Neurocognitive theories and computational models

Most of what is known about the AB has been gathered through behavioural work. A few neu-roimaging studies nonetheless provided useful information as to the structure of the brain circuitry involved in this particular deficit (Figure 2.2) and the dynamics of information processing expressed within this network of structures.

Hommel et al. (2006) describe a neurocognitive scenario involving a temporo-parieto-frontal net-work to account for the AB. They build upon two-stage models to circumscribe where in the brain the processing bottleneck lies. In this scenario, the stimulus processing begins with a nonselective stage in occipital areas, in which both targets and masks are encoded to the same extent. No effect of the lags has been reported showing the modulation of the early activation of these brain areas⁷, which supports the claim that the AB does not purely reflect a perceptual phenomena. The second stage of the processing involves temporo-frontal areas: Stimuli are identified, and targeted by modulations originating from lateral-frontal and posterior-parietal areas. There, task-specific target stimuli undergo a more extensive task-related processing (Kessler et al., 2005). Different types of targets have been shown to activate different, content-specific temporal areas (Marois et al., 2004). Hommel et al. (2006) further suggest that temporal areas represent a kind of workspace where bottom-up information meets top-down influences to fulfil task-related goals.

A slightly different interpretation is provided by Dehaene and colleagues who used recordings of event-related potentials (ERP) to compare the temporal dynamics of seen and unseen (blinked) words in a typical AB experiment (Sergent & Dehaene, 2004). Describing the cortical activations of unseen words, the authors report a drop in the waveform of components peaking around 300

Figure 2.2: The “Attentional Blink matrix” superimposed on cortical areas underlying bottom-up (dark arrows) and top-down (light arrows) attentional influences (after Desimone

& Duncan, 1995; Kanwisher & Wojciulik, 2000; Kastner & Ungerleider, 2000; Pessoa et al., 2002; Vuilleumier, 2005; Pessoa, 2008). Dashed lines indicate subcortical areas, and areas not visible on the drawing. PFC: Pre-Frontal Cortex; PG: Parietal Gyri; TE: Inferior Temporal areas; TEO: Temporo-Occipital areas; V1/V4: Visual cortices. Studies used fMRI if not otherwise specified. (Original work elaborated in the course of this thesis.)

milliseconds, which correlated with behavioural visibility ratings. Whereas ERP methodology cannot be used to make unambiguous inferences about brain localisations, estimations techniques allow one to roughly determine the sources of cortical electrical activity. Using this approach, the authors report that seen words, compared to unseen words, initiated an intense spread of activation within left temporal and inferior frontal regions (about 300 milliseconds after stimulus onset), which would then spread to lateral prefrontal and anterior cingulate cortices (about 440 milliseconds), before extending in more posterior regions (about 580 milliseconds). In their inter-pretation, these authors introduce the concept of a global workspace, which refers to the notion that the different high-level, specialised brain areas involved in the processing of visual stimuli in-terconnect to each other, to form a global workspace processing the stimuli into a unitary assembly supporting conscious reportability (Baars, 1988, 2002; Shanahan, 2007). Perceived stimuli would thus compete to recruit this global workspace that once activated, only affords exclusive access, leading to the inability to process subsequent stimuli for a transient period of time. Areas in the global workspace theoretically map onto the description of the processing streams involved in visual perception, from perceptual areas to higher associative areas of temporal, parietal, frontal, and cingulate cortex ; which is to say that the bottleneck described in AB studies would therefore lay in the influence of higher-level areas, like the PFC or more parietal areas, over the lower-level

areas involved in the visual streams. Dehaene et al. (2003) constructed an artificial neural network model to put to test this hypothesis. They structured the model to represent early sensory regions and higher association areas. Results compared to their ERP results, suggesting that the compe-tition between T1 and T2 may prevent the second target from reaching the stages of processing necessary for report (see also Shanahan, 2007).

An alternative hypothesis is proposed by Nieuwenhuis, Gilzenrat, et al. (2005). According to these authors, the AB may be explained by the particular biphasic response of the locus coeruleus–

norepinephrine system (LC–NE). The LC is a tiny structure in the brain stem that has been implicated in the maintenance of arousal states (Robbins, 1997, see also Hebb, 1955) as well as in the regulation of cognitive performance (Aston-Jones et al., 1998, 1999; Aston-Jones & Cohen, 2005; Dayan & Yu, 2006). It is heavily activated during the processing of motivationally relevant stimuli (Dayan), resulting in the release of norepinephrine (also known as noradrenaline) through widespread cortical projections (Aston-Jones et al., 1984). This diffuse response generally increases the responsiveness of efferent target neurons (Berridge & Waterhouse, 2003). The phasic release of NE within the LC is quickly followed by a transient refractory period during which the LC–

NE is silent (Usher et al., 1999), a time course resembling the natural course of the AB effect.

Nieuwenhuis, Gilzenrat, et al. (2005) thus hypothesised that the boost of activation due to the release of NE supports the consolidation of targets in WM, and that the ensuing attentional blink is the result of the transient refractory period following LC activity. One strong prediction that can be made from this hypothesis is that any stimulus presented before this refractory period should not have much of an impact on the AB. Nieuwenhuis, Gilzenrat, et al. confirmed this hypothesis in two experiments, showing that varying the type of masks immediately following T1 has an impact on the report of T1 but does not affect the report on T2. The authors replicated these results using a computational model of the LC activity (Gilzenrat et al., 2002), suggesting that NE activity plays a critical role in the dynamics of the AB. More weight is offered in support of this hypothesis in a series of double-blind experiments by De Martino, Strange, & Dolan (2008) in which different types ofβ–adrenoceptor agonists (propanolol hydrochloride, reboxetine methansulphonate, or nadolol) not only generally rendered the AB worse compared to placebo, but emotional/arousing words, hypothesised as eliciting an increase in NE release, were still detected more frequently than neutral words. One study however failed to show such pharmacologically-elicited deterioration of the AB with anα2–adrenoreceptor agonist (clonidine), emphasising the complexity of the LC–NE system and its involvement in higher-level tasks like the orienting of attention (Nieuwenhuis et al., 2007).

Compared to other psychological phenomena the attentional blink has aroused the appetite of

many computational modellers (Taylor, 2003; Fragopanagos et al., 2005; Dehaene et al., 2003;

Nieuwenhuis, Gilzenrat, et al., 2005; M. Lundqvist et al., 2006; Shih, 2007; Bowman & Wyble, 2007). The reason for this sustained attention may simply lay in the relative ease of building a model that reproduces a U-shaped curve resembling the AB. This ease may however lead to the mistaken conclusion that a model producing a U-shaped curve therefore fully accounts for the mechanisms underlying the AB. Nonetheless, provided that there is a sensible benchmark, much can be learnt about the feasibility (i.e., implementability) of the proposed theories. The description of this benchmark exceeding the framework of this thesis, we refer the reader to an earlier publication (Roesch et al., 2007).

Temporal perspectives on attention describe the unfolding competition between incoming stimuli.

This competition biases each stage of the processing in favour of one stimulus, which is then used to perform a particular task (e.g., identification, report, fight, flight). Addressing the unfolding stages of this processing has been the main topic of interest of emotion and attention researchers.

However it is only recently that they have used experimental paradigms that explicitly allow to tap into the evolving processing of stimuli⁸. The AB paradigm in particular sets an explicit emphasis on the sequence of processes yielding a correct response, providing researchers with ways to rephrase classical questions about emotion and attention, and formulate new ones.