The poetry of psychological distance: Bidirectional associations between stimulus speed and its psychological distance and construal level.

Based on the cognitive-ecological approach and on logical-functional principles, in 12 studies (11 preregistered), we examine the novel hypotheses that psychological distance and construal level (CL) are associated in people's minds with stimulus speed: the psychologically distant/abstract is slow, and the psychologically close/concrete is fast. The findings support our expectations. Study Set I examined the association between psychological distance and speed. Findings show that psychological distance is implicitly and explicitly associated with speed (Study 1), that psychological distance is seen as compatible with slow and proximity with fast (Study 2), that stimulus psychological distance affects its perceived speed (Study 3), and that stimulus speed affects its psychological distance (Study 4). Study Set II examined the association between construal level and speed. Findings show that construal level is explicitly associated with speed (Study 5), that abstract is seen as compatible with slow and concrete with fast (Study 6), that natural language word distribution structures reflect an association between abstractness and speed (Study 7), that construal level affects speed (Study 8), and that speed affects stimulus construal level (Study 9). Study Set III examined implications for communication and person perception. Findings suggest that slow-paced (vs. fast-paced) speech is associated with larger perceived spatial and social distance between speaker and audience and larger audiences (Studies 10a, 10b) and that people infer an expansive (contractive) regulatory scope from slow-paced (fast-paced) spoken messages (Study 11). We elaborate on possible mechanisms and their theoretical and practical implications in domains including decision making and urban design. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Detecting recollection: Human evaluators can successfully assess the veracity of others' memories.

Humans have the highly adaptive ability to learn from others' memories. However, because memories are prone to errors, in order for others' memories to be a valuable source of information, we need to assess their veracity. Previous studies have shown that linguistic information conveyed in self-reported justifications can be used to train a machine-learner to distinguish true from false memories. But can humans also perform this task, and if so, do they do so in the same way the machine-learner does? Participants were presented with justifications corresponding to Hits and False Alarms and were asked to directly assess whether the witness's recognition was correct or incorrect. In addition, participants assessed justifications' recollective qualities: their vividness, specificity, and the degree of confidence they conveyed. Results show that human evaluators can discriminate Hits from False Alarms above chance levels, based on the justifications provided per item. Their performance was on par with the machine learner. Furthermore, through assessment of the perceived recollective qualities of justifications, participants were able to glean more information from the justifications than they used in their own direct decisions and than the machine learner did.

Emotion Classification Based on Pulsatile Images Extracted from Short Facial Videos via Deep Learning.

Most human emotion recognition methods largely depend on classifying stereotypical facial expressions that represent emotions. However, such facial expressions do not necessarily correspond to actual emotional states and may correspond to communicative intentions. In other cases, emotions are hidden, cannot be expressed, or may have lower arousal manifested by less pronounced facial expressions, as may occur during passive video viewing. This study improves an emotion classification approach developed in a previous study, which classifies emotions remotely without relying on stereotypical facial expressions or contact-based methods, using short facial video data. In this approach, we desire to remotely sense transdermal cardiovascular spatiotemporal facial patterns associated with different emotional states and analyze this data via machine learning. In this paper, we propose several improvements, which include a better remote heart rate estimation via a preliminary skin segmentation, improvement of the heartbeat peaks and troughs detection process, and obtaining a better emotion classification accuracy by employing an appropriate deep learning classifier using an RGB camera input only with data. We used the dataset obtained in the previous study, which contains facial videos of 110 participants who passively viewed 150 short videos that elicited the following five emotion types: amusement, disgust, fear, sexual arousal, and no emotion, while three cameras with different wavelength sensitivities (visible spectrum, near-infrared, and longwave infrared) recorded them simultaneously. From the short facial videos, we extracted unique high-resolution spatiotemporal, physiologically affected features and examined them as input features with different deep-learning approaches. An EfficientNet-B0 model type was able to classify participants' emotional states with an overall average accuracy of 47.36% using a single input spatiotemporal feature map obtained from a regular RGB camera.

Is it better to be happy or right? Examining the relative role of the pragmatic and epistemic imperatives in momentary affective evaluations.

According to research highlighting the importance of predictions, the confirmation of expectations may be a positively-laden experience. A strong test of this principle is the case of the "doomsayer's delight": the possibility that belief confirmation can be rewarding even when negative expectations are realized. In order to investigate this idea, we conducted two high-powered experiments examining people's immediate affective reactions following exposure to expected or unexpected positive and negative stimuli. The results show that people feel significantly worse when their pessimistic expectations are confirmed than when their optimistic expectations are violated. This finding was not moderated by several theoretically relevant individual difference measures or temporal dynamics. Findings from this study contribute to our understanding of the interplay between epistemic and pragmatic motivations in guiding emotional responses. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The recursive nature of ownership intuitions.

The proposed model overlooks the self-referential and self-perpetuating nature of ownership intuitions. Human knowledge is primarily formed through social interaction within power dynamics. Accordingly, we suggest that legitimate ownership of one object can influence perceptions of legitimate ownership of another object. Ultimately, we argue that ownership intuitions are not independent but embedded in a self-referential system that perpetuates inequality.

Development and validation of the Mental-Physical Verb Norms (MPVN): A text analysis measure of mental state attribution.

Attribution of mental states to self and others, i.e., mentalizing, is central to human life. Current measures are lacking in the ability to directly gauge the extent to which individuals engage in spontaneous mentalizing. Focusing on natural language use as an expression of inner psychological processes, we developed the Mental-Physical Verb Norms (MPVN). These norms are participant-derived ratings of the extent to which common verbs reflect mental (vs physical) activities and occurrences, covering a majority of verbs appearing in a given English text. Content validity was assessed against existing expert-compiled dictionaries of mental states and cognitive processes, as well as against normative ratings of verb concreteness. Criterion Validity was assessed through natural text analysis of both experimental data, and natural language use in a real-world online setting. Finally, incremental validity was assessed through a classification analysis. Results indicate the unique contribution of the MPVN ratings as a measure of the degree to which individuals adopt the intentional stance in describing targets, by describing both self and others in mental, opposite physical, terms. We discuss potential uses for future research across various psychological and neurocognitive disciplines, as well as theoretical implications regarding the use of mentalizing language within spontaneous contexts.

A prediction-focused approach to personality modeling.

In the current study, we set out to examine the viability of a novel approach to modeling human personality. Research in psychology suggests that people's personalities can be effectively described using five broad dimensions (the Five-Factor Model; FFM); however, the FFM potentially leaves room for improved predictive accuracy. We propose a novel approach to modeling human personality that is based on the maximization of the model's predictive accuracy. Unlike the FFM, which performs unsupervised dimensionality reduction, we utilized a supervised machine learning technique for dimensionality reduction of questionnaire data, using numerous psychologically meaningful outcomes as data labels (e.g., intelligence, well-being, sociability). The results showed that our five-dimensional personality summary, which we term the "Predictive Five" (PF), provides predictive performance that is better than the FFM on two independent validation datasets, and on a new set of outcome variables selected by an independent group of psychologists. The approach described herein has the promise of eventually providing an interpretable, low-dimensional personality representation, which is also highly predictive of behavior.

Classification of emotional states via transdermal cardiovascular spatiotemporal facial patterns using multispectral face videos.

We describe a new method for remote emotional state assessment using multispectral face videos, and present our findings: unique transdermal, cardiovascular and spatiotemporal facial patterns associated with different emotional states. The method does not rely on stereotypical facial expressions but utilizes different wavelength sensitivities (visible spectrum, near-infrared, and long-wave infrared) to gauge correlates of autonomic nervous system activity spatially and temporally distributed across the human face (e.g., blood flow, hemoglobin concentration, and temperature). We conducted an experiment where 110 participants viewed 150 short emotion-eliciting videos and reported their emotional experience, while three cameras recorded facial videos with multiple wavelengths. Spatiotemporal multispectral features from the multispectral videos were used as inputs to a machine learning model that was able to classify participants' emotional state (i.e., amusement, disgust, fear, sexual arousal, or no emotion) with satisfactory results (average ROC AUC score of 0.75), while providing feature importance analysis that allows the examination of facial occurrences per emotional state. We discuss findings concerning the different spatiotemporal patterns associated with different emotional states as well as the different advantages of the current method over existing approaches to emotion detection.

The four different modes of psychological explanation, and their proper evaluative schemas.

I apply Benjamin's (1941) taxonomy of common scientific "modes of explanation" to the psychological context. I argue that: (i) in a "naming" mode, generalizability is not necessary; (ii) in an "analysis," generalizability is desired; (iii) in a "causal ontology," generalizability is merely one of the means to an end (theory-challenge); (iv) in a "synthesis," generalizability is (eventually) critical. A better appreciation of the diversity in psychologists' modes of explanation is crucial for cogent meta-psychological discussions.

Grounded separation: can the sensorimotor be grounded in the symbolic?

According to Lee and Schwarz, the sensorimotor experience of cleansing involves separating one physical entity from another and grounds mental separation of one psychological entity from another. We propose that cleansing effects may result from symbolic cognition. Instead of viewing abstract meanings as emerging from concrete physical acts of cleansing, this physical act may be appended with pre-existing, symbolic meaning.

To which world regions does the valence-dominance model of social perception apply?

Over the past 10 years, Oosterhof and Todorov's valence-dominance model has emerged as the most prominent account of how people evaluate faces on social dimensions. In this model, two dimensions (valence and dominance) underpin social judgements of faces. Because this model has primarily been developed and tested in Western regions, it is unclear whether these findings apply to other regions. We addressed this question by replicating Oosterhof and Todorov's methodology across 11 world regions, 41 countries and 11,570 participants. When we used Oosterhof and Todorov's original analysis strategy, the valence-dominance model generalized across regions. When we used an alternative methodology to allow for correlated dimensions, we observed much less generalization. Collectively, these results suggest that, while the valence-dominance model generalizes very well across regions when dimensions are forced to be orthogonal, regional differences are revealed when we use different extraction methods and correlate and rotate the dimension reduction solution. PROTOCOL REGISTRATION: The stage 1 protocol for this Registered Report was accepted in principle on 5 November 2018. The protocol, as accepted by the journal, can be found at https://doi.org/10.6084/m9.figshare.7611443.v1 .

Above and beyond "Above and beyond the concrete".

The commentaries address our view of abstraction, our ontology of abstract entities, and our account of predictive cognition as relying on relatively concrete simulation or relatively abstract theory-based inference. These responses revisit classic questions concerning mental representation and abstraction in the context of current models of predictive cognition. The counter arguments to our article echo: constructivist theories of knowledge, "neat" approaches in artificial intelligence and decision theory, neo-empiricist models of concepts, and externalist views of cognition. We offer several empirical predictions that address points of contention and that highlight the generative potential of our model.

Political depression? A big-data, multimethod investigation of Americans' emotional response to the Trump presidency.

Previous studies suggested that the 2016 presidential elections gave rise to pathological levels of election-related distress in liberal Americans; however, it has also been suggested that the public discourse and the professional discourse have increasingly overgeneralized concepts of trauma and psychopathology. In light of this, in the current research, we utilized an array of big data measures and asked whether a political loss in a participatory democracy can indeed lead to psychopathology. We observed that liberals report being more depressed when asked directly about the effects of the election; however, more indirect measures show a short-lived or nonexistent effect. We examined self-report measures of clinical depression with and without a reference to the election (Studies 1A & 1B), analyzed Twitter discourse and measured users' levels of depression using a machine-learning-based model (Study 2), conducted time-series analysis of depression-related search behavior on Google (Study 3), examined the proportion of antidepressants consumption in Medicaid data (Study 4), and analyzed daily surveys of hundreds of thousands of Americans (Study 5), and saw that at the aggregate level, empirical data reject the accounts of "Trump Depression." We discuss possible interpretations of the discrepancies between the direct and indirect measures. The current investigation demonstrates how big-data sources can provide an unprecedented view of the psychological consequences of political events and sheds light on the complex relationship between the political and the personal spheres. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Above and beyond the concrete: The diverse representational substrates of the predictive brain.

In recent years, scientists have increasingly taken to investigate the predictive nature of cognition. We argue that prediction relies on abstraction, and thus theories of predictive cognition need an explicit theory of abstract representation. We propose such a theory of the abstract representational capacities that allow humans to transcend the "here-and-now." Consistent with the predictive cognition literature, we suggest that the representational substrates of the mind are built as a hierarchy, ranging from the concrete to the abstract; however, we argue that there are qualitative differences between elements along this hierarchy, generating meaningful, often unacknowledged, diversity. Echoing views from philosophy, we suggest that the representational hierarchy can be parsed into: modality-specific representations, instantiated on perceptual similarity; multimodal representations, instantiated primarily on the discovery of spatiotemporal contiguity; and categorical representations, instantiated primarily on social interaction. These elements serve as the building blocks of complex structures discussed in cognitive psychology (e.g., episodes, scripts) and are the inputs for mental representations that behave like functions, typically discussed in linguistics (i.e., predicators). We support our argument for representational diversity by explaining how the elements in our ontology are all required to account for humans' predictive cognition (e.g., in subserving logic-based prediction; in optimizing the trade-off between accurate and detailed predictions) and by examining how the neuroscientific evidence coheres with our account. In doing so, we provide a testable model of the neural bases of conceptual cognition and highlight several important implications to research on self-projection, reinforcement learning, and predictive-processing models of psychopathology.

Goal-Directed Allostasis: The Unique Challenge of Keeping Things as They Are and Strategies to Overcome It.

We introduce the concept of goal-directed allostasis (GDA), the mental process that underlies individuals' deliberate and proactive attempts to maintain the current state of affairs. GDA is distinct from goal-directed progress (GDP), the mental process that underlies the pursuit of change in the current state of affairs. We argue that GDA plays a crucial role in human life but that it has been largely overlooked in psychological research. We discuss the unique cognitive and motivational challenges that arise during GDA and suggest strategies to overcome these challenges. Finally, we outline how acknowledging the distinction between GDA and GDP might contribute to the study and treatment of mental illness and highlight several directions for future research.

Self-regulation via neural simulation.

Can taking the perspective of other people modify our own affective responses to stimuli? To address this question, we examined the neurobiological mechanisms supporting the ability to take another person's perspective and thereby emotionally experience the world as they would. We measured participants' neural activity as they attempted to predict the emotional responses of two individuals that differed in terms of their proneness to experience negative affect. Results showed that behavioral and neural signatures of negative affect (amygdala activity and a distributed multivoxel pattern reflecting affective negativity) simulated the presumed affective state of the target person. Furthermore, the anterior medial prefrontal cortex (mPFC)-a region implicated in mental state inference-exhibited a perspective-dependent pattern of connectivity with the amygdala, and the multivoxel pattern of activity within the mPFC differentiated between the two targets. We discuss the implications of these findings for research on perspective-taking and self-regulation.

The effects of an action's "age-of-acquisition" on action-sentence processing.

How does our brain allow us comprehend abstract/symbolic descriptions of human action? Whereas past research suggested that processing action language relies on sensorimotor brain regions, recent work suggests that sensorimotor activation depends on participants' task goals, such that focusing on abstract (vs. concrete) aspects of an action activates "default mode network" (rather than sensorimotor) regions. Following a Piagetian framework, we hypothesized that for actions acquired at an age wherein abstract/symbolic cognition is fully-developed, even when participants focus on the concrete aspects of an action, they should retrieve abstract-symbolic mental representations. In two studies, participants processed the concrete (i.e., "how") and abstract (i.e., "why") aspects of late-acquired and early-acquired actions. Consistent with previous research, focusing on the abstract (vs. concrete) aspects of an action resulted in greater activation in the "default mode network". Importantly, the activation in these regions was higher when processing later-acquired (vs. earlier acquired) actions-also when participants' goal was to focus on the concrete aspects of the action. We discuss the implications of the current findings to research on the involvement of concrete representations in abstract cognition.

Neural correlates of processing "self-conscious" vs. "basic" emotions.

Self-conscious emotions are prevalent in our daily lives and play an important role in both normal and pathological behavior. Despite their immense significance, the neural substrates that are involved in the processing of such emotions are surprisingly under-studied. In light of this, we conducted an fMRI study in which participants thought of various personal events which elicited feelings of negative and positive self-conscious (i.e., guilt, pride) or basic (i.e., anger, joy) emotions. We performed a conjunction analysis to investigate the neural correlates associated with processing events that are related to self-conscious vs. basic emotions, irrespective of valence. The results show that processing self-conscious emotions resulted in activation within frontal areas associated with self-processing and self-control, namely, the mPFC extending to the dACC, and within the lateral-dorsal prefrontal cortex. Processing basic emotions resulted in activation throughout relatively phylogenetically-ancient regions of the cortex, namely in visual and tactile processing areas and in the insular cortex. Furthermore, self-conscious emotions differentially activated the mPFC such that the negative self-conscious emotion (guilt) was associated with a more dorsal activation, and the positive self-conscious emotion (pride) was associated with a more ventral activation. We discuss how these results shed light on the nature of mental representations and neural systems involved in self-reflective and affective processing.

Delineating the effect of semantic congruency on episodic memory: the role of integration and relatedness.

A fundamental challenge in the study of learning and memory is to understand the role of existing knowledge in the encoding and retrieval of new episodic information. The importance of prior knowledge in memory is demonstrated in the congruency effect-the robust finding wherein participants display better memory for items that are compatible, rather than incompatible, with their pre-existing semantic knowledge. Despite its robustness, the mechanism underlying this effect is not well understood. In four studies, we provide evidence that demonstrates the privileged explanatory power of the elaboration-integration account over alternative hypotheses. Furthermore, we question the implicit assumption that the congruency effect pertains to the truthfulness/sensibility of a subject-predicate proposition, and show that congruency is a function of semantic relatedness between item and context words.