Predictive Memory and the Surprising Gap

where P(E|H i ) determines the likelihood ratio that E is the case given H i (i.e., the context factor), and P(H i ) gives the prior probability that a particular memory will be needed (i.e., the history factor). For present purposes, two consequences that follow from this formulation are relevant. First, as Anderson and Milson (1989) remarked, given the multiplicity of elements present in a retrieval context, the likelihood ratio representing the context factor is best understood as the multiplicative product of all the likelihood ratios for every element of the context given H i . As a result, certain contextual elements are going to be better cues than others (i.e., representing a larger positive contribution to the overall product), as it is the case with elements that were present in the context of encoding (Craik and Tulving, 1975). The second thing to notice is that the prior probability, according to the ACT-R model, depends on the history of previous retrievals. Originally, Anderson and Milson (1989) noted that determining the history factor could be daunting, if not impossible, as one “would have to follow people about their daily lives, keeping a complete record of when they use various facts [and] such an objective study of human information is close to impossible” (p. 705). To get around this problem, Anderson and Schooler (1991) suggested extracting prior probabilities from the statistical distribution of existent databases that, according to them, would capture “coherent slices of the environment. ” One such environmental database, for instance, contained 2 years worth of word usage in the New York Times headlines. They found that the odds that a particular word was used in a certain headline was inversely correlated to its having occurred in a previous headline, with the probability diminishing the more time had passed since its last usage. Importantly, Anderson and Schooler (1991) showed that this model could fit extant data on recency and frequency effects on memory retrieval remarkably well. Taken together, the context and the history factors suggest that the probability that a certain memory will be needed in a particular context can be predicted from the probability that it has been needed in the recent past in relevantly similar contexts. From the point of view of Clark’s HPM approach then, context and history factors combine in a hierarchical model that tries to find the most predictable memory – i.e., that which minimizes prediction error – for a needed memory given a cue. Clark (in press) has offered a forceful defense of the “hierarchical prediction machine” (HPM) approach to the brain. Roughly, HPM suggests that brains are in the business of making sense of incoming information by generating top-down models aimed at providing the optimal fit for the input data. A better fit between the model and the data minimizes prediction error, which Clark – following Friston (e.g., Friston, 2010) – construes as tantamount to reducing surprisal, i.e., “the subpersonally computed implausibility of some sensory state given the model of the world” (p. 17). Notwithstanding the breadth of his defense, Clark’s case is entirely built upon research on perception, attention, and action, all of which are on-line cognitive processes. With practically no mention of offline cognition, the theoretical pretensions of the HPM approach, which Clark so vigorously defends as a “single unifying explanatory framework” (p. 61) in cognitive science, are questionable. I suggest that this conspicuous absence might be partially remedied, at least for the case of remembering, by looking at recent Bayesian accounts of memory retrieval developed after Anderson’s Adaptive Control of Thought-Rational (ACTR) model (Anderson and Milson, 1989; Anderson, 1990; Anderson and Schooler, 1991, 2000). Specifically, I suggest that the ACT-R model can be read as describing how memory retrieval attempts to minimize prediction error when finding the optimal memory given the costs of its retrieval and the organism’s current needs. Originally, the ACT-R model stated that remembering is a cognitive operation whose costs are offset by the gains attained when retrieval is successful. As such, our adaptive memory system would search for a particular memory as long as the probability of recovering it given our current needs is greater than the costs of its retrieval. The ACT-R model captures this insight in Bayesian terms thus: let H i be the hypothesis that a particular memory Predictive memory and the surprising gap