Randall K. Jamieson

Applying an exemplar model to the artificial-grammar task: Inferring grammaticality from similarity

We present three artificial-grammar experiments. The first used position constraints, and the second used sequential constraints. The third varied both the amount of training and the degree of sequential constraint. Increasing both the amount of training and the redundancy of the grammar benefited participants’ ability to infer grammatical status; nevertheless, they were unable to describe the grammar. We applied a multitrace model of memory to the task. The model used a global measure of similarity to assess the grammatical status of the probe and captured performance both in our experiments and in three classic studies from the literature. The model shows that retrieval is sensitive to structure in memory, even when individual exemplars are encoded sparsely. The work ties an understanding of performance in the artificial-grammar task to the principles used to understand performance in episodic-memory tasks.

Applying an exemplar model to the serial reaction-time task: Anticipating from experience:

We present a serial reaction time (SRT) task in which participants identified the location of a target by pressing a key mapped to the location. The location of successive targets was determined by the rules of a grammar, and we varied the redundancy of the grammar. Increasing both practice and the redundancy of the grammar reduced response time, but the participants were unable to describe the grammar. Such results are usually discussed as examples of implicit learning. Instead, we treat performance in terms of retrieval from a multitrace memory. In our account, after each trial, participants store a trace comprising the current stimulus, the response associated with it, and the context provided by the immediately preceding response. When a target is presented, it is used as a prompt to retrieve the response mapped to it. As participants practise the task, the redundancy of the series helps point to the correct response and, thereby, speeds retrieval of the response. The model captured performance in the experiment and in classic SRT studies from the literature. Its success shows that the SRT task can be understood in terms of retrieval from memory without implying implicit learning.

An instance theory of associative learning

We present and test an instance model of associative learning. The model, Minerva-AL, treats associative learning as cued recall. Memory preserves the events of individual trials in separate traces. A probe presented to memory contacts all traces in parallel and retrieves a weighted sum of the traces, a structure called the echo. Learning of a cue–outcome relationship is measured by the cue’s ability to retrieve a target outcome. The theory predicts a number of associative learning phenomena, including acquisition, extinction, reacquisition, conditioned inhibition, external inhibition, latent inhibition, discrimination, generalization, blocking, overshadowing, overexpectation, superconditioning, recovery from blocking, recovery from overshadowing, recovery from overexpectation, backward blocking, backward conditioned inhibition, and second-order retrospective revaluation. We argue that associative learning is consistent with an instance-based approach to learning and memory.

Grammaticality is inferred from global similarity: A reply to Kinder (2010)

Jamieson and Mewhort (2009b) proposed an account of performance in the artificial-grammar judgement-of-grammaticality task based on Hintzmans (1986) model of retrieval, Minerva 2. In the account, each letter is represented by a unique vector of random elements, and each exemplar is represented by concatenating its constituent letter vectors. Although successful in simulating several experiments, Kinder (2010) showed that the model fails for three selected experiments. We track the models failure to a constraint introduced by concatenating letter vectors to construct the exemplar representation. To fix the problem, we use a holographic representation. Holographic representation not only provides the flexibility missing with the concatenation scheme but also acknowledges variability in what subjects notice when they inspect training exemplars. Armed with holographic representations, we show that the model successfully captures the three problematic data sets. We argue for retrospective accounts, like the present one, that acknowledge subjects’ skill in drawing unexpected inferences based on memory of studied items against prospective accounts that require subjects to learn statistical regularities in the training set in anticipation of an undefined classification test.

Applying an exemplar model to the artificial-grammar task: String completion and performance on individual items.

Jamieson and Mewhort (2009a) demonstrated that performance in the artificial-grammar task could be understood using an exemplar model of memory. We reinforce the position by testing the model against data for individual test items both in a standard artificial-grammar experiment and in a string-completion variant of the standard procedure. We argue that retrieval is sensitive to structure in memory. The work ties performance in the artificial-grammar task to principles of explicit memory.

Global Similarity Predicts Dissociation of Classification and Recognition: Evidence Questioning the Implicit-Explicit Learning Distinction in Amnesia.

Dissociation of classification and recognition in amnesia is widely taken to imply 2 functional systems: an implicit procedural-learning system that is spared in amnesia and an explicit episodic-learning system that is compromised. We argue that both tasks reflect the global similarity of probes to memory. In classification, subjects sort unstudied grammatical exemplars from lures, whereas in recognition, they sort studied grammatical exemplars from lures. Hence, global similarity is necessarily greater in recognition than in classification. Moreover, a grammatical exemplars similarity to studied exemplars is a nonlinear function of the integrity of the data in memory. Assuming that data integrity is better for control subjects than for subjects with amnesia, the nonlinear relation combined with the advantage for recognition over classification predicts the dissociation of recognition and classification. To illustrate the dissociation of recognition and classification in healthy undergraduates, we manipulated study time to vary the integrity of the data in memory and brought the dissociation under experimental control. We argue that the dissociation reflects a general cost in memory rather than a selective impairment of separate procedural and episodic systems.

Recoding and representation in artificial grammar learning

We apply an exemplar model of memory to explain performance in the artificial grammar task. The model blends the convolution-based method for representation developed in Jones and Mewhort’s BEAGLE model of semantic memory (Psychological Review 114:1–37, 2007) with the storage and retrieval assumptions in Hintzman’s MINERVA 2 model of episodic memory (Behavior Research Methods, Instruments, and Computers, 16:96–101, 1984). The model captures differences in encoding to fit data from two experiments that document the influence of encoding on implicit learning. We provide code so that researchers can adapt the model and techniques to their own experiments.

A computational account of the production effect: Still playing twenty questions with nature.

People remember words that they read aloud better than words that they read silently, a result known as the production effect. The standing explanation for the production effect is that producing a word renders it distinctive in memory and, thus, memorable at test. By 1 key account, distinctiveness is defined in terms of sensory feedback. We formalize the sensory-feedback account using MINERVA 2, a standard model of memory. The model accommodates the basic result in recognition as well as the fact that the mixed-list production effect is larger than its pure-list counterpart, that the production effect is robust to forgetting, and that the production and generation effects have additive influences on performance. A final simulation addresses the strength-based account and suggests that it will be more difficult to distinguish a strength-based versus distinctiveness-based explanation than is typically thought. We conclude that the production effect is consistent with existing theory and discuss our analysis in relation to Alan Newells (1973) classic criticism of psychology and call for an analysis of psychological principles instead of laboratory phenomena. (PsycINFO Database Record

The offline production effect.

People remember words they say aloud better than ones they do not, a result called the production effect. The standing explanation for the production effect is that producing a word renders it distinctive in memory and thus memorable at test. Whereas it is now clear that motoric production benefits remembering over nonproduction, and that more intense motoric production benefits remembering to a greater extent than less intense motoric production, there has been no comparison of the memorial benefit conferred by motoric versus imagined production. One reason for the gap is that the standard production-by-vocalization procedure confounds the analysis. To make the comparison, we used a production-by-typing procedure and tested memory for words that people typed, imagined typing, and did not type. Whereas participants remembered the words that they typed and imagined typing better than words that they did not, they remembered the words they typed better than the ones they imagined typing; an advantage that was consistent over tests of recognition memory and source discrimination. We conclude that motoric production is a sufficient and facilitative (but not a necessary) condition to observe the production effect. We explain our results by a sensory feedback account of the production effect and sketch a computational framework to implement that approach.

Applying an exemplar model to an implicit rule-learning task: Implicit learning of semantic structure

Studies of implicit learning often examine peoples’ sensitivity to sequential structure. Computational accounts have evolved to reflect this bias. An experiment conducted by Neil and Higham [Neil, G. J., & Higham, P. A.(2012). Implicit learning of conjunctive rule sets: An alternative to artificial grammars. Consciousness and Cognition, 21, 1393–1400] points to limitations in the sequential approach. In the experiment, participants studied words selected according to a conjunctive rule. At test, participants discriminated rule-consistent from rule-violating words but could not verbalize the rule. Although the data elude explanation by sequential models, an exemplar model of implicit learning can explain them. To make the case, we simulate the full pattern of results by incorporating vector representations for the words used in the experiment, derived from the large-scale semantic space models LSA and BEAGLE, into an exemplar model of memory, MINERVA 2. We show that basic memory processes in a classic model of memory capture implicit learning of non-sequential rules, provided that stimuli are appropriately represented.