CCC3 Face Processing 2024 PDF

Module Overview Lecture Date Memory 1. Intro and Background (RS) 26th Sept 12. Working Memory (DV) 1st Nov 13. Long Term Memory & Amnesia (CB) 7th Nov...

Module Overview Lecture Date Memory 1. Intro and Background (RS) 26th Sept 12. Working Memory (DV) 1st Nov 13. Long Term Memory & Amnesia (CB) 7th Nov Perception 14. Memory loss in old age: Dementia (AM) 8th Nov 2. Object Recognition (RS) 27th Sept Thought 3. Face Recognition (RS) 3rd Oct 15. Knowledge (RS) 14th Nov 4. Agnosia / Prosopagnosia (RS) 4th Oct 16. Reasoning / Decision Making (RS) 15th Nov 5. Synaesthesia (JW) 10th Oct 17. Dysexecutive Syndrome (FDL) 21st Nov Attention Behaviour 6. Attention (RS) 11th Oct 18. Volition (RS) 22nd Nov 7. Attentional Biases in Anxiety (SF) 17th Oct 19. Cognitive Behavioural Therapy (KC) 28th Nov 8. Neglect (SF) 18th Oct 20. Cognition and Appetite (MY) 29th Nov 9. Autism and Attention (SA) 24th Oct 21. Impulse Control (HC) 5th Dec 10. Lecture on Experiment (RS) 25th Oct 22. Revision and exam preparation (RS) 6th Dec 11. Academic Misconduct (BS) 31st Oct Ryan Jamie Sophie Bryan Dominika Chris Alexa Sophie Flavia Kate Martin Hans Scott Ward Forster Singer Varga Bird Morcom Anns De Luca Cavanagh Yeomans Crombag  Lecture 3: Face Recognition Lecture Overview Bruce & Young’s early model The IAC model of face recognition What information is used for recognition? Where is face processing done?  Bruce & Young’s model of face recognition Bruce & Young (1986) Modular model in that different sub-functions are processed independently. Distinct pathways for recognising familiar faces vs. recognising expressions etc. Parallel pathways dealing with facial expression, facial speech, and “visually derived semantic information” such as sex, age, and race. The Bruce and Young Bruce (1986) modelmodel & Young’s of face recognition: of face recognition Bruce & Young (1986) 1. Different representations constructed for different purposes and for familiar vs. unfamiliar faces. 2. For recognition, a familiar face activates a “Face Recognition Unit” – faces previously encountered. 3. FRUs are linked to “Person Identity Nodes”, gateways to semantic information about the person. 4. PINs are linked to name generation.  Early evidence for Bruce & Young Memory loss diary study (Young, Hay and Ellis,1985) Most common errors: – Person not recognized (i.e., ‘blanked’). – Feeling of familiarity without identity. – Person recognized but no name retrieved. – Person misidentified. Repetition priming found for familiarity decisions but not for gender or expression decisions (Ellis et al. 1990). Familiarity does not influence: – gender decisions (Bruce, 1986). – expression analysis (Young et al.1986) since disputed Humans can selectively attend to identity or emotion in sorting tasks (Etcoff, 1984). Neurological support for Bruce & Young Neuropsychological support for parallelism: Double dissociation between the processing of facial expression and face recognition. Some have a deficit in Identity but not expression and vice versa – Identity , Expression (Bruyer et al., 1983). – Identity , Expression  (Humpherys et al.1993). Neuro-imaging support for parallelism: Different cortical sites are active in the processing of identity versus emotion (don’t worry about which sites!) – Inferior occipital and lateral fusiform gyri activity: Identity Expression (Sergent et al. 1992). – Amygdala and superior temporal sulcus activity: Identity Expression (Posamentier & Abdi, 2003).  The Challenge of Semantic Priming ‘Semantic’ priming – a face is responded to faster if it follows a closely related face (e.g., Prince Charles followed by Diana) compared to an or unrelated face. (Bruce & Valentine, 1986). No means to account for this using Bruce & Youngs model. Interactive Activation and Competition (The IAC model) McClelland (1981) proposed parallel distributed networks that have interactive activation and competition built in as basic processes. Concepts and category learning (e.g., Jets and Sharks in West Side Story) via an IAC model. IAC Model of Jets vs. Sharks Semantic information is ‘pooled’. Knowledge is represented in pools. Relationships between different bits of knowledge are represented in the connections between the pools. Connections within a pool are mutually inhibitory connections between pools are mutually facilitatory. IAC Model of Face Processing (Burton, Bruce and Johnston, 1990) FRU activated Inhibits others in the pool. IAC – Interactive Activation and Competition FRU – face recognition unit PIN – Person Identity node IAC Model of Face Processing (Burton, Bruce and Johnston, 1990) Activation spreads along connections FRU –> PIN PIN –> Semantic info PIN –> Name input etc. IAC – Interactive Activation and Competition FRU – face recognition unit PIN – Person Identity node IAC Model of Face Processing (Burton, Bruce and Johnston, 1990) Inhibitory connections within pools are activated. IAC – Interactive Activation and Competition FRU – face recognition unit PIN – Person Identity node IAC Model of Face Processing (Burton, Bruce and Johnston, 1990) PINS can be partially activated through shared semantics. IAC – Interactive Activation and Competition FRU – face recognition unit PIN – Person Identity node IAC Model of Face Processing (Burton, Bruce and Johnston, 1990) Activity can spread from PINs facilitating semantic priming even across modalities. IAC – Interactive Activation and Competition FRU – face recognition unit PIN – Person Identity node  IAC Model Summary FRUs signal face familiarity, PINs are modality-free gateways to semantic information. Details of connectivity and spread of activation and inhibition clarified. No separate nodes for names; simply part of semantic information. Can explain more empirical data than earlier models. – Relative timing of familiarity, semantic access, and naming (familiarity faster than semantics which is faster than naming) – Repetition priming: Laurel’s face primes Laurel’s face. – Semantic priming: Laurel primes Hardy. – Cross-modal semantic priming: e.g. Laurel’s spoken name primes Hardy’s face etc.  What do we use to recognise faces? Human beings are exceptionally capable of recognizing individual faces. The challenge of reliably individuating faces is made apparent by the fact that all faces share a basic configuration. Every individual face consists of facial features such as eyes, nose, and a mouth that have the same first-order relations such as two eyes above a nose and mouth. Although these features are ample for rendering the percept of ‘a’ face, they are wholly inadequate in rendering a percept of ‘that’ face. What about second order relationships? While there are instances of features that are rather distinctive and sufficiently accurate in signaling the identity of an individual, they are rare. Thus, it has been suggested that the efficacy of face coding for the purposes of recognition must exploit second-order relations i.e., the Fine-grained spatial interrelationship between “features”. e.g. Richler (2009): “…subtle differences in spatial relations between face features being encoded…” e.g. Tanaka and Gordon (2011): “…encoding of metric distances between features”.  Second order account challenges Hole, George, Eaves and Rasek (2002): Stretching, squashing, shearing alter second order relationships but faces remain easily identifiable.  Second order account challenges Hole, George and Dunsmore (1999): Negative faces are very difficult to recognize despite preserving configural information. Where does face processing happen? Face processing is widely distributed Core aspects of face recognition are localizable in the superior temporal sulcus and the inferior temporal cortex This is where we see face selective neurons Inferior temporal cortex Superior temporal sulcus  Hierarchical Accounts of Processing Cells in the inferior temporal cortex are selective to complex stimuli giving credence to hierarchical theories of object perception. According to this view early visual cortex codes elementary features such line orientation and colour. Outputs are combined to form detectors of higher-order features such as corners or T-junctions. Cells at the highest level in the hierarchy code specific shapes such as hands or faces.  Summary Models of face recognition divide the task into that of recognizing familiar individuals from that of deciphering other information from faces such as sex and expression. The Bruce & Young model has been extremely useful as a basic description of face processing The IAC model captures many features of human facial recognition and related effects such as semantic priming. It remains unclear exactly how face recognition is achieved – some kind of “configural” processing occurs but recognition cannot be achieved solely by via fine-grain configural processing as is often claimed. Face processing is widely distributed in the brain but with some core functions localizable in the superior temporal sulcus and the inferior temporal cortex.