People Like Logical Truth: Testing the Intuitive Detection of Logical Value in Basic Propositions

<div><p>Recent studies on logical reasoning have suggested that people are intuitively aware of the logical validity of syllogisms or that they intuitively detect conflict between heuristic responses and logical norms via slight changes in their feelings. According to logical intuition studies, logically valid or heuristic logic no-conflict reasoning is fluently processed and induces positive feelings without conscious awareness. One criticism states that such effects of logicality disappear when confounding factors such as the content of syllogisms are controlled. The present study used abstract propositions and tested whether people intuitively detect logical value. Experiment 1 presented four logical propositions (conjunctive, biconditional, conditional, and material implications) regarding a target case and asked the participants to rate the extent to which they liked the statement. Experiment 2 tested the effects of matching bias, as well as intuitive logic, on the reasoners’ feelings by manipulating whether the antecedent or consequent (or both) of the conditional was affirmed or negated. The results showed that both logicality and matching bias affected the reasoners’ feelings, and people preferred logically true targets over logically false ones for all forms of propositions. These results suggest that people intuitively detect what is true from what is false during abstract reasoning. Additionally, a Bayesian mixed model meta-analysis of conditionals indicated that people’s intuitive interpretation of the conditional “<i>if p then q”</i> fits better with the conditional probability, <i>q given p</i>.</p></div

Summary of the Results of the Bayesian Mixed Model Meta-analysis [log(BF)].

<p>Summary of the Results of the Bayesian Mixed Model Meta-analysis [log(BF)].</p

Percentages for the Response Categories (True, False, and Void) in the Truth Table Task in Experiment 2.

<p>Percentages for the Response Categories (True, False, and Void) in the Truth Table Task in Experiment 2.</p

Density and Credible Intervals of 1000 Posterior Samples for the relevant fixed effects of the Maximal Random Effect Structure for the Fixed Effects of the Suppositional * Matching Model.

<p>Vertical lines depict the 25–75% posterior probability intervals.</p

Mean Liking Ratings and Cousineau-Morey Difference Adjusted 95% Confidence Intervals for the Four Targets and Matching in Experiment 2.

<p>Note: ++ double matching, +- antecedent matching, -+ consequent matching,—double mismatching</p

Mean Liking Ratings and Cousineau-Morey Difference Adjusted 95% Confidence Intervals for the Four Logical Forms and Targets in Experiment 1.

<p>Mean Liking Ratings and Cousineau-Morey Difference Adjusted 95% Confidence Intervals for the Four Logical Forms and Targets in Experiment 1.</p

A Negated Paradigm of a Conditional With an Example of the Conditional Statements and Targets.

<p>A Negated Paradigm of a Conditional With an Example of the Conditional Statements and Targets.</p

Example of the Liking Rating Task in Experiment 1 (Conjunctive Form and <i>FT</i> Target).

<p>Example of the Liking Rating Task in Experiment 1 (Conjunctive Form and <i>FT</i> Target).</p

Why Verbalization of Non-Verbal Memory Reduces Recognition Accuracy: A Computational Approach to Verbal Overshadowing

<div><p>Verbal overshadowing refers to a phenomenon whereby verbalization of non-verbal stimuli (e.g., facial features) during the maintenance phase (after the target information is no longer available from the sensory inputs) impairs subsequent non-verbal recognition accuracy. Two primary mechanisms have been proposed for verbal overshadowing, namely the recoding interference hypothesis, and the transfer-inappropriate processing shift. The former assumes that verbalization renders non-verbal representations less accurate. In contrast, the latter assumes that verbalization shifts processing operations to a verbal mode and increases the chance of failing to return to non-verbal, face-specific processing operations (i.e., intact, yet inaccessible non-verbal representations). To date, certain psychological phenomena have been advocated as inconsistent with the recoding-interference hypothesis. These include a decline in non-verbal memory performance following verbalization of non-target faces, and occasional failures to detect a significant correlation between the accuracy of verbal descriptions and the non-verbal memory performance. Contrary to these arguments against the recoding interference hypothesis, however, the present computational model instantiated core processing principles of the recoding interference hypothesis to simulate face recognition, and nonetheless successfully reproduced these behavioral phenomena, as well as the standard verbal overshadowing. These results demonstrate the plausibility of the recoding interference hypothesis to account for verbal overshadowing, and suggest there is no need to implement separable mechanisms (e.g., operation-specific representations, different processing principles, etc.). In addition, detailed inspections of the internal processing of the model clarified how verbalization rendered internal representations less accurate and how such representations led to reduced recognition accuracy, thereby offering a computationally grounded explanation. Finally, the model also provided an explanation as to why some studies have failed to report verbal overshadowing. Thus, the present study suggests it is not constructive to discuss whether verbal overshadowing exists or not in an all-or-none manner, and instead suggests a better experimental paradigm to further explore this phenomenon.</p></div

Distribution of polarity values for the “old” and “new” faces.

<p>Simulation 1 (without verbalization). The dotted vertical line indicates the decision criterion that produces “old”/“new” judgment performance comparable to humans.</p