Task switching in rhesus macaques (Macaca mulatta) and tufted capuchin monkeys (Cebus apella) during computerized categorization tasks.
ABSTRACT: The present experiments extended to monkeys a previously used abstract categorization procedure (Castro & Wasserman, 2016) where pigeons had categorized arrays of clipart icons based upon two task rules: the number of clipart objects in the array or the variability of objects in the array. Experiment 1 replicated Castro and Wasserman by using capuchin monkeys and rhesus monkeys and reported that monkeys' performances were similar to pigeons' in terms of acquisition, pattern of errors, and the absence of switch costs. Furthermore, monkeys' insensitivity to the added irrelevant information suggested that an associative (rather than rule-based) categorization mechanism was dominant. Experiment 2 was conducted to include categorization cue reversals to determine (a) whether the monkeys would quickly adapt to the reversals and inhibit interference from a prereversal task rule (consistent with a rule-based mechanism) and (b) whether the latency to make a response prior to a correct or incorrect outcome was informative about the presence of a cognitive mechanism. The cue reassignment produced profound and long-lasting performance deficits, and a long reacquisition phase suggested the involvement of associative learning processes; however, monkeys also displayed longer latencies to choose prior to correct responses on challenging trials, suggesting the involvement of nonassociative processes. Together these performances suggest a mix of associative and cognitive-control processes governing monkey categorization judgments. (PsycINFO Database Record
Project description: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).
Project description:Might there be parallels between category learning in animals and word learning in children? To examine this possibility, we devised a new associative learning technique for teaching pigeons to sort 128 photographs of objects into 16 human language categories. We found that pigeons learned all 16 categories in parallel, they perceived the perceptual coherence of the different object categories, and they generalized their categorization behavior to novel photographs from the training categories. More detailed analyses of the factors that predict trial-by-trial learning implicated a number of factors that may shape learning. First, we found considerable trial-by-trial dependency of pigeons' categorization responses, consistent with several recent studies that invoke this dependency to claim that humans acquire words via symbolic or inferential mechanisms; this finding suggests that such dependencies may also arise in associative systems. Second, our trial-by-trial analyses divulged seemingly irrelevant aspects of the categorization task, like the spatial location of the report responses, which influenced learning. Third, those trial-by-trial analyses also supported the possibility that learning may be determined both by strengthening correct stimulus-response associations and by weakening incorrect stimulus-response associations. The parallel between all these findings and important aspects of human word learning suggests that associative learning mechanisms may play a much stronger part in complex human behavior than is commonly believed.
Project description: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.
Project description: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.
Project description:BACKGROUND: Comparative studies of cognitive processes find similarities between humans and apes but also monkeys. Even high-level processes, like the ability to categorize classes of object from any natural scene under ultra-rapid time constraints, seem to be present in rhesus macaque monkeys (despite a smaller brain and the lack of language and a cultural background). An interesting and still open question concerns the degree to which the same images are treated with the same efficacy by humans and monkeys when a low level cue, the spatial frequency content, is controlled. METHODOLOGY/PRINCIPAL FINDINGS: We used a set of natural images equalized in Fourier spectrum and asked whether it is still possible to categorize them as containing an animal and at what speed. One rhesus macaque monkey performed a forced-choice saccadic task with a good accuracy (67.5% and 76% for new and familiar images respectively) although performance was lower than with non-equalized images. Importantly, the minimum reaction time was still very fast (100 ms). We compared the performances of human subjects with the same setup and the same set of (new) images. Overall mean performance of humans was also lower than with original images (64% correct) but the minimum reaction time was still short (140 ms). CONCLUSION: Performances on individual images (% correct but not reaction times) for both humans and the monkey were significantly correlated suggesting that both species use similar features to perform the task. A similar advantage for full-face images was seen for both species. The results also suggest that local low spatial frequency information could be important, a finding that fits the theory that fast categorization relies on a rapid feedforward magnocellular signal.
Project description:The assumption in some current theories of probabilistic categorization is that people gradually attenuate their learning in response to unavoidable error. However, existing evidence for this error discounting is sparse and open to alternative interpretations. We report 2 probabilistic-categorization experiments in which we investigated error discounting by shifting feedback probabilities to new values after different amounts of training. In both experiments, responding gradually became less responsive to errors, and learning was slowed for some time after the feedback shift. Both results were indicative of error discounting. Quantitative modeling of the data revealed that adding a mechanism for error discounting significantly improved the fits of an exemplar-based and a rule-based associative learning model, as well as of a recency-based model of categorization. We conclude that error discounting is an important component of probabilistic learning.
Project description: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.
Project description:The ability to learn abstract relational concepts is fundamental to higher level cognition. In contrast to item-specific concepts (e.g. pictures containing trees versus pictures containing cars), abstract relational concepts are not bound to particular stimulus features, but instead involve the relationship between stimuli and therefore may be extrapolated to novel stimuli. Previous research investigating the same/different abstract concept has suggested that primates might be specially adapted to extract relations among items and would require fewer exemplars of a rule to learn an abstract concept than non-primate species. We assessed abstract-concept learning in an avian species, Clark's nutcracker (Nucifraga columbiana), using a small number of exemplars (eight pairs of the same rule, and 56 pairs of the different rule) identical to that previously used to compare rhesus monkeys, capuchin monkeys and pigeons. Nutcrackers as a group (N = 9) showed more novel stimulus transfer than any previous species tested with this small number of exemplars. Two nutcrackers showed full concept learning and four more showed transfer considerably above chance performance, indicating partial concept learning. These results show that the Clark's nutcracker, a corvid species well known for its amazing feats of spatial memory, learns the same/different abstract concept better than any non-human species (including non-human primates) yet tested on this same task.
Project description:A model proposing error-driven learning of associations between representations of stimulus properties and responses can account for many findings in the literature on object categorization by nonhuman animals. Furthermore, the model generates predictions that have been confirmed in both pigeons and people, suggesting that these learning processes are widespread across distantly related species. The present work reports evidence of a category-overshadowing effect in pigeons' categorization of natural objects, a novel behavioral phenomenon predicted by the model. Object categorization learning was impaired when a second category of objects provided redundant information about correct responses. The same impairment was not observed when single objects provided redundant information, but the category to which they belonged was uninformative, suggesting that this effect is different from simple overshadowing, arising from competition among stimulus categories rather than individual stimuli during learning.
Project description:Prefrontal cortex influences behavior largely through its connections with other association cortices; however, the nature of the information conveyed by prefrontal output signals and what effect these signals have on computations performed by target structures is largely unknown. To address these questions, we simultaneously recorded the activity of neurons in prefrontal and posterior parietal cortices of monkeys performing a rule-based spatial categorization task. Parietal cortex receives direct prefrontal input, and parietal neurons, like their prefrontal counterparts, exhibit signals that reflect rule-based cognitive processing in this task. By analyzing rapid fluctuations in the cognitive information encoded by activity in the two areas, we obtained evidence that signals reflecting rule-dependent categories were selectively transmitted in a top-down direction from prefrontal to parietal neurons, suggesting that prefrontal output is important for the executive control of distributed cognitive processing.