The mediating effect of geospatial thinking on the relationship between family capital and sense of place

Few studies have examined how family capital affects the sense of place, and the effect of spatial thinking on the relationship between the two is unclear. This study constructs a mediation model to examine the impact of family capital on sense of place and the mediation effect of geospatial thinking. A total of 1,004 upper-secondary-school students were surveyed using the Family Capital Questionnaire, the Geospatial Thinking Test, and the Sense of Place Scale. The correlation analysis showed that family capital has a positive effect on both sense of place and geospatial thinking. Moreover, there is also a significant positive correlation between geospatial thinking and sense of place. The results of mediation analysis indicated that geospatial thinking plays mediating and buffering roles in the relationship between family capital and sense of place after controlling for gender and residential address. The direct and indirect effects accounted for 73.31 and 26.69% of the total effect, respectively. Specifically, family capital is a significant positive predictor of both sense of place and geospatial thinking, and geospatial thinking partially mediates the relationship between family capital and sense of place. Students from better family backgrounds are more likely to have a better geospatial thinking and sense of place, as well as geospatial thinking promotes the development of a sense of place. Therefore, both family capital and geospatial thinking should be considered when we want to examine and develop individuals’ level of sense of place.

Naming the Parents of the THOG: Mental Representation and Reasoning

It was hypothesized that subjects misrepresent the THOG problem by confusing data and hypotheses. An abstract version of the problem in which the given exemplars and the hypothetical ones were designated by two different labels was used in three experiments. Experiment 1 showed that this version elicits a better performance than the standard version of the problem. Experiments 2 and 3 confirmed these results, by ruling out a possible alternative account of the facilitatory effect obtained in Experiment 1. The present results are discussed in relationship to the general issues of content effects and non-consequentialism in reasoning.

Inference-driven attention in symbolic and perceptual tasks: Biases toward expected and unexpected inputs

The aims of this paper are (a) to gather support for the hypothesis that some basic mechanisms of attentional deployment (i.e., its high efficiency in dealing with expected and unexpected inputs) meet the requirements of the inferential system and have possibly evolved to support its functioning, and (b) to show that these orienting mechanisms function in very similar ways in two perceptual tasks and in a symbolic task. The general hypothesis and its predictions are sketched in the Introduction, after a discussion of current findings concerning visual attention and the generalities of the inferential system. In the empirical section, three experiments are presented where participants tracked visual trajectories (Experiments 1 and 3) or arithmetic series (Experiments 2 and 3), responding to the onset of a target event (e.g., to a specific number) and to the repetition of an event (e.g., to a number appearing twice consecutively). Target events could be anticipated when they were embedded in regular series/trajectories; they could be anticipated, with the anticipation later disconfirmed, when a regular series/trajectory was abruptly interrupted before the target event occurred; and they could not be anticipated when the series/trajectory was random. Repeated events could not be anticipated. Results show a very similar pattern of allocation in tracking visual trajectories and arithmetic series: Attention is focused on anticipated events; it is defocused and redistributed when an anticipation is not confirmed by ensuing events; however, performance decreases when dealing with random series/trajectory—that is, in the absence of anticipations. In our view, this is due to the fact that confirmed and disconfirmed anticipations are crucial events for “knowledge revision”—that is, the fine tuning of the inferential system to the environment; attentional mechanisms have developed so as to enhance detection of these events, possibly at all levels of inferential processing.

Base-rate sensitivity through implicit learning

Two experiments assessed the contributions of implicit and explicit learning to base-rate sensitivity. Using a factorial design that included both implicit and explicit learning disruptions, we tested the hypothesis that implicit learning underlies base-rate sensitivity from experience (and that explicit learning contributes comparatively little). Participants learned to classify two categories of simple stimuli (bar graph heights) presented in a 3:1 base-rate ratio. Participants learned either from “observational” training to disrupt implicit learning or “response” training which supports implicit learning. Category label feedback on each trial was followed either immediately or after a 2.5 second delay by onset of a working memory task intended to disrupt explicit reasoning about category membership feedback. Decision criterion values were significantly larger following response training, suggesting that implicit learning underlies base-rate sensitivity. Disrupting explicit processing had no effect on base-rate learning as long as implicit learning was supported. These results suggest base-rate sensitivity develops from experience primarily through implicit learning, consistent with separate learning systems accounts of categorization.

Intuitive Cognition and Models of Human-Automation Interaction

OBJECTIVE

The aim of this study was to provide an analysis of the implications of the dominance of intuitive cognition in human reasoning and decision making for conceptualizing models and taxonomies of human-automation interaction, focusing on the Parasuraman et al. model and taxonomy.

BACKGROUND

Knowledge about how humans reason and make decisions, which has been shown to be largely intuitive, has implications for the design of future human-machine systems.

METHOD

One hundred twenty articles and books cited in other works as well as those obtained from an Internet search were reviewed. Works were deemed eligible if they were published within the past 50 years and common to a given literature.

RESULTS

Analysis shows that intuitive cognition dominates human reasoning and decision making in all situations examined. The implications of the dominance of intuitive cognition for the Parasuraman et al. model and taxonomy are discussed. A taxonomy of human-automation interaction that incorporates intuitive cognition is suggested.

APPLICATION

Understanding the ways in which human reasoning and decision making is intuitive can provide insight for future models and taxonomies of human-automation interaction.

Emotional Gestalts: Appraisal, Change, and the Dynamics of Affect

This article interprets emotional change as a transition in a complex dynamical system. We argue that the appropriate kind of dynamical system is one that extends recent work on how neural networks can perform parallel constraint satisfaction. Parallel processes that integrate both cognitive and affective constraints can give rise to states that we call emotional gestalts, and transitions can be understood as emotional gestalt shifts. We describe computational models that simulate such phenomena in ways that show how dynamical and gestalt metaphors can be given a concrete realization.

Control Processes and Self-Organization as Complementary Principles Underlying Behavior

This article addresses the convergence and complementarity between self-regulatory control-process models of behavior and dynamic systems models. The control-process view holds that people have a goal in mind and try to move toward it (or away from it), monitoring the extent to which a discrepancy remains between the goal and one's present state and taking steps to reduce the discrepancy (or enlarge it). Dynamic systems models tend to emphasize a bottom-up self-organization process, in which a coherence arises from among many simultaneous influences, moving the system toward attractors and away from repellers. We suggest that these differences in emphasis reflect two facets of a more complex reality involving both types of processes. Discussion focuses on how self-organization may occur within constituent elements of a feedback system—the input function, the output function, and goal values being used by the system—and how feedback processes themselves can reflect self-organizing tendencies.

Does office space occupation matter? The role of the number of persons per enclosed office space, psychosocial work characteristics, and environmental satisfaction in the physical and mental health of employees

The study examined the effects of office space occupation, psychosocial work characteristics, and environmental satisfaction on physical and mental health of office workers in small-sized and open-plan offices as well as possible underlying mechanisms. Office space occupation was characterized as number of persons per one enclosed office space. A total of 207 office employees with similar jobs in offices with different space occupation were surveyed regarding their work situation (psychosocial work characteristics, satisfaction with privacy, acoustics, and control) and health (psychosomatic complaints, irritation, mental well-being, and work ability). Binary logistic and linear regression analyses as well as bootstrapped mediation analyses were used to determine associations and underlying mechanisms. Employee health was significantly associated with all work characteristics. Psychosocial work stressors had the strongest relation to physical and mental health (OR range: 1.66-3.72). The effect of office space occupation on employee health was mediated by stressors and environmental satisfaction, but not by psychosocial work resources. As assumed by sociotechnical approaches, a higher number of persons per enclosed office space was associated with adverse health effects. However, the strongest associations were found with psychosocial work stressors. When revising office design, a holistic approach to work (re)design is needed.

Cognitive Behavioral and Psychodynamic Therapies: Points of Intersection and Divergence

Cognitive behavioral therapy (CBT) and psychodynamic therapy (PDT) are two major paradigms in the mental health care field. The present article reviews broad similarities and differences between each tradition while acknowledging that such generalizations may overlook heterogeneity within each. However, it is believed that a comparison between CBT and PDT is beneficial in dispelling myths about each tradition, fostering dialogue, encouraging further scholarship and research. While not an exhaustive account, this article will examine how CBT and PDT differ in how they view several topics such as the unconscious, the therapeutic alliance, the role of homework, symptom reduction, and therapeutic heuristics. Commentary is also offered on how research may be more effectively and collaboratively integrated with clinical work from both traditions. Future directions for partnership and improving mental health treatments are also discussed.

Implicit learning mediates base rate acquisition in perceptual categorization

We explored the possibility, suggested by Koehler (Behavioral and Brain Sciences, 19, 1-53, 1996; also Spellman Behavioral and Brain Sciences, 19, 38, 1996), that implicit learning mediates the influence of base-rates on category knowledge acquired through direct experience. In two experiments, participants learned simple perceptual categories with unequal base-rates (i.e., presentation frequency). In Experiment 1, participants received either response training or observational training. In Experiment 2, participants received response training with either immediate or delayed feedback. In previous studies, observational training and delayed feedback training have been shown to disrupt implicit learning. We found that base-rate influence was weaker in these conditions when category discriminability was low (i.e., when category membership was difficult to determine). This conclusion was based on signal detection β values as well as decision-bound modeling results. Because these disruptions to implicit learning attenuate the base-rate effect, we conclude that implicit learning does indeed underlie the influence of base-rates learned through direct experience. This suggests that the implicit learning system postulated by the COVIS theory of categorization (Ashby, Alfonso-Reese, Turken, & Waldron Psychological Review, 105, 442-481, 1998) may be involved in developing sensitivity to category base-rates.

Yes It Can

Understanding the division of labor between conscious processes and unconscious ones is central to our understanding of the human mind. This article proposes a simple “Yes It Can” (or YIC) principle: It argues that unconscious processes can perform the same fundamental, high-level functions that conscious processes can perform. The author presents considerations of evolutionary pressures and of the availability of mental resources that render YIC a reasonable hypothesis. Evidence is then reviewed from various subfields of the cognitive sciences, which shows that functions that were traditionally thought of as requiring consciousness can occur nonconsciously. On the basis of these data and arguments, it is proposed that an answer to the question “What is it that consciousness does?” would not be in the form of “Consciousness is necessary for F,” where F is a fundamental, high-level cognitive function. In Marr’s (1982) terms, the argument is that computationally conscious and unconscious processes are very similar. Yet differences in how these processes kick in and in the ways in which they play out (Marr’s algorithmic-representational level) are likely to have interesting implications for human cognition, motivation, and emotion.

Decisions beyond boundaries: when more information is processed faster than less

Bounded rationality models usually converge in claiming that decision time and the amount of computational steps needed to come to a decision are positively correlated. The empirical evidence for this claim is, however, equivocal. We conducted a study that tests this claim by adding and omitting information. We demonstrate that even an increase in information amount can yield a decrease in decision time if the added information increases coherence in the information set. Rather than being influenced by amount of information, decision time systematically increased with decreasing coherence. The results are discussed with reference to a parallel constraint satisfaction approach to decision making, which assumes that information integration is operated in an automatic, holistic manner.

Recruitment of intuitive versus analytic thinking strategies affects the role of working memory in a gambling task

Previous studies found mixed results concerning the role of working memory (WM) in the gambling task (GT). Here, we aimed at reconciling inconsistencies by showing that the standard version of the task can be solved using intuitive strategies operating automatically, while more complex versions require analytic strategies drawing on executive functions. In Study 1, where good performance on the GT could be achieved using intuitive strategies, participants performed well both with and without a concurrent WM load. In Study 2, where analytical strategies were required to solve a more complex version of the GT, participants without WM load performed well, while participants with WM load performed poorly. In Study 3, where the complexity of the GT was further increased, participants in both conditions performed poorly. In addition to the standard performance measure, we used participants' subjective expected utility, showing that it differs from the standard measure in some important aspects.

Group-based trajectory modeling in clinical research

Group-based trajectory models are increasingly being applied in clinical research to map the developmental course of symptoms and assess heterogeneity in response to clinical interventions. In this review, we provide a nontechnical overview of group-based trajectory and growth mixture modeling alongside a sampling of how these models have been applied in clinical research. We discuss the challenges associated with the application of both types of group-based models and propose a set of preliminary guidelines for applied researchers to follow when reporting model results. Future directions in group-based modeling applications are discussed, including the use of trajectory models to facilitate causal inference when random assignment to treatment condition is not possible.

Physiological arousal in processing recognition information: Ignoring or integrating cognitive cues?

The recognition heuristic (RH; Goldstein & Gigerenzer, 2002) suggests that, when applicable, probabilistic inferences are based on a noncompensatory examination of whether an object is recognized or not. The overall findings on the processes that underlie this fast and frugal heuristic are somewhat mixed, and many studies have expressed the need for considering a more compensatory integration of recognition information. Regardless of the mechanism involved, it is clear that recognition has a strong influence on choices, and this finding might be explained by the fact that recognition cues arouse affect and thus receive more attention than cognitive cues. To test this assumption, we investigated whether recognition results in a direct affective signal by measuring physiological arousal (i.e., peripheral arterial tone) in the established city-size task. We found that recognition of cities does not directly result in increased physiological arousal. Moreover, the results show that physiological arousal increased with increasing inconsistency between recognition information and additional cue information. These findings support predictions derived by a compensatory Parallel Constraint Satisfaction model rather than predictions of noncompensatory models. Additional results concerning confidence ratings, response times, and choice proportions further demonstrated that recognition information and other cognitive cues are integrated in a compensatory manner.

The Dreyfus model of clinical problem-solving skills acquisition: a critical perspective

CONTEXT

The Dreyfus model describes how individuals progress through various levels in their acquisition of skills and subsumes ideas with regard to how individuals learn. Such a model is being accepted almost without debate from physicians to explain the 'acquisition' of clinical skills.

OBJECTIVES

This paper reviews such a model, discusses several controversial points, clarifies what kind of knowledge the model is about, and examines its coherence in terms of problem-solving skills. Dreyfus' main idea that intuition is a major aspect of expertise is also discussed in some detail. Relevant scientific evidence from cognitive science, psychology, and neuroscience is reviewed to accomplish these aims.

CONCLUSIONS

Although the Dreyfus model may partially explain the 'acquisition' of some skills, it is debatable if it can explain the acquisition of clinical skills. The complex nature of clinical problem-solving skills and the rich interplay between the implicit and explicit forms of knowledge must be taken into consideration when we want to explain 'acquisition' of clinical skills. The idea that experts work from intuition, not from reason, should be evaluated carefully.

Studying the use of base rates: Normal science or shifting paradigm?

The underutilization of base rates is a consistent finding. The strong claim that base rates are ignored has been rejected and this needs no further emphasis. Following the path of “normal science,” research examines the conditions predicting changes in the degree of underutilization. A scientific revolution that might dethrone the heuristics and biases paradigm is not in sight.

The base rate fallacy reconsidered: Descriptive, normative, and methodological challenges

We have been oversold on the base rate fallacy in probabilistic judgment from an empirical, normative, and methodological standpoint. At the empirical level, a thorough examination of the base rate literature (including the famous lawyer–engineer problem) does not support the conventional wisdom that people routinely ignore base rates. Quite the contrary, the literature shows that base rates are almost always used and that their degree of use depends on task structure and representation. Specifically, base rates play a relatively larger role in tasks where base rates are implicitly learned or can be represented in frequentist terms. Base rates are also used more when they are reliable and relatively more diagnostic than available individuating information. At the normative level, the base rate fallacy should be rejected because few tasks map unambiguously into the narrow framework that is held up as the standard of good decision making. Mechanical applications of Bayes's theorem to identify performance errors are inappropriate when (1) key assumptions of the model are either unchecked or grossly violated, and (2) no attempt is made to identify the decision maker's goals, values, and task assumptions. Methodologically, the current approach is criticized for its failure to consider how the ambiguous, unreliable, and unstable base rates of the real world are and should be used. Where decision makers' assumptions and goals vary, and where performance criteria are complex, the traditional Bayesian standard is insufficient. Even where predictive accuracy is the goal in commonly defined problems, there may be situations (e.g., informationally redundant environments) in which base rates can be ignored with impunity. A more ecologically valid research program is called for. This program should emphasize the development of prescriptive theory in rich, realistic decision environments.

The implicit use of base rates in experiential and ecologically valid tasks

When base rates are learned and used in an experiential manner subjects show better base rate use, perhaps because the implicit learning system is engaged. A causal framework in which base rates are relevant might also be necessary. Humans might thus perform better on more ecologically valid tasks, which are likely to contain those three components.

Base rates do not constrain nonprobability judgments

Base rates have no necessary relation to judgments that are not themselves probabilities. There is no logical imperative, for instance, that behavioral base rates must affect causal attributions or that base rate information should affect judgments of legal liability. Decision theorists should be cautious in arguing that base rates place normative constraints on judgments of anything other than posterior probabilities.

Judgment under uncertainty: Evolution may not favor a probabilistic calculus

The environment in which humans evolved is strongly and positively autocorrelated in space and time. Probabilistic judgments based on the assumption of independence may not yield evolutionarily adaptive behavior. A number of “faults” of human reasoning are not faulty under fuzzy arithmetic, a nonprobabilistic calculus of reasoning under uncertainty that may be closer to that underlying human decision making.

Fallacy and controversy about base rates

Koehler's target article attempts a balanced view of the relevance of knowledge of base rates to judgments of subjective or credal probability, but he is not sensitive enough to the difference between requiring and permitting the equation of probability judgments with base rates, the interaction between precision of base rate and reference class information, and the possibility of indeterminate probability judgment.

The purpose of experiments: Ecological validity versus comparing hypotheses

As illustrated by research Koehler himself cites (Dawes et al. 1993), the purpose of experiments is to choose between contrasting explanations of past observations – rather than to seek statistical generalizations about the prevalence of effects. True external validity results not from sampling various problems that are representative of “real world” decision making, but from reproducing an effect in the laboratory with minimal contamination (including from real world factors).

Physicians neglect base rates, and it matters

A recent study showed physicians' reasoning about a realistic case to be ignorant of base rate. It also showed physicians interpreting information pertinent to base rate differently, depending on whether it was presented early or late in the case. Although these adult reasoners might do better if given hints through talk of relative frequencies, this would not prove that they had no problem of base rate neglect.

Why do frequency formats improve Bayesian reasoning? Cognitive algorithms work on information, which needs representation

In contrast to traditional research on base-rate neglect, an ecologically-oriented research program would analyze the correspondence between cognitive algorithms and the nature of information in the environment. Bayesian computations turn out to be simpler when information is represented in frequency formats as opposed to the probability formats used in previous research. Frequency formats often enable even uninstructed subjects to perform Bayesian reasoning.

Base rates, experience, and the big picture

The important question is how people process probabilistic information, not whether they process it in accordance with a normative model that we never should have expected them to be capable of following. Experience is not the cure, as widely thought, to problems with utilizing base rate information.

Base rates, stereotypes, and judgmental accuracy

The base rate literature has an opposite twin in the social psychological literature on stereotypes, which concludes that people use their preexisting beliefs about probabilistic category attributes too much, rather than not enough. This ironic discrepancy arises because beliefs about category attributes enhance accuracy when the beliefs are accurate and diminish accuracy when they are not. To determine the accuracy of base rate/stereotype beliefs requires research that addresses specific content.

Characteristics of dissociable human learning systems

A number of ways of taxonomizing human learning have been proposed. We examine the evidence for one such proposal, namely, that there exist independent explicit and implicit learning systems. This combines two further distinctions, (1) between learning that takes place with versus without concurrent awareness, and (2) between learning that involves the encoding of instances (or fragments) versus the induction of abstract rules or hypotheses. Implicit learning is assumed to involve unconscious rule learning. We examine the evidence for implicit learning derived from subliminal learning, conditioning, artificial grammar learning, instrumental learning, and reaction times in sequence learning. We conclude that unconscious learning has not been satisfactorily established in any of these areas. The assumption that learning in some of these tasks (e.g., artificial grammar learning) is predominantly based on rule abstraction is questionable. When subjects cannot report the “implicitly learned” rules that govern stimulus selection, this is often because their knowledge consists of instances or fragments of the training stimuli rather than rules. In contrast to the distinction between conscious and unconscious learning, the distinction between instance and rule learning is a sound and meaningful way of taxonomizing human learning. We discuss various computational models of these two forms of learning.

Cognitive algebra versus representativeness heuristic

Cognitive algebra strongly disproved the representativeness heuristic almost before it was published; and therewith it also disproved the base rate fallacy. Cognitive algebra provides a theoretical foundation for judgment-decision theory through its joint solution to the two fundamental problems – true measurement of subjective values, and cognitive rules for integration of multiple determinants.

Throwing out the baby with the bathwater? Let's not overstate the overselling of the base rate fallacy

Koehler's summary and critique of research on the base rate fallacy is cogent and persuasive. However, he may have overstated the case, and his suggestions for future research may be too restrictive. We agree that methodological approaches to this topic should be broadened, but we argue that experimental laboratory research and the Bayesian normative standard are useful and should not be abandoned.

Probabilistic fallacies

Two distinct issues are sometimes confused in the base rate literature: Why do people make logical mistakes in the assessment of probabilities? and why do subjects not use base rates the way experimenters do? The latter problem may often reflect differences in an implicit reference class rather than a disinclination to update a base rate by Bayes' theorem. Also important are considerations concerning the interaction of several potentially relevant base rates.

Which reference class is evoked?

Any instance (i.e., event, behavior, trait) belongs to infinitely many reference classes, hence there are infinitely many base rates from which to choose. People clearly do not entertain all possible reference classes, however, so something must be limiting the search space. We suggest some possible mechanisms that determine which reference class is evoked for the purpose of judgment and decision.