Reliability and consistency affect the integration of visual depth cues

Abstract Originalsprache (englisch) Visual perception relies on a two-dimensional projection of the viewed scene on the retinas of both eyes. Thus, visual depth has to be reconstructed from a number of different cues that are subsequently integrated to obtain robust depth percepts. Existing models of sensory integration are mainly based on the reliabilities of individual cues and disregard potential cue interactions. In the current study, an extended Bayesian model is proposed that takes into account both cue reliability and consistency. Four experiments were carried out to test this model's predictions. Observers had to judge visual displays of hemi-cylinders with an elliptical cross section, which were constructed to allow for an orthogonal variation of several competing depth cues. In Experiment 1 and 2, observers estimated the cylinder's depth as defined by shading, texture, and motion gradients. The degree of consistency among these cues was systematically varied. It turned out that the extended Bayesian model provided a better fit to the empirical data compared to the traditional model which disregards covariations among cues. To circumvent the potentially problematic assessment of single-cue reliabilities, Experiment 3 used a multiple-observation task, which allowed for estimating perceptual weights from multiple-cue stimuli. Using the same multiple-observation task, the integration of stereoscopic disparity, shading, and texture gradients was examined in Experiment 4. It turned out that less reliable cues were downweighted in the combined percept. Moreover, a specific influence of cue consistency was revealed. Shading and disparity seemed to be processed interactively while other cue combinations could be well described by additive integration rules. These results suggest that cue combination in visual depth perception is highly flexible and depends on single-cue properties as well as on interrelations among cues. The extension of the traditional cue combination model is defended in terms of the necessity for robust perception in ecologically valid environments and the current findings are discussed in the light of emerging computational theories and neuroscientific approaches.

Contour integration and principles of perceptual grouping

Ziel der Arbeit ist die Analyse von Prinzipien der Konturintegration im menschlichen visuellen System. Die perzeptuelle Verbindung benachbarter Teile in einer visuellen Szene zu einem Ganzen wird durch zwei gestalttheoretisch begründete Propositionen gekennzeichnet, die komplementäre lokale Mechanismen der Konturintegration beschreiben. Das erste Prinzip der Konturintegration fordert, dass lokale Ähnlichkeit von Elementen in einem anderen Merkmal als Orientierung nicht hinreicht für die Entdeckung von Konturen, sondern ein zusätzlicher statistischer Merkmalsunterschied von Konturelementen und Umgebung vorliegen muss, um Konturentdeckung zu ermöglichen. Das zweite Prinzip der Konturintegration behauptet, dass eine kollineare Ausrichtung von Konturelementen für Konturintegration hinreicht, und es bei deren Vorliegen zu robuster Konturintegrationsleistung kommt, auch wenn die lokalen merkmalstragenden Elemente in anderen Merkmalen in hohem Maße zufällig variieren und damit keine nachbarschaftliche Ähnlichkeitsbeziehung entlang der Kontur aufweisen. Als empirische Grundlage für die beiden vorgeschlagenen Prinzipien der Konturintegration werden drei Experimente berichtet, die zunächst die untergeordnete Rolle globaler Konturmerkmale wie Geschlossenheit bei der Konturentdeckung aufweisen und daraufhin die Bedeutung lokaler Mechanismen für die Konturintegration anhand der Merkmale Kollinearität, Ortsfrequenz sowie der spezifischen Art der Interaktion zwischen beiden Merkmalen beleuchten. Im ersten Experiment wird das globale Merkmal der Geschlossenheit untersucht und gezeigt, dass geschlossene Konturen nicht effektiver entdeckt werden als offene Konturen. Das zweite Experiment zeigt die Robustheit von über Kollinearität definierten Konturen über die zufällige Variation im Merkmal Ortsfrequenz entlang der Kontur und im Hintergrund, sowie die Unmöglichkeit der Konturintegration bei nachbarschaftlicher Ähnlichkeit der Konturelemente, wenn Ähnlichkeit statt über kollineare Orientierung über gleiche Ortsfrequenzen realisiert ist. Im dritten Experiment wird gezeigt, dass eine redundante Kombination von kollinearer Orientierung mit einem statistischen Unterschied im Merkmal Ortsfrequenz zu erheblichen Sichtbarkeitsgewinnen bei der Konturentdeckung führt. Aufgrund der Stärke der Summationswirkung wird vorgeschlagen, dass durch die Kombination mehrerer Hinweisreize neue kortikale Mechanismen angesprochen werden, die die Konturentdeckung unterstützen. Die Resultate der drei Experimente werden in den Kontext aktueller Forschung zur Objektwahrnehmung gestellt und ihre Bedeutung für die postulierten allgemeinen Prinzipien visueller Gruppierung in der Konturintegration diskutiert. Anhand phänomenologischer Beispiele mit anderen Merkmalen als Orientierung und Ortsfrequenz wird gezeigt, dass die gefundenen Prinzipien Generalisierbarkeit für die Verarbeitung von Konturen im visuellen System beanspruchen können.

The importance of visual and olfactory stimuli during flower visits in Apis mellifera

Flowers attract honeybees using colour and scent signals. Bimodality (having both scent and colour) in flowers leads to increased visitation rates, but how the signals influence each other in a foraging situation is still quite controversial. We studied four basic questions: When faced with conflicting scent and colour information, will bees choose by scent and ignore the “wrong” colour, or vice versa? To get to the bottom of this question, we trained bees on scent-colour combination AX (rewarded) versus BY (unrewarded) and tested them on AY (previously rewarded colour and unrewarded scent) versus BX (previously rewarded scent and unrewarded colour). It turned out that the result depends on stimulus quality: if the colours are very similar (unsaturated blue and blue-green), bees choose by scent. If they are very different (saturated blue and yellow), bees choose by colour. We used the same scents, lavender and rosemary, in both cases. Our second question was: Are individual bees hardwired to use colour and ignore scent (or vice versa), or can this behaviour be modified, depending on which cue is more readily available in the current foraging context? To study this question, we picked colour-preferring bees and gave them extra training on scent-only stimuli. Afterwards, we tested if their preference had changed, and if they still remembered the scent stimulus they had originally used as their main cue. We came to the conclusion that a colour preference can be reversed through scent-only training. We also gave scent-preferring bees extra training on colour-only stimuli, and tested for a change in their preference. The number of animals tested was too small for statistical tests (n = 4), but a common tendency suggested that colour-only training leads to a preference for colour. A preference to forage by a certain sensory modality therefore appears to be not fixed but flexible, and adapted to the bee’s surroundings. Our third question was: Do bees learn bimodal stimuli as the sum of their parts (elemental learning), or as a new stimulus which is different from the sum of the components’ parts (configural learning)? We trained bees on bimodal stimuli, then tested them on the colour components only, and the scent components only. We performed this experiment with a similar colour set (unsaturated blue and blue-green, as above), and a very different colour set (saturated blue and yellow), but used lavender and rosemary for scent stimuli in both cases. Our experiment yielded unexpected results: with the different colours, the results were best explained by elemental learning, but with the similar colour set, bees exhibited configural learning. Still, their memory of the bimodal compound was excellent. Finally, we looked at reverse-learning. We reverse-trained bees with bimodal stimuli to find out whether bimodality leads to better reverse-learning compared to monomodal stimuli. We trained bees on AX (rewarded) versus BY (unrewarded), then on AX (unrewarded) versus BY (rewarded), and finally on AX (rewarded) and BY (unrewarded) again. We performed this experiment with both colour sets, always using the same two scents (lavender and rosemary). It turned out that bimodality does not help bees “see the pattern” and anticipate the switch. Generally, bees trained on the different colour set performed better than bees trained on the similar colour set, indicating that stimulus salience influences reverse-learning.