Hmax Model Paper

In the present contemporary era, facial recognition technologies are being installed by the companies in an extensive sense that surely reflects a continuum of growing hi-tech superiority and complexity. At the most ordinary level, facial detection is done by this technology which means that a photo is just detected and located for a face ("Facing Facts: Best Practices for Common Uses of Facial Recognition Technologies," 2012).

This double dissociation therefore suggests that recognition of faces and common objects is served by different mechanisms that are independent for each other. Although brain cells of face recognition have not been located, some functional models could help us interpret face recognition and prosopagnosia. One of the most influential models was proposed by Bruce and Young (1986). In this model face recognition involves several steps, and three steps more relate to prosopagnosia.

The face recognition model developed by Bruce and Young has eight key parts and it suggests how we process familiar and unfamiliar faces, including facial expressions. The diagram below shows how these parts are interconnected. Structural encoding is where facial features and expressions are encoded. This information is translated at the same time, down two different pathways, to various units. One being expression analysis, where the emotional state of the person is shown by facial features. By using facial speech analysis we can process auditory information. This was shown by McGurk (1976) who created two video clips, one with lip movements indicating 'Ba' and other indicating 'Fa'. Both clips had the sound 'Ba' played over the clip.

In the video, Neurons to Nirvana: The Great Medicines; it presents many discussion around the social taboos of psychedelics. Interviews featuring professors, writers, and researcher adds more insight to the field of psychedelic psychotherapy. Throughout this hour and nine minutes video, three ideas and/or concepts stood out to me. Beginning within the first following eight minutes, in the 1940s, it mentioned how those who were administered into the psychiatric hospital. People suffering from schizophrenic were said to be the cause of mothers failing in child care. Whether if the mother weren’t nurturing or she didn’t breastfeed her child. Although professionals back in the 1940s stated this, there weren’t any connection to what happened in

As mentioned before, a more recent study by Balas and Koldewyn (2013) compared N170 amplitudes between real and artificial faces of dogs and humans. In result, Balas and Koldewyn (2013) only found a significant difference in N170 amplitude between dogs and humans when the active task was species specific. Overall, the researchers did not report a difference in N170 across conditions. However, as our study involves different active tasks and less conditions (i.e., only human faces and doll faces) a direct comparison should be taken lightly.

The current study examined the neural representations of faces and different objects in the human brain with a univariate analysis. The results demonstrate that unique distributed activation across the whole brain area conveys semantic categorical information. Further, it is revealed that faces evoke great activation in temporal occipital fusiform cortex, consistent with the notion that FFA is face-responsive and face-selective. Additionally, activation in motor cortex was only evoked by images of scissors, which might indicate the close association between the neural representations of images of tools and tool-related actions. Together, these findings emphasize both the unique distribution and the functional specificity of the neural

Overly, the model provides coherent explanation of many kind of information about faces and how they relate to each other. It provides evidence about significant differences in processing of familiar and unfamiliar faces. Nonetheless there are limitations. For instance, there is not enough information about processing unfamiliar faces. Burton et al. (1999)

Prosopagnosia, also known as face blindness, is a form of visual agnosia characterized by an inability to recognize faces. Usually those with Prosopagnosia are able to see the features on a person 's appearance with little difficulty; however, it is the retention and synthetization of those images that challenges them. Many people also report deficits in other aspects of face processing, such as judging age or gender, recognizing certain emotional expressions, or following the direction of a person 's eye gaze. Currently, scientists have yet to come to a consensus on the actual cause of the disease, but many suspect it is linked to extensive damage of the occipital and temporal lobes. Presently, one speculation is that Prosopagnosia is connected to the fusiform gyrus, an area of the brain located on the inferior/median temporal lobe. As found by researchers, “the fusiform gyrus is thought to constitute a “core” visual representation system for faces, in part because they show face selectivity and face repetition suppression” (Furl, N., Garrido, L., Dolan, R. J., Driver, J., & Duchaine, B., 2011, 1723-1740). However, looking at recent fMRI studies, scientists are questioning whether these conditions and damages to the fusiform gyrus relate to face processing skills at all (Furl, N. et al, 2011). Due to this uncertainty another theory has evolved. As stated by Gainotti (2010, 763-766), “prosopagnosia is due to disruption of purely visual entities, the face

Prosopagnosia is defined as the difficulty in recognizing an individuals face; it is broken down into 3 main types; Apperceptive prosopagnosia, associative prosopagnosia and developmental prosopagnosia. Perception is an important aspect when recognizing faces, without recognizing the stimulus (face) you would not be able to identify a person. Therefore the individuals who sufferer from this disorder is unable to accurately recognize a face whether that be the face of a familiar person such as a family member or close friend, the face of a famous person, or even their own face. This paper will look at prosopagnosia in greater detail, it will explain the 3 main types, as well as give insight as to why face recognition is important in our

There are several models of abnormality in use today (Comer, 2009) lists “The Biological Model…”, “The Psychodynamic Model…”, The Behavioral Model…”, The Cognitive Model…”, The Humanistic-Existential Model…”, The Sociocultural Model…” (p.33). The biggest contrasts would be the Biological model, and the other models. Comparing the biological model, and the cognitive model will highlight those differences.

The model claims that there is one facial recognition unit for each face and that viewing a face will in turn activate the appropriate face recognition unit. The Person Identity Nodes identify who the person is by classifying or associating them with something. Once again there is only one unit of identification per person. It is at this point that all points of domain and recognition occur. (Burton,A.M; Bruce, V;Johnston,R.A, 1990) (Burton,A.M; Bruce,V& Handcock,P.J,

Information conveyed by the synapses on the dendrites is processed and projected from the axon. The axon is extraordinary, it is specialized for signal conduction to the next neuron. Axons vary in length, the ones in your brain are relatively short in comparison to the axons that run from your spinal cord down to your foot (about a meter long).

MSM is an extension of Subspace Method~\cite{smguide:2007} and is based on estimation of multiple face image patterns obtained under changes of facial expressions, face directions, lighting and other factors. In MSM, two sets of patterns to be compared are represented by different linear subspaces in a high-dimensional vector space, respectively. Each subspace is generated by applying PCA to the set of patterns. It works pretty well in most cases. However, it is commonly known that traditional PCA is not robust in the sense that outlying data can arbitrarily skew the solution from the desired solution. The problem is that PCA is optimal in a least-squares sense. By the traditional PCA, only the few first components are kept, supposed to preserve most of the information expressed by the data. If the dataset contains too many noisy vectors, the principal components will encode only the variation due to the

Humphreys and Bruce (1989) proposed a model of object recognition that fits a wider context of cognition. According to them, the recognition of objects occurs in a series of stages. First, sensory input is generated, leading to perceptual classification, where the information is compared with previously stored descriptions of objects. Then, the object is recognized and can be semantically classified and subsequently named. This approach is, however, over-simplified. Other theories like Marr and Nishihara’s and Biederman’s

The connectionist model relies on the current perspectives on the brains anatomical and physiological organization. It represents the computational architecture of the brain while numerous types of information are being processes (those of human cognition: reasoning, problem solving). It differs from the historical perspective, because current information on how the brain works goes against the strict modularity perspectives of neuroanatomy and neurophysiology. Recent research shows how the given brain structures can vary in functions in accordance with other brain structures or cortical areas, with which they interact. Along those lines, others studies have gone to show how certain specific areas of the brain can take on other functions when