The who and the where: Attention to identities and locations in groups.

While it is widely accepted that the single gaze of another person elicits shifts of attention, there is limited work on the effects of multiple gazes on attention, despite real-world social cues often occurring in groups. Further, less is known regarding the role of unequal reliability of varying social and nonsocial information on attention. We addressed these gaps by employing a variant of the gaze cueing paradigm, simultaneously presenting participants with three faces. Block-wise, we manipulated whether one face (Identity condition) or one location (Location condition) contained a gaze cue entirely predictive of target location; all other cues were uninformative. Across trials, we manipulated the number of valid cues (number of faces gazing at target). We examined whether these two types of information (Identity vs. Location) were learned at a similar rate by statistically modelling cueing effects by trial count. Preregistered analyses returned no evidence for an interaction between condition, number of valid faces, and presence of the predictive element, indicating type of information did not affect participants' ability to employ the predictive element to alter behaviour. Exploratory analyses demonstrated (i) response times (RT) decreased faster across trials for the Identity compared with Location condition, with greater decreases when the predictive element was present versus absent, (ii) RTs decreased across trials for the Location condition only when it was completed first, and (iii) social competence altered RTs across conditions and trial number. Our work demonstrates a nuanced relationship between cue utility, condition type, and social competence on group cueing.

You read my mind: Generating and minimizing intention uncertainty under different social contexts in a two-player online game.

We investigate an ecologically pertinent form of social uncertainty regarding the ability to read another's intentions. We use classic measures (response time, accuracy) and dynamic measures (mouse trajectories) to investigate how people generate or minimize uncertainty regarding their own intentions under different social contexts, and how uncertainty regarding other's intentions affects decision making. Ninety-six participants (N = 48 dyads) completed a two-player online card game, where the goal was to collect cards with a certain feature (e.g., triangles), with participant cursor movements projected to both players. Participants played six games, three cooperatively and three competitively (Social Decision Context). Points were awarded for two decisions: collecting a card matching one's goal (ability to achieve personal goal) and correctly guessing the other player's goal (ability to guess intention). Data revealed: (a) Card scores did not vary with Social Decision Context, (b) Guess scores did vary with Social Decision Context, with more correct guesses when cooperating compared to competing, and (c) Mouse trajectories (durations and mouse distance traveled) decreased when cooperating compared to competing. These results indicate that better guessing during cooperative play is not due to explicit communication (i.e., circling desired cards), but may be due to increased speed and confidence when making decisions in a cooperative context. Additionally, participants could be actively hiding their intention in a competitive context. Thus, social uncertainty when reading another's intentions is both adaptive-affected by the prescribed social context, and automatic-indirectly inferred from the way another moves their mouse when acting with intention. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Embodying cognitive ethology.

Cognitive psychology considers the environment as providing information, not affecting fundamental information processes. Thus, cognitive psychology's traditional paradigms study responses to precisely timed stimuli in controlled environments. However, new research demonstrates the environment does influence cognitive processes and offers cognitive psychology new methods. The authors examine one such proposal: cognitive ethology. Cognitive ethology improves cognitive psychology's ecological validity through first drawing inspiration from robust phenomena in the real world, then moving into the lab to test those phenomena. To support such methods, cognitive ethologists appeal to embodied cognition, or 4E cognition, for its rich relationships between agents and environments. However, the authors note while cognitive ethology focuses on new methods (epistemology) inspired by embodied cognition, it preserves most traditional assumptions about cognitive processes (ontology). But embodied cognition-particularly its radical variants-also provides strong ontological challenges to cognitive psychology, which work against cognitive ethology. The authors argue cognitive ethology should align with the ontology of less radical embodied cognition, which produces epistemological implications, offering alternative methodologies. For example, cognitive ethology can explore differences between real-world and lab studies to fully understand how cognition depends on environments.

How Do Artificial Neural Networks Classify Musical Triads? A Case Study in Eluding Bonini's Paradox.

How might artificial neural networks (ANNs) inform cognitive science? Often cognitive scientists use ANNs but do not examine their internal structures. In this paper, we use ANNs to explore how cognition might represent musical properties. We train ANNs to classify musical chords, and we interpret network structure to determine what representations ANNs discover and use. We find connection weights between input units and hidden units can be described using Fourier phase spaces, a representation studied in musical set theory. We find the total signal coming through these weighted connection weights is a measure of the similarity between two Fourier structures: the structure of the hidden unit's weights and the structure of the stimulus. This is surprising because neither of these Fourier structures is computed by the hidden unit. We then show how output units use such similarity measures to classify chords. However, we also find different types of units-units that use different activation functions-use this similarity measure very differently. This result, combined with other findings, indicates that while our networks are related to the Fourier analysis of musical sets, they do not perform Fourier analyses of the kind usually described in musical set theory. Our results show Fourier representations of music are not limited to musical set theory. Our results also suggest how cognitive psychologists might explore Fourier representations in musical cognition. Critically, such theoretical and empirical implications require researchers to understand how network structure converts stimuli into responses.