From a face to its category via a few information processing states in the brain

Abstract

Cognitive neuroscience assumes a correspondence between specific spatio-temporal patterns of neural activity and the states of a mechanism that processes cognitive information. Mechanistic explanations of cognition should therefore translate patterns of neural activity into the components of a formal mechanism: a set of information processing states and their transitions. For the first time, we carried out this research programme with four naive observers instructed to categorise randomly presented face information. With classification image techniques, we revealed the diagnostic features that the brain requires to produce correct behaviour (i.e., two eyes for gender categorisation in one session; the mouth for expression in the other session). With the same techniques applied to brain signals, we revealed the features processing states associated with modulations of oscillatory EEG energy (measured on occipito-temporal face-sensitive electrodes). Here we show how transitions between distinct feature processing states in the theta/alpha [4-12 Hz] oscillatory bands implement two face categorisations. On the left and right occipito-temporal electrodes of each observer, processing of the contra-lateral eye precedes bilateral integration of the features required for behaviour. For the first time, we relate stimulus information to behaviour via sequences of categorisation-specific feature processing states in the brain. © 2007 Elsevier Inc. All rights reserved.To study face categorisation mechanisms in an information system such as the brain, automata theory (Hopcroft and Ullman, 1979) specifies three generic questions that must be addressed to relate brain activity to the states of an information processing mechanism (i.e., a formal automaton). The first question is that of form: what is the nature of the brain activity supporting face processing-i.e., the states of the brain correlated with face processing? The second question is that of content: what is the information content processed in these brain states? The third question is that of transition: how does information flow from one brain state to the next between stimulus onset and behavioural response? So far, cognitive neuroimaging studies have not simultaneously addressed these three questions. Until this is resolved, inferences about cognitive information processing mechanisms will be severely restricted.To illustrate the problem, researchers have proposed that slow brain rhythms could form distinct states of information processing, on the basis of considerable evidence of a consistent relationship between low frequency, theta (4-8 Hz) and alpha (8-12 Hz), oscillatory EEG activity of the neural substrate and behavioural variables (Van Rullen and Koch, 2003;Ward, 2003, for reviews). However, definitive evidence is lacking in part because it has been proven difficult to identify precisely the information content of these states from the parametric modulations (i.e., phase and amplitude) of slow brain rhythms. Consequently, little is known about the detailed f...

Dissociating the Effect of Noise on Sensory Processing and Overall Decision Difficulty

“…In addition to the modulation of P1 and P2 components, adding phase noise to the face stimuli decreased the amplitudes of the N170 ERP component as well as the phase synchrony in the theta band oscillations over the ventral occipitotemporal electrodes. These findings are again consistent with the previous research showing that N170 amplitudes are strongly reduced in the presence of both phase and white noise (Jemel et al, 2003;Tanskanen et al, 2005;Schneider et al, 2007;Rousselet et al, 2008) as well as with the earlier results suggesting that the N170 component is strongly associated with the evoked oscillations in the theta and alpha band (Hsiao et al, 2006;Rousselet et al, 2007;Smith et al, 2007;Hansen et al, 2010).…”

“…Object processing in the visual cortex involves a cascade of neural events, which are reflected in the P1-N1-P2 ERP complex (Murray et al, 2006;Rousselet et al, 2007;Smith et al, 2007;Barbeau et al, 2008;Okazaki et al, 2008). The P2 component, in particular, might reflect grouping processes (Schendan and Kutas, 2007;Schendan and Lucia, 2010) as well as high-level object processing (Halit et al, 2000;Itier and Taylor, 2002;Latinus and Taylor, 2005;Mercure et al, 2008) and might be modulated by the recurrent and feedback processes within the visual cortical hierarchy in the case of increased processing demands, such as during visual masking (Kotsoni et al, 2007).…”

Extracting the internal representation of faces from human brain activity: An analogue to reverse correlation

“…A similar EEG approach to ours has been carried out before using the bubbles technique (Gosselin and Schyns, 2001), a technique similar to that reported by Haig (1985Haig ( , 1986. However, that approach made use of actual face images as stimuli (Smith et al, 2004(Smith et al, , 2006(Smith et al, , 2007. Given the arguments described in the Introduction section, our approach employed completely non-biased fractal noise stimuli, similar to preliminary reports by Goffaux and colleagues (2003) and Smith and colleagues (2009).…”

“…Yet another issue concerns the depth of the face representation as assessed through the N170. One view is that the representation consists of an initial global (i.e., holistic) representation (e.g., Sergent, 1984;Young et al, 1987;Goffaux and Rossion, 2006;Jacques and Rossion, 2009), with an opposing view that the initial representation is built up through an iterative part-based integration (e.g., Harris and Aguirre, 2008;Smith et al, 2007).…”

“…One promising technique that would allow for such an approach is a behavioral analogue to reverse correlation, referred to as classification image analysis (Ahumada, 1996;Beard and Ahumada, 1998;Ahumada, 2002). Along that vein, several neurophysiological studies have begun to utilize reverse correlation techniques using actual faces (or parts of faces) in hopes of better understanding the nature of different face selective brain signals (Smith et al, 2004(Smith et al, , 2006(Smith et al, , 2007. However, such studies cannot bypass the high-level cognitive attributes which accompany actual faces (or parts of faces) as stimuli.…”