Lael J. Schooler

Reflections of the Environment in Memory

Availability of human memories for specific items shows reliable relationships to frequency, recency, and pattern of prior exposures to the item. These relationships have defied a systematic theoretical treatment. A number of environmental sources (New York Times, parental speech, electronic mail) are examined to show that the probability that a memory will be needed also shows reliable relationships to frequency, recency, and pattern of prior exposures. Moreover, the environmental relationships are the same as the memory relationships. It is argued that human memory has the form it does because it is adapted to these environmental relationships. Models for both the environment and human memory are described. Among the memory phenomena addressed are the practice function, the retention function, the effect of spacing of practice, and the relationship between degree of practice and retention.

Cognitive niches: An ecological model of strategy selection.

How do people select among different strategies to accomplish a given task? Across disciplines, the strategy selection problem represents a major challenge. We propose a quantitative model that predicts how selection emerges through the interplay among strategies, cognitive capacities, and the environment. This interplay carves out for each strategy a cognitive niche, that is, a limited number of situations in which the strategy can be applied, simplifying strategy selection. To illustrate our proposal, we consider selection in the context of 2 theories: the simple heuristics framework and the ACT-R (adaptive control of thought-rational) architecture of cognition. From the heuristics framework, we adopt the thesis that people make decisions by selecting from a repertoire of simple decision strategies that exploit regularities in the environment and draw on cognitive capacities, such as memory and time perception. ACT-R provides a quantitative theory of how these capacities adapt to the environment. In 14 simulations and 10 experiments, we consider the choice between strategies that operate on the accessibility of memories and those that depend on elaborate knowledge about the world. Based on Internet statistics, our model quantitatively predicts peoples familiarity with and knowledge of real-world objects, the distributional characteristics of the associated speed of memory retrieval, and the cognitive niches of classic decision strategies, including those of the fluency, recognition, integration, lexicographic, and sequential-sampling heuristics. In doing so, the model specifies when people will be able to apply different strategies and how accurate, fast, and effortless peoples decisions will be.

From recognition to decisions: Extending and testing recognition-based models for multialternative inference

The recognition heuristic is a noncompensatory strategy for inferring which of two alternatives, one recognized and the other not, scores higher on a criterion. According to it, such inferences are based solely on recognition. We generalize this heuristic to tasks with multiple alternatives, proposing a model of how people identify the consideration sets from which they make their final decisions. In doing so, we address concerns about the heuristic’s adequacy as a model of behavior: Past experiments have led several authors to conclude that there is no evidence for a noncompensatory use of recognition but clear evidence that recognition is integrated with other information. Surprisingly, however, in no study was this competing hypothesis—the compensatory integration of recognition—formally specified as a computational model. In four studies, we specify five competing models, conducting eight model comparisons. In these model comparisons, the recognition heuristic emerges as the best predictor of people’s inferences.

The Role of Process in the Rational Analysis of Memory.

Abstract The rational analysis of memory (Anderson, 1990) proposes that memorys sensitivity to statistical structure in the environment enables it to optimally estimate the odds that a memory trace will be needed. We have analyzed sources of informational demand in the environment: speech to children and word usage in the front page headlines of the New York Times. In a previous paper (Anderson & Schooler, 1991) we have shown that factors that govern memory performance, including recency, also predict the odds that an item (e.g., a word) will be encountered. In the present paper we develop the theory to make precise predictions about how the odds of encountering an item now varies as a joint function of (1) the statistical associations between the item and elements of the current context and (2) how long it has been since the item was last encountered. The prediction was confirmed environmentally by analyses of the New York Times and speech to children. The corresponding behavioral prediction was tested, using a cued recall task in which the cues were either strongly associated or unassociated to the targets. In contrast to the environmental results, recall performance is more sensitive to the length of the retention interval in the presence of unassociated cues than in the presence of associated cues. Further modeling shows that incorporating estimates of the influence of non-retrieval processes (e.g., reading a word, deciding to respond, etc.) on overall performance reduces the discrepancy between the theoretical predictions and the observed data.

Why You Think Milan is Larger than Modena: Neural Correlates of the Recognition Heuristic

When ranking two alternatives by some criteria and only one of the alternatives is recognized, participants overwhelmingly adopt the strategy, termed the recognition heuristic (RH), of choosing the recognized alternative. Understanding the neural correlates underlying decisions that follow the RH could help determine whether people make judgments about the RHs applicability or simply choose the recognized alternative. We measured brain activity by using functional magnetic resonance imaging while participants indicated which of two cities they thought was larger (Experiment 1) or which city they recognized (Experiment 2). In Experiment 1, increased activation was observed within the anterior frontomedian cortex (aFMC), precuneus, and retrosplenial cortex when participants followed the RH compared to when they did not. Experiment 2 revealed that RH decisional processes cannot be reduced to recognition memory processes. As the aFMC has previously been associated with self-referential judgments, we conclude that RH decisional processes involve an assessment about the applicability of the RH.

The adaptive nature of memory

Adapted from Anderson, John R., Schooler, Lael J., 2000. Tulving, Endel, Craik, Fergus I. M. (Eds.), The Oxford Handbook of Memory, (pp. 557–570). New York, NY, USA: Oxford University Press, vol. xiv, 700 pp.

Simple predictions fueled by capacity limitations : When are they successful?

Counterintuitively, Y. Kareev, I. Lieberman, and M. Lev (1997) found that a lower short-term memory capacity benefits performance on a correlation detection task. They assumed that people with low short-term memory capacity (low spans) perceived the correlations as more extreme because they relied on smaller samples, which are known to exaggerate correlations. The authors consider, as an alternative hypothesis, that low spans do not perceive exaggerated correlations but make simpler predictions. Modeling both hypotheses in ACT-R demonstrates that simpler predictions impair performance if the environment changes, whereas a more exaggerated perception of correlation is advantageous to detect a change. Congruent with differences in the way participants make predictions, 2 experiments revealed a low capacity advantage before the environment changes but a high capacity advantage afterward, although this pattern of results surprisingly only existed for men.

The Robust Beauty of Ordinary Information.

Heuristics embodying limited information search and noncompensatory processing of information can yield robust performance relative to computationally more complex models. One criticism raised against heuristics is the argument that complexity is hidden in the calculation of the cue order used to make predictions. We discuss ways to order cues that do not entail individual learning. Then we propose and test the thesis that when orders are learned individually, peoples necessarily limited knowledge will curtail computational complexity while also achieving robustness. Using computer simulations, we compare the performance of the take-the-best heuristic--with dichotomized or undichotomized cues--to benchmarks such as the naïve Bayes algorithm across 19 environments. Even with minute sizes of training sets, take-the-best using undichotomized cues excels. For 10 environments, we probe peoples intuitions about the direction of the correlation between cues and criterion. On the basis of these intuitions, in most of the environments take-the-best achieves the level of performance that would be expected from learning cue orders from 50% of the objects in the environments. Thus, ordinary information about cues--either gleaned from small training sets or intuited--can support robust performance without requiring Herculean computations.

The recognition heuristic: A review of theory and tests

The recognition heuristic is a prime example of how, by exploiting a match between mind and environment, a simple mental strategy can lead to efficient decision making. The proposal of the heuristic initiated a debate about the processes underlying the use of recognition in decision making. We review research addressing four key aspects of the recognition heuristic: (a) that recognition is often an ecologically valid cue; (b) that people often follow recognition when making inferences; (c) that recognition supersedes further cue knowledge; (d) that its use can produce the less-is-more effect – the phenomenon that lesser states of recognition knowledge can lead to more accurate inferences than more complete states. After we contrast the recognition heuristic to other related concepts, including availability and fluency, we carve out, from the existing findings, some boundary conditions of the use of the recognition heuristic as well as key questions for future research. Moreover, we summarize developments concerning the connection of the recognition heuristic with memory models. We suggest that the recognition heuristic is used adaptively and that, compared to other cues, recognition seems to have a special status in decision making. Finally, we discuss how systematic ignorance is exploited in other cognitive mechanisms (e.g., estimation and preference).

Mapping the structure of semantic memory

Aggregating snippets from the semantic memories of many individuals may not yield a good map of an individuals semantic memory. The authors analyze the structure of semantic networks that they sampled from individuals through a new snowball sampling paradigm during approximately 6 weeks of 1-hr daily sessions. The semantic networks of individuals have a small-world structure with short distances between words and high clustering. The distribution of links follows a power law truncated by an exponential cutoff, meaning that most words are poorly connected and a minority of words has a high, although bounded, number of connections. Existing aggregate networks mirror the individual link distributions, and so they are not scale-free, as has been previously assumed; still, there are properties of individual structure that the aggregate networks do not reflect. A simulation of the new sampling process suggests that it can uncover the true structure of an individuals semantic memory.