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Discounting models are commonly applied to understand intertemporal choices. Similarity models provide an alternative, attribute-based approach where people compare the similarity of reward amounts and time delays for options and decide based on dissimilarity. Knowledge of other people's similarity judgments may affect an individual's similarity judgments, which can in turn affect subsequent intertemporal choices. We investigated the potential effects of social influence across three studies by having participants make similarity judgments and intertemporal choices before and after viewing other people's similarity judgments. We found that participants preferred larger but delayed intertemporal choice options more after they viewed similarity judgments that suggested a preference for larger, later rewards. Additionally, this change in preference seemed to result from a shift in participants' personal similarity judgments for reward amount and time delay pairs to match the social information. Our findings suggest that social information about similarity judgments can shape intertemporal choices, which can potentially be used to help increase people's preferences for options that benefit them in the long term.

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Convolutional neural networks (CNNs) are increasingly widely used in psychology and neuroscience to predict how human minds and brains respond to visual images. Typically, CNNs represent these images using thousands of features that are learned through extensive training on image datasets. This raises a question: How many of these features are really needed to model human behavior? Here, we attempt to estimate the number of dimensions in CNN representations that are required to capture human psychological representations in two ways: (1) directly, using human similarity judgments and (2) indirectly, in the context of categorization. In both cases, we find that low-dimensional projections of CNN representations are sufficient to predict human behavior. We show that these low-dimensional representations can be easily interpreted, providing further insight into how people represent visual information. A series of control studies indicate that these findings are not due to the size of the dataset we used and may be due to a high level of redundancy in the features appearing in CNN representations.

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Near-scale environments, like work desks, restaurant place settings or lab benches, are the interface of our hand-based interactions with the world. How are our conceptual representations of these environments organized? What properties distinguish among reachspaces, and why? We obtained 1.25 million similarity judgments on 990 reachspace images, and generated a 30-dimensional embedding which accurately predicts these judgments. Examination of the embedding dimensions revealed key properties underlying these judgments, such as reachspace layout, affordance, and visual appearance. Clustering performed over the embedding revealed four distinct interpretable classes of reachspaces, distinguishing among spaces related to food, electronics, analog activities, and storage or display. Finally, we found that reachspace similarity ratings were better predicted by the function of the spaces than their locations, suggesting that reachspaces are largely conceptualized in terms of the actions they support. Altogether, these results reveal the behaviorally-relevant principles that structure our internal representations of reach-relevant environments.

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Our research focuses on the perception of difference in the evaluations of positive and negative options. The literature provides evidence for two opposite effects: on the one hand, negative objects are said to be more differentiated (e.g., density hypothesis), on the other, people are shown to see greater differences between positive options (e.g., liking-breeds-differentiation principle). In our study, we investigated the perception of difference between fictitious political candidates, hypothesizing greater differences among the evaluations of favorable candidates. Additionally, we analyzed how positive and negative information affect candidate evaluation, predicting further asymmetries. In three experiments, participants evaluated various candidate profiles presented in a numeric and narrative manner. The evaluation tasks were designed as individual or joint assessments. In all three studies, we found more differentiation between positive than negative options. Our research suggests that after exceeding a certain, relatively small level of negativity, people do not see any further increase in negativity. The increase in positivity, on the other hand, is more gradual, with greater differentiation among positive options. Our findings are discussed in light of cognitive-experiential self-theory and density hypothesis.

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The success of forensic science depends heavily on human reasoning abilities. Although we typically navigate our lives well using those abilities, decades of psychological science research shows that human reasoning is not always rational. In addition, forensic science often demands that its practitioners reason in non-natural ways. This article addresses how characteristics of human reasoning (either specific to an individual or in general) and characteristics of situations (either specific to a case or in general in a lab) can contribute to errors before, during, or after forensic analyses. In feature comparison judgments, such as fingerprints or firearms, a main challenge is to avoid biases from extraneous knowledge or arising from the comparison method itself. In causal and process judgments, for example fire scenes or pathology, a main challenge is to keep multiple potential hypotheses open as the investigation continues. Considering the contributions to forensic science judgments by persons, situations, and their interaction, reveals ways to develop procedures to decrease errors and improve accuracy.

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Stimuli that evoke the same feelings can nevertheless look different and have different semantic meanings. Although we know much about the neural representation of emotion, the neural underpinnings of emotional similarity are unknown. One possibility is that the same brain regions represent similarity between emotional and neutral stimuli, perhaps with different strengths. Alternatively, emotional similarity could be coded in separate regions, possibly those sensitive to emotional valence and arousal. In behavior, the extent to which people consider similarity along emotional dimensions when they evaluate the overall similarity between stimuli has never been investigated. Although the emotional features of stimuli may dominate explicit ratings of similarity, it is also possible that people neglect emotional dimensions as irrelevant to that judgment. We contrasted these hypotheses in (male and female) healthy controls using two measures of similarity and two picture databases of complex negative and neutral scenes, the second of which provided exquisite control over semantic and visual attributes. The similarity between emotional stimuli was greater than between neutral stimuli in the inferior temporal cortex, the fusiform face area, and the precuneus. Additionally, only the similarity between emotional stimuli was significantly represented in early visual cortex, anterior insula and dorsal anterior cingulate cortex. Intriguingly, despite the stronger neural similarity between emotional stimuli, the same participants did not rate them as more similar to each other than neutral stimuli. These results contribute to our understanding of how emotion is represented within a general conceptual workspace and of the overgeneralization bias in anxiety disorders.SIGNIFICANCE STATEMENT We tested differences in similarity between emotional and neutral scenes. Arousal and negative valence did not increase similarity ratings. When conditions were equated on semantic similarity, participants rated emotional stimuli as similar to each other as neutral ones. Despite this equivalence, the similarity among the neural representations of emotional compared with neutral stimuli was higher in regions, which also expressed similarity between neutral stimuli and in unique regions. We report a striking difference between behavioral and neural similarity; strong neural similarity between emotional pictures did not influence similarity judgements in the same participants in the behavioral rating task after the scan. These findings may have an impact on research about the neural representations of emotional categories and the overgeneralization bias in anxiety disorders.

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The influence of grammatical gender on cognitive processes is an important issue in contemporary psycholinguistics and language psychology, particularly in research concerning the relations between grammar and semantics. The extent of this effect is dependent on a given language's gender system and its grammatical specifics. The aim of the presented research was to investigate grammatical gender effects in Polish - a Slavic language with three singular and two plural grammatical genders. In Experiment 1, triadic similarity judgments were used, and it turned out that the grammatical gender of nouns influenced perceived similarity of words in case of animals, but not inanimate objects or abstract concepts. In Experiment 2 we used a modified Implicit Association Test; results suggest that grammatical gender seems to be of implicit nature, as grammatical gender consistency influenced reaction times and the number of classification errors. In Experiment 3 participants assigned male and female voices to animals and inanimate objects, which were presented either as words or as pictures. Grammatical gender effects occurred for both animate and inanimate objects and were similar for verbal and visual stimuli. It turned out that in the Polish language the influence of grammatical gender may occur on the lexicosemantic level and the conceptual level, and concerns both animate and inanimate objects. Results are discussed in context of the similarity and gender and the sex and gender hypotheses.

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In this study we followed the extension of Tversky's research about features of similarity with its application to open sets. Unlike the original closed-set model in which a feature was shifted between a common and a distinctive set, we investigated how addition of new features and deletion of existing features affected similarity judgments. The model was tested empirically in a political context and we analyzed how positive and negative changes in a candidate's profile affect the similarity of the politician to his or her ideal and opposite counterpart. The results showed a positive-negative asymmetry in comparison judgments where enhancing negative features (distinctive for an ideal political candidate) had a greater effect on judgments than operations on positive (common) features. However, the effect was not observed for comparisons to a bad politician. Further analyses showed that in the case of a negative reference point, the relationship between similarity judgments and voting intention was mediated by the affective evaluation of the candidate.

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Recent advances in Deep convolutional Neural Networks (DNNs) have enabled unprecedentedly accurate computational models of brain representations, and present an exciting opportunity to model diverse cognitive functions. State-of-the-art DNNs achieve human-level performance on object categorisation, but it is unclear how well they capture human behavior on complex cognitive tasks. Recent reports suggest that DNNs can explain significant variance in one such task, judging object similarity. Here, we extend these findings by replicating them for a rich set of object images, comparing performance across layers within two DNNs of different depths, and examining how the DNNs' performance compares to that of non-computational "conceptual" models. Human observers performed similarity judgments for a set of 92 images of real-world objects. Representations of the same images were obtained in each of the layers of two DNNs of different depths (8-layer AlexNet and 16-layer VGG-16). To create conceptual models, other human observers generated visual-feature labels (e.g., "eye") and category labels (e.g., "animal") for the same image set. Feature labels were divided into parts, colors, textures and contours, while category labels were divided into subordinate, basic, and superordinate categories. We fitted models derived from the features, categories, and from each layer of each DNN to the similarity judgments, using representational similarity analysis to evaluate model performance. In both DNNs, similarity within the last layer explains most of the explainable variance in human similarity judgments. The last layer outperforms almost all feature-based models. Late and mid-level layers outperform some but not all feature-based models. Importantly, categorical models predict similarity judgments significantly better than any DNN layer. Our results provide further evidence for commonalities between DNNs and brain representations. Models derived from visual features other than object parts perform relatively poorly, perhaps because DNNs more comprehensively capture the colors, textures and contours which matter to human object perception. However, categorical models outperform DNNs, suggesting that further work may be needed to bring high-level semantic representations in DNNs closer to those extracted by humans. Modern DNNs explain similarity judgments remarkably well considering they were not trained on this task, and are promising models for many aspects of human cognition.