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.
Categorization is a fundamental cognitive function that organizes our experiences into meaningful “chunks.” This category knowledge can then be generalized to novel stimuli and situations. Multiple clinical populations, including people with Parkinson's disease, amnesia, autism, ADHD and schizophrenia, have impairments in the acquisition and use of categories. Although rodent research is well suited for examining the neural mechanisms underlying cognitive functions, many rodent cognitive tasks have limited translational value. To bridge this gap, we use touchscreens to permit greater flexibility in stimulus presentation and task design, track key dependent measures, and minimize experimenter involvement. Touchscreens offer a valuable tool for creating rodent cognitive tasks that are directly comparable to tasks used with humans. Touchscreen tasks are also readily used with cutting‐edge neuroscientific methods that are difficult to do in humans such as optogenetics, chemogenetics, neurophysiology and calcium imaging (using miniscopes). In this review, we show advantages of touchscreen‐based tasks for studying category learning in rats. We also address multiple factors for consideration when designing category learning tasks, including the limitations of the rodent visual system, experimental design, and analysis strategies.
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.
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