研究人类使用启发式之五原则(英文)

快速节俭启发式框架假定人们使用一套简捷的决策策略——启发式——进行推理、选择、评价及其他决策。这些启发式策略能顺应任务情境结构中的规律, 利用人类的基本认知能力。正基于此, 启发式成就了适应性行为。本文拟对启发式框架进行回顾, 并简要陈述引导研究者研究人类适应性工具箱的五条原则。我们强调, 启发式模型应(ⅰ)精确界定(ⅱ)对照检验(ⅲ)与策略选择理论相符(ⅳ)能评估其对新资料的预测力(ⅴ)能既在实验室又在现实世界中得以检验。     

Inferences from memory: strategy- and exemplar-based judgment models compared

What are the cognitive processes underlying people's inferences from memory? To provide an answer, the exemplar-based approach to predicting people's inferences is tested against the strategy-based approach. Exemplar models assume that people make inferences about objects by retrieving similar objects from memory. In contrast, the strategy-based approach assumes that people select cognitive strategies that make inferences based on abstracted knowledge and information the inference situation provides. In Experiment 1, in which dichotomous feedback on the level of pair-comparisons was provided, almost all participants were classified as using a simple lexicographic strategy. In Experiment 2, in which continuous feedback for single objects was provided, most participants were classified as using a compensatory strategy. Both experiments suggest that the strategy-based approach is more suitable for predicting people's inferences from memory than the exemplar-based approach. The strategy-based approach shows how people adapt to inference situations by selecting different cognitive strategies.     

The aging decision maker: cognitive aging and the adaptive selection of decision strategies

Are older adults' decision abilities fundamentally compromised by age-related cognitive decline? Or can they adaptively select decision strategies? One study (N = 163) investigated the impact of cognitive aging on the ability to select decision strategies as a function of environment structure. Participants made decisions in either an environment that favored the use of information-intensive strategies or one favoring the use of simple, information-frugal strategies. Older adults tended to (a) look up less information and take longer to process it and (b) use simpler, less cognitively demanding strategies. In accordance with the idea that age-related cognitive decline leads to reliance on simpler strategies, measures of fluid intelligence explained age-related differences in information search and strategy selection. Nevertheless, both young and older adults seem to be equally adapted decision makers in that they adjust their information search and strategy selection as a function of environment structure, suggesting that the aging decision maker is an adaptive one.     

A hybrid model of categorization

Category learning is often modeled as either an exemplar-based or a rule-based process. This paper shows that both strategies can be combined in a cognitive architecture that was developed to model other task domains. Variations on the exemplar-based random walk (EBRW) model of Nosofsky and Palmeri (1997b) and the rule-plus-exception (RULEX) rule-based model of Nosofsky, Palmeri, and McKinley (1994) were implemented in the ACT-R cognitive architecture. The architecture allows the two strategies to be mixed to produce classification behavior. The combined system reproduces latency, learning, and generalization data from three category-learning experiments--Nosofsky and Palmeri (1997b), Nosofsky et al., and Erickson and Kruschke (1998). It is concluded that EBRW and ACT-R have different but equivalent means of incorporating similarity and practice. In addition, ACT-R brings a theory of strategy selection that enables the exemplar and the rule-based strategies to be mixed.     

Decision making with the "adaptive toolbox": influence of environmental structure,intelligence, and working memory load

In 2 experiments with a total of 220 participants, the tendency to use simple heuristics such as the take the best heuristic in an adaptive manner was investigated. In a simulated stock market paradigm, the payoff structure of environments was varied, favoring either compensatory or noncompensatory decision strategies in terms of expected long-term payoff. In both experiments, the majority of participants were classified as using strategies that were adequate for the environment, supporting the notion of adaptive strategy selection. These strategy shifts were moderated by intelligence, as measured with common tests. Neither an additional learning phase (Experiment 1) nor working memory load or working memory capacity (Experiment 2) had additional effects on strategy selection.     

Stability/Instability of Cognitive Strategies Across Tasks Determine Whether Stress Will Affect Judgmental Processes'

Previous research indicates that individuals employ various cognitive heuristics and decision modes in making decisions and judgmental tasks that do not follow an expected value model. It further indicates that several cognitive faculties are affected by stress. The purpose of this study was twofold: first, to examine whether individual differences exist in the use of cognitive heuristics and risk-assessment decision modes; second, to examine whether stress would affect the use of these cognitive strategies. Three versions of a questionnaire measuring the representativeness and availability heuristics, and risk-seeking and risk-aversion decision modes were administered to three different groups of subjects. Consistent individual differences were only observed in the subscales measuring risk-seeking and risk-aversion modes, but not in the use of the heuristics. In a different group of subjects, exposure to noise and task overload stress increased the use of the representativeness heuristic, but did not alter the use of risk-seeking and risk-aversion decision modes. These results indicate that the existence of individual differences in cognitive strategies may determine, in part, whether stress will modify judgmental processes.     

Simple mechanisms for gathering social information

Social contexts are notoriously complex, yet decisions are nevertheless made by using simple strategies. We argue that the concept of fast and frugal heuristics provides a promising framework for understanding how we gather social information to make decisions in social environments. That is, we assume that under limitations of time, energy, and computational resources people use cognitively based shortcuts that rely on information from social environments to solve different types of real problems. We review three successful applications of heuristics to the social arena. We first introduce some commonly faced social inference problems (e.g., selecting a mate or deciding whether to cooperate with someone) and then discuss how individuals can use simple strategies to solve such problems. For each problem, we consider how social environments are structured, and how we take advantage of this structure when using heuristics to make inferences and decisions.     

The generation and resemblance heuristics in face recognition: cooperation and competition

Like all probabilistic decisions, recognition memory judgments are based on inferences about the strength and quality of stimulus familiarity. In recent articles, B. W. A. Whittlesea and J. Leboe (2000; J. Leboe & B. W. A. Whittlesea, 2002) proposed that such memory decisions entail various heuristics, similar to well-known heuristics in overt decision making. Using verbal stimulus materials, Whittlesea and Leboe illustrated 3 separate memory heuristics: fluency, generation, and resemblance. In the present investigation, the authors examined the generation and resemblance heuristics in face recognition. In 12 experiments, people memorized faces and later performed exclusion (source memory) tasks. Every experiment contained natural groups of facial photographs (e.g., Caucasian vs. Asian faces), but such groups were not always valid source-memory predictors. Instead, across experiments, the potential utility of generation and resemblance strategies was systematically varied. People were quite sensitive to such variations, changing from one heuristic to another as needed. However, they also combined heuristics, both improving and damaging performance across conditions. The relevance of recognition decision heuristics to eyewitness memory is considered.     

Towards a Cognitive Theory of Cyber Deception

This work is an initial step toward developing a cognitive theory of cyber deception. While widely studied, the psychology of deception has largely focused on physical cues of deception. Given that present-day communication among humans is largely electronic, we focus on the cyber domain where physical cues are unavailable and for which there is less psychological research. To improve cyber defense, researchers have used signaling theory to extended algorithms developed for the optimal allocation of limited defense resources by using deceptive signals to trick the human mind. However, the algorithms are designed to protect against adversaries that make perfectly rational decisions. In behavioral experiments using an abstract cybersecurity game (i.e., Insider Attack Game), we examined human decision-making when paired against the defense algorithm. We developed an instance-based learning (IBL) model of an attacker using the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture to investigate how humans make decisions under deception in cyber-attack scenarios. Our results show that the defense algorithm is more effective at reducing the probability of attack and protecting assets when using deceptive signaling, compared to no signaling, but is less effective than predicted against a perfectly rational adversary. Also, the IBL model replicates human attack decisions accurately. The IBL model shows how human decisions arise from experience, and how memory retrieval dynamics can give rise to cognitive biases, such as confirmation bias. The implications of these findings are discussed in the perspective of informing theories of deception and designing more effective signaling schemes that consider human bounded rationality.     

Do People Order Cues by Retrieval Fluency when Making Probabilistic Inferences?

In multiple‐cue probabilistic inferences, people infer alternatives' unknown values on decision criteria, using alternatives' attributes as cues. Some inferential strategies, like take‐the‐best, assume that people consider relevant cues sequentially in order of decreasing validity. This assumption has been deemed cognitively implausible by some, who suggest memory retrieval principles to guide cue order. We test whether memory‐based inferences are better described by a model considering cues in order of validity or in order of memory retrieval. In an experiment, we manipulated the frequency with which cues appeared in a learning phase, increasing retrieval fluency of cue values related to the more frequently appearing cue. In a subsequent decision phase, participants made a series of two‐alternative decisions based on the learned cue values. We compared two sequential sampling models, which differed in whether cues are sampled in order of subjective cue validity or in order of retrieval fluency. To model retrieval order of cues in the fluency sampling model, we used the declarative memory theory embedded in the ACT‐R cognitive architecture. Most participants' decisions were best described by the model sampling cues in order of memory retrieval. Only a minority of participants were classified as sampling cues by validity. Our result suggests that retrieval fluency is the primary driver of cue order in inferences from memory, irrespective of the cues' validities. Copyright © 2017 John Wiley & Sons, Ltd.     

Working memory and syllogistic reasoning

The purpose of this study was to examine the relation between working memory span and syllogistic reasoning performance. In addition, performance for the reasoning task was compared to predictions made by mental model theory and the probability heuristics model. According to mental model theory, syllogisms that require the use of more mental models are more difficult. According to the probability heuristics model difficulty is related to the number of probabilistic heuristics that must be applied, or (for invalid syllogisms) inconsistencies between the derived and correct conclusion. The predictions of these theories were examined across two experiments. In general, people with larger working memory capacities reasoned better. Also, the responses made by people with larger capacities were more likely to correspond to the predictions made by both mental model theory and the probability heuristics model. Relations between working memory span and performance were also consistent with both theories.     

Integrating Fast and Frugal Heuristics with a Model of Memory‐based Cue Generation

Although the fast and frugal heuristics have been studied extensively, relatively little attention has been paid to how cues are generated to be used within the heuristics. The goal of this paper is to propose and test a memory‐based model of how cues are generated and used in cue‐based inferences. The current study advances theory by integrating the fast and frugal heuristics with HyGene, a memory‐based model of how decision makers generate and evaluate hypotheses. Using archival data in which memory retrieval variables were not directly manipulated, we demonstrate that participants' cue selection behavior is consistent with memory‐based retrieval. Further, by directly manipulating memory retrieval within a cue‐based stock‐forecasting task, we demonstrate that memory processes underlie cue use. Participants' cue use varied depending on the relationship between cue validity and the frequency with which the cues were seen during learning. The HyGene model provided the best account of the empirical data, providing further evidence for the critical role of memory in judgment and decision making. Copyright © 2018 John Wiley & Sons, Ltd.     

The role of working memory in syntactic sentence realization: A modeling & simulation approach

This paper examines the effects of working memory size in incremental grammatical encoding during language production. Our experiment tests different variants of a computational-cognitive model that combines an empirically validated framework of general cognition, ACT-R, with a linguistic theory, Combinatory Categorial Grammar. The model is induced from a corpus of spoken dialogue. This methodology facilitates comparison of different strategies and working memory capacities according to the similarity of the model’s produced sentences to the corpus sentences. The experiment presented shows that while having more working memory available improves performance, using less working memory during realization does as well, even after controlling sentence length. Sentences realized with a more incremental strategy also appear to more closely track the naturalistic data. As high incrementality is correlated with low working memory usage, this study offers a possible mechanism by which syntactic incrementality can be explained. Finally, this paper proposes a multi-disciplinary modeling and simulation-based approach to empirical psycholinguistic inquiry.     

A hybrid model using neural networks and ACT-R

How do people make decisions given contradictory information? This paper presents a model of how expert DSOs (defensive system operators) on a B1 bomber examine a complex series of signals, categorize whether those signals are dangerous or not, and then make a decision on the basis of those signals. This decision is made more difficult because an automatic on-board computer sometimes identifies the signal incorrectly. Therefore, the DSO must compare the actual signal to the system ID “guess.” The proposed model is a hybrid model, combining a standard neural network and ACT-R, a production system, which achieves a high degree of success.     

Environments That Make Us Smart: Ecological Rationality

ABSTRACT— Traditional views of rationality posit general-purpose decision mechanisms based on logic or optimization. The study of ecological rationality focuses on uncovering the "adaptive toolbox" of domain-specific simple heuristics that real, computationally bounded minds employ, and explaining how these heuristics produce accurate decisions by exploiting the structures of information in the environments in which they are applied. Knowing when and how people use particular heuristics can facilitate the shaping of environments to engender better decisions.     

SSL: a theory of how people learn to select strategies

The assumption that people possess a repertoire of strategies to solve the inference problems they face has been raised repeatedly. However, a computational model specifying how people select strategies from their repertoire is still lacking. The proposed strategy selection learning (SSL) theory predicts a strategy selection process on the basis of reinforcement learning. The theory assumes that individuals develop subjective expectations for the strategies they have and select strategies proportional to their expectations, which are then updated on the basis of subsequent experience. The learning assumption was supported in 4 experimental studies. Participants substantially improved their inferences through feedback. In all 4 studies, the best-performing strategy from the participants' repertoires most accurately predicted the inferences after sufficient learning opportunities. When testing SSL against 3 models representing extensions of SSL and against an exemplar model assuming a memory-based inference process, the authors found that SSL predicted the inferences most accurately.     

Fluency heuristic: a model of how the mind exploits a by-product of information retrieval

Boundedly rational heuristics for inference can be surprisingly accurate and frugal for several reasons. They can exploit environmental structures, co-opt complex capacities, and elude effortful search by exploiting information that automatically arrives on the mental stage. The fluency heuristic is a prime example of a heuristic that makes the most of an automatic by-product of retrieval from memory, namely, retrieval fluency. In 4 experiments, the authors show that retrieval fluency can be a proxy for real-world quantities, that people can discriminate between two objects' retrieval fluencies, and that people's inferences are in line with the fluency heuristic (in particular fast inferences) and with experimentally manipulated fluency. The authors conclude that the fluency heuristic may be one tool in the mind's repertoire of strategies that artfully probes memory for encapsulated frequency information that can veridically reflect statistical regularities in the world.     

Personal Publication Assistant: Abstract recommendations by a cognitive model

This paper discusses an analysis of how scientists select relevant publications, and an application that can assist scientists in this information selection task. The application, called the Personal Publication Assistant, is based on the assumption that successful information selection is driven by recognizing familiar terms. To adapt itself to a researcher’s interests, the system takes into account what words have been used in a particular researcher’s abstracts, and when these words have been used. The user model underlying the Personal Publication Assistant is based on a rational analysis of memory, and takes the form of a model of declarative memory as developed for the cognitive architecture ACT-R. We discuss an experiment testing the assumptions of this model and present a user study that validates the implementation of the Personal Publication Assistant. The user study shows that the Personal Publication Assistant can successfully make an initial selection of relevant papers from a large collection of scientific literature.     

Set as an Instance of a Real‐World Visual‐Cognitive Task

Complex problem solving is often an integration of perceptual processing and deliberate planning. But what balances these two processes, and how do novices differ from experts? We investigate the interplay between these two in the game of SET. This article investigates how people combine bottom‐up visual processes and top‐down planning to succeed in this game. Using combinatorial and mixed‐effect regression analysis of eye‐movement protocols and a cognitive model of a human player, we show that SET players deploy both bottom‐up and top‐down processes in parallel to accomplish the same task. The combination of competition and cooperation of both types of processes is a major factor of success in the game. Finally, we explore strategies players use during the game. Our findings suggest that within‐trial strategy shifts can occur without the need of explicit meta‐cognitive control, but rather implicitly as a result of evolving memory activations.