Jeffrey Annis

On the relationship between memory and perception: sequential dependencies in recognition memory testing.

Many models of recognition are derived from models originally applied to perception tasks, which assume that decisions from trial to trial are independent. While the independence assumption is violated for many perception tasks, we present the results of several experiments intended to relate memory and perception by exploring sequential dependencies in recognition. The findings from these experiments disconfirm the independence assumption for recognition memory. In addition, the pattern of sequential dependencies observed in recognition differs from that observed for many perception tasks. This suggests that sequential dependencies arise from mnemonic or perceptual processes and not from decision processes that should be common to memory and perception tasks.

Sources of interference in recognition testing.

Recognition memory accuracy is harmed by prior testing (a.k.a., output interference [OI]; Tulving & Arbuckle, 1966). In several experiments, we interpolated various tasks between recognition test trials. The stimuli and the tasks were more similar (lexical decision [LD] of words and nonwords) or less similar (gender identification of male and female faces) to the stimuli and task used in recognition testing. Not only did the similarity between the interpolated and recognition tasks not affect recognition accuracy but performance of the interpolated task caused no interference in subsequent recognition testing. Only the addition of recognition trials caused OI. When we presented a block of LD trials or gender identification trials before the recognition test, a decrease in accuracy was observed in the subsequent recognition tests. These results suggest a distinction between temporal context and task context, such that recognition memory performance is determined by the salience of the context cues, and the use of temporal context cues is associated with OI.

Bayesian inference with Stan: A tutorial on adding custom distributions

When evaluating cognitive models based on fits to observed data (or, really, any model that has free parameters), parameter estimation is critically important. Traditional techniques like hill climbing by minimizing or maximizing a fit statistic often result in point estimates. Bayesian approaches instead estimate parameters as posterior probability distributions, and thus naturally account for the uncertainty associated with parameter estimation; Bayesian approaches also offer powerful and principled methods for model comparison. Although software applications such as WinBUGS (Lunn, Thomas, Best, & Spiegelhalter, Statistics and Computing, 10, 325–337, 2000) and JAGS (Plummer, 2003) provide “turnkey”-style packages for Bayesian inference, they can be inefficient when dealing with models whose parameters are correlated, which is often the case for cognitive models, and they can impose significant technical barriers to adding custom distributions, which is often necessary when implementing cognitive models within a Bayesian framework. A recently developed software package called Stan (Stan Development Team, 2015) can solve both problems, as well as provide a turnkey solution to Bayesian inference. We present a tutorial on how to use Stan and how to add custom distributions to it, with an example using the linear ballistic accumulator model (Brown & Heathcote, Cognitive Psychology, 57, 153–178. doi:10.1016/j.cogpsych.2007.12.002, 2008).

Consequences of Testing Memory

Abstract Studies using a wide variety of conditions and a diverse set of procedures show that testing memory affects future behavior. The studies have used differing terminology and have been ascribed to differing specialty areas of the literature. Partly, for this reason, the various phenomena have been described in ways, suggesting they differ in substance. In this chapter, we relate many of these phenomena and show that they might be due to a set of common memory processes, processes that can act through conscious, strategic or unconscious, implicit means. The critical strand that links the phenomena is that memory is a continuous process that constantly stores and retrieves information.

Bayesian statistical approaches to evaluating cognitive models: Bayesian statistical approaches

Cognitive models aim to explain complex human behavior in terms of hypothesized mechanisms of the mind. These mechanisms can be formalized in terms of mathematical structures containing parameters that are theoretically meaningful. For example, in the case of perceptual decision making, model parameters might correspond to theoretical constructs like response bias, evidence quality, response caution, and the like. Formal cognitive models go beyond verbal models in that cognitive mechanisms are instantiated in terms of mathematics and they go beyond statistical models in that cognitive model parameters are psychologically interpretable. We explore three key elements used to formally evaluate cognitive models: parameter estimation, model prediction, and model selection. We compare and contrast traditional approaches with Bayesian statistical approaches to performing each of these three elements. Traditional approaches rely on an array of seemingly ad hoc techniques, whereas Bayesian statistical approaches rely on a single, principled, internally consistent system. We illustrate the Bayesian statistical approach to evaluating cognitive models using a running example of the Linear Ballistic Accumulator model of decision making (Brown SD, Heathcote A. The simplest complete model of choice response time: linear ballistic accumulation. Cogn Psychol 2008, 57:153-178). WIREs Cogn Sci 2018, 9:e1458. doi: 10.1002/wcs.1458 This article is categorized under: Neuroscience > Computation Psychology > Reasoning and Decision Making Psychology > Theory and Methods.

Modeling memory dynamics in visual expertise.

The development of visual expertise is accompanied by enhanced visual object recognition memory within an expert domain. We aimed to understand the relationship between expertise and memory by modeling cognitive mechanisms. Participants with a measured range of birding expertise were recruited and tested on memory for birds (expert domain) and cars (novice domain). Participants performed an old-new continuous recognition memory task whereby on each trial an image of a bird or car was presented that was either new or had been presented earlier with lag j. The Linear Ballistic Accumulator model (LBA; Brown & Heathcote, 2008) was first used to decompose accuracy and response time (RT) into drift rate, response threshold, and nondecision time, with the measured level of visual expertise as a potential covariate on each model parameter. An Expertise × Category interaction was observed on drift rates such that expertise was positively correlated with memory performance recognizing bird images but not car images as old versus new. To then model the underlying processes responsible for variation in drift rate with expertise, we used a model of drift rates building on the Exemplar-Based Random Walk model (Nosofsky, Cox, Cao, & Shiffrin, 2014; Nosofsky & Palmeri, 1997), which revealed that expertise was associated with increases in memory strength and increases in the distinctiveness of stored exemplars. Taken together, we provide insight using formal cognitive modeling into how improvements in recognition memory with expertise are driven by enhancements in the representations of objects in an expert domain. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

A Bayesian Approach to Discriminating Between Biased Responding and Sequential Dependencies in Binary Choice Data.

Sequential dependencies occur when prior decisions affect subsequent decisions, and they have been observed in both memory and perception tasks. For binary response tasks, an inherent problem in measuring sequential dependencies is the ability to distinguish between sequential dependencies and response bias. The problem arises because the sequential dependency estimate within the frequentist architecture does not contain information regarding the number of observations upon which it is based. One solution to the problem is to use a Bayesian approach that takes the uncertainty of the sequential dependency estimate into account. We describe 2 Bayesian measurement models of sequential dependencies in binary response tasks and test them using simulated data with known degrees of response bias and sequential dependencies. Both models were able to distinguish between fluctuations in sequential dependencies and response bias. We then use the model to measure the contributions of sequential dependencies and response bias to the decisions made in recognition memory and perceptual categorization.