Interview 2 Flashcards
1
Q
What is title of project?
A
Using Naturalistic Viewing Paradigm to Explore the Role of Conceptual Knowledge in Face Recognition’
2
Q
W
A
3
Q
What is conceputal information of a face?
A
The conceptual information refers to person-specific details such as someone’s name, occupation, what they are as a person and personality traits like whether are they introverted or extroverted
4
Q
What is perceputal information of a face?
A
Perceptual information of a face is information such as the arrangement of someone’s facial features of the face, the colour of someone’s eyes, and the shape of the face (i.e., does someone have a round face or sharp jaw?)
5
Q
- What is the key research question you are addressing?
A
The main question I am trying to address is how conceptual knowledge helps in familiar face recognition and where in the brain this knowledge is represented
6
Q
- Tell us about your PhD project: the research question and how you hope to investigate this/What is issue with PhD and what is your proposed methodology to investigate it? - (9)
A
My PhD project aims to answer the question which is how conceptual knowledge helps in familiar face recognition and where in the brain this knowledge is represented
The conceptual information refers to person-specific details such as someone’s name, occupation, what they are as a person and personality traits like whether are they introverted or extroverted
Most of the literature has focused on the role of perceptual information (i.e., visual information from someone’s face such as the arrangement of their facial features and how round their face is) in face recognition
What I would like to do with this project is investigate the role of conceptual knowledge on face recognition and there have been limited studies on this which has limitations.
Their limitation is that faces are presented in a controlled experimental setting that does not reflect our experiences in real life of how we encounter faces
The stimuli are static and are linked with artificial conceptual knowledge like name and occupation as well and perceptual and conceptual knowledge may not be separately acquired which is assumed in the paradigms as well as facial stimuli are presented in ‘trials’ that are not assumed to be independent of each other.
Naturalistic task-free paradigms can be used to overcome these limitations where participants’ neural responses are recorded in an fMRI scanner while watching an engaging movie/documentary, however, it is hard to separate conceptual from perceptual.
A recent behavioural study using naturalistic viewing paradigm by Noads and Andrews had participants watch the original or scrambled version of an episode of Life of Mars and tested how well they understood the story of the movie immediately and after a delay and performed a face recognition task. Both groups have same perceptual information but acquire different cocneputal knowledge. They found that original group performed better in constructing narrative of movie than scrambled. But interestingly, original and scrambled performed equally on face recognition task assuming they were using perceptual features for face recognition of the actors. However, after a delay of 4-week, the original group performs better than scrambled indicating use of conceptual knowledge in face recognition after delay.
Thus, our study will utilise naturalistic viewing paradigm of Noad and Andrews’s Life on Mars paradigm while participants’ neural responses are measured in fMRI and use MVPA and ISC to identify the brain network for conceptual information representation and how perceptual and conceptual networks in brain interact using dynamic causal modelling.
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Q
- What methodology of your proposed PhD project in more detail - (4)
A
We will use the Life on Mars naturalistic viewing paradigm (see Noad and Andrews, 2023). However, participants will initially view either the Original or Scrambled version of the movie outside the scanner which they are unfamiliar with. And they are tested on narrative using structured questions of key events of movie and write a detailed narrative.
Participants in both the Original and Scrambled groups will have the same perceptual information but will acquire different conceptual knowledge.
The neural response will then be measured in participants from both groups using fMRI. They will view a movie containing excerpts from previously unseen episodes of Life on Mars.
Participants from both groups will then be scanned after a delay of 4 weeks. On this occasion, they will view a new movie containing excerpts from previously unseen episodes of Life on Mars.
8
Q
- Who developed naturalistic viewing paradigms?
A
Hasson et al 2004
9
Q
- How using naturalistic viewing paradigm in your study overcome limitations of previous neuroimagning studies? - (4)
A
- The facial stimuli are dynamic and not static
- The movie is more engaging and thus might make participants more cooperative
- The perceptual and conceptual information is integrated and not separated
- The conceptual knowledge the participants learn of actors in movie is more meaningful as it goes with the story of the movie
10
Q
- Why did original group in Noad and Andrews have higher face recognition performance after a delay? - (2)
A
This is because original group new memories of the identities of the actors are successfully consolidated over delay and their memories of new actors is enhanced as they acquired information of the actors in a coherent context which leads to them having higher face recognition of identities of actors over a longer period.
This is not the case of scrambled group as they watched the movie in scrambled order and their memories of actors they have obtained is not in a coherent context and leads to less stable recognition of them and thus leads to poor performance
11
Q
- How are we defining region of interests (ROIs) in your study? - (2)
A
For an MVPA approach, we are not defining regions of interest apriori as we are using search light approach with MVPA as looking at the whole brain to identify brain regions which correctly classify actor’s identity above chance level.
For DCM, focusing on direction of flow of information between core and extended system. Therefore, these regions needed to be defined. To do this, we can pick up coordinates that show in ISC and MVPA analysis. Picking up those areas showing above chance classification.
12
Q
- If not mentioning core and extended systems in more detail, we can say? - (2)
A
In core network, brain regions responsible for identifying identity of someone
In extended network, brain regions responsible for retrieval of person-specific knowledge
13
Q
- How are you using MVPA in your study/whats the aim? - (3)
A
Using MVPA, we will ask whether the pattern of response in different regions of the brain can predict the identity of a person.
To do this, we will compare the pattern of response when one identity is viewed in one part of the video with the pattern of response from a different part of the video.
The MVPA analysis will be performed using a searchlight approach on the whole brain to identify brain regions which correctly classify identity above chance level immediately after participants watch movie and after a delay.
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Q
- What are the 2 ways to do MVPA? - (2)
A
region based
search light = draw 6mm sphere and run classifer sphere , contains one or more regions
15
Q
- Whatis the difference between traditional univarate and MVPA?- (4)
A
most of the fMRI studies for face recognition use univariate method of analysis in which each voxel is considered in isolation of others while its activation in response to task or stimulus is estimated.
It is, therefore, assumed that difference in response between the two stimuli, for example, familiar vs non-familiar faces can be observed at the singe voxel level.
The possibility that information could be encoded jointly by the voxels is ignored in the univariate method.
The multivariate pattern analysis (MVPA) (Haxby et al., 2001) overcomes this limitation by considering spatially distributed pattern of activity across voxels. The MVPA makes distinctions between the stimuli or tasks based on the pattern of activity
16
Q
- What is MVPA’s classifer? - (3)
A
A classifier is machine learning algorithm learns to associate patterns of brain activity with specific labels
Classifier is ‘trained’ on portion of dataset and told that certain brain activity correspond to different labels
The classifier is then tested on separate portion of dataset not used on training to classify in appropriate labels and its accuracy is observed
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Q
- What are the key hypotheses driving your PhD project? – MVPA- immediate: - (2)
A
Since immediately after the movie, both groups of participants predominantly use perceptual information for recognition, the hypothesis is that brain areas of the core network will have above chance classification in both groups and that the performance between the two groups will not be significantly different.
In contrast, brain regions from the extended network will not show above chance classification in either group
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Q
- What are the key hypotheses driving your PhD project? – MVPA- delay - (2)
A
Given that after a delay, there is a difference in the role of conceptual knowledge for recognition, the hypothesis is that brain areas of the extended network will have above chance classification in the Original group and that the performance will be significantly higher compared to the Scrambled group.
Since conceptual knowledge can also activate perceptual system, there will be above chance classification in the core network for Original group and this and this will not be so in the Scrambled group.
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Q
- What is inter-subject correlation? - (2)
A
inter-subject correlation (ISC) as a measure to calculate how consistent the activation of a given brain area across different participants is when they watch the same movie.
Mathematically, the ISC is simply a correlation coefficient between the time-series of activation extracted from a given voxel/brain area from two participants
20
Q
- What is difference between inter and intra subject correlation? - (2)
A
Inter-subject = measures consistency of neural responses across different participants in study
Intra subject = measure consistency of neural responses within same individual across different conditions
21
Q
- Why is inter-subject correlation used in naturalistic viewing paradigm? - (2)
A
Since the stimuli presentations in these paradigms are not controlled, a challenge lies in the analysis and interpretation of the data.
Hasson et al (Hasson et al., 2004) used inter-subject correlation (ISC) as a measure to calculate how consistent the activation of a given brain area across different participants is when they watch the same movie.
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Q
- How are you using ISC/whats your aim? - (2)
A
The second aim of the proposal is to use Inter-subject correlation (ISC) to assess the role of conceptual information in face identification
To address this question, the neural response to the movies shown without a delay and with a delay will be compared in participants from the Original and Scrambled groups.
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Q
- What are the key hypotheses driving your PhD project? – ISC – delay and immediate: - (4)
A
Since immediately after the movie, both groups of participants predominantly use perceptual information for recognition, the hypothesis is that brain areas of the core network will have high ISC in both groups and that the performance between the two groups will not be significantly different.
In contrast, brain regions from the extended network may not show high ISC in either group.
Given that after a delay, there is a difference in the role of conceptual knowledge for recognition, the hypothesis is that brain areas of the extended network will have higher ISC in the Original compared to the Scrambled group.
Since conceptual knowledge can also activate perceptual system, there may also be higher ISC in the core network for Original group.
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Q
- What is the aim of DCM and how it is used - (2)
A
The third aim is to understand causal interactions between the core and extended network using dynamic causal modelling (DCM) for fMRI
More specifically, the hypotheses to be tested are (i) the flow of information between core and extended network is influenced by conceptual knowledge and (ii) the flow of information has its directionality from extended to core network
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24. How to identify causal flow of info in DCM in study for core and extended system? - (3)
To identify causal flow of information between the core and extended network for facial recognition, fMRI data and DCM will be used to estimate three types of models:
Forward model (core network drives the extended network), backward model (extended network drives the core network) and forward-backward model (both core and extended networks drives each other).
These models will be estimated and compared against each other, and the best model will be selected.
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27. What are the key hypotheses driving your PhD project? – DCM - (2)
Since, conceptual knowledge can activate perceptual system, the hypothesis is that the backward model will be the best model in ‘Original’ group for the data acquired after the delay period when conceptual information is dominant for face recognition.
Since the backward flow of information in the Scrambled group would be absent, the best model for the Scrambled group would be the forward model.
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28. What is the difference between DCM and functional connectivity (correlational)?
Both DCM and functional connectivity give magnitude of strength of relationship of between different variables however what’s unique to DCM is that it gives the direction of flow of info whether its forward, backward or forward-backward which functional connectivity does not tell you
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30. Why are we not using Granger causality instead of DCM? - (6)
Both causal connectivity and give direciton of flow of info (regression)
o Granger is not based on neural activity and regression and can be applied to any sort of data
o DCM tries to go at neural level,what you are measuring at fMRI is indirect measurement of neural activity by measuring blood flow
o Start a model where area A and area B have neural activity between them and area A is driving area B
In DCM there is a mathematical equation of neural activity to predicted BOLD signal.
Can compare predicted and actual BOLD and if it matches well the model is true and if not correct.
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31. Are you using within or between subjects in PhD project?
It is a mixed design between the subject (original and scrambled) and within the subject as both groups watch the movie immediately and after a 4-week delay.
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32. Why is recognizing faces so important? - (2)
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.
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33. 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 hold in working memory more accurately (Jackson and Raymond, 2008).
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34. Wha are some of the behavioural methodologies showing differences of familiar faces? - Gobbini showing familiar faces detected faster than unfamiliar faces? - (4)
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
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35. What is evidence showing perceptual information helps in face recognition?
Studies showing perceptual experience helps recognises faces such as repeated experience of the Jennifer Lawerence’s face from different views, illumination and facial expression produce a robust visual representation that enables participants to recognise her in novel images from new viewpoints improved
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36. What is evidence showing conceptual info helps in face recognition - (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
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37. What did Haxby and colleagues propose?
They propose a distributed model of face processing that divides brain areas into core and extended network
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38. What is core network? - (2)
The core network includes includes brain areas such as Fusiform Face Area (FFA), Superior Temporal Sulcus (STS) and occipital face area (OFA), is thought to extract visual features about the face.
Within this network, the STS extracts dynamic features of faces (O'Toole et al., 2002) which change with time (e.g., speech, shifting gaze, expressing emotional expressions), whereas the FFA is involved in extraction of facial features which are invariant (e.g. identity).
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39. What is extended network?
The role of extended network is to extract ‘higher level’ conceptual information, such as traits and biographical information, associated with the face and includes brain areas such as the anterior temporal lobe (ATL), cingulate and precuneus.
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What is role of ATL, cingulate and prenuceus? - (3)
ATL = It retrieves sematnci memories related to people we know such as recalling its name, occupation, past experiences etc..
Cingulate involves in recalling emotions associated with person’s face like feelings of trust/empathy
Precuneus: creates mental representations of situations and past experiences
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43. What are the practical applications of your PhD project?/Impact of the project - (4)
- The ability to recognize faces is fundamental to our social and emotional development.
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- Therefore, understanding face recognition in humans is valuable in the context of mental health, as difficulties in recognizing faces can be indicative of certain neurological or psychological conditions.
Therapies can address this as a misunderstanding of faces such as their emotions and challenges in social interactions can lead to conflicts/ symptomsand therapies and interventions address to improve the social functioning of individuals wi eh h mental health
The findings may also have relevance to face recognition technology.
Efficientand accurate face recognition systems can aid in identifying and tracking individuals fo public safety and crime prevention.
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44. How many participants are you expected to recruit?
Recruit participants from local population and given sensitivity analyses from similar studies, we estimate that we will need 20-25 participants per condition
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45. What challenges you might face in the project? - (4)
- Firstly, managing my time more effectively and balancing research commitments as well as other responsibilities that I have is crucial as I transition to a more independent researcher role.
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- Secondly, participant recruitment, can be a challenge for all experiments including neuroimaging studies and require proactive strategies to encourage participants to participate
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- Also processing and analysing large-scale neuroimaging datasets can be computationally intensive and demanding and ensuring I have good data management skills to ensure I label files correctly
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- Data analysis may pose a challenge as well since I will have to analyse these large-scale neuroimaging data as it requires advanced computational skills and familiarity of specialised software of neuroimaging software which I am starting to learn about in my second semester of my degree.
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46. Why do you want to do a PhD in this field of face recognition? - (4)
Undertaking the proposed PhD to explore the role of conceptual knowledge in face
recognition’ is driven by my intellectual, personal, and professional motivations.
Personally, I have had first-hand experience of role conceptual knowledge has in familiar face recognition. As a person who travels to visit their family back in India, I was astonished that I can still recognize the faces of the uncles and aunties, which must be due at some level to my knowledge of them as people.
My intellectual motivation behind how this occurs at the brain level has led me to apply for this PhD program.
Professionally, completing this project aligns with my career aspirations of making a career as an academic researcher in the field of face
recognition. By completing this PhD program, I will have developed a deep understanding of the neural mechanisms behind face recognition as well as master both multi-voxel techniques and task-free naturalistic paradigms which will equip me with valuable skills that are sought out in world-leading research labs in this field.
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47. Why do you want to do a PhD in general?
Generally, I am motivated to do a PhD since I have always had the belief of continuing to learn throughout my life as I find learning, especially about face recognition quite exciting and pursuing a PhD aligns with those personal beliefs
Furthermore, I want to do a PhD to deepen my research interests into face perception and this PhD helps me to achieve my long-term career aspiration of becoming a neuroscientist in this field as well as this PhD will open doors for me in terms of networking in able to connect with many individuals who has expertise in face recognition.
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1. What are the sources of background noises in speech-in-noise perception?
The source of these background noises could be interfering speech sounds (e.g., many people speaking at the same time) or environmental sounds (e.g., loud TV, traffic noises, etc..)
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2. Why is SiN perception challenging?
It is challenging for listeners since the background noise degrades the speech signal and thus requires a greater degree of focus and attention of the speaker
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3. What does successful SiN perception require?
Successful SiN perception requires listeners to separate target speech from other irrelevant noises
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4. What are the mechanisms behind SiN perception/figure-ground separation? - (4)
Teki et al., (2013) proposed the temporal coherence model to explain the mechanisms behind figure detection
The model is based on the fact that the input sound in the ear is first divided parallel frequency channels (from low to high-frequency) that will then transmit auditory information to the brain
If the different frequency channels are transmitted related information tto the brain then it is grouped as a figure and if the information across the channels are not related then these are grouped together and perceived as a background
The temporal coherence model can be applied to both SiN perception as well as figure detection in SFG and HRSFG stimuli.
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5. Why is there an emphasis to develop SiN tests developed? = SiN Difficulty - (3)
Problems with SiN perception is often referred to as SiN difficulty or more informally the cocktail party effect
SiN difficulty is common among hearing impaired individuals and most associated with higher rates of anxiety symptoms, social isolation and generally lower quality of life as compared to general population
Therefore, how to diagnose SiN difficulty has been an important research topic in speech perception
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6. What tests were conventionally used to diagnose SiN difficulty and what were their limitations? = PTA and SiN
- (2)
Conventionally, the diagnosis is based on a pure-tone audiometry (PTA) test which measures an individual’s hearing threshold level based on response to pure-tone stimuli which only captured peripheral hearing loss
SiN tests used with speech in multi-talker babbler noise and more realistic to sounds we encounter and measures both impairments in brain processing and peripheral hearing loss.
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7. What is multi-talker babble noise?
Many people talking at the same time
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9. Explain more about figure-ground, what does SFG consist of? - (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
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10. How does SFG measure SiN perception?
Typically, the SFG measures SiN perception by assessing the ability to detect the presence of the figure
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11. What are the advantages of SFG compared to SiN tests?
The SFG stimulus has an advantage over SiN tests, in terms of being applicable to a wider range of participants coming from different language backgrounds because it lacks linguistic content
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12. What did prior research (i.e., Holmes and Griffiths 2019) show with the SFG and its correlation with SiN (sentence) tests?
A previous study by Holmes and Griffiths (2019) using a figure discrimination task they created using SFG found SFG thresholds only modestly positively correlated with sentence-in-noise performance thresholds.
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13. What made you create the HRSFG from previous research? - (4)
The modest correlation between SFG and sentence in noise thresholds implies:
SFG is a valid measure of SiN perception but could be further improved to stimulate speech more closely.
Thus, this research gave us rationale to create new version of SFG called HRSFG which contains dynamic and harmonic features of speech in figure which SFG did not have (i.e., static pattern as figure [fixed freq over time)
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14. Why did the figure discrimination task (by Holmes and Griffiths 2019) include gaps in figure?
Since natural speech is compromised of speech segments that are separated by short gaps, Holmes and Griffiths (2019) created a new task where participants were asked to discriminate which of the two SFG stimuli contained a gap in the figure elements.
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15. Explain more about speech containing dynamic structure and example: - (2)
Speech is dynamic as its frequencies change over time as we are not speaking with a monotonous voice like a robot
Also on average male pitch is 150 Hz but it does not mean males always talk in 150 Hz in a monotonous voice but its pitch may go to 125 or 180 Hz but always average to 150 Hz.
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16. Explain more about speech containing harmonic structure and example: - (4)
Additionally, one of the important acoustic features of speech is that a significant portion of the speech sound has a harmonic structure.
That is the spectrum of speech has frequencies which are multiples of fundamental frequencies and an associated pitch.
For example, if the fundamental frequency is 200 Hz then the second (400 Hz), third (600 Hz), and fourth (800 Hz) harmonics etc. are also present
For example, imagine you are saying the word “hello” and the vibration of your vocal cords at the starting point of the word “h” sound creates a fundamental frequency which is like a starting note of melody. As you begin to say the rest of the word, your voice does not produce that one main pitch but produces higher-pitched tones of harmonics.
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What is pitch?
Pitch refers to the perceived frequency of a sound and is sometimes determined by the physical frequency of a sound
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18. Give an example of pitch:
Pitch allows us to distinguish between high or low notes in music
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19. What is frequency (Hz)?
Frequency is the number of cycles of a sound wave that occur in one second
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20. What is the limitations of the SFG stimuli? - (3)
There is a modest correlation between SFG performance thresholds and speech-in-noise tests suggesting It can be improved to be more speech-like
Additionally, the figure in SFG is static (has a fixed frequency) and has pure-tone components that are not harmonically related to each other.
Therefore, for an SFG stimulus to mimic the speech stimulus, it may be required for the frequencies of the figure to be harmonically related.
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21. How were our HRSFG stimuli developed?
So we had natural speech of male recording in which they spoke sentences consisting of a structure of name, verb, number, adjective, and noun.
We then extracted the pitch contours of that recording (i.e., how does the male’s pitch change over time) using a software called Praat and we incorporated that pitch going up and done in our HRSFG stimulus which was created using MATLAB.
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22. How was SFG produced for the figure-discrimination task? - (3)
The SFG was not created by us but taken from Teki and colleagues
They had created the SFG stimuli using MATLAB software
In the figure discrimination task, one of the two SFG stimuli had 6 chords removed from its figure and each chord contained multiple pure tones (explain what pure tone is) of certain frequencies removed to insert a gap in the figure portion.
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26. What was your design of the study? - (3
Correlational study
IVs = PTA, SFG thresholds via figure-discrimination, HRSFG thresholds via pattern discriminatiton
DV = sentence in noise thresholds
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28. More details of the sentence-in-noise test you used: - (4)
The sentence in word presented sentences in multi-talker babble
All sentences were spoken with British accented male who spoke native English
All sentences had a structure of name, verb, number, adjective, and noun (e.g., Lucy ordered eight large sofas)
After each sentence-in-noise, 10 options were presented for each word in the sentence and participants had to report five words from the target sentence.
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29. If they ask what other ways did you measure speech in noise aside from sentence in noise? - (2)
We also employed a word-in-noise measure in which participants played word in noise in each trial and had four options to choose from, one was target and the rest was foils.
The overall performance of the WiN test for each participant was calculated, instead of using TMR thresholds, but as percentage correct (i.e., performance = number of target words correctly identified/total number of trials)
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30. What do you mean when you say TMR in TMR thresholds used in SFG , HRSFG and sentence-in-noise?
They manipulated the level of loudness of the target (e.g., figure/speech sounds) relative to the masker (e.g., background/babble noise) noise (target-to-masker ratio [TMR]
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31. How were sentence-in-noise, SFG and HRSFG thresholds measured? - 5
We obtain TMR thresholds for each of the test
The thresholds were determined using adaptive tracking where you increase/decrease signal (figure) noise level depending on participants’ performance
If participants are doing better then you decrease the noise in the signal and if becomes worse then increase the noise level
At some point, performance oscillates at certain bounds and does not increase/decrease further. = no dramatic changes in performance
At what level of noise of signal that oscillation is occurring is then recorded which can be different for each participant so each participant has different trials , not fixed, for these tests.
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33. What were the similarities between figure-discrimination and the pattern-discrimination task?
They are both forced-choice discrimination tasks as had to discriminate two non-speech stimuli
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34. What are the similarities between SFG and HRSFG?
The similarities are both non-speech stimuli measuring SiN perception, consist of a figure and tasks that utilise these stimuli involve participants to discriminate patterns of these stimuli in each trial
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36. More details on PTA - (5)
In Tim’s lab, I was trained how to give a hearing test, called pure-tone audiometry, to participants before conducting the experimental paradigm
In pure-tone audiometry, it involves presenting pure tones of frequencies of 250 Hz all the way to 8000 Hz and each level presents tones of varying levels of loudness, measured in decibels, in each ear separately
Then I recorded in each frequency level what Is the participants’ hearing threshold = what is the minimum level of loudness the participants can detect the tone
The participants respond whenever they can detect the tone with a patient signal response button.
After participants completed their PTA, I then informed them of their hearing and whether it was normal before continuing with the rest of the study
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37. What is decibels (db)?
It is a unit of measurement that is used to measure the intensity or loudness of sounds
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How can you define decibels (dB) - 6
If you have a sound, it has amplitude so how up and down its going (how big the peak is from the baseline)
If you find amplitude, not related to frequency, you square it so it becomes power (always positive and not negative as amplitude can be both positive and negative) = power of your signal
Then you compare it to some standard power
dB is defined as taking the power of the actual signal divided by standard (reference) power = saying how big
If the ratio between these two is 2 from the calculation then the power of the actual signal is x2 compared to the reference signal
You divide the actual signal over standard reference power take the log of it and multiply it by 10 to calculate decibels
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39. Why did we use decibels (dB) in our study for TMR thresholds? - (3)
We used decibels since it use a logarithmic scale which is useful for expressing a wide range of values
In human perception, decibels refer to perception of sound intensity and the human ear perceives changes in loudness on logarithmic scale and decibels allow for representation that aligns more closely with how we perceive intensity of sounds.
Additionally, decibels are commonly used to express signal-to-noise ratio (SNR) or we call it TMR (same thing) in decibels.
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40. Did participants do practice trials?
Participants completed practice trials for each computer task before beginning the experimental trials where they received feedback on whether their responses were ‘correct’ or ‘incorrect’
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41. Was the procedure counterbalanced?
The order was counterbalanced across participants.
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42. Did you perform normality tests?
We performed normality tests of Kolmogorov-Smirnov tests on each variable and if normality tests failed we log-transformed them = sentence in noise
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To test first hypothesis that SFG and HRSFG is related to SiN perception
Pearson correlation tests between SiN (sentence and word) with SFG and HRSFG thresholds separately.
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45. To test the second hypothesis of HRSFG is better SiN predictor than SFG
Separate hierarchical regressions performed in which SFG thresholds as block 1 regressors and roving stimuli as block 2
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46. What programs did you conduct your data analysis in?
I did it using R and Matlab since my supervisor also encouraged to perform same data analysis in that software as well
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48. Explain your positive correlation between (sentence) SiN threshold and SFG threshold
It means that individuals who require a higher level of noise to detect target speech relative to background noise in speech-in-noise tests also require higher level of target signal noise of figure relative to background noise in figure discrimination task
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49. Explain your positive correlation between (sentence) SiN threshold and HRSFG threshold
It means that individuals who require a higher level of noise to detect target speech relative to background noise in speech-in-noise tests also require higher level of target signal noise of harmonic figure relative to background noise in pattern discrimination task
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50. Why does low TMR threshold indicate better performance?
In terms of SiN perception, a low TMR threshold indicates listener detect and understand target at a noise levelt hatt is not much louder than background noise.
This indicates individuals effectively perceive the target in challenging listening conditions.
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51. Why does high TMR threshold indicate worse performance?
Indicates that a higher level of the target signal is needed relative to the background noise for the listener to correctly perceive or understand the target. This is considered poorer performance as it suggests more difficulty in separating the target from the background noise.
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52. Why did you perform hierarchical regression? - (2)
Since our new version is more resembling of speech than SFG, we wanted to assess the new contribution of new variable of harmonic SFG after accounting for the contirubtion of original figure-ground variable in SiN test performance.
The new variable in Block 2 adds significantly to the prediction after controlling for figure-ground, it implies that the harmonic figure in SFG has additional explanatory power beyond the basic figure-ground variable.
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53. What is theoretical and practical applications of project? - (2)
This finding had theoretical implications that harmonic figure has unique influence beyond basic figure-ground perception on speech-in-noise performance and better SiN predictor
Practical applications as can be used in settings to assess someone’s SiN Perception and assess if they have SiN difficulty which is related to hearing loss and have certain bounds indicating normal, to severe hearing loss.
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54. 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
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What is limitations of dissertation - (4)
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
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55. How would you do your dissertation project differently if you got the chance? - (2)
Since we only included participants who are native English speakers which only generalises our findings to English speakers, while doing literature searching of this project, I discovered other speech-in-noise tests in other languages such as Spanish
So next time, I could do the study with a Spanish speech-in-noise test and recruit Spanish speakers to see if we come with the same conclusions and give more stronger evidence our HRSFG stimuli is better SiN predictor.
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56. What is the future direction of your study in more detail? - (4)
The brain mechanisms behind both figure-ground perception and performances on SiN tests has been studied
More specifically, the studies have found that that brain areas activated in SiN tests do or do not overlap with brain areas that have been linked with figure-ground perception
Since the roving stimuli are a better predictor of behavioural SiN performance as compared to conventional SFG stimuli, it is therefore expected that there would be an increase in the overlap of brain areas that are activated by SiN tests and roving stimuli.
Future work needs to confirm this.
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57. Now you have learned about designs in neuroscience, how would you design brain study based on future directions? - 6
We can specifically do a study that has two sessions: a behavioural session and an fMRI scanning session.
The behavioural session is the same as before where participants do pattern, figure discrimination and sentence-in-noise task and measure their TMR thresholds on each
We will also do a scanning session in which It is an event-related design to capture participants’ responses associated with specific events/stimuli
Each trial could be a presentation of SFG and roving where participants do discrimination tasks as well as sentence-in-noise test (auditory stimuli played via MR compatible headphones to reduce impact of scanner noise) and adjust stimuli in each test to the TMR threshold acquired of behavioural session
Could do region of interest of regions relevant to stimuli associated with auditory processing as well as consider whole brain to make sure we don’t miss anything like primary auditory cortex located on superior temporal gyrus in temporal lobe.
Then do conjunction analysis to identify brain regions where activation overlaps in different conditions.
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59. What has previous research shown about brain mechanisms behind SFG and SiN? - (3)
They found regions that overlap such as the superior temporal gyrus (STS; important role in decoding acoustic features of speech) and inferior frontal gyrus (IFS; cognitive control role in focusing relevant speech/figure amidst noise)
But also non-overlapping regions in speech in noise test that are not activated in figure-ground perception like thalamus (in challenging listening siutaions thalamus enahcne sailence of relevant auditory input and direct its flow to appropriate areas0
So mixed results
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60. What is a pure tone?
A pure tone is a tone consisting of a single frequency e.g., a tone consisting of 100 Hz
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61. What is a complex tone?
A complex tone is a tone that has more than one frequency like 100, 200 , 300 Hz
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62. Example of harmonically related frequencies but not dynamic:
A tone that consists of frequencies of 100 (fundamental frequency) , 200 Hz and 300 Hz that is played over time
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63. Example of harmonically and dynamically related frequencies:
At one time point tone consisting of frequencies of 100 (fundamental freq) and 200 Hz and second point like 2 seconds its like 400 Hz and 200 Hz.
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What is harmonic not saying
Harmonic is not saying anything respect to time is just saying multiples of fundamental frequency
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65. How did you establish the validity of non-speech auditory stimuli as valid predictors?
We established validity, specifically concurrent validity of assessing the degree to which results of a new measure in this case SFG and HRSFG are related to results of an established measure of sentence in noise that both aim to capture the same or similar construct which we have shown.
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66. How does your research contribute to previous research? -
1) We have developed a new harmonic roving HRSFG which we found to be a more accurate predictor of speech in noise performance compared to previously established SFG. This is a significant advancement from static figure in SFG originally produced by Teki et al
2) Our results align with those of Holmes and Griffitths (2019) who also demonstrated a significant correlation between SFG thresholds and sentence-in noise performance (r = 0.32)
3) In addition to assessing sentence-in-noise performance, also conducted word-in-noise test in our study. Interestingly, we found a significant correlation between word-in-noise performance and SFG thresholds, a result not previously covered in my presentation. This expands upon the work of Holmes and Griffiths (2019), who exclusively utilized sentence-in-noise tasks to evaluate speech perception
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68. What was the direction of correlation between SFG/HRSFG and word-in-noise and why it is different?
- (3)
We also found a negative correlation between SFG and HRSFG thresholds and word-in-noise performance.
It is different from the positive correlation we observed with sentence-in-noise and we suspect that WiN and speech-in-babble tests used different measures (WiN measured mean accuracy of performance and speech-in-babble measured thresholds)
This issue needs to be investigated further using a common measure for both the tasks.
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1. What is the process of fitting GLM to fMRI data? -
. We begin to specificy time periods of modelled neural activity to specific task/stimulus
2. From this, we produce the hemodynamic regressors (by modelled neural activity with canoical hemodynamic response function)
3. This is done for each experimental condition
4. We then fit regressors (G1 and G2) to the data of fMRI signal of specific voxel with aim of determining how much each regressor and bits leftover we can't explain contributes to observed pattern of signal change
5. We fit constant to the regression model as well
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How does fMRI work - (3)
Functional Magnetic Resonance Imaging (fMRI) works by detecting changes in blood oxygenation levels in the brain, which are associated with neural activity. During an fMRI scan, when a specific brain region becomes active, there is an increase in blood flow to that area to meet the metabolic demands of the neurons.
This increased blood flow leads to a higher concentration of oxygenated hemoglobin in the capillaries and veins within the active region
Fresh oxygenated blood generates has less MRI signal decay due to T2 effects and remain brighter as have stronger T2* signal (i.e., decay of transverse magnetisation of hydrogen atoms not aligning to main magnetic z field) than stale deoxygenated blood
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3. What is the advantages and disadvantages of fMRI? - (2)
The advantages of fMRI is that it has excellent spatial resolution allowing researchers to pinpoint specific regions within brain and invasive.
The disadvantage of using fMRI is the limited temporal resolution of capturing rapid changes in brain activity as compared to EEG and MEG. It is expensive and indirect measure of neural activity as measures changes in blood flow and oxygenation
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What is hemodynamic response?
The changes in MR signal triggered by neuronal activity is known as hemodynamic response or HDR
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1. Why have you applied to do a PhD in York? What is it about working with Professor Andrews that appeals to you?
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 as 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 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.
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2. What do you hope to achieve or contribute through your PhD research/towards the end of PhD?- 4
What I hope to achieve at the end of the PhD is to contribute novel insights to cognitive neuroscience, particularly in face perception field by investigating the neural basis of the role of conceptual knowledge in face recognition.
These novel insights could contribute to existing theoretical models we have of face processing as all based mainly on studies on faces presented in artificially controlled experimental settings with artificial knowledge
I also aim to achieve in disseminating the findings of my PhD project not only through published papers but also opportunity to disseminate findings in conferences over in the USA to share my findings in the broader scientific community
More personally, I am to become a more independent researcher and achieve excellen time management skills.
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3. 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.
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4. What skills and attributes do you have that make you suitable for a PhD in area of research? - 8
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.
Overall I am equipped with both theoretical knowledge as well as technical skills which makes me qualified for this PhD
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5. Do you have any teaching experience/expertise? - (3)
I do have some teaching experience throughout my academic journey as during my A-levels I worked as a numeracy leader where I assisted in year 7 mathematics lessons by answering any questions the students had about math, helping the maths teacher with getting printouts for the lesson and making their tests
I also acted as a peer mentor during my third-year undergraduate degree in psychology where I had the responsibility of teachingundergraduate students how to do APA referencing properly, how to write academic essays and how to use referencing softwares such as Endnote
These experiences made me passionate about making teaching a career as well and I hope to gain more teaching experience with this PhD studentship at the University of York
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5.What personal attributes makes you qualfied for Phd? - (3)
Aside from my technical skills in programming and educational knowledge, I have had extensive research experience working in various labs. More specifically, During my bachelor’s degree, I worked as a research assistant at a psychology lab for a year. As part of my assigned tasks, I conducted in-person interviews for a deception detection study and conducted literature searches with Web of Science. I developed excellent teamwork skills as well as time management skills by completing my supervisor’s tasks to the highest of standards alongside my university studies.
I am also quite adaptable and resourceful as able to learn new methodologies and techniques quite quickly. For example, for the dissertation project, I presented I was assigned to this project two months later than other undergraduate dissertation students due to having second thoughts of the dissertation project I was originally assigned due as it turned out to not align with my research interests. I was able to learn new methodologies that I have not done before such as conducting hearing tests as well as learning how to conduct MATLAB scripts to run experimental paradigms. I was able to achieve a high 2:1 in this project.
I also have excellent communication skills as I have conducted many presentations during my undergraduate degree which broke down complex concepts into simple information for the audience to understand and have achieved first-class marks for these presentations
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What are my strength and weaknesses?
My strengths are that I have acquired programming skills such as R, and MATLAB as well as working knowledge of Python that I have recently learned in a Python course I have just began as well as in the second semester learning how to use Python to analyse neuroimaging and behavioural data analysis and these programming skills are needed for fMRI data analysis as well as a neuroscientist to have many tools in their statistical tool kit.
I have also gained a strong theoretical foundation for my PhD proposal by learning about face perception in Professor Andrews's module of the Neuroimaging of Vision, learning about how fMRI works as well as its advanced data analysis techniques such as intersubject correlation and MVPA that I am going to utilise in my PhD project. I also gained relevant research experience by assisting on a project similar to my PhD proposal.
My weaknesses, however, are that I have yet to master and become fluent Python and MATLAB which are the most utilised programs In neuroscience which I will over time and something that I need to work on. Also I can recognise I am a perfectionist sometimes and tend to invest a significant amount of time that every task I do meets highest standards which results in occasionally spending more than than necessary on certain tasks which is something I am actively working on to find a balance on delivering excellence as well as managing my time by setting realistic goals and prioritising tasks to avoid perfectionism.
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1. What is replication crisis? - (2)
The replication crisis refers to the phenomenon in which many findings in scientific research, particularly in fields like psychology and neurscience, have been difficult or impossible to replicate in subsequent studies.
This has raised concerns about the reliability and validity of scientific research, highlighting issues such as publication bias, low statistical power, and questionable research practices.
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Whatt are solutions to replication crisis?
Solutions including transparent and rigorous research practices such as pre-registring study protocls, opening sharing data and materials and conducting robust statistical analyses
Embracing open science initiatives such as having open access to articles and have open data sharing platforms can have transparency and collaboration among researchers
Having improved statistical training in researchers to enhance quality of scientific research
As well as promotoing quality of reproductivity of research such as giving rewards.
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3. What is open science? - (2)
Open science refers to movement of making scientific research and data more freely available to public, promoting transparency, collaboration and reproductibility
It encompasses practices such as open access publishing, pre-registration of studies, data sharing, and open peer review.
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4. What is your view on open science? - (3)
I strongly support open science as it promotes transparency, collaboration, and accessibility in research.
Open access publishing and data sharing initiatives break down paywalls, enabling students like myself to access and engage with research findings more easily.
This fosters a culture of knowledge exchange, accelerates scientific discovery, and enhances research integrity and accountability
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5. Can neuroimaging data show us anything behavioural data can not? - (5)
Neuroimaging data can reveal underlying neural mechanisms and patterns of brain activity that may not be directly observable through behavioral data alone.
While behavioral data provide insights into observable actions and responses, they do not capture the complex neural processes that underlie behavior.
Neuroimaging techniques such as functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and magnetoencephalography (MEG) allow researchers to directly measure brain activity associated with cognitive processes, emotions, and behaviors.
This enables a deeper understanding of the neural correlates of behavior and provides insights into the underlying cognitive processes and mechanisms involved.
Therefore, neuroimaging data complement behavioral data by offering a more comprehensive perspective on the neural basis of behavior.
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6. Any questions - (3)
when can I expect to hear back about my application?
Will I have the opportunity to attend neuroscience conferences across the world?
Will I have the opportunity to publish and present conferences?
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7. What is a paper you read recently that you found to be interesting? - (70
A paper I read recently that I found to be extremely interesting while conducting literature searches for my advanced optional module for my Neuroimagning of Vision Essay in semester 1 was a paper by Veraat et al
So generally,given absence of visual sensory input one might expect the visual cortex in blind individuals would be inactive
But neuroimagning research conducted by Veerat et al suggests otherwise
Veerat had blind individuals and control group of sighted individuals with eyes open and closed underwent a resting state PET scan.
In PET scan, both groups injected radioactive tracer of flurodexoyglcuose (FDG) into their bloodstream and taken by active brain cells allowing them to measure glucose metabolism.
Interestingly, the metabolic rate of V1 (the primary visual cortex, responsible for processing basic visual info) in early blind participants (i.e., congenitally blind or became blind before visual developmentt) was the same as those in sighted participants with eyes open – not case for participants who were blind later on
Thus highlights plasticity of brain, even in adulthood, and how it adapts to sensory deprivation
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9. How do you plan to address potential biases or confounding variables in your research? - (4)
In order to address potential biases or confounding variables in my research, I plan to implement rigorous research design and methodology.
This includes carefully considering factors such as sample selection, randomization procedures, blinding techniques, and controlling for relevant variables in statistical analyses.
Additionally, I will conduct thorough pilot studies to identify and mitigate any potential sources of bias or confounding.
Transparency in reporting methods and results will also be emphasized to ensure the validity and reliability of findings.
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10. How will you ensure your research is ethically conducted with human participants? - 11
Data collection is approved by the ethics committee of department of psychology and York neuroimagning committee.
Behavioural testing will require participants to complete simple computer-based tasks that are widely used to measure face perception.
Provided sensible precautions are taken (e.g., participants are given regular breaks) this approach poses little risk to the participants or researchers.
Neuroimaging experiments will take place at the York Neuroimaging Centre, which has an established record in scanning patient groups.
It uses the highest standards of safety and ethical procedures.
All participants will be screened before they are registered for scanning using standard protocols at YNiC.
During participant recruitment, participants will be asked to give consent to have their name and email address stored as part of the participant database.
They will also receive a Privacy Notice explaining how their personal data are stored in line with GDPR requirements.
Participants will receive an information sheet before the start of each session.
We will also ask them before each session to sign a consent form. At the end of each session, participants will receive a debriefing about the specific tasks they completed in that specific session.
All participants will have anonymous ID codes
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11. How do you plan to disseminate your research findings tto both scientific audience and general public? - (2)
I have the aspiration to publish my findings in peer-reviewed journals and as well as allowed to present them at relevant academic conferences
I could also utilise various social media platforms to engage with general public and communicate the significance of my research in an accessible and understandable way
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- (2)1. What is z score ?
It is a standardised score which expresses the relative position of data point within data set
It measures how many SD a particular data point is from the mean of data set
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2. How to calculate z score?
Z = X (individual data point) – mean of data set / standard deviation of dataset
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What does positive and negative z score mean? - (2)
A positive z score indicates that the data point is above the mean
A negative z score indicates data point is below the mean
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6. Why is z-score useful to use in research?
They are useful as they are standardised so they can allow researchers to compare scores from different distributions, different variables regardless of original measurement units.
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7. How do we know sample mean estimate is representative of population mean?
Via computing standard error of mean (SEM), larger the sample the smaller the SEM and be confident of sample mean Is representative of population
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8. How to calculate standard error of mean?
It is calculated by dividng the standard deviation of sample by the square root of the number in the sample
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9. What is standard deviation a measure of?
How representative mean was of observed data, if SD is small then data points close to mean but if large then data points distant from mean
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10. 95% confidence interval for 100 samples (CI constructured for each) means
95 of these samples the confidence intervals constructured would contain the true value of mean in population and if 95% CI, the z scores fall between -1.96 and 1.96 and this would be limits of CI
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11. How to calculate CI? (Rearranging z score formula) - (2)
LB: Mean – (1.96 * SEM)
UB: Mean + (1.96 * SEM)
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Why is there more focus to do parametric than non-parametric tests in research? - (3)
They are more rigorous, powerful and sensitive than non-parametric tests to answer your question
This means more higher chance of detecting a true effect or difference if it exists
They allow you to make generalisaitons and predicitons about population based on sample data
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12. If sample mean represent true population mean then
CI would be small/narrow and small SEM
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2. What are the 4 types of outcomes?
Ratio, Interval, Ordinal, Nominal
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3. Wha is continuous variable? - (2
There is infinite number of possible values the variables can take on
Entites get a distinct score
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4. What is interval, continuous variable?
Equal intervals on variable represent equal differences in property being measured e.g., difference between 600 ms and 800 ms is equivalent to difference between 1300ms and 1500 ms
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5. Wha is ratio, continuous variable?
It is the same as interval variable but also has clear definition of 0.0 e.g., height, weight
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6. What is categorical variable?
A variable that can not take on all values within the limits of the variable, entities are divided into distinct categories
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7. What is nominal, categorical variable?
A variable with categories that do not have a natural order or ranking, has two or more categories e.g., blood type
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8. What is ordinal, categorical variable?
Categories that have a logical, incremental order e.g., levels of education
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9. What type of variable is SiN , SFG, HRSFG thresholds? - (3)
It is a continuous ratio variable.
The TMR is often measured in decibels (Db) and indicates difference between target speech/figure signal level and background noise level required for participants to detect the target
Since decibels have a true 0 point (i.e., no sound) and consistent interval scale, TMR is ratio-scaled variable.
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10. What is measurement error?
The discrepancy between actual value we are trying to measure and number we use to represent that value
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11. What are the 2 types of measurement error? - (2)
Systematic measurement error = Predictable and constant to true value and always affect results of experiment in predictable direction e.g., if I know im 5 ft 2 and measured told 6 ft so happens when there is a problem with your experiment
Random measurement error is when measurable values are being inconsistent when repeated measures of constant attribute or quantity is taken e.g., height measurement taken in morning is 5 ft 2 but in evening is 5 ft = taken at different times so some variability
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12. Wha is validity/criterion validity ?
Instrument measures what it sets out to measure = refers to accuracy of the measure
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13. Wha is reliability?
The ability of measure to produce same results under same condition
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14. What is systematic variation?
Differences in performance due tto specific experimental manipulation
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16. To test if model fits data we compare15. What is unsystematic variation?
Differences in performance created by unknown factors which can be controlled by inclusion/exclusion of pps
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16. To test if model fits data we compare
Systematic variation with unsystematic variation
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17. Randomisation reduces
Unsystemattic variation but does nott remove it completely
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18. What does p less than 0.05 mean? - (2)
p-value less than 0.05 typically indicates that the observed result is statistically significant at the 5% significance level.
This means that there is less than a 5% chance of observing the result (or one more extreme)
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19. Effect of experimental manipulation is likely more apparent and have more power in repeated-measures than independent design because..?
Differences between the characteristics of the people allocated to each of the groups is likely to create considerable random variation within each condition and between them
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20. What are the 2 sources of systematic variation in repeated designs and what are they and how to eliminate them? - (3)
- Practice effects: Participants perform differently in second condition because familiarity of experimental situation and/or measures being used
- Boredom effects: Participants may perform differently in second condition as they are tired from having completed first condition
- Solve both by counterbalancing order the person participates in condition
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21. What is IV?
Hypothesised cause, predictor variable
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22. What is DV?
The proposed effect, outcome variable, measure not manipulated in exp
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