John R Anderson

Computational cognitive models aim to simulate the cognitive processes humans go through when performing a particular task. In this chapter, we discuss a machine learning approach that can discover such cognitive processes in M/EEG data. The method uses a combination of multivariate pattern analysis (MVPA) and hidden semi-Markov models (HsMMs), to take both the spatial extent and the temporal duration of cognitive processes into account. In the first part of this chapter, we will introduce the HsMM-MVPA method and demonstrate its application to an associative recognition dataset. Next, we will use the results of the analysis to inform a high-level cognitive model developed in the ACT-R (adaptive control of thought – rational) architecture. Finally, we will discuss how the HsMM-MVPA method can be extended and how it can inform other modeling paradigms.

Memory should make more available things that are more likely to be needed. Across multiple environmental domains, it has been shown that such a system would match qualitatively the memory effects involving repetition, delay, and spacing (Schooler & Anderson, 2017). To obtain data of sufficient size to study how detailed patterns of past appearance predict probability of being needed again, we examined the patterns with which words appear in large two data sets: tweets from popular sources and comments on popular subreddits. The two data sets show remarkably similar statistics, which are also consistent with earlier, smaller studies of environmental statistics. None of a candidate set of mathematical models of memory do well at predicting the observed patterns in these environments. A new model of human memory based on the environmental model proposed by Anderson and Milson (1989) did better at …

Bock (1986) showed that using a particular syntactic pattern in one's speech primes that use of that pattern in subsequent speech. Bock et al.(1996) suggested that implicit learning may account for these priming effects. To support this claim, they showed that priming exerts its effect on test trials that are separated from the most recent priming trial by as many as ten neutral trials. However, the amount of time spanned by ten trials is only on the order of one minute, much shorter than the typical time span covered in implicit learning experiments (Seger, 1994). This abstract describes the first evidence in normal subjects that syntactic priming lasts longer than a few minutes and thus proceeds on the same time scale as implicit learning. In the experiment described here, we found evidence for syntactic priming occurring at a delay of at least 20 minutes, which is an order of magnitude longer than has previously been …

In using eye movements to develop cognitive models, researchers typically analyze eye movement protocols with aggregate measures and test models with respect to these measures. Because aggregate analyses sometimes conceal informative low-level behavior, protocol analyses comparing model predictions to individual trial protocols are frequently desirable; however, protocol analysis for eye movement data is often tedious and time-consuming. We describe how to automate the protocol analysis of eye movements using hidden Markov models. Working with data from an equation-solving task, we demonstrate two methods of tracing eye movement data—that is, mapping eye movements to the sequential predictions of a cognitive process model. We evaluated these tracing methods in an experiment where participants were instructed to execute given equation-solving strategies. When coding the experimental …

Control and content of feedback was manipulated as students practiced coding functions with the Lisp Tutor. Four feedback conditions were employed: (1) immediate error feedback and correction, (2) immediate error flagging but immediate correction not required (3) feedback on demand and (4) no tutorial assistance. The wide range in feedback conditions did not affect mean learning rate as measured by individual production firing, time to complete the exercises or post-test performance. However, post-test results were more highly correlated with student ability as tutorial assistance decreased across conditions. Feedback conditions also affected students’ monitoring of the learning process. Across groups, students found the material was easier and believed they had learned it better as assistance decreased across conditions. However, students who received more assistance estimated their mastery of the …

Three experiments investigate the influence of feedback timing on skill acquisition in the context of learning LISP. In experiment 1 subjects receiving immediate feedback went through the training material in 40% less time than did those receiving delayed feedback, but learning was not impaired. A second experiment involved the use of an improved editor and less supportive testing conditions. Though subjects in the immediate condition went through the training problems 18% faster than did those in the delay condition, they were slower on the test problems and made twice as many errors. The results of experiment 3, a partial replication of the first two experiments, indicated a general advantage for delayed feedback in terms of errors, time on task, and the percentage of errors that subjects self-corrected. A protocol analysis suggests that immediate feedback competes for working memory resources, forcing out …

We examined the detailed behavioral characteristics of transfer of skill and the ability of the adaptive control of thought rational (ACT-R) architecture to account for this with its new Controller module. We employed a simple action video game called Auto Orbit and investigated the control tuning of timing skills across speed perturbations of the environment. In Auto Orbit, players needed to learn to alternate turn and shot actions to blow and burst balloons under time constraints imposed by balloon resets and deflations. Cognitive and motor skill transfer was assessed both in terms of game performance and in terms of the details of their motor actions. We found that skill transfer across speeds necessitated the recalibration of action timing skills. In addition, we found that acquiring skill in Auto Orbit involved a progressive decrease in variability of behavior. Finally, we found that players with higher skill levels tended to be less variable in terms of action chunking and action timing. These findings further shed light on the complex cognitive and motor mechanisms of skill transfer across speeds in complex task environments.

This paper shows how identical skills can emerge either from instruction or discovery when both result in an understanding of the causal structure of the task domain. The paper focuses on the discovery process, extending the skill acquisition model of Anderson et al. (2019) to address learning by discovery. The discovery process involves exploring the environment and developing associations between discontinuities in the task and events that precede them. The growth of associative strength in ACT-R serves to identify potential causal connections. The model can derive operators from these discovered causal relations just as does with the instructed causal information. Subjects were given a task of learning to play a video game either with a description of the game’s causal structure (Instruction) or not (Discovery). The Instruction subjects learned faster, but successful Discovery subjects caught up. After 20 3 …