Neural competition for conscious representation across time: An fMRI study

Background The information processing capacity of the human mind is limited, as is evidenced by the attentional blink (AB) - a deficit in identifying the second of two temporally-close targets (T1 and T2) embedded in a rapid stream of distracters. Theories of the AB generally agree that it results from competition between stimuli for conscious representation. However, they disagree in the specific mechanisms, in particular about how attentional processing of T1 determines the AB to T2. Methodology/Principal Findings The present study used the high spatial resolution of functional magnetic resonance imaging (fMRI) to examine the neural mechanisms underlying the AB. Our research approach was to design T1 and T2 stimuli that activate distinguishable brain areas involved in visual categorization and representation. ROI and functional connectivity analyses were then used to examine how attentional processing of T1, as indexed by activity in the T1 representation area, affected T2 processing. Our main finding was that attentional processing of T1 at the level of the visual cortex predicted T2 detection rates Those individuals who activated the T1 encoding area more strongly in blink versus no-blink trials generally detected T2 on a lower percentage of trials. The coupling of activity between T1 and T2 representation areas did not vary as a function of conscious T2 perception. Conclusions/Significance These data are consistent with the notion that the AB is related to attentional demands of T1 for selection, and indicate that these demands are reflected at the level of visual cortex. They also highlight the importance of individual differences in attentional settings in explaining AB task performance.

Digital identity for the retired

A This Is Me workbook for the retired or vulnerable internet user.

Application of Gaussian multi-scale representation to feature tracking in meteorological satellite imagery

Gaussian multi-scale representation is a mathematical framework that allows to analyse images at different scales in a consistent manner, and to handle derivatives in a way deeply connected to scale. This paper uses Gaussian multi-scale representation to investigate several aspects of the derivation of atmospheric motion vectors (AMVs) from water vapour imagery. The contribution of different spatial frequencies to the tracking is studied, for a range of tracer sizes, and a number of tracer selection methods are presented and compared, using WV 6.2 images from the geostationary satellite MSG-2.

Democracy and identity: educating the student voice

Considering the role of student voice in music education in connection with the role of music in identity formation. A report on a small-scale study.

The representation of fathers by children of depressed mothers: refining the meaning of parentification in high-risk samples

Background: Children’s representations of mothers in doll-play are associated with child adjustment. Despite the importance of fathers for children’s adjustment, especially in the context of maternal psychopathology, few studies have considered children’s representations of their fathers. Method: We examined the portrayal of fathers by 5-year-old children of depressed (N = 55) and non-depressed (N = 39) mothers in a doll-play procedure concerning family experience. Results: Children gave equal prominence in their play to mothers and fathers. Representations of fathers were unrelated to maternal mood, but were associated with parental conflict. Representations of child care for the father that was unreciprocated predicted poor child adjustment in school, but only in children exposed to maternal postnatal depression. Conclusions: It may be clinically useful to consider children’s distinctive representations of their mother and father; but the concept of parentification in relation to risk and resilience effects requires refinement.

Dynamic knowledge representation in connectionist systems

One of the most pervading concepts underlying computational models of information processing in the brain is linear input integration of rate coded uni-variate information by neurons. After a suitable learning process this results in neuronal structures that statically represent knowledge as a vector of real valued synaptic weights. Although this general framework has contributed to the many successes of connectionism, in this paper we argue that for all but the most basic of cognitive processes, a more complex, multi-variate dynamic neural coding mechanism is required - knowledge should not be spacially bound to a particular neuron or group of neurons. We conclude the paper with discussion of a simple experiment that illustrates dynamic knowledge representation in a spiking neuron connectionist system.