Hidehito Honda

Directional Verbal Probabilities

Verbal probability expressions (e.g., it is possible or doubtful) convey not only vague numerical meanings (i.e., probability) but also semantic functions, called directionality. We performed two experiments to examine whether preferential judgments are consistent with numerical meanings of verbal probabilities regardless of directionality. The results showed that because of the effects of directionality, perceived degrees of certainty for verbal probabilities differed between a binary choice and a numerical translation (Experiment 1), and decisions based on a verbal probability do not correspond to those based on a numerical translation for verbal probabilities (Experiment 2). These findings suggest that directionality of verbal probabilities is an independent feature from numerical meanings; hence numerical meanings of verbal probability alone remain insufficient to explain the effects of directionality on preferential judgments.

The role of familiarity in binary choice inferences

In research on the recognition heuristic (Goldstein & Gigerenzer, Psychological Review, 109, 75–90, 2002), knowledge of recognized objects has been categorized as “recognized” or “unrecognized” without regard to the degree of familiarity of the recognized object. In the present article, we propose a new inference model—familiarity-based inference. We hypothesize that when subjective knowledge levels (familiarity) of recognized objects differ, the degree of familiarity of recognized objects will influence inferences. Specifically, people are predicted to infer that the more familiar object in a pair of two objects has a higher criterion value on the to-be-judged dimension. In two experiments, using a binary choice task, we examined inferences about populations in a pair of two cities. Results support predictions of familiarity-based inference. Participants inferred that the more familiar city in a pair was more populous. Statistical modeling showed that individual differences in familiarity-based inference lie in the sensitivity to differences in familiarity. In addition, we found that familiarity-based inference can be generally regarded as an ecologically rational inference. Furthermore, when cue knowledge about the inference criterion was available, participants made inferences based on the cue knowledge about population instead of familiarity. Implications of the role of familiarity in psychological processes are discussed.

Communicative functions of directional verbal probabilities: Speaker's choice, listener's inference, and reference points.

Verbal probabilities have directional communicative functions, and most can be categorized as positive (e.g., “it is likely”) or negative (e.g., “it is doubtful”). We examined the communicative functions of verbal probabilities based on the reference point hypothesis According to this hypothesis, listeners are sensitive to and can infer a speakers reference points based on the speakers selected directionality. In four experiments (two of which examined speakers’ choice of directionality and two of which examined listeners’ inferences about a speakers reference point), we found that listeners could make inferences about speakers’ reference points based on the stated directionality of verbal probability. Thus, the directionality of verbal probabilities serves the communicative function of conveying information about a speakers reference point.

Memory‐Based Simple Heuristics as Attribute Substitution: Competitive Tests of Binary Choice Inference Models

Some researchers on binary choice inference have argued that people make inferences based on simple heuristics, such as recognition, fluency, or familiarity. Others have argued that people make inferences based on available knowledge. To examine the boundary between heuristic and knowledge usage, we examine binary choice inference processes in terms of attribute substitution in heuristic use (Kahneman & Frederick, 2005). In this framework, it is predicted that people will rely on heuristic or knowledge-based inference depending on the subjective difficulty of the inference task. We conducted competitive tests of binary choice inference models representing simple heuristics (fluency and familiarity heuristics) and knowledge-based inference models. We found that a simple heuristic model (especially a familiarity heuristic model) explained inference patterns for subjectively difficult inference tasks, and that a knowledge-based inference model explained subjectively easy inference tasks. These results were consistent with the predictions of the attribute substitution framework. Issues on usage of simple heuristics and psychological processes are discussed.

Diversity of inference strategies can enhance the ‘wisdom-of-crowds’ effect

Studies on inference have shown that people use a variety of inference strategies depending on the situation. Despite a great deal of discussion on the use of these strategies at an individual level, very little research has examined how the strategies people use affect group performance. To address this issue, we conducted two computer simulation studies on group decision-making. Our focus was primarily the diversity of strategies used in groups, as previous studies have suggested that diversity plays a critical role in the wisdom of crowds. Therefore, we systematically manipulated the diversity of inference strategies among group members and examined the effect on group performance. In Study 1, we conducted computer simulations using behavioural data from a previous study and found that diversity of strategies could improve group performance. That is, the group whose members used diverse strategies had higher accuracy than groups where all members used an identical strategy. We also investigated how such a phenomenon emerged. In Study 2, we created multiple hypothetical environmental settings and examined the effect. The environmental settings in Study 1 was limited to the ‘kind’ setting, in which correct inferences could be made for most problems by using a certain strategy, and the results of Study 2 showed that the findings of Study 1 could be generalized to other settings. For example, diversity could improve group performance in the ‘wicked’ environment where an inference strategy tends to lead an individual to the wrong answer. We also identified conditions in which the diversity enhanced group performance in each environment. Finally, for Study 1, we conducted additional simulations and discussed the conditions in which diversity would improve group performance more. The contributions to the research on the wisdom of crowds and human inference are discussed.

Typicality or Fluency

The modern Japanese writing system comprises different scripts, such as Kanji, Hiragana, and Katakana. These scripts differ greatly in both typicality and frequency of usage. In two experimental studies using names of cities or prefectures in Japan as target stimuli, we examined two hypotheses, the typicality hypothesis and fluency hypothesis, in order to assess effects of Japanese script on psychological processes. It was found that Kanji names induced typical thinking in a participant’s description of a location, whereas Katakana names induced rather nontypical thinking. In contrast, we found that script differences did not affect distance estimations. We discuss these effects of Japanese script on psychological processes in terms of the typicality hypothesis (differences in typical usage habits between Kanji and Katakana that affect psychological processes).

On the relationships between social structures and acquired knowledge in societies

Many existing studies on human learning pay almost exclusive attention to how individuals learn. Unlike those studies, we examined influence of social structures on knowledge acquired by societies using computer simulations. We compared four types of social networks, namely regular, random, small world, and scale-free networks. When individual differences and the principle of homophily (i.e., people who have similar beliefs tend to have close relationships with each other) exist in societies, the societies would acquire pareto-optimal knowledge. We also investigated influences of highly connected individuals on knowledge acquired by societies. The results inarguably indicate that highly connected individuals play important roles in social learning, setting the standards for what type of knowledge to be acquired by societies.

Effects of Individual Differences on Knowledge and Wisdom of Society: A Social Modeling Approach

Categorically organized knowledge is the main vehicle in high-level cognitive processes. The previous empirical and theoretical studies on categorization paid almost exclusive attention to how individuals learn categorical knowledge. In the real world, however, people acquire knowledge not only through individual learning, but also through interacting with others. In the present study, using computational modeling, we explored how social interactions would produce unique dynamics of knowledge acquisition that cannot be examined by studies on micro level processes. The results of simulation studies showed that when there were several clusters of individuals in a society where individuals held different beliefs about what constitutes ”good” knowledge, then the society as a whole formed Pareto-optimal knowledge. That is, there was no cluster of knowledge that was simultaneously worse in two important aspects of knowledge (i.e., accuracy and simplicity) as compared with those of other clusters in a mature society.

Modeling high-order human intelligence with intelligence of swarm

Particle Swarm Optimization (PSO) is a type of meta-heuristic optimization method built on the basis of the principle of collective behaviors exhibited by simple organisms. Although PSO is a model of social behaviors, the present research attempts to model learning behaviors of an individual human with PSO in order to evaluate our hypothesis that the dynamics of knowledge that are being acquired and updated in our mind resemble the dynamics of social interactions exhibited by swarms. A simulation study showed that a cognitive model with PSO was able to replicate not only manifested cognitive behaviors but also latent cognitive behaviors, resulting in the acquisition of at least two dissimilar yet functional solutions for a given task.

On the Knowledge Organization in Concept Formation: An Exploratory Cognitive Modeling Study

Recent cognitive modeling studies suggest the effectiveness of meta-heuristic optimization in describing human cognitive behaviors. Such models are built on the basis of population-based algorithm (e.g., genetic algorithm) and thus hold multiple solutions or notions. There are, however, important yet unaddressed issues in cognitive mechanisms associated with possession of multiple notions. The issues we address in the present research is about how multiple notions are organized in our mind. In particular, we paid close attention to how each notion interact with other notions while learning a new concept. In so doing, we incorporated Particle Swarm Optimization in a cognitive model of concept learning. Three PSO-based concept learning models were developed and compared in the present exploratory cognitive modeling study.