While action selection strategies in well‐defined domains have received considerable attention, little is yet known about how people choose what to do next in ill‐defined tasks. In this contribution, we shed light on this issue by considering everyday tasks, which in many cases have a multitude of possible solutions (e.g., it does not matter in which order the items are brought to the table when setting a table) and are thus categorized as ill‐defined problems. Even if there are no hard (...) constraints on the ordering of subtasks in everyday activities, our research shows that people exhibit specific preferences. We propose that these preferences arise from bounded rationality, that is, people only have limited knowledge and processing power available, which results in a preference to minimize the overall physical and cognitive effort. In the context of everyday activities, this can be achieved by (a) taking properties of the spatial environment into account to use them to one's advantage, and (b) employing a stepwise‐optimal action selection strategy. We present the Opportunistic Planning Model as an explanatory cognitive model, which instantiates these assumptions, and show that the model is able to generalize to new everyday tasks, outperforming machine learning models such as neural networks during generalization. (shrink)

Topics in Cognitive Science, Volume 14, Issue 2, Page 214-222, April 2022.

This study explores the way that belief systems, interactions with social or experimental environments, and skills at the “control” level in decision‐making shape people's behavior as they solve problems. It is argued that problem‐solvers' beliefs (not necessarily consciously held) about what is useful in mathematics may determine the set of “cognitive resources” at their disposal as they do mathematics. Such beliefs may, for example, render inaccessible to them large bodies of information that are stored in long‐term memory and that are (...) easily retrieved in other circumstances. In other cases, individuals' reactions to an experimental setting (fear of failure, or the desire to “look mathematical” while being videotaped) may induce behavior that is almost pathological—and at the same time, so consistent that it can be modeled. In general, such “environmental” factors establish the context within which individuals access and utilize the information potentially at their disposal.Protocols illustrating these points are presented and discussed. A model based on an axiomatization of students' beliefs about plane geometry is outlined, and is shown to correspond closely to their problem‐solving performance. A framework is offered for analyzing problem‐solving performance at three qualitatively different levels: access to cognitive resources stored in LTM, executive or control decision‐making, and belief systems. (shrink)

Most models of computer programming explain the programmer's behaviour by a single design strategy. This article presents a cognitive architecture that uses cue‐based search to model multiple design strategies including procedural, functional, means‐end or focal, and opportunistic design. The model has been implemented in an artificial intelligence (AI) system that generates Pascal programs from English specifications.Knowledge is represented as nodes that reside in internal or external memory, where a node encodes an action that may range from a line of code (...) to a routine in size. A program is built by linking nodes through a search cue of the form. The cue is broadcast to memory, and any matching node is returned; the cue provides a question to ask, and the return provides the answer. A cue on the newly linked node is then selected as a new focus, and the search process repeated. Each design strategy defines a specific node visiting order that traverses the program structure through its links. (shrink)

This paper examines human planning abilities, using as its inspiration planning techniques developed in artificial intelligence. AI research has shown that in certain problems partial‐order planners, which manipulate partial plans while not committing to a particular ordering of those partial plans, are more efficient than total‐order planners, which represent all partial plans as totally ordered. This research asks whether total‐order planning and/or partial‐order planning are accurate descriptions of human planning, and if different populations use different planning techniques. Using a simple (...) planning task modeled after tasks designed in artificial intelligence we tested 7–8 year‐old children, 11–13 year‐old children, adult controls, and adults with damage to the prefrontal cortex. We found that adults and older children exhibited performance on planning tasks of varying complexity which matched that of artificial partial‐order planners, and that this pattern of performance did not vary with multiple presentations of the planning task. In contrast, young children and adults with damage to the prefrontal cortex exhibited performance matching that of artificial total‐order planners. This pattern of performance did vary, however, with multiple presentations of the planning task, with the young children and adults with cortical damage displaying aspects of total‐order planning. In a further study we found that adolescents who had sustained damage to the prefrontal cortex as children displayed two different patterns of performance; when measures of reaction time were analyzed they revealed a pattern of performance suggestive of partial‐order plan representations. However, analyses of the adolescents' protocols revealed a pattern of performance suggestive of total‐order plan representations. The significance of these results to psychology, neuroscience, and artificial intelligence are discussed. (shrink)

Exposing the chimpanzee to language training appears to enhance the animal's ability to perform some kinds of tasks but not others. The abilities that are enhanced involve abstract judgment, as in analogical reasoning, matching proportions of physically unlike exemplars, and completing incomplete representations of action. The abilities that do not improve concern the location of items in space and the inferences one might make in attempting to obtain them. Representing items in space and making inferences about them could be done (...) with an imaginal code, but representing relations and judging the relations between them, as in analogies, requires a more abstract code. Language training cannot instill such an abstract code, but for species that have the code to start with, it may enhance the animal's ability to use it. (shrink)

This study examined the role of key causal analysis strategies in forecasting and ethical decision-making. Undergraduate participants took on the role of the key actor in several ethical problems and were asked to identify and analyze the causes, forecast potential outcomes, and make a decision about each problem. Time pressure and analytic mindset were manipulated while participants worked through these problems. The results indicated that forecast quality was associated with decision ethicality, and the identification of the critical causes of the (...) problem was associated with both higher quality forecasts and higher ethicality of decisions. Neither time pressure nor analytic mindset impacted forecasts or ethicality of decisions. Theoretical and practical implications of these findings are discussed. (shrink)

Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking (...) machines will have to reach beyond current engineering trends in both what they learn and how they learn it. Specifically, we argue that these machines should build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; ground learning in intuitive theories of physics and psychology to support and enrich the knowledge that is learned; and harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes toward these goals that can combine the strengths of recent neural network advances with more structured cognitive models. (shrink)