Just do it: A neuropsychological theory of agency, cognition, mood, and dopamine.

Agency is the sense that one has control over one's own actions and the consequences of those actions. Despite the critical role that agency plays in the human condition, little is known about its neural basis. A novel theory proposes that increases in agency disinhibit the dopamine system and thereby increase the number of tonically active dopamine neurons in the ventral tegmental area.

Sex differences in brain transcriptomes of juvenile Cynomolgus macaques.

Background: Behavioral, social, and physical characteristics are posited to distinguish the sexes, yet research on transcription-level sexual differences in the brain is limited. Here, we investigated sexually divergent brain transcriptomics in prepubertal cynomolgus macaques, a commonly used surrogate species to humans.

Methods: A transcriptomic profile using RNA sequencing was generated for the temporal lobe, ventral midbrain, and cerebellum of 3 female and 3 male cynomolgus macaques previously treated with an Adeno-associated virus vector mix.

Femoral Structure and Biomechanical Characteristics in Sanfilippo Syndrome Type-B Mice.

Sanfilippo syndrome Type-B, also known as mucopolysaccharidosis IIIB (MPS IIIB), accounts for approximately one-third of all Sanfilippo syndrome patients and is characterized by a similar natural history as Type-A. Patients suffer from developmental regression, bone malformation, organomegaly, GI distress, and profound neurological deficits. Despite human trials of enzyme replacement therapy (ERT) (SBC-103, AX250) in MPS IIIB, there is currently no FDA approved treatment and a few palliative options.

Economic Burden of Sanfilippo Syndrome in the United States.

Introduction: Sanfilippo syndrome is a rare disease and fatal genetic disorder with no FDA-approved treatment in the United States (US), and no comprehensive assessment of economic disease burden is available.

Objectives: To develop a model to estimate the economic burden associated with Sanfilippo syndrome in the US using direct costs, indirect costs and valued intangibles (disability-adjusted life years, or DALYs) from 2023 onward.

Design And Setting: A multistage comorbidity model was generated based on Sanfilippo syndrome symptoms, and disability weights from the 2010 Global Burden of Disease Study.

A general recognition theory model for identifying an ideal stimulus.

A probabilistic, multidimensional model is described that accounts for sensory and hedonic ratings that are collected from the same experiment. The model combines a general recognition theory model of the sensory ratings with Coombs' unfolding model of the hedonic ratings. The model uses sensory ratings to build a probabilistic, multidimensional representation of the sensory experiences elicited by exposure to each stimulus, and it also builds a similar representation of the hypothetical ideal stimulus in this same space.

On what it means to automatize a rule.

The results of two experiments are reported that included a combined total of approximately 633,000 categorization trials. The experiments investigated the nature of what is automatized after lengthy practice with a rule-guided behavior. The results of both experiments suggest that an abstract rule, if interpreted as a verbal-based strategy, was not automatized during training, but rather the automatization linked a set of stimuli with similar values on one visual dimension to a common motor response.

The significance of triple-capsid-mutant AAV8 for treatment of Sanfilippo Syndrome Type B.

Sanfilippo Syndrome Type-B remains an untreatable childhood neurodegenerative disease with great burden for both patient and caregiver. Very few clinical trials have been undertaken to treat the disease, and none of these have yet yielded clinically obtainable products for patients. Caused by a simple enzyme function deficiency, Sanfilippo Syndrome Type-B has been considered a great prospect for gene-therapy interventions.

When instructions don't help: Knowing the optimal strategy facilitates rule-based but not information-integration category learning.

Providing verbal or written instructions on how to perform optimally in a task is one of the most common ways to teach beginners. This practice is so widely accepted that scholarship primarily focuses on how to provide instructions, not whether these instructions help or not. Here we investigate the benefits of prior instruction on rule-based (RB) category-learning, in which the optimal strategy is a simple explicit rule, and information-integration (II) category-learning, in which the optimal strategy is similarity-based.

Modulation of Dopamine for Adaptive Learning: A Neurocomputational Model.

There have been many proposals that learning rates in the brain are adaptive, in the sense that they increase or decrease depending on environmental conditions. The majority of these models are abstract and make no attempt to describe the neural circuitry that implements the proposed computations. This article describes a biologically detailed computational model that overcomes this shortcoming.

Linear separability, irrelevant variability, and categorization difficulty.

In rule-based (RB) category-learning tasks, the optimal strategy is a simple explicit rule, whereas in information-integration (II) tasks, the optimal strategy is impossible to describe verbally. This study investigates the effects of two different category properties on learning difficulty in category learning tasks-namely, linear separability and variability on stimulus dimensions that are irrelevant to the categorization decision. Previous research had reported that linearly separable II categories are easier to learn than nonlinearly separable categories, but Experiment 1, which compared performance on linearly and nonlinearly separable categories that were equated as closely as possible on all other factors that might affect difficulty, found that linear separability had no effect on learning.

A neurocomputational theory of how rule-guided behaviors become automatic.

This article introduces a biologically detailed computational model of how rule-guided behaviors become automatic. The model assumes that initially, rule-guided behaviors are controlled by a distributed neural network centered in the prefrontal cortex, and that in addition to initiating behavior, this network also trains a faster and more direct network that includes projections from sensory association cortex directly to rule-sensitive neurons in the premotor cortex. After much practice, the direct network is sufficient to control the behavior, without prefrontal involvement.

A role for the medial temporal lobes in category learning.

Despite much research, the role of the medial temporal lobes (MTL) in category learning is unclear. Two unstructured categorization experiments explored conditions that might recruit MTL category learning and memory systems-namely, whether the stimulus display includes one or two stimuli, and whether category membership depends on configural properties of the stimulus features. The results supported three conclusions.

Role of Essential Metal Ions in AAV Vector-Mediated Transduction.

Metal elements are essential components of approximately half of all cellular proteins, and approximately one-third of all known enzymes thus far are metalloenzymes. Several cellular proteins and enzymes undoubtedly impact the transduction efficiency of recombinant adeno-associated virus (AAV) vectors, but the precise role of metal ions in this process has not been studied in detail. In the present studies, we systematically evaluated the effects of all 10 essential metal ions (calcium, cobalt, copper, iron, magnesium, manganese, molybdenum, potassium, sodium, and zinc) on the transduction efficiency of AAV vectors.

Dissociations between rule-based and information-integration categorization are not caused by differences in task difficulty.

In rule-based (RB) category-learning tasks, the optimal strategy is a simple explicit rule, whereas in information-integration (II) tasks, the optimal strategy is impossible to describe verbally. Many studies have reported qualitative dissociations between training and performance in RB and II tasks. Virtually all of these studies were testing predictions of the dual-systems model of category learning called COVIS.

A difficulty predictor for perceptual category learning.

Predicting human performance in perceptual categorization tasks in which category membership is determined by similarity has been historically difficult. This article proposes a novel biologically motivated difficulty measure that can be generalized across stimulus types and category structures. The new measure is compared to 12 previously proposed measures on four extensive data sets that each included multiple conditions that varied in difficulty.

Retinal-specific category learning.

Virtually all cognitive theories of category learning (such as prototype theory and exemplar theory) view this important skill as a high-level process that uses abstract representations of objects in the world. Because these representations are removed from visual characteristics of the display, such theories suggest that category learning occurs in higher-level (such as association) areas and therefore should be immune to the visual field dependencies that characterize processing of objects mediated by representations in low-level visual areas. Here we challenge that view by describing a fully controlled demonstration of visual-field dependence in category learning.

Novel representations that support rule-based categorization are acquired on-the-fly during category learning.

Humans learn categorization rules that are aligned with separable dimensions through a rule-based learning system, which makes learning faster and easier to generalize than categorization rules that require integration of information from different dimensions. Recent research suggests that learning to categorize objects along a completely novel dimension changes its perceptual representation, making it more separable and discriminable. Here, we asked whether such newly learned dimensions could support rule-based category learning.

Testing analogical rule transfer in pigeons (Columba livia).

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.

Linking signal detection theory and encoding models to reveal independent neural representations from neuroimaging data.

Many research questions in visual perception involve determining whether stimulus properties are represented and processed independently. In visual neuroscience, there is great interest in determining whether important object dimensions are represented independently in the brain. For example, theories of face recognition have proposed either completely or partially independent processing of identity and emotional expression.

Increased cognitive load enables unlearning in procedural category learning.

Interventions for drug abuse and other maladaptive habitual behaviors may yield temporary success but are often fragile and relapse is common. This implies that current interventions do not erase or substantially modify the representations that support the underlying addictive behavior-that is, they do not cause true unlearning. One example of an intervention that fails to induce true unlearning comes from Crossley, Ashby, and Maddox (2013, Journal of Experimental Psychology: General), who reported that a sudden shift to random feedback did not cause unlearning of category knowledge obtained through procedural systems, and they also reported results suggesting that this failure is because random feedback is noncontingent on behavior.

Quantitative modeling of category learning deficits in various patient populations.

Objective: To provide a select review of our applications of quantitative modeling to highlight the utility of such approaches to better understand the neuropsychological deficits associated with various neurologic and psychiatric diseases.

Method: We review our work examining category learning in various patient populations, including individuals with basal ganglia disorders (Huntington's Disease and Parkinson's disease), amnesia and Eating Disorders.

Results: Our review suggests that the use of quantitative models has enabled a better understanding of the learning deficits often observed in these conditions and has allowed us to form novel hypotheses about the neurobiological bases of their deficits.

Testing Separability and Independence of Perceptual Dimensions with General Recognition Theory: A Tutorial and New R Package ().

Determining whether perceptual properties are processed independently is an important goal in perceptual science, and tools to test independence should be widely available to experimental researchers. The best analytical tools to test for perceptual independence are provided by General Recognition Theory (GRT), a multidimensional extension of signal detection theory. Unfortunately, there is currently a lack of software implementing GRT analyses that is ready-to-use by experimental psychologists and neuroscientists with little training in computational modeling.

A neural interpretation of exemplar theory.

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.

Perceptual category learning and visual processing: An exercise in computational cognitive neuroscience.

The field of computational cognitive neuroscience (CCN) builds and tests neurobiologically detailed computational models that account for both behavioral and neuroscience data. This article leverages a key advantage of CCN-namely, that it should be possible to interface different CCN models in a plug-and-play fashion-to produce a new and biologically detailed model of perceptual category learning. The new model was created from two existing CCN models: the HMAX model of visual object processing and the COVIS model of category learning.