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Human memory has various deficits such as forgetting. Such deficits are generally regarded as human irrationality. However, superficial deficits in human cognition can be understood differently as rational aspects in terms of the interaction between human cognition and the environmental feature. Based on this idea, the present study analyzed the nature of memory-based judgment errors. We hypothesized that systematic errors are produced when ecologically rational inferences based on statistical regularity in the environment are performed in uncertain situations. To verify this hypothesis, we proposed a benchmark for a rational inference model of memory-based judgments under uncertainty, and tested it by analyzing real-world data, computer simulations, and a behavioral experiment. We found that the error patterns participants showed in the memory-based judgment were consistent with those predicted by the rational inference benchmark. These findings provide new insights into the errors produced by memory-based judgments from the rational side of cognition.

Previous studies have shown that people’s decisions are af-fected by context in various ways, even when they are providedwith the same or analogous information. In the present study,we analyzed decisions based on verbally expressed probabilis-tic phrases (verbal probabilities) and examined how contextualfactors affected such decisions. In particular, we focused on thedifference in contexts that produced different probabilistic be-liefs with regards to uncertain events. We hypothesized thatsuch contextual effects could be explained in terms of a Deci-sion by Sampling (DbS) account (Stewart et al., 2006). In orderto examine our hypothesis, we proposed a modified version ofDbS, Decision by Belief Sampling (DbBS), and conducted be-havioral experiment about decision making. In this experiment,we set different contexts that we expected to produce differentprobabilistic beliefs regarding uncertain events for decision-makers and examined how such differences affected decisionmaking. Results showed that decisions were significantly af-fected by the variation in contexts, and DbBS well explainedsuch effects.

Verbal probabilistic expressions (verbal probabilities) contain a communicative function called directionality and can becategorized as positive (e.g., likely or probable) or negative (e.g., unlikely or doubtful) on the ba-sis of their directionality.Previous studies have demonstrated that the directionality of phrases affects decisions. In particular, people tend to be morerisk seeking when presented with positive phrases and risk averse when presented with negative phrases. The rationality(i.e., maximizing utility) of such seemingly biased decisions is examined in this study. We hypothesize that because aspeaker tends to choose a positive or negative expression on the basis of context, the selected phrase works as an adaptivecue for understanding the situational change, and that decision biases based on differences in expressions will lead tomore rational decision making. Computer simulations were conducted regarding decisions with uncertainty based onverbal probabilities. We found that despite speaker biases in probability judgments, miscommunication generated by thevagueness of verbal expressions, and individual differences in subjective values, biased deci-sion makers who changedtheir risk attitude on the basis of the directionality of verbal probabilities could make more decisions that were rationalthan could those who did not show such decision biases.

Previous studies have shown that people can make adaptive in-ferences based on memory-based simple heuristics such asrecognition, fluency, or familiarity heuristic. In the presentstudy, we discussed the adaptive nature of memory-based sim-ple heuristics in a group decision making setting. In particular,we examined how the diversity of memory affected group de-cision making when group members were assumed to make in-ferences based on the familiarity heuristic. We predicted that,when the group members’ memories were diverse, group deci-sion making would become more accurate. To examine thisprediction, we conducted a behavioral experiment and com-puter simulations, and our results generally supported the pre-diction. We discuss the role of diverse memories in generatingadaptive group decision making.

We examined whether probability weighting in decisions madeunder risk changed depending on the difference in evaluationmethods. In particular, we focused on two methods, joint eval-uation (JE) and separate evaluation (SE). We conducted a be-havioral experiment and found that participants put more prob-ability weight on small probability when using the SE methodthan when using JE, and that for large probabilities, the inversewas observed (i.e., participants put more weight in JE). We an-alyzed these results using a cognitive model and found that par-ticipants’ subjective value of money does not change owing todifferences in evaluation methods. However, beliefs concern-ing uncertain events shifted depending on evaluation methods,which led to the differences in probability weight. In this paper,we also discuss psychological mechanisms that produce differ-ent judgments or evaluations between SE and JE.

We examined decisions based on verbal probability phrases,such as "small chance," "likely," or "doubtful" (we call thesephrases verbal probabilities). Verbal probabilities havecommunicative functions called directionality and can becategorized into positive (e.g., "likely" or "probable") ornegative (e.g., “unlikely,” “doubtful”) phrases in terms oftheir directionality. Previous studies have shown that thedirectionality of phrases affects decisions. Although suchdecisions seem biased, we argue that they are not. Wehypothesize that since a speaker has the option to choose thedirectionality used during communication, the selecteddirectionality becomes relevant information to a decisionmaker, and is taken into account in making decisions. Wemodeled these processes using the Decision by BeliefSampling (DbBS) model. We found that the observed datacould be well explained by our hypothesis, and that the DbBSmodel could be one of the best potential models for decisionsbased on verbal probability information.

Previous studies have shown that individuals often makeinferences based on heuristics using recognition, fluency, orfamiliarity. In the present study, we propose a new heuristiccalled familiarity-matching, which predicts that when a decisionmaker is familiar (or unfamiliar) with an object in a questionsentence, s/he will choose the more (or less) familiar object fromthe two alternatives. We examined inference processes andecological rationality regarding familiarity-matching throughthree studies including behavioral experiments and ecologicalanalyses. Results showed that participants often used familiarity-matching in solving difficult binary choice problems, and thatfamiliarity-matching could be applied in an ecologically rationalmanner in real-world situations. A new perspective on humancognitive processes is discussed in this study.

Previous studies have shown that people’s memories arechangeable, and systematic incorrect memories (e.g., falsememory) can be created. We hypothesize that people’s beliefsabout the real world can be changed similarly to the waysystematic incorrect memories and systematic incorrectbeliefs (which we call memory-based false belief) aregenerated. We also predict that since memory-based falsebeliefs are consistent with abstract knowledge that isconsisted with prototypical patterns and organization found inthe real world, false beliefs work adaptively in makinginferences about environmental information in the real world.We conducted behavioral and simulation studies in order toexamine our hypotheses on people’s beliefs and inferencesabout the real world. The results showed that participants hadsystematic false beliefs about cities’ attributes (e.g., whetherthey have a professional baseball team), and that such falsebeliefs worked adaptively in making inferences aboutpopulation size.

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.

In two empirical studies of attention allocation during category learning, we investigate the idea that category learners learn to allocate attention optimally across stimulus dimensions. We argue that 'optimal' patterns of attention allocation are model or process specific, that human learners do not always optimize attention, and that one reason they rail to do so is that under certain conditions the cost of information retrieval of use may affect the attentional strategy adopted by learners. We empirically investigate these issues using a computer interface incorporating an 'information-board' display that collects detailed information on participants' patterns of' attention allocation and information search during learning trials. Experiment 1 investigated the effects on attention allocation of distributing perfectly diagnostic features across stimulus dimensions versus within one dimension. The overall pattern of viewing times supported the optimal attention allocation hypothesis, but a more detailed analysis produced evidence of instance- or category-specific attention allocation, a phenomenon not predicted by prominent computational models of category learning. Experiment 2 investigated the strategies adopted by category learners encountering redundant perfectly predictive cues. Here, the majority of participants learned to distribute attention optimally in a cost-benefit sense, allocating attention primarily to only one of the two perfectly predictive dimensions. These results suggest that learners may take situational costs and benefits into account, and they present challenges for computational models of learning that allocate attention by weighting stimulus dimensions.