Computational and empirical simulations of selective memory impairments: Converging evidence for a single-system account of memory dissociations

Instance theory predicts categorization decisions in the absence of categorical structure: A computational analysis of artificial grammar learning without a grammar

Theories of categorization have historically focused on the stimulus characteristics to which people are sensitive. Artificial grammar learning (AGL) provides a clear example of this phenomenon, with theorists debating between knowledge of rules, fragments, whole strings, and so on as the basis of categorization decisions (i.e., stimulus-driven explanations). We argue that this focus loses sight of the more important question of how participants make categorization decisions on a mechanistic level (i.e., process-driven explanations). To address the problem, we derived predictions from an instance-based model of human memory in a pseudo-AGL task in which all study and test strings were generated randomly, a task that stimulus-driven explanations of AGL would have difficulty accommodating. We conducted a standard AGL experiment with human participants using the same strings. The model's predictions corresponded to participants' decisions well, even in the absence of any experimenter-generated structure and regardless of whether test stimuli contained any incidental structure. We argue that theories of categorization ought to continue shifting towards the goal of modeling categorization at the level of cognitive processes rather than primarily attempting to identify the stimulus characteristics to which participants are drawn.

Generalization and the hippocampus: More than one story?

Memory-based cognition depends on both the ability to remember specific details of individual experiences and the ability to combine information across experiences to generalize and derive new knowledge. A hippocampal role in rapid encoding of specific events is long established. More recent research also demonstrates hippocampal contributions to generalization, but their nature is still debated. The current review provides an overview of hippocampal-based generalization in two lines of research-episodic inference and categorization-and discusses evidence for four candidate mechanisms and representational schemes that may underpin such generalization. We highlight evidence showing that the hippocampus contributes specific memories to generalization decisions, but also forms generalized representations that integrate information across experiences. Multiple views are currently plausible of how such generalized representations form and relate to specific memories. Future research that uses behavioral and neural indices of both generalization and specificity may help resolve between the candidate generalization mechanisms, with the possibility that more than one view of hippocampal-based generalization may be valid. Importantly, all views share the emphasis on the broader role of the hippocampus in cognition that goes beyond remembering the past.

Modeling test learning and dual-task dissociations

Much of cognitive psychology is premised on the distinction between automatic and intentional processes, but the distinction often remains vague in practice and alternative explanations are often not followed through. For example, Hendricks, Conway and Kellogg (Journal of Experimental Psychology: Learning, Memory, and Cognition, 39, 491–1500, 2013) found that dual tasks at training versus at test dissociated performance in two different artificial grammar learning tasks. This was taken as evidence for underlying automatic and intentional processes. In this article, a different explanation is considered based on test learning and similarity, where participants are assumed to update their knowledge at test. Contrasting formal memory models of test learning are implemented, and it is concluded that the models account for the relevant dissociations without assuming a distinction between automatic and intentional processes.

Perceptual identification task points to continuity between implicit memory and recall

Adopting a continuous identification task (CID-R) with embedded questions about prior occurrence, recent research has proposed that implicit and explicit memory are underpinned by a single memory system, since there is a systematic relationship between implicit memory (measured by identification) and explicit memory (measured by subjective report of recognition; for an example, see Berry, Shanks, & Henson, 2008). We were interested in whether this pattern would extend to recall of information from a study phase (Experiment 1) or recall from semantic memory (Experiment 2). We developed a degraded face identification version of the CID-R task using Gaussian blur. We reproduced previous results regarding the relationship between explicit responses on the recognition task (old/new) and stimuli identification, pointing to a continuity between explicit and implicit memory. Critically, we also found that the strength of the implicit effect (i.e., stimuli identification) was predicted by the accuracy in recall (retrieval of context in Experiment 1 and correct responses to general knowledge questions about the face in Experiment 2). Our results support the idea that memory is unidimensional and related to memory trace strength; both for recall and recognition, and interestingly, for semantic and episodic recall.

Repeated exposure to exemplars does not enhance implicit learning: A puzzle for models of learning and memory

We learn regularities in the world around us, frequently without conscious effort, a phenomenon known as implicit learning. These regularities are often impossible to verbalise. One example of implicit learning is the structural effect, in which participants learn a rule set combining two factors, such as lexical frequency and concreteness. Theories of implicit learning predict that repetition of exemplar words would result in improved learning of the rule set, increasing the magnitude of the structural effect. Over four experiments, we demonstrate that this is, in fact, not the case. In Experiments 1 and 2, three repetitions of exemplar words result in superior item memory, but no change in the magnitude of the structural effect, compared with individually presented words. In Experiments 3 and 4, the structural effect is shown to be invariant to five repetitions of exemplar words and at high and low numbers of exemplars. In all four experiments, participants were unable to describe the rule set underlying the structural effect. However, confidence ratings demonstrated sensitivity to the structure and this sensitivity, unlike endorsements, increased with strength. The results are discussed in reference to differentiation, structural versus judgement knowledge, and flexible learning systems.