Deconfounded and mixed-symmetry versions of the Ponzo illusion figure

One of the original Ponzo illusion figures, which consists of two converging lines between which two parallel lines of similar length have been inserted orthogonal to the figure's axis of mirror symmetry, was itself mirror-reflected so that the overall shape of the figure became "< >" or "> <", and one line at a time was inserted into each half. The usual illusion - the overestimation of the length of a line that is nearer to a vertex than a farther-away comparison line - occurred. Experiments 1 and 2 used different distances of target and comparison lines to the vertices, but identical distances of these lines from the converging lines, and so, as a tandem, deconfounded the two variables. Experiments 3 and 4 changed the symmetries of the modified Ponzo figure by reducing opposing half-angles of the converging lines or by tilting target and comparison lines concordantly or discordantly. The first measure, which created unequal distances of the endpoints of the target and comparison lines from the converging lines, hardly affected the amount of illusion. The second measure often attenuated the illusion - equally so for concordant and discordant tilts - suggesting that global and local symmetries of the stimuli, and their accordance, were less important than the vertical versus oblique orientation of target and comparison lines. Descriptively, the main cause of the Ponzo illusion seems to be the size of the gap between target and converging lines. The neural substrate of the effect may be interactions between orientation-sensitive and end-inhibited neurons.

Simulating Small Neural Circuits with a Discrete Computational Model

Simulations of neural activity are commonly based on differential equations. We address the question what can be achieved with a simplified discrete model. The proposed model resembles artificial neural networks enriched with additional biologically inspired features. A neuron has several states, and the state transitions follow endogenous patterns which roughly correspond to firing behavior observed in biological neurons: oscillatory, tonic, plateauing, etc. Neural interactions consist of two components: synaptic connections and extrasynaptic emission of neurotransmitters. The dynamics is asynchronous and event-based; the events correspond to the changes in neurons activity. This model is innovative in introducing discrete framework for modeling neurotransmitter interactions which play the important role in neuromodulation. We simulate rhythmic activity of small neural ensembles like central pattern generators (CPG). The modeled examples include: the biphasic rhythm generated by the half-center mechanism with the post-inhibitory rebound (like the leech heartbeat CPG), the triphasic rhythm (like in pond snail feeding CPG) and the pattern switch in the system of several neurons (like the switch between ingestion and egestion in Aplysia feeding CPG). The asynchronous dynamics allows to obtain multi-phasic rhythms with phase durations close to their biological prototypes. The perspectives of discrete modeling in biological research are discussed in the conclusion.

Titchener's T in context 2 - Symmetric patterns of two Ts

Patterns of two Ts, materializing different symmetry groups, were used to explore conditions that would lead to a modulation of the typically observed overestimation of the length of a T's undivided line relative to its divided line. Observers either had to compare the lengths of the lines of one or the other of the Ts in a pattern, or noncorresponding lines between the two Ts. For both tasks alike, the T-illusion was found to be markedly greater with twofold mirror-symmetric 2-T patterns than it usually is with individual Ts. A control experiment suggested that the effect was probably due to the collinearity of the two Ts' undivided lines in these patterns rather than the additional axis of mirror symmetry. Findings are interpreted in terms of interactions between orientation-sensitive neurons that respond to the Ts' individual lines.

From intermodulation components to visual perception and cognition-a review

Perception results from complex interactions among sensory and cognitive processes across hierarchical levels in the brain. Intermodulation (IM) components, used in frequency tagging neuroimaging designs, have emerged as a promising direct measure of such neural interactions. IMs have initially been used in electroencephalography (EEG) to investigate low-level visual processing. In a more recent trend, IMs in EEG and other neuroimaging methods are being used to shed light on mechanisms of mid- and high-level perceptual processes, including the involvement of cognitive functions such as attention and expectation. Here, we provide an account of various mechanisms that may give rise to IMs in neuroimaging data, and what these IMs may look like. We discuss methodologies that can be implemented for different uses of IMs and we demonstrate how IMs can provide insights into the existence, the degree and the type of neural integration mechanisms at hand. We then review a range of recent studies exploiting IMs in visual perception research, placing an emphasis on high-level vision and the influence of awareness and cognition on visual processing. We conclude by suggesting future directions that can enhance the benefits of IM-methodology in perception research.