaging methods such as 3 Tesla fMRI systems in YNICas well as having the ability to work with many experts in the field of neuroscience research, especially face recognition.
I have developed a deep passion for the field of face recognition as I read a couple of Professor Andrews’s papers on the subject as well as learned about neural basis of face perception in neuroimaging of vision as well as currently helping one of Professor Andrew’s PhD student on a project on face recognition.
I very much enjoy working with him.
I have found him to be very supportive and encouraging. His expertise on face recognition using advanced methods like MVPA aligns with my research interests.
What are some of the papers you read from Professor Andrews and what you found interesting? - (2)
The study had a series of experiments where participants identified manipulated averaged images of familiar faces
Images had either shape or texture altered to measure importance of each feature in recognition
Key finding was texture plays a dominant role in recognising faces and participants significantly better at identifying faces with preserved texture even when shape was altered compared to opposite scenario.
What skills and attributes do you have that make you a suitable for PhD in area of research? - (7)
Over the years, I have also acquired programming skills using R, MATLAB, which I
used in my undergraduate dissertation to conduct correlational tests and as well as use MATLAB specifically to run the experimental script for dissertation.
I have also a working knowledge of Python which I used during my GCSE and will soon learn Python in second semester in introduction to programming course python used to analyse neuroimagning and behavioural data.
These programming skills are needed for fMRI data analysis.
I have a completed a module called ‘Neuroimaging of Vision’ taught by Professor Andrews. This module covers the brain mechanism including face perception.
In addition, I have also done a module on Principles of Cognitive Neuroscience, in which both Professor Andrews and Hartley wcovers how fMRI works and Professor Hartley convered advanced data analysis methods like intersubject correlation and MVPA.
Both these modules form the foundation of my preparation for the PhD.
As I mentioned, I am already involved in working on a similar project in which I
have gained experience on analysis of behavioural data where I acted as a
second coder and some hands-on experience in fMRI data analysis using FSL.
- Can you tell us in simple terms about a research project that you have been involved in – what were you investigating and what did you find? - (6)
My final dissertation project at Newcastle University was called ‘Prediction of
Speech-In-Noise Performance Using Non-Speech Stimuli’ under the supervision of
Professor Tim Griffiths.
Speech-in-noise perception is basically how well people can hear speech in
background noise. For example, in a pub how well we can hear a friend wile lot of people are talking in the background (This is also called the cocktail party effect).
Now the question how to measure the speech-in -noise performance? The typical
test use a sample of speech (like a word or sentence) within noise added to it. The
task is to recognize the word/sentence.
The speech content is typically recorded in specific language (e.g. English) and thus
the tests can not be used with participants who are not fluent in that language.
To address this limitation a non-speech stimulus called Figure-Ground was
developed. This stimulus consisted of a pattern of coherent tones , which is the
figure a background of non-coherent tones. After hearing two figure-ground stimuli,
participants were asked if the two patterns were same or different.
We then investigated if the performance on the Figure-Ground stimulus was
correlated with performance on conventional speech -in-noise test. We found that
that this was indeed the case. (Typical correlations around 0.4)
- What were the correlation values you find in new test and how did it compare to old tests? - (3)
- We measured speech-in-noise using both sentence and word in noise and a typically hearing test called pure-tone
- Figure-ground between sentence was 0.40 and word was 0.38
- Did regression showing figure-ground significantly added 10-20% of variance explained in sentence and word in noise tests more than PTA
What is advantages and practical application of dissertation study? - (2)
- Auditory processing disorder is charactercised by speech in noise difficulty and used as a test than convential speech in noise
- Used for children who do not possess adult level language proficiency, biliuginal and individuals who are non-native speakers
- What is figure-ground? - (2)
The SFG stimulus consists of a set of tones of varying frequencies that change randomly and a specific subset of tones that remains the same over time.
The fixed frequencies are perceived as the auditory target amidst the ‘background’ of random variation of frequencies
- What is limitations of dissertation? - (5)
There are some limitations of the study.
The study only considered individuals with “normal” hearing thresholds and native English speakers.
Therefore our results are not extendable to those have hearing deficits and/or not native English speakers.
The performance of the roving stimulus, in terms of predicting SiN performance, needs to be evaluated on these populations.
The criteria of including only “normal” hearing thresholds participants also led to a exclusion of a large number (10/74) of participants from the data analysis
- What is figure like in same or different pattern in figure-ground? - (2)
- Figure is same in same pattern
- Figure is different to one another in different pattern
- What is an issue you would like to address during your PhD? - (14)
The main question that I am trying to address is how conceptual knowledge helps in familiar face recognition and where in the brain this knowledge is represented.
Research has shown that familiar and unfamiliar faces are processed differently as familiar faces require less effort in recognition despite changes in appearance and are detected much faster than unfamiliar faces
As we get to know someone we acquire both perceptual information as well as conceptual knowledge of a person – like who they are as a person which both aid in the recognition of familiar faces.
There has been limited research that have tried to answer how conceptual knowledge helps in face recognition and where is it represented in brain but a number of limitations
The main being that the paradigm used lacks ecological validity as static facial stimuli associated with artificial conceptual info like name/occupation, perceptual and conceptual in these paradigms are assumed to be separated which does not happen in real life.
To overcome this limitation, task-free naturalistic paradigms have been developed
where participants are watching a engaging movie in a MRI scanner.
The naturalistic viewing paradigm is nice from the ecological perspective, but the problem is how to separate the conceptual from perceptual.
A recently collected behavioural data using naturalistic paradigm has shown that
conceptual information helps in recognition of faces after a delay period.
In this paradigm, there are two groups of participants.
One group watches a movie in its original order and the other watches a scrambled version of the movie.
The idea is that the Original group will be able to construct a narrative where as the scramble group will not be able to.
After watching the movie, subjects perform a face recognition task to identify actors in the movie. Interestingly, immediately after watching the movie, performance of both the original and scrambled version did not differ indicating that both groups are using perceptual
features for face recognition.
However, after a delay of 4-week, the original group performns better than scrambled indicating use of conceptual knowledge in face recognition after delay.
In this current we will use MVPA and ISC to identify the brain network for conceptual
information representation and how the perceptual and conceptual network interacts
using dynamic causal modelling.
- Where do you see yourself doing in 5 years? - (3)
In the next five years, I envision myself having completed a PhD gaining expertise in face recognition and working a research lab doing my independent research in this relevant field.
My long-term plan is utilising the knowledge and expertise I have gained from my MSC in CN and PhD in face recognition in University of York is to make a career in research in a laboratory and teaching in a University in the UK or USA.
This PhD program will be a foundation for this aspiration.
- Do you have any questions?
I don’t have any questions as I have been a masters student at the university of York for some time and I have asked a bunch of questions to Professor Andrews who has been helpful and clarified them.
- What is your question about your proposal:
‘Using Naturalistic Viewing Paradigm to Explore the Role of Conceptual Knowledge in Face Recognition’
- Why is recognizing faces so important?
The recognition of familiar faces is fundamental for social interactions. For example, recognizing whether a face is someone you know or a stranger dictates how you are going to interact with them.
- What are the differences between familiar and unfamiliar faces?
There is a difference between familiar and unfamiliar faces as they require less effort in recognition despite their changes in appearance (Bruce, 1982; Young and Burton, 2019) , are detected faster (Gobbini et al., 2013) are processed more automatically (Jackson and Raymond, 2006) and can held in working memory more accurately (Jackson and Raymond, 2008).
- What are some of the behavioural methodologies showing differences of familiar faces? - Gobbini - (4) processed much faster
o In an experiment doing continuous flash suppression (CFS) task, participants viewed pairs of rapidly alternating images one containing familiar/unfamiliar face and other a house, one image in each pair rendered invisible through CFS making it unconscious
o Task: Participants reported which image (face or house) they “felt” present, even though they couldn’t consciously see it.
o Finding: Participants were significantly faster at detecting the invisible familiar face compared to the invisible unfamiliar face. This suggests that familiar faces can be processed preconsciously, influencing early stages of visual perception.
o Prioritize detection of personally familiar faces even without conscious awareness
- What are the theoretical models behind familiar and unfamiliar face recognition? - (3)
Models of face processing suggest that image-invariant representations of familiar faces are constructed and stored over time (Bruce and Young, 1986)
These representations then act as a template to which an incoming face image is matched.
With repeated exposure of face, template of familiar face becomes refined and specific containing consistent facial features leading faster and accurate recognition
- What is conceptual information of a face?
The conceptual information refers to person-specific details such as what is someone’s name, occupation, what are they like as a person, their personality traits are they introverted or extroverted.
- What is the perceptual information of a face?
The perceptual information of faces is like arraignment of facial features such as eyes, nose, mouth, color of someone’s eyes for example and the shape of the face is it round or someone has a sharp jaw.
- What is the evidence showing that perceptual information helps in face recognition? - (2)
Studies have shown that the image-invariant representation is derived from experience of face image and its visual features.
For example, repeated experience of the same person’s face from different views, illumination and facial expression produce a robust visual representation (Kramer et al., 2015 study)
- Details of Kramer’s study:
Kramer et al., 2015 = so participants had faster and more accurate recognition as they saw more images of Jennifer Lawrence as ability to recognise her in novel images from new viewpoints improved
- What is the evidence showing that conceptual information helps in face recognition? - (2)
Although role of perceptual information in face recognition has received a lot of attention, it has suggested that conceptual information (e.g., name or occupation) associated with face is important for recognition
e.g., Schwartz et al., (2016)
- More details of Schwartz et al (2016) study - (3)
- perceptual learning group = saw each face from multiple angles and lighting conditions but received no additional info,
- conceptual learning group had faces with unique name and occupation,
- participants took a face recognition with novel images of familiar faces , participants in conceptual performed better than perceptual learning and control in learning faces from new viewpoints
- What did Schwartz and Yovel (2019) study demonstrate?
More recently, Schwartz and Yovel (2019) showed that the effect of conceptual advantage is not due to modifications of the perceptual representation of faces, indicating that conceptual and perceptual information are encoded distinctly.
- More details on Schwartz and Yovel (2019) study on method and findings: - (4)
Generally in their methods, they asked participants to rate 20 different identities based on their perceptual appearance and inferred personality traits In learning phase on likert scales e.g., how intelligent the face is.
Then during testing phase images of learned identities differed in lighting and asked to see if the face was new or old to them.
They varied whether participants doing perceptual judgements based on specific facial features e.g, how round the eyes are or globally e.g., how round the face is and found that the conceptual advantage in face recognition is not due to processing the face more globally than part-based or vice versa.
They also showed that participants recognition performance its accuracy was not dependent on taking more time in doing conceptual evaluations (e.g., how intelligent does a face look) of faces than perceptual (e.g., how round a face is) as they measured their reaction times e.g., more elaborative encoding of faces in conceptual.
