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& The majority of the research related to visual recognition has so far focused on bottom-up analysis, where the input is processed in a cascade of cortical regions that analyze increasingly complex information. Gradually more studies emphasize the role of top-down facilitation in cortical analysis, but it remains something of a mystery how such processing would be initiated. After all, top-down facilitation implies that high-level information is activated earlier than some relevant lower-level information. Building on previous studies, I propose a specific mechanism for the activation of top-down facilitation during visual object recognition. The gist of this hypothesis is that a partially analyzed version of the input image (i.e., a blurred image) is projected rapidly from early visual areas directly to the prefrontal cortex (PFC). This coarse representation activates in the PFC expectations about the most likely interpretations of the input image, which are then back-projected as an ‘‘initial guess’ ’ to the temporal cortex to be integrated with the bottom-up analysis. The top-down process facilitates recognition by substantially limiting the number of object representations that need to be considered. Furthermore, such a rapid mechanism may provide critical information when a quick response is necessary. &

Value-based decision making is pervasive in nature. It occurs whenever an animal makes a choice from several alternatives on the basis of a subjective value that it places on them. Examples include basic animal behaviours, such as bee foraging, and complicated human decisions, such as trading in the stock market. Neuroeconomics is a relatively new discipline that studies the computations that the brain carries out in order to make value-based decisions, as well as the neural implementation of those computations. It seeks to build a biologically sound theory of how humans make decisions that can be applied in both the natural and the social sciences. The field brings together models, tools and techniques from several disciplines. Economics provides a rich class of choice paradigms, formal models of the subjective variables that the brain needs to compute to make decisions, and some experimental protocols for how to measure these variables. Psychology provides a wealth of behavioural data that shows how animals learn and choose under different conditions, as well as theories about the nature of those processes. Neuroscience provides the knowledge of the brain and the tools to study the neural events that attend decision making. Finally, computer science provides computational models of machine learning and decision making. Ultimately, it is the computations that are central to uniting these disparate levels of description, as computational models identify the kinds of signals and signal dynamics that are required by different value-dependent learning and decision problems. However, a full understanding of choice will require a description at all these levels. In this Review we propose a framework for thinking about decision making. It has three components: first, it divides decision-making computations into five types; second, it shows that there are multiple types of valuation systems; and third, it incorporates modulating variables that affect the different valuation processes. This framework will allow us to bring together recent findings in the field, highlight some of the most important outstanding problems, define a common lexicon that bridges the different disciplines that inform neuroeconomics, and point the way to future applications. The development of a common lexicon is important because a lot of confusion has been introduced into the literature on the neurobiology of decision making by the use of the unqualified terms 'reward' and 'value'; as shown in the Review, these terms could apply to very different computations. Computations involved in decision making The first part of the framework divides the computations that are required for value-based decision making into five basic processes The first process in decision making involves the computation of a representation of the decision problem. This entails identifying internal states (for example, hunger level), external states (for example, threat level) Abstract | Neuroeconomics is the study of the neurobiological and computational basis of value-based decision making. Its goal is to provide a biologically based account of human behaviour that can be applied in both the natural and the social sciences. This Review proposes a framework to investigate different aspects of the neurobiology of decision making. The framework allows us to bring together recent findings in the field, highlight some of the most important outstanding problems, define a common lexicon that bridges the different disciplines that inform neuroeconomics, and point the way to future applications.

Attentional control theory is an approach to anxiety and cognition representing a major development of Eysenck and Calvo’s (1992) processing efficiency theory. It is assumed that anxiety impairs efficient functioning of the goal-directed attentional system and increases the extent to which processing is influenced by the stimulus-driven attentional system. In addition to decreasing attentional control, anxiety increases attention to threat-related stimuli. Adverse effects of anxiety on processing efficiency depend on two central executive functions involving attentional control: inhibition and shifting. How-ever, anxiety may not impair performance effectiveness (quality of performance) when it leads to the use of compensatory strategies (e.g., enhanced effort; increased use of processing resources). Directions for future research are discussed.

Alerting, orienting, and executive control are widely thought to be relatively independent aspects of attention that are linked to separable brain regions. However, neuroimaging studies have yet to examine evidence for the anatomical separability of these three aspects of attention in the same subjects performing the same task. The attention network test (ANT) examines the effects of cues and targets within a single reaction time task to provide a means of exploring the efficiency of the alerting, orienting, and executive control networks involved in attention. It also provides an opportunity to examine the brain activity of these three networks as they operate in a single integrated task. We used event-related functional magnetic resonance imaging (fMRI) to explore the brain areas involved in the three attention systems targeted by the ANT. The alerting contrast showed strong thalamic involvement and activation of anterior and posterior cortical sites. As expected, the orienting contrast activated parietal sites and frontal eye fields. The executive control network contrast showed activation of the anterior cingulate along with several other brain areas. With some exceptions, activation patterns of these three networks within this single task are consistent with previous fMRI studies that have been studied in separate tasks. Overall, the fMRI results suggest that the functional contrasts within this single task differentially activate three separable anatomical networks related to the components of attention.

A load theory of attention in which distractor rejection depends on the level and type of load involved in current processing was tested. A series of experiments demonstrates that whereas high perceptual load reduces distractor interference, working memory load or dual-task coordination load increases distractor interference. These findings suggest 2 selective attention mechanisms: a perceptual selection mechanism serving to reduce distractor perception in situations of high perceptual load that exhaust perceptual capacity in processing relevant stimuli and a cognitive control mechanism that reduces interference from perceived distractors as long as cognitive control functions are available to maintain current priorities (low cognitive load). This theory resolves the long-standing early versus late selection debate and clarifies the role of cognitive control in selective attention. Goal-directed behavior requires focusing attention on goal-relevant stimuli while ignoring irrelevant distractors. However, the mechanisms for such behavioral control by selective attention remain to be elucidated. In this article, we present a load theory of attention that proposes two mechanisms of selective attention. The first is a perceptual selection mechanism that allows for excluding

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When subjects switch between a pair of stimulus–response tasks, reaction time is slower on trial N if a different task was performed on trial N 2 1. We present a parallel distributed processing (PDP) model that simulates this effect when subjects switch between word reading and color naming in response to Stroop stimuli. Reac-tion time on ‘‘switch trials’ ’ can be slowed by an extended response selection process which results from (a) persisting, inappropriate states of activation and inhibition of task-controlling representations; and (b) associative learning, which allows stimuli to evoke tasks sets with which they have recently been associated (as proposed by Allport & Wylie, 2000). The model provides a good fit to a large body of empirical data, including findings which have been seen as problematic for this explanation of switch costs, and shows similar behavior when the parameters are set to random values, supporting Allport and Wylie’s proposal. ª 2001 Elsevier Science (USA) Key Words: task switching; task set; Stroop effect; parallel distributed processing; executive functions. Atkinson and Shiffrin (1968) proposed a distinction between relatively permanent cognitive structures, such as short- and long-term memory, and control processes which harness those fixed structures in order to attain spe-cific goals. This distinction was elaborated in the following years (e.g.,

This paper reports a meta-analysis of neuroimaging studies of attention shifting and executive processes in working memory. We analyzed peak activation coordinates from 31 fMRI and PET studies of five types of shifting using kernel-based methods [NeuroImage 19 (2003) 513]. Analyses collapsing across different types of shifting gave more consistent results overall than analysis within individual types, suggesting a commonality across types of shifting. These areas shared substantial, significant overlap with regions derived from kernel-based analyses of reported peaks for executive processes in working memory (WM). The results suggest that there is a common set of brain regions active in diverse executive control operations, including medial prefrontal, superior and inferior parietal, medial parietal, and premotor cortices. However, within several of these regions, different types of switching produced spatially discriminable activation foci. Precise locations of meta analysis-derived regions from both attention shifting and working memory are defined electronically and may be used as regions of interest in future studies.