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Young children tend to prioritize objects over layouts in their drawings, often juxtaposing “floating” objects in the picture plane instead of grounding those objects in drawn representations of the extended layout. In the present study, we explore whether implicitly directing children’s attention to elements of the extended layout through a drawing’s communicative goal—to indicate the location of a hidden target to someone else—might lead children to draw more layout information. By comparing children’s drawings to a different group of children’s verbal descriptions, moreover, we explore how communicative medium affects children’s inclusion of layout and object information. If attention modulates children’s symbolic communication about layouts and objects, then children should both draw and talk about layouts and objects when they are relevant to the communicative task. If there are challenges or advantages specific to either medium, then children might treat layouts and objects differently when drawing versus describing them. We find evidence for both of these possibilities: Attention affects what children include in symbolic communication, like drawings and language, but children are more concise in their inclusion of relevant layout or object information in language versus drawings.

CogSci 2022

Young children’s drawings and descriptions of layouts and objects

technical paper

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We examined the signatures of music improvisation following a targeted manipulation that involves a mirroring task. In 18 pairs participants completed a solo music improvisation performance using a percussion instrument (cajon), a mirroring task with a partner, and joint music improvisation. Across pairs, we manipulated three different types of mirroring to examine its effects on coordination during joint music improvisation (Hierarchical mirroring, partner A leading and partner B following; Turn-Taking mirroring, partner A leading the first half and partner B leading the latter half; and Egalitarian mirroring, partners co-creating spontaneous movement together). From audio recordings, we extracted signatures of interpersonal coordination from the acoustic performances using cross-correlation. There were only marginally significant differences in interpersonal coordination during improvisation, with pairs in the Turn-Taking condition exhibiting lower levels of cross-correlation than in the Egalitarian condition. This work provides some initial theoretical insights about downstream effects of interpersonal dynamics on music improvisation.

Improving Improv: Effects of Interpersonal Coordination on Music Improvisation

The Rescorla-Wagner model has seen widespread success in modelling not only its original target of animal learning, but also several areas of human learning. However, despite its success, a number of studies with humans have found effects that are not predicted by the model, thus inspiring proposals for modifications to the model. One such proposal, by Van Hamme and Wasserman (1994, VHW), is that humans not only learn from present cues to all (present and absent) outcomes, as in the original model, but also learn from the absence of cues. They set out to test this hypothesis with a causal rating experiment. However, behaviour in learning studies may depend on the task. We propose that error-driven learning should be considered to be a form of implicit learning and that the results of VHW’s contingency judgement task might stem from explicit strategies involving logic and reasoning. The present study investigates this question by a) running simulations with both the original and modified versions of the model; b) replicating the VHW experiment (Experiment 1); and c) extending the experiment with new stimuli and by including unseen stimuli following the learning phases (Experiment 2). Simulations show that the VHW modified model predicts that cues learnt at the beginning will be unlearnt when absent over the following blocks, so that they become negative predictors over time. In contrast, the original RW predicts that the absent cues remain steady (positive) predictors over the blocks. Results showed no significant difference in cue assignment between training and test, in line with the original RW model. Moreover, predictive cues in the training phase showed significantly higher ratings than a new cue introduced in the test phase, at least in some cases, also partially supporting the original RW. We propose that in the development of human learning theory, attention should be paid to whether the behaviour (or other learning data) to be modelled results from implicit learning or involves higher level cognitive processes. We suggest that the RW may best capture implicit error-driven learning.

Does error-driven learning occur in the absence of cues? Examination of the effects of updating connection weights to absent cues

This paper presents a connectionist network simulation of Livesey and McLaren’s (2009) results. In that paper they showed that participants with post-discrimination gradients that were initially peak shifted became monotonic as they were exposed to the full range of test stimuli. While the authors suggest that this is the result of rule-based processes ‘taking over’ responding, we show how a connectionist network with an attentional parameter and realistic activation functions for the input can simulate both the peak shifted and monotonic gradients. Although we do not infer that the monotonic gradient obtained in peak shift paradigms is entirely the result of associative, rather than propositional processes, we suggest that perhaps it is a change in the allocation of attention, in conjunction with the underlying representational structures used for the stimuli that facilitates rule induction in this case.

Modelling Dual-Processes in a Connectionist Network

In the color-word contingency learning paradigm, each word appears more often in one color (HI contingency) than in other colors (LO contingency). Despite the words being irrelevant, responses to the relevant colors quickly become faster to HI than to LOs—the contingency learning effect. Across four experiments (N = 1,490), the number of response-irrelevant word stimuli linked to each of the three response-relevant colors varied from 1 to 2 to 4. Our prediction, derived from the Parallel Episodic Processing (PEP) 2.0 model, was borne out: The magnitude of the contingency learning effect declined monotonically as more words were linked as HIs to each color. Inconsistent with the PEP model, however, we observed changes in response times not only in HI but also in LO trials, indicating a need to amend the model. Associative learning may therefore be a function of prioritizing high probability items at the expense of low probability items.

Contingency learning decreases when associations are shared

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.


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

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

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