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It is generally thought that skilled behavior in human beings results from a functional hierarchy of the motor control system, within which reusable motor primitives are flexibly integrated into various sensori-motor sequence patterns. The underlying neural mechanisms governing the way in which continuous sensori-motor flows are segmented into primitives and the way in which series of primitives are integrated into various behavior sequences have, however, not yet been clarified. In earlier studies, this functional hierarchy has been realized through the use of explicit hierarchical structure, with local modules representing motor primitives in the lower level and a higher module representing sequences of primitives switched via additional mechanisms such as gate-selecting. When sequences contain similarities and overlap, however, a conflict arises in such earlier models between generalization and segmentation, induced by this separated modular structure. To address this issue, we propose a different type of neural network model. The current model neither makes use of separate local modules to represent primitives nor introduces explicit hierarchical structure. Rather than forcing architectural hierarchy onto the system, functional hierarchy emerges through a form of self-organization that is based on two distinct types of neurons, each with different time properties (\"multiple timescales\"). Through the introduction of multiple timescales, continuous sequences of behavior are segmented into reusable primitives, and the primitives, in turn, are flexibly integrated into novel sequences. In experiments, the proposed network model, coordinating the physical body of a humanoid robot through high-dimensional sensori-motor control, also successfully situated itself within a physical environment. Our results suggest that it is not only the spatial connections between neurons but also the timescales of neural activity that act as important mechanisms leading to functional hierarchy in neural systems.

An important task of the brain is to represent the outside world. It is unclear how the brain may do this, however, as it can only rely on neural responses and has no independent access to external stimuli in order to \"decode\" what those responses mean. We investigate what can be learned about a space of stimuli using only the action potentials (spikes) of cells with stereotyped -- but unknown -- receptive fields. Using hippocampal place cells as a model system, we show that one can (1) extract global features of the environment and (2) construct an accurate representation of space, up to an overall scale factor, that can be used to track the animal's position. Unlike previous approaches to reconstructing position from place cell activity, this information is derived without knowing place fields or any other functions relating neural responses to position. We find that simply knowing which groups of cells fire together reveals a surprising amount of structure in the underlying stimulus space; this may enable the brain to construct its own internal representations.

The animal preference for complexity is most clearly demonstrated when the environmental change takes the form of an increase in complexity. Therefore, one of the potential difficulties in interpretation is that the preference for perceptual novelty may be confounded with the change in environmental complexity. In this study, the environmental complexity was controlled by manipulating with tunnels inside the experimental chamber. Adding new tunnels triggered a very profound change in behaviour, which was demonstrated by the animals’ prolonged stay in the proximity of the novel objects, sniffing, touching, and climbing on top of the tunnels. The removal of the tunnels from the test arena turned out to have the least influence on behaviour compared to the other manipulations used in this study. The reduction of complexity of the tunnels had a moderate effect on rat behavior. Tunnels are important elements in the rats’ environment, since they provide various possibilities for hiding, resting or moving inside the tunnel. They may be treated as a good example of affordances in rat-environment interactions. The results of this study may therefore serve as a basis for constructing a modified theory of animal curiosity which could incorporate the concept of ecological psychology.

Artificial grammar learning (AGL) provides a useful tool for exploring rule learning strategies linked to general purpose pattern perception. To be able to directly compare performance of humans with other species with different memory capacities, we developed an AGL task in the visual domain. Presenting entire visual patterns simultaneously instead of sequentially minimizes the amount of required working memory. This approach allowed us to evaluate performance levels of two bird species, kea (Nestor notabilis) and pigeons (Columba livia), in direct comparison to human participants. After being trained to discriminate between two types of visual patterns generated by rules at different levels of computational complexity and presented on a computer screen, birds and humans received further training with a series of novel stimuli that followed the same rules, but differed in various visual features from the training stimuli. Most avian and all human subjects continued to perform well above chance during this initial generalization phase, suggesting that they were able to generalize learned rules to novel stimuli. However, detailed testing with stimuli that violated the intended rules regarding the exact number of stimulus elements indicates that neither bird species was able to successfully acquire the intended pattern rule. Our data suggest that, in contrast to humans, these birds were unable to master a simple rule above the finite-state level, even with simultaneous item presentation and despite intensive training.

The present studies tested whether African American face type (stereotypical or nonstereotypical) facilitated stereotype-consistent categorization, and whether that categorization influenced memory accuracy and errors. Previous studies have shown that stereotypically Black features are associated with crime and violence (e.g., Blair, Judd, & Chapleau Psychological Science 15:674–679, 2004 ; Blair, Judd, & Fallman Journal of Personality and Social Psychology 87:763–778, 2004 ; Blair, Judd, Sadler, & Jenkins Journal of Personality and Social Psychology 83:5–25 2002 ); here, we extended this finding to investigate whether there is a bias toward remembering and recategorizing stereotypical faces as criminals. Using category labels, consistent (or inconsistent) with race-based expectations, we tested whether face recognition and recategorization were driven by the similarity between a target’s facial features and a stereotyped category (i.e., stereotypical Black faces associated with crime/violence). The results revealed that stereotypical faces were associated more often with a stereotype-consistent label (Study 1), were remembered and correctly recategorized as criminals (Studies 2–4), and were miscategorized as criminals when memory failed. These effects occurred regardless of race or gender. Together, these findings suggest that face types have strong category associations that can promote stereotype-motivated recognition errors. Implications for eyewitness accuracy are discussed.

This paper presents a novel representational framework for the Temporal Difference (TD) model of learning, which allows the computation of configural stimuli--cumulative compounds of stimuli that generate perceptual emergents known as configural cues. This Simultaneous and Serial Configural-cue Compound Stimuli Temporal Difference model (SSCC TD) can model both simultaneous and serial stimulus compounds, as well as compounds including the experimental context. This modification significantly broadens the range of phenomena which the TD paradigm can explain, and allows it to predict phenomena which traditional TD solutions cannot, particularly effects that depend on compound stimuli functioning as a whole, such as pattern learning and serial structural discriminations, and context-related effects.

Categorization refers to the classification of perceptual input into defined functional groups. We present and discuss evidence suggesting that stimulus categorization can also be found in an invertebrate, the honeybee Apis mellifera, thus underlining the generality across species of this cognitive process. Honeybees show positive transfer of appropriate responding from a trained to a novel set of visual stimuli. Such a transfer was demonstrated for specific isolated features such as symmetry or orientation, but also for assemblies (layouts) of features. Although transfer from training to novel stimuli can be achieved by stimulus generalization of the training stimuli, most of these transfer tests involved clearly distinguishable stimuli for which generalization would be reduced. Though in most cases specific experimental controls such as stimulus balance and discriminability are still required, it seems appropriate to characterize the performance of honeybees as reflecting categorization. Further experiments should address the issue of which categorization theory accounts better for the visual performances of honeybees.

Impossible figures are striking examples of inconsistencies between global and local perceptual structures, in which the overall spatial configuration of the depicted image does not yield a coherent three-dimensional object. In order to investigate whether structural “impossibility” is an important perceptual property of depicted objects, we used a category formation task in which subjects were asked to divide pictures of shapes into groups that seemed most natural to them. Category formation is usually unidimensional, such that sorting is dominated by a single perceptual property, so this task can serve as a measure of which dimensions are most salient. In Experiment 1 , subjects received sets of 12 line drawings consisting of six possible and six impossible objects. Very few subjects grouped the figures by impossibility on the first try, and only half did so after multiple attempts at sorting. In Experiment 2 , we investigated other global properties of figures: symmetry and complexity. Subjects readily sorted objects by complexity, but seldom by symmetry. In Experiment 3 , subjects were asked to draw each of the figures before sorting them, which had only a minimal effect on categorization. Finally, in Experiment 4 , subjects were explicitly instructed to divide the shapes by symmetry or impossibility. Performance on this task was perfect for symmetry, but not for impossibility. Although global properties of figures seem extremely important to our perception, the results suggest that some of these cues are not immediately obvious or salient for most observers.

A major theoretical debate in language acquisition research regards the learnability of hierarchical structures. The artificial grammar learning methodology is increasingly influential in approaching this question. Studies using an artificial centre-embedded AnBn grammar without semantics draw conflicting conclusions. This study investigates the facilitating effect of distributional biases in simple AB adjacencies in the input sample—caused in natural languages, among others, by semantic biases—on learning a centre-embedded structure. A mathematical simulation of the linguistic input and the learning, comparing various distributional biases in AB pairs, suggests that strong distributional biases might help us to grasp the complex AnBn hierarchical structure in a later stage. This theoretical investigation might contribute to our understanding of how distributional features of the input—including those caused by semantic variation—help learning complex structures in natural languages.