A category-overshadowing effect in pigeons: support for the Common Elements Model of object categorization learning

Emergent categorization in the recognition of black and white paintings through conditional discrimination

Background

The emergent categorization involving paintings by renowned painters and their corresponding names was demonstrated by previous studies. However, the results of these studies suggest that the colors of the pictures may have played a preponderant role, obscuring other aspects of the stimuli that could be more directly related to the style of each painter. To verify this possibility, the present study used the same methodology of Ferreira et al. to investigate the establishment of emergent conditional relations between categories composed of black and white paintings and the names of their authors.

Method

The procedure consisted of the training of relations between each of the ten paintings and an abstract picture, for each of the three painters Botticelli, Monet, and Picasso. Relations between each of the three abstract figures and the printed name of one of the painters were verified in sequence. Finally, tests of relations between five trained and five untrained paintings of each artist and the printed names were conducted.

Results

The participants’ performance suggests that the outcome was properly controlled by aspects pertinent to the paintings that belonged to each painter’s category.

Conclusions

The results reinforced the data obtained previously with colored pictures, suggesting that the process of emergent categorization involving artificial categories of paintings is robust. It also indicates possibilities for future investigations, for example, using stimuli of other artistic productions, such as sculpture and music.

Beyond the "Conceptual Nervous System": Can computational cognitive neuroscience transform learning theory?

In the last century, learning theory has been dominated by an approach assuming that associations between hypothetical representational nodes can support the acquisition of knowledge about the environment. The similarities between this approach and connectionism did not go unnoticed to learning theorists, with many of them explicitly adopting a neural network approach in the modeling of learning phenomena. Skinner famously criticized such use of hypothetical neural structures for the explanation of behavior (the "Conceptual Nervous System"), and one aspect of his criticism has proven to be correct: theory underdetermination is a pervasive problem in cognitive modeling in general, and in associationist and connectionist models in particular. That is, models implementing two very different cognitive processes often make the exact same behavioral predictions, meaning that important theoretical questions posed by contrasting the two models remain unanswered. We show through several examples that theory underdetermination is common in the learning theory literature, affecting the solvability of some of the most important theoretical problems that have been posed in the last decades. Computational cognitive neuroscience (CCN) offers a solution to this problem, by including neurobiological constraints in computational models of behavior and cognition. Rather than simply being inspired by neural computation, CCN models are built to reflect as much as possible about the actual neural structures thought to underlie a particular behavior. They go beyond the "Conceptual Nervous System" and offer a true integration of behavioral and neural levels of analysis.

An interplay of fusiform gyrus and hippocampus enables prototype- and exemplar-based category learning

The aim of the present study was to examine the contributions of different brain structures to prototype- and exemplar-based category learning using functional magnetic resonance imaging (fMRI). Twenty-eight subjects performed a categorization task in which they had to assign prototypes and exceptions to two different families. This test procedure usually produces different learning curves for prototype and exception stimuli. Our behavioral data replicated these previous findings by showing an initially superior performance for prototypes and typical stimuli and a switch from a prototype-based to an exemplar-based categorization for exceptions in the later learning phases. Since performance varied, we divided participants into learners and non-learners. Analysis of the functional imaging data revealed that the interaction of group (learners vs. non-learners) and block (Block 5 vs. Block 1) yielded an activation of the left fusiform gyrus for the processing of prototypes, and an activation of the right hippocampus for exceptions after learning the categories. Thus, successful prototype- and exemplar-based category learning is associated with activations of complementary neural substrates that constitute object-based processes of the ventral visual stream and their interaction with unique-cue representations, possibly based on sparse coding within the hippocampus.

Promoting rotational-invariance in object recognition despite experience with only a single view

Different processes are assumed to underlie invariant object recognition across affine transformations, such as changes in size, and non-affine transformations, such as rotations in depth. From this perspective, promoting invariant object recognition across rotations in depth requires visual experience with the object from multiple viewpoints. One learning mechanism potentially contributing to invariant recognition is the error-driven learning of associations between relatively view-invariant visual properties and motor responses or object labels. This account uniquely predicts that experience with affine transformations of a single object view may also promote view-invariance, if view-invariant properties are also invariant across such affine transformations. We empirically confirmed this prediction in both people and pigeons, thereby suggesting that: (a) the hypothesized mechanism participates in view-invariance learning, (b) this mechanism is present across distantly-related vertebrates, and (c) the distinction between affine and non-affine transformations may not be fundamental for biological visual systems, as previously assumed.

Mechanisms of object recognition: what we have learned from pigeons

Behavioral studies of object recognition in pigeons have been conducted for 50 years, yielding a large body of data. Recent work has been directed toward synthesizing this evidence and understanding the visual, associative, and cognitive mechanisms that are involved. The outcome is that pigeons are likely to be the non-primate species for which the computational mechanisms of object recognition are best understood. Here, we review this research and suggest that a core set of mechanisms for object recognition might be present in all vertebrates, including pigeons and people, making pigeons an excellent candidate model to study the neural mechanisms of object recognition. Behavioral and computational evidence suggests that error-driven learning participates in object category learning by pigeons and people, and recent neuroscientific research suggests that the basal ganglia, which are homologous in these species, may implement error-driven learning of stimulus-response associations. Furthermore, learning of abstract category representations can be observed in pigeons and other vertebrates. Finally, there is evidence that feedforward visual processing, a central mechanism in models of object recognition in the primate ventral stream, plays a role in object recognition by pigeons. We also highlight differences between pigeons and people in object recognition abilities, and propose candidate adaptive specializations which may explain them, such as holistic face processing and rule-based category learning in primates. From a modern comparative perspective, such specializations are to be expected regardless of the model species under study. The fact that we have a good idea of which aspects of object recognition differ in people and pigeons should be seen as an advantage over other animal models. From this perspective, we suggest that there is much to learn about human object recognition from studying the "simple" brains of pigeons.

Object-specific and relational learning in pigeons

Abstract or relational stimulus processing requires an organism to appreciate the interrelations between or among two or more stimuli (e.g., same or different, less than or greater than). In the current study, we explored the role of concrete and abstract information processing in pigeons performing a visual categorization task which could be solved by attending to either the specific objects presented or the relation among the objects. In Experiment 1, we gave pigeons three training phases in which we gradually increased the variability (that is, the number of object arrays) in the training set. In Experiment 2, we trained a second group of pigeons with an even larger number of object arrays from the outset. We found that, the larger the variability in the training exemplars, the lesser the pigeons' attention to object-specific information and the greater their attention to relational information; nevertheless, the contribution of object-specific information to categorization performance was never completely eliminated. This pervasive influence of object-specific information is not peculiar to animals, but has been observed in young children and human adults as well.