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This work explores the influence of motivation on choice behavior in a dynamic decision-making environment, where the payoffs from each choice depend on one's recent choice history. Previous research reveals that participants in a regulatory fit exhibit increased levels of exploratory choice and flexible use of multiple strategies over the course of an experiment. The present study placed promotion and prevention-focused participants in a dynamic environment for which optimal performance is facilitated by systematic exploration of the decision space. These participants either gained or lost points with each choice. Our experiment revealed that participants in a regulatory fit were more likely to engage in systematic exploration of the task environment than were participants in a regulatory mismatch and performed more optimally as a result. Implications for contemporary models of human reinforcement learning are discussed. Keywords: decision making, reinforcement learning, exploration, motivation, regulatory fit DOI: 10.1037/a0018999

We evaluate two broad classes of cognitive mechanisms that might support the learning of sequential patterns. According to the first, learning is based on the gradual accumulation of direct associations between events based on simple conditioning principles. The other view describes learning as the process of inducing the transformational structure that defines the material. Each of these learning mechanisms predicts differences in the rate of acquisition for differently organized sequences. Across a set of empirical studies, we compare the predictions of each class of model with the behavior of human subjects. We find that learning mechanisms based on transformations of an internal state, such as recurrent network architectures (e.g., Elman, 1990), have difficulty accounting for the pattern of human results relative to a simpler (but more limited) learning mechanism based on learning direct associations. Our results suggest new constraints on the cognitive mechanisms supporting sequential learning behavior. Keywords: Sequence learning; Skill acquisition and learning; Learning constraints; Simple recurrent networks; Associative learing DOI: 10.1111/j.1551-6709.2009.01076.x

Previous research suggests that learning categories by classifying new instances highlights information that is useful for discriminating between categories. In contrast, learning categories by making predictive inferences focuses learners on an abstract summary of each category (e.g., the prototype). To test this characterization of classification and inference learning further, the authors evaluated the two learning procedures with nonlinearly separable categories. In contrast to previous research involving cohesive, linearly separable categories, the authors found that it is more difficult to learn nonlinearly separable categories by making inferences about features than it is to learn them by classifying instances. This finding reflects that the prototype of a nonlinearly separable category does not provide a good summary of the category members. The results from this study suggest that having a cohesive category structure is more important for inference than it is for classification.

Notions of mechanism, emergence, reduction and explanation are all tied to levels of analysis. I cover the relationship between lower and higher levels, suggest a level of mechanism approach for neuroscience in which the components of a mechanism can themselves be further decomposed and argue that scientists' goals are best realized by focusing on pragmatic concerns rather than on metaphysical claims about what is 'real'. Inexplicably, neuroscientists are enchanted by both reduction and emergence. A fascination with reduction is misplaced given that theory is neither sufficiently developed nor formal to allow it, whereas metaphysical claims of emergence bring physicalism into question. Moreover, neuroscience's existence as a discipline is owed to higher-level concepts that prove useful in practice. Claims of biological plausibility are shown to be incoherent from a level of mechanism view and more generally are vacuous. Instead, the relevant findings to address should be specified so that model selection procedures can adjudicate between competing accounts. Model selection can help reduce theoretical confusions and direct empirical investigations. Although measures themselves, such as behaviour, blood-oxygen-level-dependent (BOLD) and single-unit recordings, are not levels of analysis, like levels, no measure is fundamental and understanding how measures relate can hasten scientific progress. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Prefrontal cortex (PFC) is thought to support the ability to focus on goal-relevant information by filtering out irrelevant information, a process akin to dimensionality reduction. Here, we test this dimensionality reduction hypothesis by relating a data-driven approach to characterizing the complexity of neural representation with a theoretically-supported computational model of learning. We find evidence of goal-directed dimensionality reduction within human ventromedial PFC during learning. Importantly, by using computational predictions of each participant's attentional strategies during learning, we find that that the degree of neural compression predicts an individual's ability to selectively attend to concept-specific information. These findings suggest a domain-general mechanism of learning through compression in ventromedial PFC. Efficient learning is akin to goal-directed dimensionality reduction, in which relevant information is highlighted and irrelevant input is ignored. Here, the authors show that ventromedial prefrontal cortex uniquely supports such learning by compressing neural codes to represent goal-specific information. [ABSTRACT FROM AUTHOR]