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Optimizing competence in the service of collaboration
By collaborating with others, humans can pool their limited knowledge, skills, and resources to achieve goals that outstrip the abilities of any one person. What cognitive capacities make human collaboration possible? Here, we propose that collaboration is grounded in an intuitive understanding of how others think and of what they can do-in other words, of their mental states and competence. We present a belief-desire-competence framework that formalizes this proposal by extending existing models of commonsense psychological reasoning. Our framework predicts that agents recursively reason how much effort they and their partner will allocate to a task, based on the rewards at stake and on their own and their collaborator's competence. Across three experiments (N = 249), we show that the belief-desire-competence framework captures human judgments in a variety of contexts that are critical to collaboration, including predicting whether a joint activity will succeed (Experiment 1), selecting incentives for collaborators (Experiment 2), and choosing which individuals to recruit for a collaborative task (Experiment 3). Our work provides a theoretical framework for understanding how commonsense psychological reasoning contributes to collaborative achievements. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
We face tasks every day that we can solve alone but decide to solve together with others. When do we choose to act together vs. alone? How long do we persist in working together when doing so is difficult? Do we prefer to act together when times are uncertain? An open question in joint action research is under what conditions humans prefer to act together or alone to achieve a certain goal, and whether their preference is based on a utility calculus that takes into account the costs and benefits associated with individual and joint action alternatives. Research on cooperation reveals that frequent engagement in joint activities provides high survival benefits, as it allows individuals to achieve goals together that are otherwise unavailable. Yet, survival advantage does not wholly explain the reasons for human cooperative behavior. In fact, humans are motivated to cooperate even when it is not necessary to achieve an outcome. Research in cognitive science suggests that navigating the potential costs of joint actions is a challenge for humans, and that joint actions might provide individuals with rewards that go beyond the achievement of instrumental goals. We here address the influence of key factors on the decision to engage in joint action, such as the coordination costs arising when acting together compared to alone and the social and instrumental rewards expected when acting together compared to alone. Addressing these questions will provide critical insight for the design of cognitive models of human decisions for cooperation.
Strategic collaboration according to the law of comparative advantage involves dividing tasks based on the relative capabilities of group members. Three experiments (N = 405, primarily White and Asian, 45% female, collected 2016–2019 in Canada) examined how this strategy develops in children when dividing cognitive labor. Children divided questions about numbers between two partners. By 7 years, children allocated difficult questions to the skilled partner (Experiment 1, d = 1.42; Experiment 2, d = 0.87). However, younger children demonstrated a self‐serving bias, choosing the easiest questions for themselves. Only when engaging in a third‐party collaborative task did 5‐year‐olds assign harder questions to the more skilled individual (Experiment 3, d = 0.55). These findings demonstrate early understanding of strategic collaboration subject to a self‐serving bias.
Successful performance in cooperative activities relies on efficient task distribution between co-actors. Previous research found that people often forgo individual efficiency in favor of co-efficiency (i.e., joint-cost minimization) when planning a joint action. The present study investigated the cost computations underlying co-efficient decisions. We report a series of experiments that tested the hypothesis that people compute the joint costs of a cooperative action sequence by summing the individual action costs of their co-actor and themselves. We independently manipulated the parameters quantifying individual and joint action costs and tested their effects on decision-making by fitting and comparing Bayesian logistic regression models. Our hypothesis was confirmed: people weighed their own and their partner’s costs similarly to estimate the joint action costs as the sum of the two individual parameters. Participants minimized the aggregate cost to ensure co-efficiency. The results provide empirical support for behavioral economics and computational approaches that formalize cooperation as joint utility maximization based on a weighted sum of individual action costs.
When people perform simple actions, they often behave efficiently, minimizing the costs of movement for the expected benefit. The present study addressed the question whether this efficiency scales up to dyads working together to achieve a shared goal: do people act efficiently as a group, or do they minimize their own or their partner’s individual costs even if this increases the overall cost for the group? We devised a novel, touchscreen-based, sequential object transfer task to measure how people choose between different paths to coordinate with a partner. Across multiple experiments, we found that participants did not simply minimize their own or their partner’s movement costs but made co-efficient decisions about paths, which ensured that the aggregate costs of movement for the dyad were minimized. These results suggest that people are able and motivated to make co-efficient, collectively rational decisions when acting together.
Preschoolers are sensitive to differences in individuals’ access to external resources (e.g., tools) in division of labor tasks. However, little is known about whether children consider differences in individuals’ internal resources (e.g., abilities) and whether children can flexibly allocate roles across different goal contexts. Critically, factors that are relevant to role allocation in collaborative contexts may be irrelevant in competitive and prosocial ones. In three preregistered experiments, we found that 4- and 5-year-olds (mean: 54 months; range: 42–66 months; N = 132) used age differences to infer relative ability and appropriately allocate the harder and easier of two tasks in a dyadic cooperative interaction (Experiment 1), and appropriately ignored relative ability in competitive (Experiment 2) and prosocial (Experiment 3) contexts, instead assigning others the harder and easier roles, respectively. Thus, 3-and-a-half- to 5-year-olds evaluate their own abilities relative to others and effectively allocate roles to achieve diverse goals.
The equity heuristic is a decision rule specifying that parents should attempt to subdivide resources more or less equally among their children. This investment rule coincides with the prescription from optimality models in economics and biology in cases in which expected future return for each offspring is equal. In this article, the authors present a counterintuitive implication of the equity heuristic: Whereas an equity motive produces a fair distribution at any given point in time, it yields a cumulative distribution of investments that is unequal. The authors test this analytical observation against evidence reported in studies exploring parental investment and show how the equity heuristic can provide an explanation of why the literature reports a diversity of birth order effects with respect to parental resource allocation.
How do people hold others responsible for the consequences of their actions? We propose a computational model that attributes responsibility as a function of what the observed action reveals about the person, and the causal role that the person's action played in bringing about the outcome. The model first infers what type of person someone is from having observed their action. It then compares a prior expectation of how a person would behave with a posterior expectation after having observed the person's action. The model predicts that a person is blamed for negative outcomes to the extent that the posterior expectation is lower than the prior, and credited for positive outcomes if the posterior is greater than the prior. We model the causal role of a person's action by using a counterfactual model that considers how close the action was to having been pivotal for the outcome. The model captures participants' responsibility judgments to a high degree of quantitative accuracy across three experiments that cover a range of different situations. It also solves an existing puzzle in the literature on the relationship between action expectations and responsibility judgments. Whether an unexpected action yields more or less credit depends on whether the action was diagnostic for good or bad future performance.
Ranking valuations on the basis of observed choices
Obliged to make a choice between two goals, we evaluate the benefits of achieving the goals compared with the costs of the actions required before deciding what to do. This seems perfectly straightforward, and it is unsurprising to learn that we can also apply this reasoning to others; that is, someone that we see choosing a goal that requires a more costly action must value that goal more highly. What is remarkable, as Liu et al. report, is that preverbal children can reason in this same fashion.
Science , this issue p. 1038
Significance
Many problems, particularly sequential planning problems, are computationally very demanding. How humans combine strategies to approximate and simplify these problems is not understood. Using modelling to unpick performance in a planning task, we find that humans are able to exploit the structure of the task to subdivide it and reduce processing requirements nearly optimally. Subtasks are combined in a simple, greedy manner, however, and within subtasks there is evidence of inhibitory reflexes in response to losses.
Despite many debates in the first half of the twentieth century, it is now largely a truism that humans and other animals build models of their environments and use them for prediction and control. However, model-based (MB) reasoning presents severe computational challenges. Alternative, computationally simpler, model-free (MF) schemes have been suggested in the reinforcement learning literature, and have afforded influential accounts of behavioural and neural data. Here, we study the realization of MB calculations, and the ways that this might be woven together with MF values and evaluation methods. There are as yet mostly only hints in the literature as to the resulting tapestry, so we offer more preview than review.
Training has become the buzz word in the dynamic competitive market environment.Human capital differentiates a great organization from a good one. Organizations investing in effective training and development for human resource tend to achieve both short and long term benefits. This study presents a literature review on the significant of training and development on employee productivity. Employees tends to become absolute, and therefore making the need to adapt to the continuous learning and updating of the skill and knowledge invaluable, due to the organizational, technological and social dynamics. Thus, in order for organizations to achieve optimum returns from their investment, there is imperative need to effectively manage training and development programs. However, the most vital asset of every organization under stiff and dynamic competition is its human capital. Training and development is an instrument that aid human capital in exploring their dexterity. Therefore training and development is vital to the productivity of organization's workforce.
An individual has a theory of mind if he imputes mental states to himself and others. A system of inferences of this kind is properly viewed as a theory because such states are not directly observable, and the system can be used to make predictions about the behavior of others. As to the mental states the chimpanzee may infer, consider those inferred by our own species, for example, purpose or intention, as well as knowledge, belief, thinking, doubt, guessing, pretending, liking, and so forth. To determine whether or not the chimpanzee infers states of this kind, we showed an adult chimpanzee a series of videotaped scenes of a human actor struggling with a variety of problems. Some problems were simple, involving inaccessible food – bananas vertically or horizontally out of reach, behind a box, and so forth – as in the original Kohler problems; others were more complex, involving an actor unable to extricate himself from a locked cage, shivering because of a malfunctioning heater, or unable to play a phonograph because it was unplugged. With each videotape the chimpanzee was given several photographs, one a solution to the problem, such as a stick for the inaccessible bananas, a key for the locked up actor, a lit wick for the malfunctioning heater. The chimpanzee's consistent choice of the correct photographs can be understood by assuming that the animal recognized the videotape as representing a problem, understood the actor's purpose, and chose alternatives compatible with that purpose.
Everyday social interactions are heavily influenced by our snap judgments about
others’ goals. Even young infants can infer the goals of intentional agents from
observing how they interact with objects and other agents in their environment:
e.g., that one agent is ‘helping’ or ‘hindering’ another’s attempt to get up a hill
or open a box. We propose a model for how people can infer these social goals
from actions, based on inverse planning in multiagent Markov decision problems
(MDPs). The model infers the goal most likely to be driving an agent’s behavior
by assuming the agent acts approximately rationally given environmental constraints
and its model of other agents present. We also present behavioral evidence
in support of this model over a simpler, perceptual cue-based alternative.
The equity heuristic is a decision rule specifying that parents should attempt to subdivide resources more or less equally among their children. This investment rule coincides with the prescription from optimality models in economics and biology in cases in which expected future return for each offspring is equal. In this article, the authors present a counterintuitive implication of the equity heuristic: Whereas an equity motive produces a fair distribution at any given point in time, it yields a cumulative distribution of investments that is unequal. The authors test this analytical observation against evidence reported in studies exploring parental investment and show how the equity heuristic can provide an explanation of why the literature reports a diversity of birth order effects with respect to parental resource allocation.
Understanding Theory of Mind should begin with an analysis of the problems it solves. The traditional answer is that Theory of Mind is used for predicting others’ thoughts and actions. However, the same Theory of Mind is also used for planning to change others’ thoughts and actions. Planning requires that Theory of Mind consists of abstract structured causal representations and supports efficient search and selection from innumerable possible actions. Theory of Mind contrasts with less cognitively demanding alternatives: statistical predictive models of other people’s actions, or model-free reinforcement of actions by their effects on other people. Theory of Mind is likely used to plan novel interventions and predict their effects, for example, in pedagogy, emotion regulation, and impression management.
Reinforcement learning models have been productively applied to identify neural correlates of the value of social information. However, by operationalizing social information as a lean, reward-predictive cue, this literature underestimates the richness of human social learning: Humans readily go beyond action-outcome mappings and can draw flexible inferences from a single observation. We argue that computational models of social learning need minds, that is, a generative model of how others’ unobservable mental states cause their observable actions. Recent advances in inferential social learning suggest that even young children learn from others by using an intuitive, generative model of other minds. Bridging developmental, Bayesian, and reinforcement learning perspectives can enrich our understanding of the neural bases of distinctively human social learning.
Resistance exercise training (RET)-induced increases in voluntary 1RM strength are greater with higher loads and training by replicating (or close) the strength test. In contrast, RET-induced muscular hypertrophy is primarily mediated by intensity of effort, which is achieved by performing RET to volitional fatigue and with an internal focus on contracting a muscle throughout the exercise range of motion. In addition, RET-induced muscular hypertrophy is augmented by increasing training volume, but with diminishing returns. Other training variables such as volume-load, inter-set rest, and time under tension have negligible effects on RET-induced changes in muscle size or strength. We conclude that an uncomplicated, evidence-based approach to optimizing RET-induced changes in muscle size and strength follows the FITT principle: frequency, intensity (effort), type, and time.
Social cognition depends on our capacity for ‘mentalizing’, or explaining an agent’s behaviour in terms of their mental states. The development and neural substrates of mentalizing are well-studied, but its computational basis is only beginning to be probed. Here we present a model of core mentalizing computations: inferring jointly an actor’s beliefs, desires and percepts from how they move in the local spatial environment. Our Bayesian theory of mind (BToM) model is based on probabilistically inverting artificial-intelligence approaches to rational planning and state estimation, which extend classical expected-utility agent models to sequential actions in complex, partially observable domains. The model accurately captures the quantitative mental-state judgements of human participants in two experiments, each varying multiple stimulus dimensions across a large number of stimuli. Comparative model fits with both simpler ‘lesioned’ BToM models and a family of simpler non-mentalistic motion features reveal the value contributed by each component of our model.
We propose that human social cognition is structured around a basic understanding of ourselves and others as intuitive utility maximizers: from a young age, humans implicitly assume that agents choose goals and actions to maximize the rewards they expect to obtain relative to the costs they expect to incur. This 'naïve utility calculus' allows both children and adults observe the behavior of others and infer their beliefs and desires, their longer-term knowledge and preferences, and even their character: who is knowledgeable or competent, who is praiseworthy or blameworthy, who is friendly, indifferent, or an enemy. We review studies providing support for the naïve utility calculus, and we show how it captures much of the rich social reasoning humans engage in from infancy.
In this paper, we revisit the problem of Bayesian model selection (BMS) at the group level. We originally addressed this issue in Stephan et al. (2009), where models are treated as random effects that could differ between subjects, with an unknown population distribution. Here, we extend this work, by (i) introducing the Bayesian omnibus risk (BOR) as a measure of the statistical risk incurred when performing group BMS, (ii) highlighting the difference between random effects BMS and classical random effects analyses of parameter estimates, and (iii) addressing the problem of between group or condition model comparisons. We address the first issue by quantifying the chance likelihood of apparent differences in model frequencies. This leads to the notion of protected exceedance probabilities. The second issue arises when people want to ask “whether a model parameter is zero or not” at the group level. Here, we provide guidance as to whether to use a classical second-level analysis of parameter estimates, or random effects BMS. The third issue rests on the evidence for a difference in model labels or frequencies across groups or conditions. Overall, we hope that the material presented in this paper finesses the problems of group-level BMS in the analysis of neuroimaging and behavioural data.
One important component of collaborative problem solving is the ability to plan one's own action in relation to that of a partner. We presented 3- and 5-year-old peer pairs with two different tool choice situations in which they had to choose complementary tools with which to subsequently work on a collaborative problem-solving apparatus. In the bidirectional condition, exemplars of the two necessary tools appeared in front of each child. In the unidirectional condition, one child had to choose between two different tools first, while the other child had only one tool available. Thus, both conditions required close attention to the actions of the partner, but the unidirectional condition additionally required the anticipation of the partner's constrained tool choice. Five-year-olds were proficient planners in both conditions, whereas 3-year-olds did not consistently make the correct choice. However, 3-year-olds who had first experienced the unidirectional condition chose the correct tool at an above-chance level. Moreover, communication during the tool choice led to greater success among 3-year-olds, but not among 5-year-olds. These results provide the first experimental evidence that between 3 and 5 years of age children develop the ability to plan the division of labor in a collaborative task. We discuss our findings regarding planning for a collaborative task in relation to prior research on planning abilities for individual problem-solving that appear to undergo developmental change between 3 and 5 years of age.
We derive a simple operational definition of teaching that distinguishes it from other forms of social learning where there is no active participation of instructors, and then discuss the constituent parts of the definition in detail. From a functional perspective, it is argued that the instructor's sensitivity to the pupil's changing skills or knowledge, and the instructor's ability to attribute mental states to others, are not necessary conditions of teaching in nonhuman animals, as assumed by previous work, because guided instruction without these prerequisites could still be favored by natural selection. A number of cases of social interaction in several orders of mammals and birds that have been interpreted as evidence of teaching are then reviewed. These cases fall into two categories: situations where offspring are provided with opportunities to practice skills ("opportunity teaching"), and instances where the behavior of young is either encouraged or punished by adults ("coaching"). Although certain taxonomic orders appear to use one form of teaching more often than the other, this may have more to do with the quality of the current data set than with inherent species-specific constraints. We suggest several directions for future research on teaching in nonhuman animals that will lead to a more thorough understanding of this poorly documented phenomenon. We argue throughout that adherence to conventional, narrow definitions of teaching, generally derived from observations of human adult-infant interactions, has caused many related but simpler phenomena in other species to go unstudied or unrecorded, and severely limits further exploration of this topic.
Humans are adept at inferring the mental states underlying other agents’ actions, such as goals, beliefs, desires, emotions and other thoughts. We propose a computational framework based on Bayesian inverse planning for modeling human action understanding. The framework represents an intuitive theory of intentional agents’ behavior based on the principle of rationality: the expectation that agents will plan approximately rationally to achieve their goals, given their beliefs about the world. The mental states that caused an agent’s behavior are inferred by inverting this model of rational planning using Bayesian inference, integrating the likelihood of the observed actions with the prior over mental states. This approach formalizes in precise probabilistic terms the essence of previous qualitative approaches to action understanding based on an “intentional stance” [Dennett, D. C. (1987). The intentional stance. Cambridge, MA: MIT Press] or a “teleological stance” [Gergely, G., Nádasdy, Z., Csibra, G., & Biró, S. (1995). Taking the intentional stance at 12 months of age. Cognition, 56, 165–193]. In three psychophysical experiments using animated stimuli of agents moving in simple mazes, we assess how well different inverse planning models based on different goal priors can predict human goal inferences. The results provide quantitative evidence for an approximately rational inference mechanism in human goal inference within our simplified stimulus paradigm, and for the flexible nature of goal representations that human observers can adopt. We discuss the implications of our experimental results for human action understanding in real-world contexts, and suggest how our framework might be extended to capture other kinds of mental state inferences, such as inferences about beliefs, or inferring whether an entity is an intentional agent.
This paper examines how decision makers cope when faced with trade-offs between a higher quality alternative and a lower price alternative in situations where both alternatives involve relatively unfavorable versus relatively favorable values for quality. We hypothesize that choices between alternatives defined by unfavorable quality values will generate negative emotion, resulting in emotion-focused coping behavior. Choosing the higher quality alternative (i.e., maximizing the quality attribute in choice) appears to function as a coping mechanism in these situations. These apparently coping-motivated choice effects are found even after methods are implemented to control for more cognitive factors associated with manipulations of quality-attribute value, such as the possibility that unfavorable attribute values are associated with increased attribute ranges and therefore increased relative importance for quality.
People are often expected to make decisions based on all of the relevant information, weighted and combined appropriately. Under many conditions, however, people use heuristic strategies that depart from this ideal. I tested the ability of two models to predict bail decisions made by judges in two courts. In both courts, a simple heuristic proved to be a better predictor of judicial decisions than a more complex model that instantiated the principles of due process. Specifically, judges were "passing the buck" because they relied on decisions made by the police, prosecution, and previous bench. Problematically, these earlier decisions were not significantly related to case characteristics. These findings have implications for the types of models researchers use to capture professional decision-making policies.
It is widely recognised that one of the greatest economic problems facing developed countries is unemployment. An example of this recognition is the recent reports by the OECD ("The OECD Jobs Study", 1994) on unemployment, its causes and possible policies. One issue that is closely associated with unemployment in many people's minds is competitiveness and associated with that is the use of new technology. Indeed, the OECD Jobs Study plots out the relative importance of 'high-tech' manufacturing in each member country. This paper aims to contribute to the debate on this issue by examining the impact of the introduction of new technology on employment growth and profitability. We use two complementary datasets: two large representative cross-sections of establishments in Britain in 1990 and Australia in 1989/90. We investigate the effect of innovation in each country and then compare the outcome.
Decison-making dilemmas can arise because errors may result either from deciding too soon or from delaying decisions too long. Delay can result in lost opportunities or reductions in payoffs from the most accurate decision. This paper investigates decision processes in environments where there is time stress due to the opportunity cost of delaying decisions. First, using computer simulation, the relative accuracy of alternative decision strategies is examined in environments that differ in terms of the levels of opportunity cost of delay. The lexicographic choice rule is shown to be a very attractive decision process in situations where there is opportunity-cost time pressure. Two experiments test the adaptivity of actual decision behavior to the presence or absence of opportunity-cost time pressure along with variations in goals (accuracy emphasized vs. effort savings emphasized), dispersion in probabilities or weights across the outcomes of the choice options, and the degree of correlation among the outcomes. Subjects were generally adaptive to opportunity-cost time pressure. However, failures in adaptivity were identified when choice environment properties with conflicting implications for adaptation were present simultaneously. In particular, under opportunity-cost time pressure, subjects received a lower expected payoff when the goal was to emphasize choice accuracy than when the goal was to emphasize savings in effort. The question of when adaptivity in decision making might fail is discussed.
Preschoolers use minimal statistical information about social groups to infer the preferences and group membership of individuals
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