Canada

The human voice effectively communicates a range of emotions with nuanced variations in acoustics. Existing emotional speech corpora are limited in that they are either (a) highly curated to induce specific emotions with predefined categories that may not capture the full extent of emotional experiences, or (b) entangled in their semantic and prosodic cues, limiting the ability to study these cues separately. To overcome this challenge, we propose a new approach called &#39;Genetic Algorithm with People&#39; (GAP), which integrates human decision and production into a genetic algorithm. In our design, we allow creators and raters to jointly optimize the emotional prosody over generations. We demonstrate that GAP can efficiently sample from the emotional speech space and capture a broad range of emotions, and show comparable results to state-of-the-art emotional speech corpora. GAP is language-independent and supports large crowd-sourcing, thus can support future large-scale cross-cultural research.


CogSci 2022

Bridging the prosody GAP: Genetic Algorithm with People to efficiently sample emotional prosody

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

AAAI 2022

The human voice effectively communicates a range of emotions with nuanced variations in acoustics. Existing emotional speech corpora are limited in that they are either (a) highly curated to induce specific emotions with predefined categories that may not capture the full extent of emotional experiences, or (b) entangled in their semantic and prosodic cues, limiting the ability to study these cues separately. To overcome this challenge, we propose a new approach called 'Genetic Algorithm with People' (GAP), which integrates human decision and production into a genetic algorithm. In our design, we allow creators and raters to jointly optimize the emotional prosody over generations. We demonstrate that GAP can efficiently sample from the emotional speech space and capture a broad range of emotions, and show comparable results to state-of-the-art emotional speech corpora. GAP is language-independent and supports large crowd-sourcing, thus can support future large-scale cross-cultural research.


poster

Join us for CogSci2022! Our 44th Annual Conference will be centered around the theme of diversity, with an accompanying range of keynotes and invited speakers who themselves highlight the diversity of our field. Our three symposia are Cognition across Diverse Populations, Cognition within Diverse Societies, and Global Challenges for Cognitive Science. The conference is being offered in a fully hybrid environment for onsite and online attendees.
 
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Bayesian inference has been used in the past to model visual perception (Kerstenm 2004), accounting for the Helmholtz principle of perception as “unconscious inference” that is constrained by bottom-up sensory evidence (likelihood) while subject to top-down expectation, priming, or other contextual influences (prior bias); here "unconsciousness" merely relates to the "directness" of perception in the sense of Gibson. Here, we adopt the same Bayesian framework to model emotion process in accordance with Schachter-Singer’s Two-Factor theory, which argues that emotion is the outcome of cognitive labeling or attribution of a diffuse pattern of autonomic arousal (Schachter & Singer, 1962). In analogous to visual perception, we conceptualize the emotion process, in which emotional labels are constructed, as an instance of Bayesian inference, either consciously or unconsciously combining the contextual information with a person’s physiological arousal patterns. Drift-diffusion models were constructed to simulate emotional processes, where the decision boundaries correspond to the emotional state experienced by the participants, and boundary-crossing constitutes “labeling” in Schachter-Singer’s sense. Our model is tested against experimental data from the Schachter & Singer's study (1962) on context-modulated emotional state labeling and the Ross et al. study (1969) on fear reduction through mis-attribution. Two model scenarios are investigated, in which arousal pattern as one factor is pitted against contextual interaction with an confederate (in Schachter-Singer case) or explicitly instructed mis-attribution (in Ross et al. case) as another factor, mapping onto the Bayesian prior (initial position of the drift) and the likelihood function (evidence accumulation or drift rate). 
We find that the first scenario (arousal as the prior and context as the likelihood) has a better fit with Schachter & Singer (1962) whereas the second scenario (context as the prior and arousal as the likelihood) has a better fit with Ross et al. (1969).

A Bayesian Dri-Diffusion Model of Schachter-Singer’s Two-Factor Theory of Emotion

Many of our decisions are based on probabilistic information. While probability theory is a useful tool for quantifying probabilities mathematically, subjective probability is a complex psychological phenomenon. We investigated developmental changes in subjective probability and the modulating role of emotions in probabilistic cognition. For this, we asked N = 45 children (M = 10.59, SD = 2.28, range 7-15) and N = 160 adults (M = 25.20, SD = 14.35, range 18-88) to estimate the probability of a series of three-item compound events generated from a known probability distribution. While children’s estimates largely resembled those of adults, conservatism (avoidance of the extremes) and representativeness judgments (basing estimates on similarity) were modulated by age and emotions. Our findings suggest that the way in which people use the representativeness heuristic develops with age and that emotions modulate subjective probability in children and adults.

Emotions, age, and subjective probability in children

Prior studies indicate that sex, race, and age biases in assumed pain sensitivity produce treatment disparities: Women are seen as more sensitive than men; white people as more sensitive than Black people; and both seniors and infants are deprioritized. We recruited online participants (N=69) to assess fictional characters’ pain in 8 scenarios. Characters varied in sex (male/female), race (Black/white), age (adult/child), and injury (e.g., bruised leg, splinter). Using a mixed effects regression, we found an effect of character age (p<0.01) and a marginal effect of character sex (p=0.08). These results suggest that adults assume children feel more pain than adults. Additionally, we found that male participants rated female characters’ pain as higher than male characters’ pain (p<0.01), whereas female participants rated male and female pain equally. Also, white participants gave lower pain ratings overall than nonwhite participants (p<0.01). In sum, character and participant demographics both affected pain assessment outcomes.

The demographics of pain: How age, race, and gender affect pain assessments

Is a cow more closely related to grass or to a chicken? Responses vary by culture and age, among other factors. Those from western societies (or independent-leaning regions within interdependent non-western societies) are more likely to endorse the taxonomic match, the chicken, over the thematic match, grass (Chiu, 1972; Talhelm et al., 2014). This preference has been documented -- largely in western cultures -- to increase over development (e.g., Smiley & Brown, 1979). While neither development nor culture occur independently of the other, comparisons across these areas are problematic. We address one potential barrier to comparing cultural and developmental research using this classic paradigm -- stimulus format -- and show that the use of text (versus image) stimuli can bias participants toward taxonomic responding in some contexts. We present stimuli designed for cross-cultural use with children and adults and document country, regional, and demographic variation across the US and Italy.

Bridging cultural and cognitive perspectives on similarity reasoning

We investigate how humans leverage sparse observations of social interaction to infer the rich internal structure of human social collectives. We propose a computational model of this process which integrates a domain-general structure learning mechanism with domain-specific knowledge about social contexts (i.e.: “intuitive sociologies”). We test our model in two experiments where participants observe a sequence of animated interactions between agents, and then assign the agents to groups according to their role or type within the social collective. Crucially, the two experiments depict different types of social interactions which reflect different types of underlying social structures. The patterns of correspondence between model predictions and human data support our account, and demonstrate the importance of both general statistical learning and specific social knowledge when reasoning about social collectives. Keywords: Social Inference; Intergroup Cognition; Computational Modeling

Inferring the internal structure of social collectives

Intuitively, adding uninformed individuals to a group should undermine group efficiency, as they create coordination costs while lacking the expertise to meaningfully contribute. However, uninformed individuals may be able to overcome deadlocks in otherwise polarized groups by heightening conformity pressures. Modeling group members’ decision making using a sequential sampling model based on Decision Field Theory (DFT: Busemeyer & Townsend, 1993), we present an existence proof of how ignorance can, in contrast to intuition and prominent economic accounts, facilitate improved group decision making. The implications of these findings for cognitive science, organizational behavior, and social impact are discussed.

In the Dark: Agent-Based Modeling of Uninformed Individuals within Polarized Groups

Diversity of social identities can improve the performance of groups through varied cognitive and communicative pathways. Recently, research efforts have focused on identifying when we should expect to see these potential benefits in real-world settings. While most research to date has studied this topic at individual and interpersonal levels, in this paper, we develop an agent-based model to explore how various aspects of homophily, the tendency of individuals to associate with similar others, affects performance at a larger scale. Study 1 examines how two types of homophily---identity-driven and opinion-driven---impact collective performance on a sequential decision-making task via modulating network formation and trust relations. Study 2 considers how the presence of identity-based conformity pressure can affect the findings from the first study. Overall, we find that the effect of homophily on performance is complex, depending on the operative dimensions of similarity, mediating pathways, and the specific outcome of interest. Finally, we discuss the implications of our results for policy interventions aiming to improve group performance.

Diversity and homophily in social networks

Behavioural cascades through social reinforcement are ubiquitous in human and animal groups. Nonetheless, we only have a rudimentary understanding of which choices are more likely to initiate cascades. Here we investigate the role of response time (RT) asymmetries (i.e., one choice alternative being selected faster than the other) in shaping behavioural cascades by combining an empirical and modelling approach. RT asymmetries are found in a wide range of decision-making contexts, including police shooting, risky choice, and memory retrieval. How they shape collective dynamics, is, however, unknown. Applying evidence accumulation models to analyse behaviour in a sequential choice paradigm, we show that RT asymmetries crucially shape behavioural cascades. Using simulations, we show that especially start point biases (and to a less extent varying drift rates) can initiate cascades, as they lead to rapid choices for one choice alternative. Our results highlight the importance of RT asymmetries in shaping collective dynamics.

How the cognitive mechanisms underlying fast choices influence information spread and response bias amplification in groups

Children do not learn language from passively analyzing correlations between language and observations, but from interaction with caregivers or peers. The non-nativist approach claims that the main driver of language learning should be to achieve communicative goals. Imitation, on the other hand, is another natural desire that many argue influences language learning. However, there are still gaps in the research on what roles communicative goals and imitating linguistic input play in language acquisition, due to the difficulty of performing comprehensive experiments with human learners. In this paper, we propose a computational framework using simulated experiments that allows us to compare the roles of the two drivers. Specifically, we simulate a two-way communication game between a speaker, corresponding to a language learner, and a listener, corresponding to a caregiver or teacher. The speaker's communicative goals are modeled as rewards for successful completion of a referential game, and imitation is performed by mimicking feedback from the listener. The listener adaptively chooses to give feedback and makes choices based on the speaker's utterances. 

With empirical results on naturalistic visual and language data, we find that communicative goals play an important role in driving language learning, whereas imitation accelerates the learning process. We also find that (1) models trained with communicative goals tend to use minimal vocabulary and utterances and overextend them to concepts outside the original word meanings; (2) the strategy with which the listener provides feedback also influences the learning results and speed. Code and data for replicating the experiments are available\footnote{\url{https://bit.ly/interactgym}} to spur future research on models for computational studies of language learning.

Language Learning from Communicative Goals and Linguistic Input

Picture book reading is a common word-learning context from which parents repeatedly name objects to their child and it has been found to facilitate early word learning. To learn the correct word-object mappings in a book-reading context, infants need to be able to link what they see with what they hear. However, given multiple objects on every book page, it is not clear how infants direct their attention to objects named by parents. The aim of the current study is to examine how infants mechanistically discover the correct word-object mappings during book reading in real time. We used head-mounted eye-tracking during parent-infant picture book reading and measured the infant's moment-by-moment visual attention to the named referent. We also examined how gesture cues provided by both the child and the parent may influence infants' attention to the named target. We found that although parents provided many object labels during book reading, infants were not able to attend to the named objects easily. However, their abilities to follow and use gestures to direct the other social partner’s attention increase the chance of looking at the named target during parent naming.

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A Bayesian Dri-Diffusion Model of Schachter-Singer’s Two-Factor Theory of Emotion

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