The environmental basis of memory.

Memory should make more available things that are more likely to be needed. Across multiple environmental domains, it has been shown that such a system would match qualitatively the memory effects involving repetition, delay, and spacing (Schooler & Anderson, 2017). To obtain data of sufficient size to study how detailed patterns of past appearance predict probability of being needed again, we examined the patterns with which words appear in large two data sets: tweets from popular sources and comments on popular subreddits. The two data sets show remarkably similar statistics, which are also consistent with earlier, smaller studies of environmental statistics. None of a candidate set of mathematical models of memory do well at predicting the observed patterns in these environments. A new model of human memory based on the environmental model proposed by Anderson and Milson (1989) did better at predicting the environmental data and a wide range of behavioral studies that measure memory availability by probability of recall and speed of retrieval. A critical variable in this model was range, the span of time over which an item occurs, which was discovered in mining the environmental data. These results suggest that theories of memory can be guided by mining of the statistical structure of the environment. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Improving judgment accuracy by sequential adjustment.

Judging an object's value based on relevant cues can be challenging. We propose a simple method to improve judgment accuracy: Instead of estimating a value after seeing all available cues simultaneously, individuals view cues sequentially, one after another, making and adjusting their estimate at each step. The sequential procedure may alleviate computational difficulties in cue integration, leading to higher judgment accuracy. We tested this hypothesis in two real-world tasks in which participants judged either the price of diamonds or the fuel economy of cars. Two studies with professional jewelers and car salespeople show that most participants indeed judged more accurately with a sequential than with a simultaneous procedure. Another two studies with college students further support this finding and show additionally that the sequential procedure could raise the judgment accuracy of inexperienced students to the same level as that of professionals judging with the simultaneous procedure.

How does aging impact decision making? The contribution of cognitive decline and strategic compensation revealed in a cognitive architecture.

Older adults often face decline in cognitive resources. How does this impact their decision making-especially under high cognitive demands from concurrent activities? Do older adults' decision processes uniformly decline with increasing mental strain relative to younger adults, or do they compensate for decline by strategically reallocating resources? Using empirical data and computational modeling, we investigated older and younger adults' execution of two decision strategies in a multiattribute judgment task, while varying the demands from a concurrent task. One strategy (take-the-best) involves searching attributes in order of importance until one attribute favors one alternative; the other strategy (tallying) requires the integration of attributes favoring each alternative. Although older adults executed both strategies quite accurately, they performed worse and more slowly than younger adults. Moreover, when the concurrent demands increased, both age groups executed the strategies less accurately and more slowly. Crucially, when take-the-best required searching an increasing number of attributes, participants' accuracy and speed initially decreased with increasing search requirements, but accuracy recovered and the slowing lessened at the highest search requirements; this pattern was particularly prominent in older adults and most pronounced under the highest concurrent demands. Simulations with models in the cognitive architecture ACT-R showed how decline in specific cognitive resources can contribute to older adults' decrements in strategy execution. However, accommodating older adults' preserved strategy execution of take-the-best under the highest demands required assuming compensatory shifts in resource allocation. Thus, cognitive decline and strategic compensation applied under highest demands provided complementary accounts for older adults' decision behavior. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Social contact patterns can buffer costs of forgetting in the evolution of cooperation.

Analyses of the evolution of cooperation often rely on two simplifying assumptions: (i) individuals interact equally frequently with all social network members and (ii) they accurately remember each partner's past cooperation or defection. Here, we examine how more realistic, skewed patterns of contact-in which individuals interact primarily with only a subset of their network's members-influence cooperation. In addition, we test whether skewed contact patterns can counteract the decrease in cooperation caused by memory errors (i.e. forgetting). Finally, we compare two types of memory error that vary in whether forgotten interactions are replaced with random actions or with actions from previous encounters. We use evolutionary simulations of repeated prisoner's dilemma games that vary agents' contact patterns, forgetting rates and types of memory error. We find that highly skewed contact patterns foster cooperation and also buffer the detrimental effects of forgetting. The type of memory error used also influences cooperation rates. Our findings reveal previously neglected but important roles of contact pattern, type of memory error and the interaction of contact pattern and memory on cooperation. Although cognitive limitations may constrain the evolution of cooperation, social contact patterns can counteract some of these constraints.

Cognitive costs of decision-making strategies: A resource demand decomposition analysis with a cognitive architecture.

Several theories of cognition distinguish between strategies that differ in the mental effort that their use requires. But how can the effort-or cognitive costs-associated with a strategy be conceptualized and measured? We propose an approach that decomposes the effort a strategy requires into the time costs associated with the demands for using specific cognitive resources. We refer to this approach as resource demand decomposition analysis (RDDA) and instantiate it in the cognitive architecture Adaptive Control of Thought-Rational (ACT-R). ACT-R provides the means to develop computer simulations of the strategies. These simulations take into account how strategies interact with quantitative implementations of cognitive resources and incorporate the possibility of parallel processing. Using this approach, we quantified, decomposed, and compared the time costs of two prominent strategies for decision making, take-the-best and tallying. Because take-the-best often ignores information and foregoes information integration, it has been considered simpler than strategies like tallying. However, in both ACT-R simulations and an empirical study we found that under increasing cognitive demands the response times (i.e., time costs) of take-the-best sometimes exceeded those of tallying. The RDDA suggested that this pattern is driven by greater requirements for working memory updates, memory retrievals, and the coordination of mental actions when using take-the-best compared to tallying. The results illustrate that assessing the relative simplicity of strategies requires consideration of the overall cognitive system in which the strategies are embedded.

Reflections of the social environment in chimpanzee memory: applying rational analysis beyond humans.

In cognitive science, the rational analysis framework allows modelling of how physical and social environments impose information-processing demands onto cognitive systems. In humans, for example, past social contact among individuals predicts their future contact with linear and power functions. These features of the human environment constrain the optimal way to remember information and probably shape how memory records are retained and retrieved. We offer a primer on how biologists can apply rational analysis to study animal behaviour. Using chimpanzees (Pan troglodytes) as a case study, we modelled 19 years of observational data on their social contact patterns. Much like humans, the frequency of past encounters in chimpanzees linearly predicted future encounters, and the recency of past encounters predicted future encounters with a power function. Consistent with the rational analyses carried out for human memory, these findings suggest that chimpanzee memory performance should reflect those environmental regularities. In re-analysing existing chimpanzee memory data, we found that chimpanzee memory patterns mirrored their social contact patterns. Our findings hint that human and chimpanzee memory systems may have evolved to solve similar information-processing problems. Overall, rational analysis offers novel theoretical and methodological avenues for the comparative study of cognition.

Strategies for memory-based decision making: Modeling behavioral and neural signatures within a cognitive architecture.

How do people use memories to make inferences about real-world objects? We tested three strategies based on predicted patterns of response times and blood-oxygen-level-dependent (BOLD) responses: one strategy that relies solely on recognition memory, a second that retrieves additional knowledge, and a third, lexicographic (i.e., sequential) strategy, that considers knowledge conditionally on the evidence obtained from recognition memory. We implemented the strategies as computational models within the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture, which allowed us to derive behavioral and neural predictions that we then compared to the results of a functional magnetic resonance imaging (fMRI) study in which participants inferred which of two cities is larger. Overall, versions of the lexicographic strategy, according to which knowledge about many but not all alternatives is searched, provided the best account of the joint patterns of response times and BOLD responses. These results provide insights into the interplay between recognition and additional knowledge in memory, hinting at an adaptive use of these two sources of information in decision making. The results highlight the usefulness of implementing models of decision making within a cognitive architecture to derive predictions on the behavioral and neural level.

From perception to preference and on to inference: an approach-avoidance analysis of thresholds.

In a lexicographic semiorders model for preference, cues are searched in a subjective order, and an alternative is preferred if its value on a cue exceeds those of other alternatives by a threshold Δ, akin to a just noticeable difference in perception. We generalized this model from preference to inference and refer to it as Δ-inference. Unlike with preference, where accuracy is difficult to define, the problem a mind faces when making an inference is to select a Δ that can lead to accurate judgments. To find a solution to this problem, we applied Clyde Coombs's theory of single-peaked preference functions. We show that the accuracy of Δ-inference can be understood as an approach-avoidance conflict between the decreasing usefulness of the first cue and the increasing usefulness of subsequent cues as Δ grows larger, resulting in a single-peaked function between accuracy and Δ. The peak of this function varies with the properties of the task environment: The more redundant the cues and the larger the differences in their information quality, the smaller the Δ. An analysis of 39 real-world task environments led to the surprising result that the best inferences are made when Δ is 0, which implies relying almost exclusively on the best cue and ignoring the rest. This finding provides a new perspective on the take-the-best heuristic. Overall, our study demonstrates the potential of integrating and extending established concepts, models, and theories from perception and preference to improve our understanding of how the mind makes inferences.

We'll meet again: revealing distributional and temporal patterns of social contact.

What are the dynamics and regularities underlying social contact, and how can contact with the people in one's social network be predicted? In order to characterize distributional and temporal patterns underlying contact probability, we asked 40 participants to keep a diary of their social contacts for 100 consecutive days. Using a memory framework previously used to study environmental regularities, we predicted that the probability of future contact would follow in systematic ways from the frequency, recency, and spacing of previous contact. The distribution of contact probability across the members of a person's social network was highly skewed, following an exponential function. As predicted, it emerged that future contact scaled linearly with frequency of past contact, proportionally to a power function with recency of past contact, and differentially according to the spacing of past contact. These relations emerged across different contact media and irrespective of whether the participant initiated or received contact. We discuss how the identification of these regularities might inspire more realistic analyses of behavior in social networks (e.g., attitude formation, cooperation).

Are stripes beneficial? Dazzle camouflage influences perceived speed and hit rates.

In the animal kingdom, camouflage refers to patterns that help potential prey avoid detection. Mostly camouflage is thought of as helping prey blend in with their background. In contrast, disruptive or dazzle patterns protect moving targets and have been suggested as an evolutionary force in shaping the dorsal patterns of animals. Dazzle patterns, such as stripes and zigzags, are thought to reduce the probability with which moving prey will be captured by impairing predators' perception of speed. We investigated how different patterns of stripes (longitudinal-i.e., parallel to movement direction-and vertical-i.e., perpendicular to movement direction) affect the probability with which humans can hit moving objects and if differences in hitting probability are caused by a misperception of speed. A first experiment showed that longitudinally striped objects were hit more often than unicolored objects. However, vertically striped objects did not differ from unicolored objects. A second study examining the link between perceived speed and hitting probability showed that longitudinally and vertically striped objects were both perceived as moving faster and were hit more often than unicolored objects. In sum, our results provide evidence that striped patterns disrupt the perception of speed, which in turn influences how often objects are hit. However, the magnitude and the direction of the effects depend on additional factors such as speed and the task setup.

Mapping the structure of semantic memory.

Aggregating snippets from the semantic memories of many individuals may not yield a good map of an individual's 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 individual's semantic memory.

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 people's 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 people's decisions will be.

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).

A signal-detection analysis of fast-and-frugal trees.

Models of decision making are distinguished by those that aim for an optimal solution in a world that is precisely specified by a set of assumptions (a so-called "small world") and those that aim for a simple but satisfactory solution in an uncertain world where the assumptions of optimization models may not be met (a so-called "large world"). Few connections have been drawn between these 2 families of models. In this study, the authors show how psychological concepts originating in the classic signal-detection theory (SDT), a small-world approach to decision making, can be used to understand the workings of a class of simple models known as fast-and-frugal trees (FFTs). Results indicate that (a) the setting of the subjective decision criterion in SDT corresponds directly to the choice of exit structure in an FFT; (b) the sensitivity of an FFT (measured in d') is reflected by the order of cues searched and the properties of cues in an FFT, including the mean and variance of cues' individual d's, the intercue correlation, and the number of cues; and (c) compared with the ideal and the optimal sequential sampling models in SDT and a majority model with an information search component, FFTs are extremely frugal (i.e., do not search for much cue information), highly robust, and well adapted to the payoff structure of a task. These findings demonstrate the potential of theory integration in understanding the common underlying psychological structures of apparently disparate theories of cognition.

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, people's 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 people's 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.

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.

It just felt right: the neural correlates of the fluency heuristic.

Simple heuristics exploit basic human abilities, such as recognition memory, to make decisions based on sparse information. Based on the relative speed of recognizing two objects, the fluency heuristic infers that the one recognized more quickly has the higher value with respect to the criterion of interest. Behavioral data show that reliance on retrieval fluency enables quick inferences. Our goal with the present functional magnetic resonance imaging study was to isolate fluency-heuristic-based judgments to map the use of fluency onto specific brain areas that might give a better understanding of the heuristic's underlying processes. Activation within the claustrum for fluency heuristic decisions was found. Given that claustrum activation is thought to reflect the integration of perceptual and memory elements into a conscious gestalt, we suggest this activation correlates with the experience of fluency.

Ways of probing situated concepts.

Two ways of eliciting conceptual content have been to instruct participants to list the intrinsic properties that concept exemplars possess or to report any thoughts that come to mind about the concept. It has been argued that the open, unconstrained probe is better able to elicit the situational information that concepts contain. We evaluated this proposal in two experiments comparing the two probes with regard to the content that they yield for object concepts at the superordinate and basic levels. The results showed that the open probe was better able to elicit situated conceptual knowledge and point out differences in the representations of superordinate and basic concepts.

Forgetting constrains the emergence of cooperative decision strategies.

Theoretical studies of cooperative behavior have focused on decision strategies that depend on a partner's last choices. The findings from this work assume that players accurately remember past actions. The kind of memory that these strategies employ, however, does not reflect what we know about memory. Here, we show that human memory may not meet the requirements needed to use these strategies. When asked to recall the previous behavior of simulated partners in a cooperative memory task, participants performed poorly, making errors in 10-24% of the trials. Participants made more errors when required to track more partners. We conducted agent-based simulations to evaluate how well cooperative strategies cope with error. These simulations suggest that, even with few errors, cooperation could not be maintained at the error rates demonstrated by our participants. Our results indicate that the strategies typically used in the study of cooperation likely do not reflect the underlying cognitive capacities used by humans and other animals in social interactions. By including unrealistic assumptions about cognition, theoretical models may have overestimated the robustness of the existing cooperative strategies. To remedy this, future models should incorporate what we know about cognition.

Cognitive aging and the adaptive use of recognition in decision making.

The recognition heuristic, which predicts that a recognized object scores higher on some criterion than an unrecognized one, is a simple inference strategy and thus an attractive mental tool for making inferences with limited cognitive resources--for instance, in old age. In spite of its simplicity, the recognition heuristic might be negatively affected in old age by too much knowledge, inaccurate memory, or deficits in its adaptive use. Across 2 studies, we investigated the impact of cognitive aging on the applicability, accuracy, and adaptive use of the recognition heuristic. Our results show that (a) young and old adults' recognition knowledge was an equally useful cue for making inferences about the world; (b) as with young adults, old adults adjusted their use of the recognition heuristic between environments with high and low recognition validities; and (c) old adults, however, showed constraints in their ability to adaptively suspend the recognition heuristic on specific items. Measures of fluid intelligence mediated these age-related constraints.