REFRESH : a new approach to modeling dimensional biases in perceptual similarity and categorization

Much categorization behavior can be explained by family resemblance: New items are classified by comparison with previously learned exemplars. However, categorization behavior also shows a variety of dimensional biases, where the underlying space has so-called “separable” dimensions: Ease of learning categories depends on how the stimuli align with the separable dimensions of the space. For example, if a set of objects of various sizes and colors can be accurately categorized using a single separable dimension (e.g., size), then category learning will be fast, while if the category is determined by both dimensions, learning will be slow. To capture these dimensional biases, almost all models of categorization supplement family resemblance with either rule-based systems or selective attention to separable dimensions. But these models do not explain how separable dimensions initially arise; they are presumed to be unexplained psychological primitives. We develop, instead, a pure family resemblance version of the Rational Model of Categorization (RMC), which we term the Rational Exclusively Family RESemblance Hierarchy (REFRESH), which does not presuppose any separable dimensions in the space of stimuli. REFRESH infers how the stimuli are clustered and uses a hierarchical prior to learn expectations about the variability of clusters across categories. We first demonstrate the dimensional alignment of natural-category features and then show how through a lifetime of categorization experience REFRESH will learn prior expectations that clusters of stimuli will align with separable dimensions. REFRESH captures the key dimensional biases and also explains their stimulus-dependence and how they are learned and develop

Using Machine Teaching to Investigate Human Assumptions when Teaching Reinforcement Learners

Successful teaching requires an assumption of how the learner learns - how the learner uses experiences from the world to update their internal states. We investigate what expectations people have about a learner when they teach them in an online manner using rewards and punishment. We focus on a common reinforcement learning method, Q-learning, and examine what assumptions people have using a behavioral experiment. To do so, we first establish a normative standard, by formulating the problem as a machine teaching optimization problem. To solve the machine teaching optimization problem, we use a deep learning approximation method which simulates learners in the environment and learns to predict how feedback affects the learner's internal states. What do people assume about a learner's learning and discount rates when they teach them an idealized exploration-exploitation task? In a behavioral experiment, we find that people can teach the task to Q-learners in a relatively efficient and effective manner when the learner uses a small value for its discounting rate and a large value for its learning rate. However, they still are suboptimal. We also find that providing people with real-time updates of how possible feedback would affect the Q-learner's internal states weakly helps them teach. Our results reveal how people teach using evaluative feedback and provide guidance for how engineers should design machine agents in a manner that is intuitive for people.Comment: 21 pages, 4 figure

Estimating semantic networks of groups and individuals from fluency data

One popular and classic theory of how the mind encodes knowledge is an as- sociative semantic network, where concepts and associations between concepts correspond to nodes and edges, respectively. A major issue in semantic network research is that there is no consensus among researchers as to the best method for estimating the network of an individual or group. We propose a novel method (dubbed U-INVITE) for estimating semantic networks from semantic fluency data (listing items from a category) based on a censored random walk model of mem- ory retrieval. We compare this method to several other methods in the literature for estimating networks from semantic fluency data. In simulations, we find that U- INVITE can recover semantic networks with low error rates given only a moderate amount of data. U-INVITE is the only known method derived from a psychologi- cally plausible process model of memory retrieval and one of two known methods that are consistent estimators of this process: if semantic memory retrieval is con- sistent with this process, the procedure will eventually estimate the true network (given enough data). We conduct the first exploration of different methods for esti- mating psychologically-valid semantic networks by comparing people’s similarity judgments of edges estimated by each network estimation method. We conclude with a discussion of best practices for estimating networks from fluency data

PACKER: An Exemplar Model of Category Generation

Generating new concepts is an intriguing yet understudiedtopic in cognitive science. In this paper, we present a novelexemplar model of category generation: PACKER (ProducingAlike and Contrasting Knowledge using Exemplar Representa-tions). PACKER’s core design assumptions are (1) categoriesare represented as exemplars in a multidimensional psycholog-ical space, (2) generated items should be similar to exemplarsof the same category, and (3) generated categories should bedissimilar to existing categories. A behavioral study revealsstrong effects of contrast- and target-class similarity. Theseeffects are novel empirical phenomena, which are directly pre-dicted by the PACKER model but are not explained by existingformal approaches

Examining Search Processes in Low and High Creative Individuals with RandomWalks

The creative process involves several cognitive processes,such as working memory, controlled attention and taskswitching. One other process is cognitive search oversemantic memory. These search processes can be controlled(e.g., problem solving guided by a heuristic), or uncontrolled(e.g., mind wandering). However, the nature of this search inrelation to creativity has rarely been examined from a formalperspective. To do this, we use a random walk model tosimulate uncontrolled cognitive search over semanticnetworks of low and high creative individuals with an equalnumber of nodes and edges. We show that a random walkover the semantic network of high creative individuals “finds”more unique words and moves further through the networkfor a given number of steps. Our findings are consistent withthe associative theory of creativity, which posits that thestructure of semantic memory facilitates search processes tofind creative solutions

Analyzing human feature learning as nonparametric bayesian inference

Almost all successful machine learning algorithms and cognitive models require powerful representations capturing the features that are relevant to a particular problem. We draw on recent work in nonparametric Bayesian statistics to define a rational model of human feature learning that forms a featural representation from raw sensory data without pre-specifying the number of features. By comparing how the human perceptual system and our rational model use distributional and category information to infer feature representations, we seek to identify some of the forces that govern the process by which people separate and combine sensory primitives to form features.

Interpreting Asymmetries in Speech Perception with Bayesian Inference

This paper proposes a Bayesian account of asymmetriesfound in speech perception: In many languages, listenersshow greater sensitivity if a non-coronal sound (/b/, /p/, /g/,/k/) is changed to coronal sounds (/d/, /t/) than vice versa. Thecurrently predominant explanation for these asymmetries isthat they reflect innate constraints from Universal Grammar.Alternatively, we propose that the asymmetries could simplyarise from optimal inference given the statistical properties ofdifferent speech categories of the listener’s native language.In the framework of Bayesian inference, we examined twostatistical parameters of coronal and non-coronal sounds:frequencies of occurrence and variance in articulation. In thelanguages in which perceptual asymmetries have been found,coronal sounds are either more frequent or more variable thannon-coronal sounds. Given such differences, an ideal observeris more likely to perceive a non-coronal speech signal as acoronal segment than vice versa. Thus, the perceptualasymmetries can be explained as a natural consequence ofprobabilistic inference. The coronal/non-coronal asymmetryis similar to asymmetries observed in many other cognitivedomains. Thus, we argue that it is more parsimonious toexplain this asymmetry as one of many similar asymmetriesfound in cognitive processing, rather than a linguistic-specific, innate constraint