Age-related differences in the involvement of the prefrontal cortex in attentional control

We investigated the relative involvement of cortical regions supporting attentional control in older and younger adults during performance on a modified version of the Stroop task. Participants were exposed to two different types of incongruent trials. One of these, an incongruent-ineligible condition, produces conflict at the non-response level, while the second, an incongruent-eligible condition, produces conflict at both non-response and response levels of information processing. Greater attentional control is needed to perform the incongruent-eligible condition compared to other conditions. We examined the cortical recruitment associated with this task in an event-related functional magnetic resonance imaging paradigm in 25 older and 25 younger adults. Our results indicated that while younger adults demonstrated an increase in the activation of cortical regions responsible for maintaining attentional control in response to increased levels of conflict, such sensitivity and flexibility of the cortical regions to increased attentional control demands was absent in older adults. These results suggest a limitation in older adults’ capabilities for flexibly recruiting the attentional network in response to increasing attentional demands.     

Similarity and rules United: similarity- and rule-based processing in a single neural network

A central controversy in cognitive science concerns the roles of rules versus similarity. To gain some leverage on this problem, we propose that rule- versus similarity-based processes can be characterized as extremes in a multidimensional space that is composed of at least two dimensions: the number of features (Pothos, 2005) and the physical presence of features. The transition of similarity- to rule-based processing is conceptualized as a transition in this space. To illustrate this, we show how a neural network model uses input features (and in this sense produces similarity-based responses) when it has a low learning rate or in the early phases of training, but it switches to using self-generated, more abstract features (and in this sense produces rule-based responses) when it has a higher learning rate or is in the later phases of training. Relations with categorization and the psychology of learning are pointed out.     

Rating the similarity of simple perceptual stimuli: asymmetries induced by manipulating exposure frequency.

When judging the similarity of two stimuli, people's ratings often differ depending on the order in which the comparison is presented (A vs. B or B vs. A). Such directional asymmetries have typically been demonstrated using complex concepts that have a large number of semantic features and a standard explanation is that different sets of features are emphasized depending on the direction of the comparison. In this study, we show that directional asymmetries in the similarity of simple perceptual stimuli can be predictably manipulated merely by presenting each member of a pair with different frequency. Participants rated the similarity of color patches before and after performing an irrelevant training task in which a subset of colors was presented ten times more frequently than others. The similarity ratings after training were significantly more asymmetric than the ratings before training. We discuss the implications of these findings for models of similarity judgment and propose a computationally explicit explanation based on asymmetries in representational stability.     

Neurophysiology and neuroanatomy of smooth pursuit in humans

Smooth pursuit eye movements enable us to focus our eyes on moving objects by utilizing well-established mechanisms of visual motion processing, sensorimotor transformation and cognition. Novel smooth pursuit tasks and quantitative measurement techniques can help unravel the different smooth pursuit components and complex neural systems involved in its control. The maintenance of smooth pursuit is driven by a combination of the prediction of target velocity and visual feedback about performance quality, thus a combination of retinal and extraretinal information that has to be integrated in various networks. Different models of smooth pursuit with specific in- and output parameters have been developed for a better understanding of the underlying neurophysiological mechanisms and to make quantitative predictions that can be tested in experiments. Functional brain imaging and neurophysiological studies have defined motion sensitive visual area V5, frontal (FEF) and supplementary (SEF) eye fields as core cortical smooth pursuit regions. In addition, a dense neural network is involved in the adjustment of an optimal smooth pursuit response by integrating also extraretinal information. These networks facilitate interaction of the smooth pursuit system with multiple other visual and non-visual sensorimotor systems on the cortical and subcortical level. Future studies with fMRI advanced techniques (e.g., event-related fMRI) promise to provide an insight into how smooth pursuit eye movements are linked to specific brain activation. Applying this approach to neurological and also mental illness can reveal distinct disturbances within neural networks being present in these disorders and also the impact of medication on this circuitry.     

Lexical categories at the edge of the word

Language acquisition may be one of the most difficult tasks that children face during development. They have to segment words from fluent speech, figure out the meanings of these words, and discover the syntactic constraints for joining them together into meaningful sentences. Over the past couple of decades, computational modeling has emerged as a new paradigm for gaining insights into the mechanisms by which children may accomplish these feats. Unfortunately, many of these models assume a computational complexity and linguistic knowledge likely to be beyond the abilities of developing young children. This article shows that, using simple statistical procedures, significant correlations exist between the beginnings and endings of a word and its lexical category in English, Dutch, French, and Japanese. Therefore, phonetic information can contribute to individuating higher level structural properties of these languages. This article also presents a simple 2-layer connectionist model that, once trained with an initial small sample of words labeled for lexical category, can infer the lexical category of a large proportion of novel words using only word-edge phonological information, namely the first and last phoneme of a word. The results suggest that simple procedures combined with phonetic information perceptually available to children provide solid scaffolding for emerging lexical categories in language development.     

The unfolding argument: Why IIT and other causal structure theories cannot explain consciousness

How can we explain consciousness? This question has become a vibrant topic of neuroscience research in recent decades. A large body of empirical results has been accumulated, and many theories have been proposed. Certain theories suggest that consciousness should be explained in terms of brain functions, such as accessing information in a global workspace, applying higher order to lower order representations, or predictive coding. These functions could be realized by a variety of patterns of brain connectivity. Other theories, such as Information Integration Theory (IIT) and Recurrent Processing Theory (RPT), identify causal structure with consciousness. For example, according to these theories, feedforward systems are never conscious, and feedback systems always are. Here, using theorems from the theory of computation, we show that causal structure theories are either false or outside the realm of science.     

The role of levels of processing in disentangling the ERP signatures of conscious visual processing

We aimed to distinguish electrophysiological signatures of visual awareness from other task-related processes through manipulating the level of processing of visual stimuli. During an event-related EEG experiment, 36 subjects performed either color (low-level condition) or magnitude (high-level condition) evaluations of masked digits. Participants also assessed subjective visibility of each stimulus using the Perceptual Awareness Scale (PAS). Mean amplitude of the components of interest was analyzed (VAN − 140–240 ms; LP − 380–480 ms) with weighted regression mixed model. In the VAN component time window the mean amplitude correlated with PAS rating in both conditions. Mean amplitude in the LP time window correlated with PAS ratings in the high-level condition, but not in the low-level condition. Our results support the temporal unfolding of ERP makers of conscious processing, with an early component reflecting the initial perceptual experience and a late component being a correlate of the conscious experience of non-perceptual information.     

Adjoints and emergence: applications of a new theory of adjoint functors

Since its formal definition over sixty years ago, category theory has been increasingly recognized as having a foundational role in mathematics. It provides the conceptual lens to isolate and characterize the structures with importance and universality in mathematics. The notion of an adjunction (a pair of adjoint functors) has moved to center-stage as the principal lens. The central feature of an adjunction is what might be called “determination through universals” based on universal mapping properties. A recently developed “heteromorphic” theory about adjoints suggests a conceptual structure, albeit abstract and atemporal, for how new relatively autonomous behavior can emerge within a system obeying certain laws. The focus here is on applications in the life sciences (e.g., selectionist mechanisms) and human sciences (e.g., the generative grammar view of language).

Testing neural network models of personality

Attempts to develop neural network models of personality have not generally used empirical data for training and validating the models. Two illustrations are provided which demonstrate the incorporation of empirical data into the modeling of behavioral responses to situations varying in closeness and hierarchical role relationships. An event-contingent recording procedure is utilized to obtain data from the same participant in multiple events for multiple situations. This data is then used in the training and validation of the neural networks. The first illustration models dominant and submissive behaviors in response to situations varying in social role status. The second illustration models agreeable and quarrelsome behaviors in response to situations varying in closeness and gender of the interaction partner. The predictions from both neural network models are consistent with previous research.