I consider the role of cognitive modeling in cognitive science. Modeling, and the computers that enable it, are central to the field, but the role of modeling is often misunderstood. Models are not intended to capture fully the processes they attempt to elucidate. Rather, they are explorations of ideas about the nature of cognitive processes. In these explorations, simplification is essential—through simplification, the implications of the central ideas become more transparent. This is not to say that simplification has no downsides; (...) it does, and these are discussed. I then consider several contemporary frameworks for cognitive modeling, stressing the idea that each framework is useful in its own particular ways. Increases in computer power (by a factor of about 4 million) since 1958 have enabled new modeling paradigms to emerge, but these also depend on new ways of thinking. Will new paradigms emerge again with the next 1,000‐fold increase? (shrink)

The natural input memory (NIM) model is a new model for recognition memory that operates on natural visual input. A biologically informed perceptual preprocessing method takes local samples (eye fixations) from a natural image and translates these into a feature‐vector representation. During recognition, the model compares incoming preprocessed natural input to stored representations. By complementing the recognition memory process with a perceptual front end, the NIM model is able to make predictions about memorability based directly on individual natural stimuli. We (...) demonstrate that the NIM model is able to simulate experimentally obtained similarity ratings and recognition memory for individual stimuli (i.e., face images). (shrink)

I present a framework for modeling memory, retrieval, perception, and their interactions. Recent versions of the models were inspired by Bayesian induction: We chose models that make optimal decisions conditioned on a memory/perceptual system with inherently noisy storage and retrieval. The resultant models are, fortunately, largely consistent with my models dating back to the 1960s, and are therefore natural successors. My recent articles have presented simplified models in order to focus on particular applications. This article takes a larger perspective and (...) places the individual models in a more global framework. I will discuss (1) the storage of episodic traces, the accumulation of these into knowledge (e.g., lexical/semantic traces in the case of words), and the changes in knowledge caused by learning; (2) the retrieval of information from episodic memory and from general knowledge; (3) decisions concerning storage, retrieval, and responding. Examples of applications include episodic recognition and cued and free recall, perceptual identification (naming, yes–no and forced‐choice), lexical decision, and long‐term and short‐term priming. (shrink)

It is timely to assess Bayesian models, but Bayesianism is not a religion. Bayesian modeling is typically used as a tool to explain human data. Bayesian models are sometimes equivalent to other models, but have the advantage of explicitly integrating prior hypotheses with new observations. Any lack of representational or neural assumptions may be an advantage rather than a disadvantage.

Models of recognition memory have traditionally struggled with the puzzle of criterion setting, a problem that is particularly acute in cases in which items for study and test are of widely varying types, with differing degrees of baseline familiarity and experience (e.g., words vs. random dot patterns). We present a dynamic model of the recognition process that addresses the criterion setting problem and produces joint predictions for choice and reaction time. In this model, recognition decisions are based not on the (...) absolute value of familiarity, but on how familiarity changes over time as features are sampled from the test item. Decisions are the outcome of a race between two parallel accumulators: one that accumulates positive changes in familiarity (leading to an ‘‘old’’ decision) and another that accumulates negative changes (leading to a ‘‘new’’ decision). Simulations with this model make realistic predictions for recognition performance and latency regardless of the baseline familiarity of study and test items. (shrink)