Annette Kinder

Learning artificial grammars: No evidence for the acquisition of rules

Two experiments investigated whether there is evidence for acquisition of rules in implicit artificial grammar learning (AGL). Two different methods were used in meeting this goal, multiple regression analysis and analysis of receiver-operating characteristics (ROCs). By means of multiple regression analysis, several types of knowledge were identified that were used in judgments of grammaticality, for example, about single letters and about larger stimulus fragments. There was no evidence for the contribution of rule knowledge. The ROCs were in accord with a similarity-based account of AGL and thus did not support the notion that rule knowledge is acquired in AGL either. Simulations with a connectionist model corroborated the conclusion that the results were in accord with a similarity-based, associative account.

Sequence learning at optimal stimulus-response mapping: evidence from a serial reaction time task

We propose a new version of the serial reaction time (SRT) task in which participants merely looked at the target instead of responding manually. As response locations were identical to target locations, stimulus–response compatibility was maximal in this task. We demonstrated that saccadic response times decreased during training and increased again when a new sequence was presented. It is unlikely that this effect was caused by stimulus–response (S–R) learning because bonds between (visual) stimuli and (oculomotor) responses were already well established before the experiment started. Thus, the finding shows that the building of S–R bonds is not essential for learning in the SRT task.

Connectionist models of artificial grammar learning: what type of knowledge is acquired?

Two experiments are presented that test the predictions of two associative learning models of Artificial Grammar Learning. The two models are the simple recurrent network (SRN) and the competitive chunking (CC) model. The two experiments investigate acquisition of different types of knowledge in this task: knowledge of frequency and novelty of stimulus fragments (Experiment 1) and knowledge of letter positions, of small fragments, and of large fragments up to entire strings (Experiment 2). The results show that participants acquired all types of knowledge. Simulation studies demonstrate that the CC model explains the acquisition of all types of fragment knowledge but fails to account for the acquisition of positional knowledge. The SRN model, by contrast, accounts for the entire pattern of results found in the two experiments.

Are rules applied in Pavlovian electrodermal conditioning with humans general or outcome specific

There is growing evidence that in human skin conductance response (SCR) conditioning positive patterning (A-, B-, AB+) and negative pattering (A+, B+, AB-) are solved by applying two different rules. The present experiments investigated whether the representations of such rules are specific or general with regard to outcomes and response systems. In Experiment 1, we investigated SCR and eyelid conditioning with different types of training administered in an interleaved fashion. We found that positive patterning SCR conditioning interfered with negative patterning SCR conditioning, whereas eyeblink conditioning had no effect on SCR conditioning. In Experiment 2, in which eyeblink and SCR conditioning were administered in sequential fashion, the same result was obtained. We conclude that the rules involved in solving patterning tasks might be specific to outcomes and/or response systems.

Classification and recognition in artificial grammar learning : Analysis of receiver operating characteristics

In two experiments we investigated recognition and classification judgements using an artificial grammar learning paradigm. In Experiment 1, when only new test items had to be judged, analysis of z-transformed receiver operating characteristics (z-ROCs) revealed no differences between classification and recognition. In Experiment 2, where we included old test items, z-ROCs in the two tasks differed, suggesting that judgements relied on different types of information. The results are interpreted in terms of heuristics that people use when making classification and recognition judgements.

Responding under time pressure: Testing two animal learning models and a model of visual categorization:

Two experiments are reported, which employed a Pavlovian eyelid conditioning procedure with human participants. The experiments tested the predictions of three models of the time-course of processing under time pressure. These were the extended generalized context model (Lamberts, 1998), and two variants of the Rescorla-Wagner model (Rescorla & Wagner, 1972), which were activated in cascade mode. Reinforcement schedules in the experiments were equivalent either to an AND rule or to an XOR rule. The time available for processing the conditioned stimulus and initiating a conditioned response was manipulated by varying the interval from the onset of the conditioned stimulus to the onset of the unconditioned stimulus. The results were in accord with the predictions of one of the two variants of the Rescorla-Wagner model.

Multiple regression analyses in artificial-grammar learning: The importance of control groups

In artificial-grammar learning, it is crucial to ensure that above-chance performance in the test stage is due to learning in the training stage but not due to judgemental biases. Here we argue that multiple regression analysis can be successfully combined with the use of control groups to assess whether participants were able to transfer knowledge acquired during training when making judgements about test stimuli. We compared the regression weights of judgements in a transfer condition (training and test strings were constructed by the same grammar but with different letters) with those in a control condition. Predictors were identical in both conditions—judgements of control participants were treated as if they were based on knowledge gained in a standard training stage. The results of this experiment as well as reanalyses of a former study support the usefulness of our approach.

Is grammaticality inferred from global similarity? Comment on Jamieson and Mewhort (2009)

In a recent article, Jamieson and Mewhort (2009) proposed a novel account of artificial grammar learning (AGL), which is based on a multitrace model of episodic memory, the Minerva 2 model. According to this account, test performance in AGL is based on an assessment of global similarity of the test strings to the memory traces of the training strings. In this article, simulation studies are presented, showing for three different AGL experiments that the predictions of the Minerva 2 model strikingly deviate from participants’ performance. It is argued that participants’ test performance is not generally based on general similarity.