The quest for simplicity in human learning: Identifying the constraints on attention.

For better or worse, humans live a resource-constrained existence; only a fraction of physical sensations ever reach conscious awareness, and we store a shockingly small subset of these experiences in memory for later use. Here, we examined the effects of attention constraints on learning. Among models that frame selective attention as an optimization problem, attention orients toward information that will reduce errors. Using this framing as a basis, we developed a suite of models with a range of constraints on the attention available during each learning event. We fit these models to both choice and eye-fixation data from four benchmark category-learning data sets, and choice data from another dynamic categorization data set. We found consistent evidence for computations we refer to as "simplicity", where attention is deployed to as few dimensions of information as possible during learning, and "competition", where dimensions compete for selective attention via lateral inhibition.

As within, so without, as above, so below: Common mechanisms can support between- and within-trial category learning dynamics.

Two fundamental difficulties when learning novel categories are deciding (a) what information is relevant and (b) when to use that information. Although previous theories have specified how observers learn to attend to relevant dimensions over time, those theories have largely remained silent about how attention should be allocated on a within-trial basis, which dimensions of information should be sampled, and how the temporal order of information sampling influences learning. Here, we use the adaptive attention representation model (AARM) to demonstrate that a common set of mechanisms can be used to specify: (a) How the distribution of attention is updated between trials over the course of learning and (b) how attention dynamically shifts among dimensions within a trial. We validate our proposed set of mechanisms by comparing AARM's predictions to observed behavior in four case studies, which collectively encompass different theoretical aspects of selective attention. We use both eye-tracking and choice response data to provide a stringent test of how attention and decision processes dynamically interact during category learning. Specifically, how does attention to selected stimulus dimensions gives rise to decision dynamics, and in turn, how do decision dynamics influence which dimensions are attended to via gaze fixations? (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Distributed Neural Systems Support Flexible Attention Updating during Category Learning.

To accurately categorize items, humans learn to selectively attend to the stimulus dimensions that are most relevant to the task. Models of category learning describe how attention changes across trials as labeled stimuli are progressively observed. The Adaptive Attention Representation Model (AARM), for example, provides an account in which categorization decisions are based on the perceptual similarity of a new stimulus to stored exemplars, and dimension-wise attention is updated on every trial in the direction of a feedback-based error gradient. As such, attention modulation as described by AARM requires interactions among processes of orienting, visual perception, memory retrieval, prediction error, and goal maintenance to facilitate learning. The current study explored the neural bases of attention mechanisms using quantitative predictions from AARM to analyze behavioral and fMRI data collected while participants learned novel categories. Generalized linear model analyses revealed patterns of BOLD activation in the parietal cortex (orienting), visual cortex (perception), medial temporal lobe (memory retrieval), basal ganglia (prediction error), and pFC (goal maintenance) that covaried with the magnitude of model-predicted attentional tuning. Results are consistent with AARM's specification of attention modulation as a dynamic property of distributed cognitive systems.

Constraining functional coactivation with a cluster-based structural connectivity network.

In this article, we propose a two-step pipeline to explore task-dependent functional coactivations of brain clusters with constraints from the structural connectivity network. In the first step, the pipeline employs a nonparametric Bayesian clustering method that can estimate the optimal number of clusters, cluster assignments of brain regions of interest (ROIs), and the strength of within- and between-cluster connections without any prior knowledge. In the second step, a factor analysis model is applied to functional data with factors defined as the obtained structural clusters and the factor structure informed by the structural network. The coactivations of ROIs and their clusters can be studied by correlations between factors, which can largely differ by ongoing cognitive task. We provide a simulation study to validate that the pipeline can recover the underlying structural and functional network. We also apply the proposed pipeline to empirical data to explore the structural network of ROIs obtained by the Gordon parcellation and study their functional coactivations across eight cognitive tasks and a resting-state condition.

In this article, we propose a two-step pipeline to explore task-dependent functional coactivations of brain clusters with constraints imposed from structural connectivity networks. In the first step, the pipeline employs a nonparametric Bayesian clustering method that can estimate the optimal number of clusters, cluster assignments of brain regions of interest, and the strength of within- and between-cluster connections without any prior knowledge. In the second step, a factor analysis model is applied to functional data with factors defined as the obtained structural clusters and the factor structure informed by the structural network.

Gaussian process linking functions for mind, brain, and behavior.

The link between mind, brain, and behavior has mystified philosophers and scientists for millennia. Recent progress has been made by forming statistical associations between manifest variables of the brain (e.g., electroencephalogram [EEG], functional MRI [fMRI]) and manifest variables of behavior (e.g., response times, accuracy) through hierarchical latent variable models. Within this framework, one can make inferences about the mind in a statistically principled way, such that complex patterns of brain-behavior associations drive the inference procedure. However, previous approaches were limited in the flexibility of the linking function, which has proved prohibitive for understanding the complex dynamics exhibited by the brain. In this article, we propose a data-driven, nonparametric approach that allows complex linking functions to emerge from fitting a hierarchical latent representation of the mind to multivariate, multimodal data. Furthermore, to enforce biological plausibility, we impose both spatial and temporal structure so that the types of realizable system dynamics are constrained. To illustrate the benefits of our approach, we investigate the model's performance in a simulation study and apply it to experimental data. In the simulation study, we verify that the model can be accurately fitted to simulated data, and latent dynamics can be well recovered. In an experimental application, we simultaneously fit the model to fMRI and behavioral data from a continuous motion tracking task. We show that the model accurately recovers both neural and behavioral data and reveals interesting latent cognitive dynamics, the topology of which can be contrasted with several aspects of the experiment.

Variational Bayesian methods for cognitive science.

Bayesian inference has become a powerful and popular technique for understanding psychological phenomena. However, compared with frequentist statistics, current methods employing Bayesian statistics typically require time-intensive computations, often hindering our ability to evaluate alternatives in a thorough manner. In this article, we advocate for an alternative strategy for performing Bayesian inference, called variational Bayes (VB). VB methods posit a parametric family of distributions that could conceivably contain the target posterior distribution, and then attempt to identify the best parameters for matching the target. In this sense, acquiring the posterior becomes an optimization problem, rather than a complex integration problem. VB methods have enjoyed considerable success in fields such as neuroscience and machine learning, yet have received surprisingly little attention in fields such as psychology. Here, we identify and discuss both the advantages and disadvantages of using VB methods. In our consideration of possible strategies to make VB methods appropriate for psychological models, we develop the differential evolution variational inference algorithm, and compare its performance with a widely used VB algorithm. As test problems, we evaluate the algorithms on their ability to recover the posterior distribution of the linear ballistic accumulator model and a hierarchical signal detection model. Although we cannot endorse VB methods in their current form as a complete replacement for conventional methods, we argue that their accuracy and speed warrant inclusion within the cognitive scientist's toolkit. (PsycInfo Database Record (c) 2020 APA, all rights reserved).