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The Lords of the Rings: People and pigeons take different paths mastering the concentric-rings categorization task
Abstract
COVIS (COmpetition between Verbal and Implicit Systems; Ashby, Alfonso-Reese, & Waldron, 1998) is a prominent model of categorization which hypothesizes that humans have two independent categorization systems – one declarative, one associative – that can be recruited to solve category learning tasks. To date, most COVIS-related research has focused on just two experimental tasks: linear rule-based (RB) tasks, which purportedly encourage declarative rule use, and linear information-integration (II) tasks, which purportedly require associative learning mechanisms. We introduce and investigate a novel alternative: the concentric-rings task, a nonlinear category structure that both humans and pigeons can successfully learn and transfer to untrained exemplars. Yet, despite their broad behavioral similarities, humans and pigeons achieve their successful learning through decidedly different means. As predicted by COVIS, pigeons appear to rely solely on associative learning mechanisms, whereas humans appear to initially test but subsequently reject unidimensional rules. We discuss how variants of our concentric-rings task might yield further insights into which category-learning mechanisms are shared across species, as well as how categorization strategies might change throughout training.
... Figure 1depicts two variants of the sectioned-rings task-the ''+ cut'' (left panel) and the ''3 cut'' (right panel)-along with representative stimuli from each of the eight sections in the two training rings. The sectioned-rings task was inspired by the concentric-rings task that we recently devised in our laboratory, 13 and it was designed to disadvantage declarative rule use; critically, no single unidimensional or bidimensional decision rule can yield above-chance accuracy on either task variant. ...
... Perhaps this relative neglect has contributed to the puzzling paradox that the power of associative processes in humans and non-human animals has been underestimated, whereas the power of associative mechanisms in AI has been celebrated. [4][5][6] Our present findings with the sectioned-rings task and our prior findings with the concentric-rings task 13 reveal that a basic ll associative learning mechanism can solve particularly demanding visual categorization tasks that strongly disadvantage more elaborate rule-based learning systems. Not only does the basic associative mechanism enable pigeons to learn categorization tasks involving highly artificial stimuli such as those used here, 13,18,19 but it also allows pigeons to learn categorization tasks involving much more complex and life-like photographic stimuli. ...
... [4][5][6] Our present findings with the sectioned-rings task and our prior findings with the concentric-rings task 13 reveal that a basic ll associative learning mechanism can solve particularly demanding visual categorization tasks that strongly disadvantage more elaborate rule-based learning systems. Not only does the basic associative mechanism enable pigeons to learn categorization tasks involving highly artificial stimuli such as those used here, 13,18,19 but it also allows pigeons to learn categorization tasks involving much more complex and life-like photographic stimuli. [20][21][22] We do not doubt that, given sufficient time and motivation, human participants could also succeed at learning the sectionedrings task. ...
A wealth of evidence indicates that humans can engage two types of mechanisms to solve category-learning tasks: declarative mechanisms, which involve forming and testing verbalizable decision rules, and associative mechanisms, which involve gradually linking stimuli to appropriate behavioral responses.1,2,3 In contrast to declarative mechanisms, associative mechanisms have received surprisingly little attention in the broader category-learning literature. Although various forms of associatively driven artificial intelligence (AI) have matched-and even surpassed-humans' performance on several challenging problems,3,4,5,6 associative learning is routinely dismissed as being too simple to power the impressive cognitive achievements of both humans and non-human species.6,7,8,9 Here, we attempt to resolve this paradox by demonstrating that pigeons-which appear to rely solely on associative learning mechanisms in several tasks that promote declarative rule use by humans3,10,11,12-succeed at learning a novel, highly demanding category structure that ought to hinder declarative rule use: the sectioned-rings task. Our findings highlight the power and flexibility that associative mechanisms afford in the realm of category learning.
... Although pigeons can learn to selectively attend to categoryrelevant features, they do not produce rule-like behaviour and, instead, they learn these selective attention weightings gradually, associatively and non-analytically 141,142,147,154 . A series of experiments with pigeons and humans provides further support for this dissociation: they learned a novel and complex category structure based on concentric rings 155,156 (Fig. 3b). Unlike two-dimensional rule-based and information-integration categories, the concentric rings category structure is non-linear and cannot be solved by abstracting a prototype or comparing the similarity to previous exemplars but only through associative learning. ...
... Unlike two-dimensional rule-based and information-integration categories, the concentric rings category structure is non-linear and cannot be solved by abstracting a prototype or comparing the similarity to previous exemplars but only through associative learning. Although humans and pigeons both learned this category structure, they did so in very different ways 155 . Consistent with the predictions of COVIS, pigeons started out guessing and slowly transitioned to a strategy that was fit well by a complex quadratic boundary, but humans preferred single dimension rules early in the learning phase, even though those rules did not work. ...
... The third task we studied involved another type of categorization structure that requires information integration: the concentric-rings task as reported in O'Donoghue et al. 29 In this task, categories are defined by two concentric rings, illustrated in Figures 7A and 7C. For a rule-based learner to acquire this task, they would not only need to use both dimensions, but they would also need to apply a bimodal decision rule along those two dimensions. ...
... This type of bimodal response criterion is especially hard to learn relative to unimodal response criteria, especially for humans. [28][29][30] However, because a purely associative learner would not need to use such a complex decision rule, they should more readily acquire the task. Figures 7B and 7D illustrate the associations learned by the model over time. ...
Never known for its smarts, the pigeon has proven to be a prodigious classifier of complex visual stimuli. What explains its surprising success? Does it possess elaborate executive functions akin to those deployed by humans? Or does it effectively deploy an unheralded, but powerful associative learning mechanism? In a series of experiments, we first confirm that pigeons can learn a variety of category structures – some devised to foil the use of advanced cognitive processes. We then contrive a simple associative learning model to see how effectively the model learns the same tasks given to pigeons. The close fit of the associative model to pigeons’ categorization behavior provides unprecedented support for associative learning as a viable mechanism for mastering complex category structures and for the pigeon’s using this mechanism to adapt to a rich visual world. This model will help guide future neuroscientific research into the biological substrates of visual cognition.
... In the CC structure, category exemplars are arranged about a circular boundary, which allows us to test category learning models along continuous perceptual dimensions. Our work was influenced by recent work in comparative psychology that pioneered a concentric ring category structure O'Donoghue, Broschard, Freeman, and Wasserman (2022). Earlier precedence exists within machine learning literature Wen, Xie, and Pei (2016). ...
... Earlier precedence exists within machine learning literature Wen, Xie, and Pei (2016). The finalized category structure proposed in our paper is similar to the one put forth by O'Donoghue and colleagues, though the methods by which the category sets are generated differ, as well as the nature of the investigation to which they are applied O'Donoghue et al. (2022). ...
Prototype, exemplar, and boundary models compete to explain representational-level abstractions during human category learning. Vast majority of previous work use linear categories structures to evaluate learning. We present the development of a novel, circular category structure and leverage it to explore limitations of prototype, exemplar and boundary models. We find that circular categories are readily learned by human participants, and the induced representation is most likely a quadratic boundary. We deductively eliminate prototype theories as an explanation of these circular categories and show that exemplar models, though viable, provide a weaker explanation than boundary models which are best fitted to the present data. These circular category structures offer a promising new technique to studying implicit category learning.
... In addition, some researchers have called for additional paradigms beyond the RB-II task to shed light on which mechanisms of category learning are shared between species. In fact, other categorization tasks show additional differences between the categorization strategies of different species that are not evident in RB-II tasks (O'Donoghue et al. 2022). ...
Category learning gives rise to category formation, which is a crucial ability in human cognition. Category learning is also one of the required learning abilities in language development. Understanding the evolution of category learning thus can shed light on the evolution of human cognition and language. The current paper emphasizes its foundational role in language evolution by reviewing behavioral and neurological studies on category learning across species. In doing so, we first review studies on the critical role of category learning in learning sounds, words, and grammatical patterns of language. Next, from a comparative perspective, we review studies on category learning conducted on different species of nonhuman animals, including invertebrates and vertebrates, suggesting that category learning displays evolutionary continuity. Then, from a neurological perspective, we focus on the prefrontal cortex and the basal ganglia. Reviewing the involvement of these structures in vertebrates and the proposed homologous brain structure to the basal ganglia in invertebrates in category learning, as well as in language processing in humans, suggests that the neural basis of category learning likely has an ancient origin dating back to invertebrates. With evidence from both behavioral and neurological levels in both nonhuman animals and humans, we conclude that category learning lays a crucial cognitive foundation for language evolution.
Over the past 30 years, behavioral, computational, and neuroscientific investigations have yielded fresh insights into how pigeons adapt to the diverse complexities of their visual world. A prime area of interest has been how pigeons categorize the innumerable individual stimuli they encounter. Most studies involve either photorealistic representations of actual objects thus affording the virtue of being naturalistic, or highly artificial stimuli thus affording the virtue of being experimentally manipulable. Together those studies have revealed the pigeon to be a prodigious classifier of both naturalistic and artificial visual stimuli. In each case, new computational models suggest that elementary associative learning lies at the root of the pigeon’s category learning and generalization. In addition, ongoing computational and neuroscientific investigations suggest how naturalistic and artificial stimuli may be processed along the pigeon’s visual pathway. Given the pigeon’s availability and affordability, there are compelling reasons for this animal model to gain increasing prominence in contemporary neuroscientific research.
Categorical learning plays a foundational role in language development. By reviewing comparative studies on categorical learning in humans and nonhuman animals, we show that categorical learning displays evolutionary continuity across invertebrates and vertebrates. Great apes and parrots can be trained to produce categories of (proto-)language-like symbols in different modalities. From the neurological perspective, we show that as a conserved brain structure, the basal ganglia are involved in categorical learning across species, and language processing in humans. This raises the possibility that categorical learning is one of the crucial cognitive foundations for language evolution.
A well replicated result in humans is that performance, whether good or bad, is consistent across a wide variety of cognitive tasks. Factor analysis consistently extracts one factor that can account for approximately half of the variance in performance. This factor is termed g and almost all cognitive tasks positively load onto this factor. While some neurobiological correlates of g have been identified in humans, causal experiments are only feasible in animals. When mice and some avian species are assessed with cognitive test batteries, performance positively correlates, and the first component extracted has similar properties to g . There are some limitations to the species tested thus far, including comparability in the cognitive domains assessed across species and homogeneous samples. The pigeon is an ideal subject to overcome these issues since pigeons, humans, and other primates are frequently given similar tasks and many neural correlates of performance have been identified in the pigeon. We created a test battery that assessed different domains, including associative learning, memory, cognitive flexibility, and reaction time. Yet we did not consistently extract a g like factor. Analyses indicated a two-component structure with differential task loadings. Possible interpretations of the components are associative learning/memorization versus a general rule, degree of automaticity, and sensitivity to age related decline. Reasons and implications for this two-component structure are discussed.
Pigeons Post are sophisticated birds with a good memory, navigation skills and an instinct to return to the nest after a long absence. Over time, has become popular as a pet, and a sport. In Indonesia, the Pigeons Post championship is legally protected through POMSI (in Bahasa Indonesia: Perkumpulan Olahraga Merpati Pos Seluruh Indonesia), held by local, and national communities, this hobby continues to grow. This competition technique is that participants register, bring the bird to a place have prepared, and fly it with applicable conditions. Many competition participants cause problems because the conventional processes lead to data entry errors, flight speed calculations, lost registrations, and human errors. This community service activity is to help, and facilitate the implementation system of the Pigeons Post competition in the community - POMP Galaxy (in Bahasa Indonesia: Perhimpunan Olahraga Merpati Pos). The results prove that the system has been created to facilitate the committee in managing and improving the competition's judging accuracy. This system is tested using alpha testing, which shows all features are running well. On the other hand, the acceptance test results through the questionnaire resulted in a response percentage of 89.99%, with the system's calculation criteria strongly agreeing.
An early achievement in language is carving a variable acoustic space into categories. The canonical story is that infants accomplish this by the second year, when only unsupervised learning is plausible. I challenge this view, synthesising five lines of developmental, phonetic and computational work. First, unsupervised learning may be insufficient given the statistics of speech (including infant-directed). Second, evidence that infants “have” speech categories rests on tenuous methodological assumptions. Third, the fact that the ecology of the learning environment is unsupervised does not rule out more powerful error driven learning mechanisms. Fourth, several implicit supervisory signals are available to older infants. Finally, development is protracted through adolescence, enabling richer avenues for development. Infancy may be a time of organising the auditory space, but true categorisation only arises via complex developmental cascades later in life. This has implications for critical periods, second language acquisition, and our basic framing of speech perception.
A neuropsychological theory is proposed that assumes category learning is a competition between separate verbal and implicit (i.e., procedural-learning-based) categorization systems. The theory assumes that the caudate nucleus is an important component of the implicit system and that the anterior cingulate and prefrontal cortices are critical to the verbal system. In addition to making predictions for normal human adults, the theory makes specific predictions for children, elderly people, and patients suffering from Parkinson's disease, Huntington's disease, major depression, amnesia, or lesions of the prefrontal cortex. Two separate formal descriptions of the theory are also provided. One describes trial-by-trial learning, and the other describes global dynamics. The theory is tested on published neuropsychological data and on category learning data with normal adults.
Both humans and pigeons are highly adept at task switching. However, unlike humans, pigeons do not show measurable switch costs: decreased accuracy and/or increased response times when required to switch tasks on successive trials. This striking disparity suggests that humans and pigeons may succeed at task switching via different means: humans may rely on a combination of executive control and associative learning, whereas pigeons may rely solely on associative learning. Here, we further explored the limits of pigeons' associative learning in an expanded task-switching paradigm. We trained pigeons to switch among four tasks, each signaled by two redundant types of task cues. The pigeons benefited from the availability of both task cues despite their redundancy: their accuracies were higher when both cues were available than when only a single cue was available. Additionally, we assessed the possibility that the lack of switch costs reported in the pigeon literature might stem from methodological discrepancies between pigeon and human task-switching paradigms. Across experimental phases, we modified pigeons' trial structures to more closely mimic those typically used in human task-switching research. Despite these modifications, pigeons did not display switch costs, consistent with their sole reliance on associative learning. Overall, our data highlight the power and flexibility inherent in the pigeon's associative learning system. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
A prominent model of categorization (Ashby, Alfonso-Reese, Turken, & Waldron, 1998) posits that 2 separate mechanisms-one declarative, one associative-can be recruited in category learning. These 2 systems can effectively be distinguished by 2 task structures: rule-based (RB) tasks are unidimensional and encourage analytic processing, whereas information-integration (II) tasks are bidimensional and encourage nonanalytic associative learning. Humans and nonhuman primates have been reported to learn RB tasks more quickly than II tasks; however, pigeons and rats have shown no learning speed differences are thus believed to lack the declarative system. In the present trio of experiments, we further explored pigeons' dimensional category learning. We replicated the finding that pigeons learn RB and II tasks at equal speeds. Further, we found that stimulus generalization performance was equivalent on both tasks. We also explored the effect of switching from one task to another. Task switches between phases of training as well as within individual training sessions posed little difficulty for pigeons; they quickly and flexibly switched their categorization responses with no cost in choice speed or accuracy. Together, our data indicate that, although pigeons may lack the capacity to form explicit dimensional rules, their associative learning system is both powerful and flexible. Further exploration of this associative system would help us better appreciate possible contributions of the declarative system. (PsycINFO Database Record (c) 2020 APA, all rights reserved).
Categorization allows organisms to generalize existing knowledge to novel stimuli and to discriminate between physically similar yet conceptually different stimuli. Humans, nonhuman primates, and rodents can readily learn arbitrary categories defined by low-level visual features, and learning distorts perceptual sensitivity for category-defining features such that differences between physically similar yet categorically distinct exemplars are enhanced, whereas differences between equally similar but categorically identical stimuli are reduced. We report a possible basis for these distortions in human occipitoparietal cortex. In three experiments, we used an inverted encoding model to recover population-level representations of stimuli from multivoxel and multielectrode patterns of human brain activity while human participants (both sexes) classified continuous stimulus sets into discrete groups. In each experiment, reconstructed representations of to-be-categorized stimuli were systematically biased toward the center of the appropriate category. These biases were largest for exemplars near a category boundary, predicted participants’ overt category judgments, emerged shortly after stimulus onset, and could not be explained by mechanisms of response selection or motor preparation. Collectively, our findings suggest that category learning can influence processing at the earliest stages of cortical visual processing.
In a seminal study, Shepard, Hovland, and Jenkins (1961; henceforth SHJ) assessed potential mechanisms involved in categorization learning. To do so, they sequentially trained human participants with 6 different visual categorization tasks that varied in structural complexity. Humans' exceptionally strong performance on 1 of these tasks (Type 2, organized around exclusive-or relations) could not be solely explained by structural complexity, and has since been considered the hallmark of rule-use in these tasks. In the present project, we concurrently trained pigeons on all 6 SHJ tasks. Our results revealed that the structural complexity of the tasks was highly correlated with group-level performance. Nevertheless, we observed notable individual differences in performance. Two extensions of a prominent categorization model, ALCOVE (Kruschke, 1992), suggested that disparities in the discriminability of the dimensions used to construct the experimental stimuli could account for these differences. Overall, our pigeons' generally weak performance on the Type 2 task provides no evidence of rule-use on the SHJ tasks. Pigeons thus join monkeys in the contingent of species that solve these categorization tasks solely on the basis of the physical properties of the training stimuli. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
A prominent theory of category learning, COVIS, posits that new categories are learned with either a declarative or procedural system, depending on the task. The declarative system uses the prefrontal cortex (PFC) to learn rule-based (RB) category tasks in which there is one relevant sensory dimension that can be used to establish a rule for solving the task, whereas the procedural system uses corticostriatal circuits for information integration (II) tasks in which there are multiple relevant dimensions, precluding use of explicit rules. Previous studies have found faster learning of RB versus II tasks in humans and monkeys but not in pigeons. The absence of a learning rate difference in pigeons has been attributed to their lacking a PFC. A major gap in this comparative analysis, however, is the lack of data from a nonprimate mammalian species, such as rats, that have a PFC but a less differentiated PFC than primates. Here, we investigated RB and II category learning in rats. Similar to pigeons, RB and II tasks were learned at the same rate. After reaching a learning criterion, wider distributions of stimuli were presented to examine generalization. A second experiment found equivalent RB and II learning with wider category distributions. Computational modeling revealed that rats extract and selectively attend to category-relevant information but do not consistently use rules to solve the RB task. These findings suggest rats are on a continuum of PFC function between birds and primates, with selective attention but limited ability to utilize rules relative to primates. © 2019 Broschard et al.; Published by Cold Spring Harbor Laboratory Press.
Behavioral evidence for the COVIS dual‐process model of category learning has been widely reported in over a hundred publications (Ashby & Valentin, 2016). It is generally accepted that the validity of such evidence depends on the accurate identification of individual participants' categorization strategies, a task that usually falls to Decision Bound analysis (Maddox & Ashby, 1993). Here, we examine the accuracy of this analysis in a series of model‐recovery simulations. In Simulation 1, over a third of simulated participants using an Explicit (conjunctive) strategy were misidentified as using a Procedural strategy. In Simulation 2, nearly all simulated participants using a Procedural strategy were misidentified as using an Explicit strategy. In Simulation 3, we re‐examined a recently reported COVIS‐supporting dissociation (Smith et al., 2014) and found that these misidentification errors permit an alternative, single‐process, explanation of the results. Implications for due process in the future evaluation of dual‐process theories, including recommendations for future practice, are discussed.
Two experiments tested whether a peak-shifted generalization gradient could be explained by the averaging of distinct gradients displayed in subgroups reporting different generalization rules. Across experiments using a causal judgment task (Experiment 1) and a fear conditioning paradigm (Experiment 2), we found a close concordance between self-reported rules and generalization gradients using a continuous stimulus dimension (hue). Both experiments also showed an overall peak-shifted gradient after differential conditioning, but not after single cue conditioning. Importantly, the peak shift could be decomposed into linear and peaked gradients when participants were divided into rule subgroups. Our results highlight the need to consider individual differences in the rules that participants derive in human generalization studies, and suggest that in some situations, peak shift may be a consequence of averaging across diverse rule subgroups.
In this paper, a hybrid structure-adaptive RBF-ELM (HSARBF-ELM) network classifier is presented. HSARBF-ELM consists of a structure-adaptive radial basis function (SARBF) network and an extreme learning machine (ELM) network of cascade, where the output of the SARBF network hidden layer is used as the input layer of the ELM network. In the HSARBF-ELM network classifier, the SARBF network is utilized to achieve adaptively localizing kernel mapping of input vectors, After that step, the ELM network is utilized to implement global classification of mapping samples in the kernel space. HSARBF-ELM indicates the combination of localized kernel mapping learning and the global nonlinear classification, which combines the advantages of the SARBF network and the ELM network. The quantitative conditions for the separability enhancement and the corresponding theoretical explanation for the HSARBF-ELM network are given, which demonstrate that when input vectors go through the SARBF network, adaptively adjusting the RBF kernel parameters can boost the separability of the original sample space. Thus, the classification performance of the HSARBF-ELM network can be guaranteed theoretically. An appropriate learning algorithm for the HSARBF-ELM network is subsequently presented, which effectively combines the methods of density clustering with a potential function, center-oriented unidirectional repulsive force and the existing ELM algorithm, and the optimized complementary HSARBF-ELM network can be constructed. The experimental results show that the classification performance of the HSARBF-ELM network clearly outperforms the ELM network, and outperforms other classifiers on most classification problems.
Exemplar theory assumes that people categorize a novel object by comparing its similarity to the memory representations of all previous exemplars from each relevant category. Exemplar theory has been the most prominent cognitive theory of categorization for more than 30 years. Despite its considerable success in providing good quantitative fits to a wide variety of accuracy data, it has never had a detailed neurobiological interpretation. This article proposes a neural interpretation of exemplar theory in which category learning is mediated by synaptic plasticity at cortical-striatal synapses. In this model, categorization training does not create new memory representations, rather it alters connectivity between striatal neurons and neurons in sensory association cortex. The new model makes identical quantitative predictions as exemplar theory, yet it can account for many empirical phenomena that are either incompatible with or outside the scope of the cognitive version of exemplar theory.
The prototype distortion task demonstrates that it is possible to learn about a category of physically similar stimuli through mere observation. However, there have been few attempts to test whether different encoding conditions affect learning in this task. This study compared prototypicality gradients produced under incidental learning conditions in which participants performed a visual search task, with those produced under intentional learning conditions in which participants were required to memorize the stimuli. Experiment 1 showed that similar prototypicality gradients could be obtained for category endorsement and familiarity ratings, but also found (weaker) prototypicality gradients in the absence of exposure. In Experiments 2 and 3, memorization was found to strengthen prototypicality gradients in familiarity ratings in comparison to visual search, but there were no group differences in participants' ability to discriminate between novel and presented exemplars. Although the Search groups in Experiments 2 and 3 produced prototypicality gradients, they were no different in magnitude to those produced in the absence of stimulus exposure in Experiment 1, suggesting that incidental learning during visual search was not conducive to producing prototypicality gradients. This study suggests that learning in the prototype distortion task is not implicit in the sense of resulting automatically from exposure, is affected by the nature of encoding, and should be considered in light of potential learning-at-test effects.
This paper presents a structure-adaptive hybrid RBF-BP (SAHRBF-BP) classifier with an optimized learning strategy. SAHRBF-BP is composed of a structure-adaptive RBF network and a BP network of cascade, where the number of RBF hidden nodes is adjusted adaptively according to the distribution of sample space, the adaptive RBF network is used for nonlinear kernel mapping and the BP network is used for nonlinear classification. The optimized learning strategy is as follows: firstly, a potential function is introduced into training sample space to adaptively determine the number of initial RBF hidden nodes and node parameters, and a form of heterogeneous samples repulsive force is designed to further optimize each generated RBF hidden node parameters, the optimized structure-adaptive RBF network is used for adaptively nonlinear mapping the sample space; then, according to the number of adaptively generated RBF hidden nodes, the number of subsequent BP input nodes can be determined, and the overall SAHRBF-BP classifier is built up; finally, different training sample sets are used to train the BP network parameters in SAHRBF-BP. Compared with other algorithms applied to different data sets, experiments show the superiority of SAHRBF-BP. Especially on most low dimensional and large number of data sets, the classification performance of SAHRBF-BP outperforms other training SLFNs algorithms.
Exemplar, prototype, and rule theory have organized much of the enormous literature on categorization. From this theoretical foundation have arisen the two primary debates in the literature—the prototype-exemplar debate and the single system-multiple systems debate. We review these theories and debates. Then, we examine the contribution that animal-cognition studies have made to them. Animals have been crucial behavioral ambassadors to the literature on categorization. They reveal the roots of human categorization, the basic assumptions of vertebrates entering category tasks, the surprising weakness of exemplar memory as a category-learning strategy. They show that a unitary exemplar theory of categorization is insufficient to explain human and animal categorization. They show that a multiple-systems theoretical account—encompassing exemplars, prototypes, and rules—will be required for a complete explanation. They show the value of a fitness perspective in understanding categorization, and the value of giving categorization an evolutionary depth and phylogenetic breadth. They raise important questions about the internal similarity structure of natural kinds and categories. They demonstrate strong continuities with humans in categorization, but discontinuities, too. Categorization’s great debates are resolving themselves, and to these resolutions animals have made crucial contributions.
Human performance in task-switching paradigms is seen as a hallmark of executive-control processes: switching between tasks induces switch costs (such that performance when changing from Task A to Task B is worse than on trials where the task repeats), which is generally attributed to executive control suppressing one task-set and activating the other. However, even in cases where task-sets are not employed, as well as in computational modeling of task switching, switch costs can still be found. This observation has led to the hypothesis that associative-learning processes might be responsible for all or part of the switch costs in task-switching paradigms. To test which cognitive processes contribute to the presence of task-switch costs, pigeons performed two different tasks on the same set of stimuli in rapid alternation. The pigeons showed no sign of switch costs, even though performance on Trial N was influenced by Trial N - 1, showing that they were sensitive to sequential effects. Using Pearce's (1987) model for stimulus generalization, we conclude that they learned the task associatively-in particular, a form of Pavlovian-conditioned approach was involved-and that this was responsible for the lack of any detectable switch costs. Pearce's model also allows us to make interferences about the common occurrence of switch costs in the absence of task-sets in human participants and in computational models, in that they are likely due to instrumental learning and the establishment of an equivalence between cues signaling the same task. (PsycINFO Database Record
Pathologists and radiologists spend years acquiring and refining their medically essential visual skills, so it is of considerable interest to understand how this process actually unfolds and what image features and properties are critical for accurate diagnostic performance. Key insights into human behavioral tasks can often be obtained by using appropriate animal models. We report here that pigeons (Columba livia)-which share many visual system properties with humans-can serve as promising surrogate observers of medical images, a capability not previously documented. The birds proved to have a remarkable ability to distinguish benign from malignant human breast histopathology after training with differential food reinforcement; even more importantly, the pigeons were able to generalize what they had learned when confronted with novel image sets. The birds' histological accuracy, like that of humans, was modestly affected by the presence or absence of color as well as by degrees of image compression, but these impacts could be ameliorated with further training. Turning to radiology, the birds proved to be similarly capable of detecting cancer-relevant microcalcifications on mammogram images. However, when given a different (and for humans quite difficult) task-namely, classification of suspicious mammographic densities (masses)-the pigeons proved to be capable only of image memorization and were unable to successfully generalize when shown novel examples. The birds' successes and difficulties suggest that pigeons are well-suited to help us better understand human medical image perception, and may also prove useful in performance assessment and development of medical imaging hardware, image processing, and image analysis tools.
Humans can spontaneously create rules that allow them to efficiently generalize what they have learned to novel situations. An enduring question is whether rule-based generalization is uniquely human or whether other animals can also abstract rules and apply them to novel situations. In recent years, there have been a number of high-profile claims that animals such as rats can learn rules. Most of those claims are quite weak because it is possible to demonstrate that simple associative systems (which do not learn rules) can account for the behavior in those tasks. Using a procedure that allows us to clearly distinguish feature-based from rule-based generalization (the Shanks–Darby procedure), we demonstrate that adult humans show rule-based generalization in this task, while generalization in rats and pigeons was based on featural overlap between stimuli. In brief, when learning that a stimulus made of two components (“AB”) predicts a different outcome than its elements (“A” and “B”), people spontaneously abstract an opposites rule and apply it to new stimuli (e.g., knowing that “C” and “D” predict one outcome, they will predict that “CD” predicts the opposite outcome). Rats and pigeons show the reverse behavior—they generalize what they have learned, but on the basis of similarity (e.g., “CD” is similar to “C” and “D”, so the same outcome is predicted for the compound stimulus as for the components). Genuinely rule-based behavior is observed in humans, but not in rats and pigeons, in the current procedure.
Electronic supplementary material
The online version of this article (doi:10.1007/s10071-015-0895-8) contains supplementary material, which is available to authorized users.
Nonhuman animals show evidence for three types of concept learning: perceptual or similarity-based in which objects/stimuli are categorized based on physical similarity; relational in which one object/stimulus is categorized relative to another (e.g., same/different); and associative in which arbitrary stimuli become interchangeable with one another by virtue of a common association with another stimulus, outcome, or response. In this article, we focus on various methods for establishing associative concepts in nonhuman animals and evaluate data documenting the development of associative classes of stimuli. We also examine the nature of the common within-class representation of samples that have been associated with the same reinforced comparison response (i.e., many-to-one matching) by describing manipulations for distinguishing possible representations. Associative concepts provide one foundation for human language such that spoken and written words and the objects they represent become members of a class of interchangeable stimuli. The mechanisms of associative concept learning and the behavioral flexibility it allows, however, are also evident in the adaptive behaviors of animals lacking language.
Humans can perform several different tasks on the same set of stimuli in rapid alternation. Each task, signaled by a distinct task cue, may require the classification of stimuli using a different stimulus attribute. However, such "task switching" performance comes at a cost, as expressed by weaker performance when switching rather than repeating tasks. This cost is often claimed to be the consequence of a mental reorientation away from the previous task and towards the new task, requiring executive control of behavior. Alternatively, task switching could simply be based on the retrieval of different cue-stimulus-response associations. In this experiment, pigeons learned go-left/go-right discriminations between grating patterns according to either their spatial frequency or their orientation, depending on the color of the pattern (the task cue). When humans solved the same tasks on the basis of verbalizable rules, they responded more slowly and made more errors on trials where they had to switch between tasks than when repeating the same task. Pigeons did not show this "switch cost"; but like humans, their performance was significantly worse when the response (left or right) to a given stimulus varied between tasks than when it stayed the same (the "congruency effect"). Larger effects of both switch costs and congruency were observed in humans learning the tasks by trial and error. We discuss the potential driving factors behind these very different patterns of performance for both humans and pigeons.
Categorizations which humans make of the concrete world are not arbitrary but highly determined. In taxonomies of concrete objects, there is one level of abstraction at which the most basic category cuts are made. Basic categories are those which carry the most information, possess the highest category cue validity, and are, thus, the most differentiated from one another. The four experiments of Part I define basic objects by demonstrating that in taxonomies of common concrete nouns in English based on class inclusion, basic objects are the most inclusive categories whose members: (a) possess significant numbers of attributes in common, (b) have motor programs which are similar to one another, (c) have similar shapes, and (d) can be identified from averaged shapes of members of the class. The eight experiments of Part II explore implications of the structure of categories. Basic objects are shown to be the most inclusive categories for which a concrete image of the category as a whole can be formed, to be the first categorizations made during perception of the environment, to be the earliest categories sorted and earliest named by children, and to be the categories most codable, most coded, and most necessary in language.
A new theory of similarity, rooted in the detection and recognition literatures, is developed. The general recognition theory assumes that the perceptual effect of a stimulus is random but that on any single trial it can be represented as a point in a multidimensional space. Similarity is a function of the overlap of perceptual distributions. It is shown that the general recognition theory contains Euclidean distance models of similarity as a special case but that unlike them, it is not constrained by any distance axioms. Three experiments are reported that test the empirical validity of the theory. In these experiments the general recognition theory accounts for similarity data as well as the currently popular similarity theories do, and it accounts for identification data as well as the longstanding "champion" identification model does. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Derives a model which predicts data related to generalization and discrimination among stimuli, using data resulting from a reference experiment with 6 naive male White Carneaux pigeons. A 2nd experiment with 3 experienced Ss provided data corresponding to the prediction of step-function results from the model. This steady-state procedure yielded positive, negative, and combination gradients of stimulus control on a wavelength continuum. Results are predicted by computer simulations based on a linear difference equation. The model applies to a set of stimuli that activate common elements; a gaussian weighting function controls the degree of activation of an element by any given stimulus. The model is conceptually similar to, and compatible with, a model of conditioning previously stated by R. A. Rescorla and A. R. Wagner (1972). (24 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Reviews the peak-shift literature in relation to operant and classical conditioning procedures, errorless discrimination training, and physiological studies. Results indicate that peak shift is a reliable behavioral phenomenon affected by stimulus dimension, positive-negative stimulus separation, training procedure, and testing procedure. There are many concepts (e.g., inhibition and excitation), behavioral phenomena (e.g., behavioral contrast and negative peak shift), and experimental procedures (e.g., reinforcement density-frequency, successive training, and simultaneous training) interwoven throughout the peak-shift literature. Many questions have been raised regarding these aspects of peak shift yet few questions have been answered to date. (96 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Formal models in psychology are used to make theoretical ideas precise and allow them to be evaluated quantitatively against data. We focus on one important-but under-used and incorrectly maligned-method for building theoretical assumptions into formal models, offered by the Bayesian statistical approach. This method involves capturing theoretical assumptions about the psychological variables in models by placing informative prior distributions on the parameters representing those variables. We demonstrate this approach of casting basic theoretical assumptions in an informative prior by considering a case study that involves the generalized context model (GCM) of category learning. We capture existing theorizing about the optimal allocation of attention in an informative prior distribution to yield a model that is higher in psychological content and lower in complexity than the standard implementation. We also highlight that formalizing psychological theory within an informative prior distribution allows standard Bayesian model selection methods to be applied without concerns about the sensitivity of results to the prior. We then use Bayesian model selection to test the theoretical assumptions about optimal allocation formalized in the prior. We argue that the general approach of using psychological theory to guide the specification of informative prior distributions is widely applicable and should be routinely used in psychological modeling.
A rational model of human categorization behavior is presented that assumes that categorization reflects the derivation of optimal estimates of the probability of unseen features of objects. A Bayesian analysis is performed of what optimal estimations would be if categories formed a disjoint partitioning of the object space and if features were independently displayed within a category. This Bayesian analysis is placed within an incremental categorization algorithm. The resulting rational model accounts for effects of central tendency of categories, effects of specific instances, learning of linearly nonseparable categories, effects of category labels, extraction of basic level categories, base-rate effects, probability matching in categorization, and trial-by-trial learning functions. Although the rational model considers just 1 level of categorization, it is shown how predictions can be enhanced by considering higher and lower levels. Considering prediction at the lower, individual level allows integration of this rational analysis of categorization with the earlier rational analysis of memory (Anderson & Milson, 1989).
Recent theoretical and empirical developments in human category learning have differentiated an analytic, rule-based system of category learning from a nonanalytic system that integrates information across stimulus dimensions. In the present study, the researchers applied this theoretical distinction to pigeons' category learning. Pigeons learned to categorize stimuli varying in the tilt and width of their internal striping. The matched category problems had either a unidimensional (rule-based) or multidimensional (information-integration) solution. Whereas humans and nonhuman primates strongly dimensionalize these stimuli and learn rule-based tasks far more quickly than information-integration tasks, pigeons learned the two tasks equally quickly to the same accuracy level. Pigeons may represent a cognitive system in which the commitment to dimensional analysis and category rules was not strongly made. Their performance could suggest the character of the ancestral vertebrate categorization system from which that of primates emerged.
A wealth of empirical evidence has now accumulated concerning animals' categorizing photographs of real-world objects. Although these complex stimuli have the advantage of fostering rapid category learning, they are difficult to manipulate experimentally and to represent in formal models of behavior. We present a solution to the representation problem in modeling natural categorization by adopting a common-elements approach. A common-elements stimulus representation, in conjunction with an error-driven learning rule, can explain a wide range of experimental outcomes in animals' categorization of naturalistic images. The model also generates novel predictions that can be empirically tested. We report 2 experiments that show how entirely hypothetical representational elements can nevertheless be subject to experimental manipulation. The results represent the first evidence of error-driven learning in natural image categorization, and they support the idea that basic associative processes underlie this important form of animal cognition.
The authors explored whether pigeons can learn to discriminate simultaneously presented arrays of 16 identical (Same) visual items from arrays of 16 nonidentical (Different) visual items, when the correct choice was conditional on the presence of another cue: the color of the background. In one experiment, pigeons rapidly learned this task and, after training with arrays created from a 72-icon set, they exhibited nearly perfect transfer to novel testing arrays. In a second experiment, pigeons' accuracy to 24-, 20-, 12-, and 8-icon arrays during later testing remained as high as accuracy to training arrays; although accuracy declined with 4- and 2-icon arrays, it was still significantly above chance. In both experiments, pigeons' choice reaction time scores nicely complemented their choice accuracy scores. These results suggest that the conditional discrimination procedure is well suited to disclose same-different discrimination in pigeons and to elucidate the interaction between perception and abstraction in conceptual learning.
Pigeons and undergraduates learned conditional discriminations involving multiple spatially separated stimulus dimensions. Under some conditions, the dimensions were made available sequentially. In 3 experiments, the dimensions were all perfectly valid predictors of the response that would be reinforced and mutually redundant; in 2 others, they varied in validity. In tests with stimuli in which 1 of the 3 dimensions took an anomalous value, most but not all individuals of both species categorized them in terms of single dimensions. When information was delivered as a function of the passage of time, some students, but no pigeons, waited for the most useful information, especially when the cues differed in objective validity. When the subjects could control information delivery, both species obtained information selectively. When cue validities varied, almost all students tended to choose the most valid cues, and when all cues were valid, some chose the cues by which they classified test stimuli. Only a few pigeons chose the most useful information in either situation. Despite their tendency to unidimensional categorization, the pigeons showed no evidence of rule-governed behavior, but students followed a simple "take-the-best" rule.
A unified quantitative approach to modeling subjects' identification and categorization of multidimensional perceptual stimuli is proposed and tested. Two subjects identified and categorized the same set of perceptually confusable stimuli varying on separable dimensions. The identification data were modeled using Shepard's (1957) multidimensional scaling-choice framework. This framework was then extended to model the subjects' categorization performance. The categorization model, which generalizes the context theory of classification developed by Medin and Schaffer (1978), assumes that subjects store category exemplars in memory. Classification decisions are based on the similarity of stimuli to the stored exemplars. It is assumed that the same multidimensional perceptual representation underlies performance in both the identification and categorization paradigms. However, because of the influence of selective attention, similarity relationships change systematically across the two paradigms. Some support was gained for the hypothesis that subjects distribute attention among component dimensions so as to optimize categorization performance. Evidence was also obtained that subjects may have augmented their category representations with inferred exemplars. Implications of the results for theories of multidimensional scaling and categorization are discussed.
PREVIOUS WORK INDICATES THAT SS CAN LEARN TO CLASSIFY SETS OF PATTERNS WHICH ARE DISTORTIONS OF A PROTOTYPE THEY HAVE NOT SEEN. IT IS SHOWN THAT AFTER LEARNING A SET OF PATTERNS, THE PROTOTYPE (SCHEMA) OF THAT SET IS MORE EASILY CLASSIFIED THAN CONTROL PATTERNS ALSO WITHIN THE LEARNED CATEGORY. AS THE VARIABILITY AMONG THE MEMORIZED PATTERNS INCREASES, SO DOES THE ABILITY OF SS TO CLASSIFY HIGHLY DISTORTED NEW INSTANCES. THESE FINDINGS ARGUE THAT INFORMATION ABOUT THE SCHEMA IS ABSTRACTED FROM THE STORED INSTANCES WITH VERY HIGH EFFICIENCY. IT IS UNCLEAR WHETHER THE ABSTRACTION OF INFORMATION INVOLVED IN CLASSIFYING THE SCHEMA OCCURS WHILE LEARNING THE ORIGINAL PATTERNS OR WHETHER THE ABSTRACTION PROCESS OCCURS AT THE TIME OF THE 1ST PRESENTATION OF THE SCHEMA.
The pigeon's discrimination of visual displays comprising from 2 to 16 computer icons that were either the same as or different from one another was studied. Discrimination of Same from Different displays improved when the displays contained more icons, both after training with just 16-icon displays (Experiment 1) and after training with 2-, 4-, 8-, 12-, and 16-icon displays (Experiment 2). That improvement was specific to displays of different icons; accuracy to displays of same icons did not differ as a function of icon number. These results were well described by the degree of variability or entropy in multielement visual displays.
A neuropsychological theory is proposed that assumes category learning is a competition between separate verbal and implicit (i.e., procedural-learning-based) categorization systems. The theory assumes that the caudate nucleus is an important component of the implicit system and that the anterior cingulate and prefrontal cortices are critical to the verbal system. In addition to making predictions for normal human adults, the theory makes specific predictions for children, elderly people, and patients suffering from Parkinson's disease, Huntington's disease, major depression, amnesia, or lesions of the prefrontal cortex. Two separate formal descriptions of the theory are also provided. One describes trial-by-trial learning, and the other describes global dynamics. The theory is tested on published neuropsychological data and on category learning data with normal adults.
Categorization is an essential cognitive process useful for transferring knowledge from previous experience to novel situations. The mechanisms by which trained categorization behavior extends to novel stimuli, especially in animals, are insufficiently understood. To understand how pigeons learn and transfer category membership, seven pigeons were trained to classify controlled, bi-dimensional stimuli in a two-alternative forced-choice task. Following either dimensional, rule-based (RB) or information integration (II) training, tests were conducted focusing on the "analogical" extension of the learned discrimination to novel regions of the stimulus space (Casale, Roeder, & Ashby, 2012). The pigeons' results mirrored those from human and non-human primates evaluated using the same analogical task structure, training and testing: the pigeons transferred their discriminative behavior to the new extended values following RB training, but not after II training. Further experiments evaluating rule-based models and association-based models suggested the pigeons use dimensions and associations to learn the task and mediate transfer to stimuli within the novel region of the parametric stimulus space.
Concepts embody our knowledge of the kinds of things there are in the world. Tying our past experiences to our present interactions with the environment, they enable us to recognize and understand new objects and events. Concepts are also relevant to understanding domains such as social situations, personality types, and even artistic styles. Yet like other phenomenologically simple cognitive processes such as walking or understanding speech, concept formation and use are maddeningly complex.
Research since the 1970s and the decline of the "classical view" of concepts have greatly illuminated the psychology of concepts. But persistent theoretical disputes have sometimes obscured this progress. The Big Book of Concepts goes beyond those disputes to reveal the advances that have been made, focusing on the major empirical discoveries. By reviewing and evaluating research on diverse topics such as category learning, word meaning, conceptual development in infants and children, and the basic level of categorization, the book develops a much broader range of criteria than is usual for evaluating theories of concepts.
Bradford Books imprint
Comparative and cognitive psychologists interpret performance in different ways. Animal researchers invoke a dominant construct of associative learning. Human researchers acknowledge humans’ capacity for explicit-declarative cognition. This article offers a way to bridge a divide that defeats productive cross-talk. We show that animals often challenge the associative-learning construct, and that it does not work to try to stretch the associative-learning construct to encompass these performances. This approach thins and impoverishes that important construct. We describe an alternative approach that restrains the construct of associative learning by giving it a clear operational definition. We apply this approach in several comparative domains to show that different task variants change—in concert—the level of awareness, the declarative nature of knowledge, the dimensional breadth of knowledge, and the brain systems that organize learning. These changes reveal dissociable learning processes that a unitary associative construct cannot explain but a neural-systems framework can explain. These changes define the limit of associative learning and the threshold of explicit cognition. The neural-systems framework can broaden empirical horizons in comparative psychology. It can offer animal models of explicit cognition to cognitive researchers and neuroscientists. It can offer simple behavioral paradigms for exploring explicit cognition to developmental researchers. It can enliven the synergy between human and animal research, promising a productive future for both.
Identifying the strategy that participants use in laboratory experiments is crucial in interpreting the results of behavioral experiments. This article introduces a new modeling procedure called iterative decision-bound modeling (iDBM), which iteratively fits decision-bound models to the trial-by-trial responses generated from single participants in perceptual categorization experiments. The goals of iDBM are to identify: (1) all response strategies used by a participant, (2) changes in response strategy, and (3) the trial number at which each change occurs. The new method is validated by testing its ability to identify the response strategies used in noisy simulated data. The benchmark simulation results show that iDBM is able to detect and identify strategy switches during an experiment and accurately estimate the trial number at which the strategy change occurs in low to moderate noise conditions. The new method is then used to reanalyze data from Ell and Ashby (2006). Applying iDBM revealed that increasing category overlap in an information-integration category learning task increased the proportion of participants who abandoned explicit rules, and reduced the number of training trials needed to abandon rules in favor of a procedural strategy. Finally, we discuss new research questions made possible through iDBM.
General Recognition Theory (GRT; e.g., Ashby and Townsend, 1986, inter alia) is a two-stage, multidimensional model of encoding and response selection. In this tutorial, we present the basic conceptual and mathematical structure of GRT and review the three notions of dimensional interaction defined in the GRT framework: perceptual independence, perceptual separability, and decisional separability. Experimental protocols and data closely linked to the GRT model are discussed, and two sets of empirical tests of dimensional interaction are presented. These test procedures are illustrated via functions the new R package mdsdt.
Categorization is our ability to flexibly assign sensory stimuli into discrete, behaviorally relevant groupings. Categorical decisions can be used to study decision making more generally by dissociating category identity of stimuli from the actions subjects use to signal their decisions. Here we discuss the evidence for such abstract categorical encoding in the primate brain and consider the relationship with other perceptual decision paradigms. Recent work on visual categorization has examined neuronal activity across a hierarchically organized network of cortical areas in monkeys trained to group visual stimuli into arbitrary categories. This has revealed a transformation of visual-feature encoding in early visual cortical areas into more flexible categorical representations in downstream parietal and prefrontal areas. These neuronal category representations are encoded as abstract internal cognitive states because they are not rigidly linked with either specific sensory stimuli or the actions that the monkeys use to signal their categorical choices. Expected final online publication date for the Annual Review of Neuroscience Volume 39 is July 08, 2016. Please see http://www.annualreviews.org/catalog/pubdates.aspx for revised estimates.
J. D. Smith and colleagues (J. P. Minda & J. D. Smith, 2001; J. D. Smith & J. P. Minda, 1998, 2000; J. D. Smith, M. J. Murray, & J. P. Minda, 1997) presented evidence that they claimed challenged the predictions of exemplar models and that supported prototype models. In the authors' view, this evidence confounded the issue of the nature of the category representation with the type of response rule (probabilistic vs. deterministic) that was used. Also, their designs did not test whether the prototype models correctly predicted generalization performance. The present work demonstrates that an exemplar model that includes a response-scaling mechanism provides a natural account of all of Smith et al.'s experimental results. Furthermore, the exemplar model predicts classification performance better than the prototype models when novel transfer stimuli are included in the experimental designs.
The Bayesian information criterion (BIC) is one of the most widely known and pervasively used tools in statistical model selection. Its popularity is derived from its computational simplicity and effective performance in many modeling frameworks, including Bayesian applications where prior distributions may be elusive. The criterion was derived by Schwarz ( Ann Stat 1978, 6:461–464) to serve as an asymptotic approximation to a transformation of the Bayesian posterior probability of a candidate model. This article reviews the conceptual and theoretical foundations for BIC, and also discusses its properties and applications. WIREs Comput Stat 2012, 4:199–203. doi: 10.1002/wics.199
This article is categorized under: Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory
Statistical and Graphical Methods of Data Analysis > Information Theoretic Methods
Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods
American psychologists anticipated the Gestalt movement in recognizing the necessity of interpreting the differential response of animals to stimuli differing in degree in terms of the relational character of the stimulus situation. But experiments show that the response to relationship is not universal. A previous article by the author set forth a theoretical schema, based on stimulus-response principles, explaining discrimination learning as a cumulative process of strengthening the excitatory tendency of the positive stimulus cue by reinforcements of responses to it while the negative stimulus receives no such reinforcement. Eventually the difference between the excitatory strengths of the positive and negative cue stimuli reaches a minimum necessary to insure a consistent response to the positive stimulus. In the present article it is pointed out that where continuous dimensions as size and brightness are concerned, some transfer of training could be expected, at least between nearby members of a series. Experimental results on animals and humans are shown to be consistent with a theoretical curve of irradiation in which extent of generalization varies with size of stimulus. Gestalt theory fails to explain them. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
We evaluate the ability of artificial neural network models (multilayer perceptrons) to predict stimulus–response relationships. A variety of empirical results are considered, such as generalization, peak shift (supernormality) and stimulus intensity effects. The networks were trained on the same tasks as the animals in the experiments considered. The subsequent generalization tests on the networks showed that the model replicates correctly the empirical results. We conclude that these models are valuable tools in the study of animal behaviour.
This review describes a case of convergence in the evolution of brain and cognition. Both mammals and birds can organize their behavior flexibly over time and evolved similar cognitive skills. The avian forebrain displays no lamination that corresponds to the mammalian neocortex; hence, lamination does not seem to be a requirement for higher cognitive functions. In mammals, executive functions are associated with the prefrontal cortex. The corresponding structure in birds is the nidopallium caudolaterale. Anatomic, neurochemical, electrophysiologic and behavioral studies show these structures to be highly similar, but not homologous. Thus, despite the presence (mammals) or the absence (birds) of a laminated forebrain, 'prefrontal' areas in mammals and birds converged over evolutionary time into a highly similar neural architecture. The neuroarchitectonic degrees of freedom to create different neural architectures that generate identical prefrontal functions seem to be very limited.
The scientific study of associative learning began nearly 100 years ago with the pioneering studies of Thorndike and Pavlov, and it continues today as an active area of research and theory. Associative learning should be the foundation for our understanding of other forms of behavior and cognition in human and nonhuman animals. The laws of associative learning are complex, and many modern theorists posit the involvement of attention, memory, and information processing in such basic conditioning phenomena as overshadowing and blocking, and the effects of stimulus preexposure on later conditioning. An unresolved problem for learning theory is distinguishing the formation of associations from their behavioral expression. This and other problems will occupy future generations of behavioral scientists interested in the experimental investigation of associative learning. Neuroscientists and cognitive scientists will both contribute to and benefit from that effort in the next 100 years of inquiry.