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How do humans adapt when others exploit patterns in their behavior? When can people modify such patterns and when are they simply trapped? The present work explores these questions using the children's game of rock, paper, scissors (RPS). Adult participants played 300 rounds of RPS against one of eight bot opponents. The bots chose a move each round by exploiting unique sequential regularities in participant move choices. In order to avoid losing against their bot opponent, participants needed to recognize the ways in which their own behavior was predictable and disrupt the pattern. We find that for simple biases, participants were able to recognize that they were being exploited and even counter-exploit their opponents. However, for more complex sequential dependencies, participants were unable to change their behavior and lost reliably to the bots. Results provide a quantitative delineation of people's ability to identify and alter patterns in their past decisions.

How do humans adapt when others exploit patterns in their behavior? When can people modify such patterns and when are they simply trapped? The present work explores these questions using the children's game of rock, paper, scissors (RPS). Adult participants played 300 rounds of RPS against one of eight bot opponents. The bots chose a move each round by exploiting unique sequential regularities in participant move choices. In order to avoid losing against their bot opponent, participants needed to recognize the ways in which their own behavior was predictable and disrupt the pattern. We find that for simple biases, participants were able to recognize that they were being exploited and even counter-exploit their opponents. However, for more complex sequential dependencies, participants were unable to change their behavior and lost reliably to the bots. Results provide a quantitative delineation of people's ability to identify and alter patterns in their past decisions.

This is a modern horror story about an innocently misbehaving projector, and why we beseech everyone to report minimal col- orimetric data about stimulus displays. We present anecdotal experience of configuring a projector to display video stimuli in a high-tesla MRI room, along with all the gotchas, (broken) technical assumptions, and theoretical rehashings that should be considered by every scientist who uses computers to dis- play visual stimuli. The moral of our story is: (1) check that your monitor/projector is actually showing the colors and lu- minances that you think it is, (2) make explicit assumptions regarding the physical/perceptual space of your stimuli and how they relate to any model analysis you will perform. This is especially important when modeling non-human animals, since most equipment and data formats implicitly assume hu- man perception. We show that innocent changes to display settings such as brightness reduction can cause dangerously unexpected results. Understanding and reporting colorimetric data in scientific publications is important for two reasons: (1) reproducibility, and (2) model fidelity.

This is a modern horror story about an innocently misbehaving projector, and why we beseech everyone to report minimal col- orimetric data about stimulus displays. We present anecdotal experience of configuring a projector to display video stimuli in a high-tesla MRI room, along with all the gotchas, (broken) technical assumptions, and theoretical rehashings that should be considered by every scientist who uses computers to dis- play visual stimuli. The moral of our story is: (1) check that your monitor/projector is actually showing the colors and lu- minances that you think it is, (2) make explicit assumptions regarding the physical/perceptual space of your stimuli and how they relate to any model analysis you will perform. This is especially important when modeling non-human animals, since most equipment and data formats implicitly assume hu- man perception. We show that innocent changes to display settings such as brightness reduction can cause dangerously unexpected results. Understanding and reporting colorimetric data in scientific publications is important for two reasons: (1) reproducibility, and (2) model fidelity.

Deep reinforcement learning (Deep RL) methods can train artificial agents (AAs) to reach or exceed human-level performance. However, in multiagent contexts requiring competitive behavior or where the aim is to use AAs for human training, the qualitative behaviors AAs adopt may be just as important as their performance to ensure representative training. This paper compares human behaviors and performance when competing against either a human expert or an AA opponent trained using Deep RL on a 2-dimensional version of Pong. Results show that participants were not sensitive to the movement differences between the human expert and AA. Further, the participants did not alter their behaviors, except to compensate for differences in the environmental states caused by the opponents. The paper concludes with discussion on the potential impacts of AA training on human behavior with regard to representative design in the areas of skill development and team training.

This paper studies social interaction in newborns and of various species, whose behavioral developments attributed to the circular sensory motor Stage 3 of behavior development. The article has associated 16 exciting facts of the social behavior in groups of different species, highlighting three questions: (1) how can newborns successfully classify social phenomena that are abstract or absent from their reality; (2) whether emotional contagion can appear through cues of body language that subjects cannot consciously perceive; (3) how do organisms distinguish identical stimuli by their importance (value) without perceptual driver stimuli. The analysis suggests that organisms can interact at initial stages of development by distinguishing cues of a similar modality by their significance (their value). This ability contributes to the assimilation of knowledge about the initial social phenomena. This paper proposes the model of Coherent intelligence, which is strictly based on experimental evidence in modern literature and laws of physics.

It has been shown that in hand-written transcription tasks temporal micro-behavioural chunk signals hold promise as measures of competence in various domains (e.g., Cheng, 2014). But data capture under that an approach requires the use of graphics tablets which are relatively uncommon. In this theoretical paper we propose and explore an alternative method – Competence Assessment by Stimulus Matching (CASM). This new method uses simple mouse-driven interfaces to produce temporal chunk signals as measures of learner’s ability. However, it is not obvious what features of CASM will produce effective competence measures and the design space of CASM tasks is large. Thus, this paper uses GOMS modelling in order to explore the design space to find factors that will maximize the discrimination of chunk measures of competence. The modelling results of this paper show that CASM has potential in using chunk signals to measure competence in the domain of English language.

This paper explores previous arguments that language evolved not from animal communication, as naturally assumed by most scholars, but instead out of animal cognition. It is proposed here that additional support for this argument comes from Cognitive Linguistics, an interdisciplinary branch of linguistics. Cognitive Linguistics mediates communication and cognition but, as with Cognitive Discourse Analysis, studies language use in terms of what it demonstrates about underlying cognitive processes and concepts. The paper demonstrates key examples of animal cognition’s links to language, with Cognitive Linguistics support, as well as the approach’s application to animal cognition in terms of domain-general symbolism, beyond verbal language. However, because communication remains a major function of language, the communicative aspect ought to be maintained in the explanation of language evolution.

People’s causal judgments exhibit substantial variability, but the processes that lead to this variability are not currently understood. In this paper, we use a repeated-measures design to study the within-participant variability of conditional probability judgments in common-cause networks. We establish that these judgments indeed exhibit substantial within-participant variability. This variability differs by inference type and is related to the extent to which participants commit Markov violations. The consistency and systematicity of this variability suggest that it may be an important source of evidence for the cognitive processes that lead to causal judgments. The systematic study of both within- and between-person variability broadens the scope of behavior that can be studied in causal cognition and promotes the evaluation of formal models of the underlying process. The data and methods provided in this paper provide tools to enable the further study of within-participant variability in causal judgment.

Deep reinforcement learning (Deep RL) methods can train artificial agents (AAs) to reach or exceed human-level performance. However, in multiagent contexts requiring competitive behavior or where the aim is to use AAs for human training, the qualitative behaviors AAs adopt may be just as important as their performance to ensure representative training. This paper compares human behaviors and performance when competing against either a human expert or an AA opponent trained using Deep RL on a 2-dimensional version of Pong. Results show that participants were not sensitive to the movement differences between the human expert and AA. Further, the participants did not alter their behaviors, except to compensate for differences in the environmental states caused by the opponents. The paper concludes with discussion on the potential impacts of AA training on human behavior with regard to representative design in the areas of skill development and team training.

This paper explores previous arguments that language evolved not from animal communication, as naturally assumed by most scholars, but instead out of animal cognition. It is proposed here that additional support for this argument comes from Cognitive Linguistics, an interdisciplinary branch of linguistics. Cognitive Linguistics mediates communication and cognition but, as with Cognitive Discourse Analysis, studies language use in terms of what it demonstrates about underlying cognitive processes and concepts. The paper demonstrates key examples of animal cognition’s links to language, with Cognitive Linguistics support, as well as the approach’s application to animal cognition in terms of domain-general symbolism, beyond verbal language. However, because communication remains a major function of language, the communicative aspect ought to be maintained in the explanation of language evolution.

People’s causal judgments exhibit substantial variability, but the processes that lead to this variability are not currently understood. In this paper, we use a repeated-measures design to study the within-participant variability of conditional probability judgments in common-cause networks. We establish that these judgments indeed exhibit substantial within-participant variability. This variability differs by inference type and is related to the extent to which participants commit Markov violations. The consistency and systematicity of this variability suggest that it may be an important source of evidence for the cognitive processes that lead to causal judgments. The systematic study of both within- and between-person variability broadens the scope of behavior that can be studied in causal cognition and promotes the evaluation of formal models of the underlying process. The data and methods provided in this paper provide tools to enable the further study of within-participant variability in causal judgment.

This paper studies social interaction in newborns and of various species, whose behavioral developments attributed to the circular sensory motor Stage 3 of behavior development. The article has associated 16 exciting facts of the social behavior in groups of different species, highlighting three questions: (1) how can newborns successfully classify social phenomena that are abstract or absent from their reality; (2) whether emotional contagion can appear through cues of body language that subjects cannot consciously perceive; (3) how do organisms distinguish identical stimuli by their importance (value) without perceptual driver stimuli. The analysis suggests that organisms can interact at initial stages of development by distinguishing cues of a similar modality by their significance (their value). This ability contributes to the assimilation of knowledge about the initial social phenomena. This paper proposes the model of Coherent intelligence, which is strictly based on experimental evidence in modern literature and laws of physics.

It has been shown that in hand-written transcription tasks temporal micro-behavioural chunk signals hold promise as measures of competence in various domains (e.g., Cheng, 2014). But data capture under that an approach requires the use of graphics tablets which are relatively uncommon. In this theoretical paper we propose and explore an alternative method – Competence Assessment by Stimulus Matching (CASM). This new method uses simple mouse-driven interfaces to produce temporal chunk signals as measures of learner’s ability. However, it is not obvious what features of CASM will produce effective competence measures and the design space of CASM tasks is large. Thus, this paper uses GOMS modelling in order to explore the design space to find factors that will maximize the discrimination of chunk measures of competence. The modelling results of this paper show that CASM has potential in using chunk signals to measure competence in the domain of English language.

In this paper, we test people’s causal judgments when the graphs have inhibitory causal relations. We find evidence that a particularly important class of errors known as Markov vio- lations extend to these settings. These Markov violations are important because they are incompatible with causal graphical models, a theoretical framework that is often used as a com- putational level account of causal cognition. In contrast, the systematic pattern of errors are in line with the predictions of a recently proposed rational process model that models peo- ple as reasoning about concrete cases (Davis & Rehder, 2020). These findings demonstrate that errors in causal reasoning ex- tend across a range of settings, and do so in line with the pre- dictions of a model that describes the process by which causal judgments are drawn.

How do we manage to understand the minds of others and usefully interact with them? In the last decade, the debate on these issues has developed from unitary to pluralist approaches. According to the latter, we make use of multiple socio-cognitive strategies when predicting, interpretating, and reacting to the behavior of others. This means a departure from the view of mindreading as the main strategy underlying social cognition. In this paper, we address the question of the controversial status of mindreading within such a pluralist framework. Contrary to many other accounts, we ascribe mindreading an equal status in a pluralist framework. Mindreading is required for a variety of central situations in life and importantly underlies the way in which we understand other people. Mindreading is also no less reliable than alternative strategies; reliability is not so much a matter of different competing socio-cognitive strategies, but rather of their complementary use.

When encountering objects, we readily perceive not only low-level properties (e.g., color and orientation), but also seemingly higher-level ones--including aspects of physics (e.g., mass). Perhaps nowhere is this contrast more salient than in the perception of soft materials such as cloths: the dynamics of these objects (including how their three-dimensional forms vary) are determined by their physical properties such as stiffness, elasticity, and mass. Here we hypothesize that the perception of cloths and their physical properties must involve not only image statistics, but also abstract object representations that incorporate "intuitive physics". We provide behavioral and computational evidence for this hypothesis. We find that humans can visually match the stiffness of cloths with unfamiliar textures from the way they undergo natural transformations (e.g. flapping in the wind) across different scenarios. A computational model that casts cloth perception as mental physics simulation explains important aspects of this behavior. Full paper can be found at https://www.biorxiv.org/content/10.1101/2021.05.12.443806v1.

Understanding a sequence of images as a visual narrative is challenging because it requires not only the understanding of what is shown at a particular moment but also what has changed, been omitted or is out of frame. The human cognitive system makes inferences about the state of the world based on transitions between sequential frames. In this paper, we present a principled analysis of the stylistic differences between two dominant styles of multi-modal narratives, western comics and manga. These two styles differ in terms of screening, ballooning, layout, language, and reading order. We first provide a systematic account of these differences based on an annotated dataset consisting of both comics and manga. We then annotate these datasets with a new feature set and evaluate the contributions of these features through development of a computational model of multi-modal comprehension. The model evaluation is presented through the cloze test that measures the accuracy of the model in predicting unseen next frames given the prior frames in a sequence. Our results provide initial benchmarks and insight into the fundamental challenges that the multi-modal narrative understanding task presents for computational models both for language and vision.

Language is multimodal: non-linguistic cues, such as prosody, gestures and mouth movements, are always present in face-to-face communication and interact to support processing. In this paper, we ask whether and how multimodal cues affect L2 processing by recording EEG for highly proficient bilinguals when watching naturalistic materials. For each word, we quantified surprisal and the informativeness of prosody, gestures, and mouth movements. We found that each cue modulates the N400: prosodic accentuation, meaningful gestures, and informative mouth movements all reduce N400. Further, effects of meaningful gestures but not mouth informativeness are enhanced by prosodic accentuation, whereas effects of mouth are enhanced by meaningful gestures but reduced by beat gestures. Compared with L1, L2 participants benefit less from cues and their interactions, except for meaningful gestures and mouth movements. Thus, in real-world language comprehension, L2 comprehenders use multimodal cues just as L1 speakers albeit to a lesser extent.

How many rhymes are possible in English? How much alliteration or assonance? This paper explores the space of common phonologically driven poetic devices. I investigate instances of these poetic devices across unique words in the dictionary, assuming perfect matching in relevant corresponding elements of word pairs. Documenting the frequencies of poetic device matches across words provides a baseline for understanding the diversity and use of these forms in the wild. It also allows us to describe the distribution of words across these patterns within each poetic device. We show that certain devices, such as alliteration and stress, support a relatively small number of unique phonological patterns, providing more consolidated and predictable resources than other devices. Forms like masculine and feminine rhyme, assonance, and consonance display relatively more sound patterns respectively, and unique words are distributed across them differently. These results are discussed in terms of their poetic and cognitive implications.