1.Summary of underlying theory to which your experiment relates
2.Description of task and questionnaire, and data you will receive
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3.Hypotheses and statistical tests we would like you to run
4.Report structure (very briefly as you’ll get a separate talk)
- Evidence that people can recognize 5000 faces on average (but wide individual differences) – Jenkins et al., 2018
- Recognition survives well if we haven’t seen someone for a long time, or they’ve changed their appearance (hairstyle etc)
- People are also generally good at deriving other information from faces such as emotion, but again evidence of individual difference – Hoffman et al., 2010
- How good we are is surprising as we see a lot of faces and their first order configuration (nose in the middle, eyes above, mouth below) is the same.
- Individual features (nose, mouth, eye etc)
- Second order configuration (spacing)
- Holistic processing (integration of the multiple parts of a face into a single holistic representation)
- See Maurer et al., 2002 for further discussion.
- Many psychologists believe faces (particularly upright faces) and non-face objects are processed differently:
- Range of effects (composite effect, inversion effect etc) found in behavioural experiments with faces are not found (or not to the same extent) with non-face objects – Robbins & McKone, 2007
- Neuro-imaging studies show differences in activation (notably fusiform face area) – Kanwisher & Yovel, 2006
- It has been proposed:
- object processing involves decomposition into parts or features (Biederman, 1987)
- faces are represented and recognised holistically (Tanaka & Farah, 2003) and in particular relying on second-order configuration (Searcy & Bartlett, 1996)
- However, it has also been argued featural processing of faces has been underplayed:
- emphasis on configural processing often relies on assumption that inversion primarily impairs configural processing, but evidence that it also impairs feature processing (Murphy & Cook, 2017)
- Your experiment asks:
- Does sensitivity to configure differences in upright faces predict self-reported face recognition ability?
- Does sensitivity to feature differences in upright faces predict self-reported face recognition ability?
- Is this pattern the same for upright houses?
- Why is it relevant?
- Face processing is important for social interactions & deficits could contribute to isolation etc
- As well as prognosticator (face blindness) as an extreme form, various groups may have some difficulties with faces – autism (Dawson et al., 2005) and older people (Ortega & Phillips, 2007)
- Could training help? If so, configure or feature, and would it be limited to faces or include other things?
- Could configure and feature processing differences be a diagnostic tool?
- Stimuli were houses and faces which differed either in features or configuration – Yovel & Kanwisher 2004
- Stimuli were either upright or inverted, giving four conditions. This replicates Y&K but we will only give you, and you should only analyze, upright conditions
- You can say there were 160 trials embedded within a larger task involving inverted stimuli
- 80 trials for each condition:
- 20 which differed in configuration but not features
- 20 which differed in features but not configuration
- 40 pairs were identical
- We’ve used a signal detection task and you will get data on sensitivity (d’)
- You don’t need to explain SDT in any detail in your report (hooray!)
- Key point – sensitivity (d’) is a measure of accuracy which is independent of response bias
- It takes into account both cases where you correctly saw there was a difference (“hits”) and where you correctly saw there was no difference (“correct rejections”)
- Superior measure than just “hits” because it doesn’t matter in theory if you are biased towards or against reporting a difference.
Facial recognition;Does sensitivity to configure differences in upright faces predict self-reported face recognition ability? essay Essay Hotline.
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