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I argue that the evolution of our life history, with its distinctively long, protected human childhood, allows an early period of broad hypothesis search and exploration, before the demands of goal-directed exploitation set in. This cognitive profile is also found in other animals and is associated with early behaviours such as neophilia and play. I relate this developmental pattern to computational ideas about explore–exploit trade-offs, search and sampling, and to neuroscience findings. I also present several lines of empirical evidence suggesting that young human learners are highly exploratory, both in terms of their search for external information and their search through hypothesis spaces. In fact, they are sometimes more exploratory than older learners and adults. This article is part of the theme issue ‘Life history and learning: how childhood, caregiving and old age shape cognition and culture in humans and other animals’.

Creativity is progressively acknowledged as the main driver for progress in all sectors of humankind’s activities: arts, science, technology, business, and social policies. Nowadays, many creative processes rely on many actors collectively contributing to an outcome. The same is true when groups of people collaborate in the solution of a complex problem. Despite the critical importance of collective actions in human endeavors, few works have tackled this topic extensively and quantitatively. Here we report about an experimental setting to single out some of the key determinants of efficient teams committed to an open-ended creative task. In this experiment, dynamically forming teams were challenged to create several artworks using LEGO bricks. The growth rate of the artworks, the dynamical network of social interactions, and the interaction patterns between the participants and the artworks were monitored in parallel. The experiment revealed that larger working teams are building at faster rates and that higher commitment leads to higher growth rates. Even more importantly, there exists an optimal number of weak ties in the social network of creators that maximizes the growth rate. Finally, the presence of influencers within the working team dramatically enhances the building efficiency. The generality of the approach makes it suitable for application in very different settings, both physical and online, whenever a creative collective outcome is required.

Firms using online advertising regularly run experiments with multiple versions of their ads since they are uncertain about which ones are most effective. During a campaign, firms try to adapt to intermediate results of their tests, optimizing what they earn while learning about their ads. Yet how should they decide what percentage of impressions to allocate to each ad? This paper answers that question, resolving the well-known “learn-and-earn” trade-off using multi-armed bandit (MAB) methods. The online advertiser’s MAB problem, however, contains particular challenges, such as a hierarchical structure (ads within a website), attributes of actions (creative elements of an ad), and batched decisions (millions of impressions at a time), that are not fully accommodated by existing MAB methods. Our approach captures how the impact of observable ad attributes on ad effectiveness differs by website in unobserved ways, and our policy generates allocations of impressions that can be used in practice. We implemented this policy in a live field experiment delivering over 750 million ad impressions in an online display campaign with a large retail bank. Over the course of two months, our policy achieved an 8% improvement in the customer acquisition rate, relative to a control policy, without any additional costs to the bank. Beyond the actual experiment, we performed counterfactual simulations to evaluate a range of alternative model specifications and allocation rules in MAB policies. Finally, we show that customer acquisition would decrease by about 10% if the firm were to optimize click-through rates instead of conversion directly, a finding that has implications for understanding the marketing funnel. Data is available at https://doi.org/10.1287/mksc.2016.1023 .

Background: Despite the established efficacy of psychological therapies for post-traumatic stress disorder (PTSD) there has been little systematic exploration of dropout rates. Objective: To ascertain rates of dropout across different modalities of psychological therapy for PTSD and to explore potential sources of heterogeneity. Method: A systematic review of dropout rates from randomized controlled trials (RCTs) of psychological therapies was conducted. The pooled rate of dropout from psychological therapies was estimated and reasons for heterogeneity explored using meta-regression. Results:: The pooled rate of dropout from RCTs of psychological therapies for PTSD was 16% (95% CI 14-18%). There was evidence of substantial heterogeneity across studies. We found evidence that psychological therapies with a trauma-focus were significantly associated with greater dropout. There was no evidence of greater dropout from therapies delivered in a group format; from studies that recruited participants from clinical services rather than via advertisements; that included only military personnel/veterans; that were limited to participants traumatized by sexual traumas; that included a higher proportion of female participants; or from studies with a lower proportion of participants who were university educated. Conclusions: Dropout rates from recommended psychological therapies for PTSD are high and this appears to be particularly true of interventions with a trauma focus. There is a need to further explore the reasons for dropout and to look at ways of increasing treatment retention.

In probabilistic and nonstationary environments, individuals must use internal and external cues to flexibly make decisions that lead to desirable outcomes. To gain insight into the process by which animals choose between actions, we trained mice in a task with time-varying reward probabilities. In our implementation of such a two-armed bandit task, thirsty mice use information about recent action and action–outcome histories to choose between two ports that deliver water probabilistically. Here we comprehensively modeled choice behavior in this task, including the trial-to-trial changes in port selection, i.e., action switching behavior. We find that mouse behavior is, at times, deterministic and, at others, apparently stochastic. The behavior deviates from that of a theoretically optimal agent performing Bayesian inference in a hidden Markov model (HMM). We formulate a set of models based on logistic regression, reinforcement learning, and sticky Bayesian inference that we demonstrate are mathematically equivalent and that accurately describe mouse behavior. The switching behavior of mice in the task is captured in each model by a stochastic action policy, a history-dependent representation of action value, and a tendency to repeat actions despite incoming evidence. The models parsimoniously capture behavior across different environmental conditionals by varying the stickiness parameter, and like the mice, they achieve nearly maximal reward rates. These results indicate that mouse behavior reaches near-maximal performance with reduced action switching and can be described by a set of equivalent models with a small number of relatively fixed parameters.

According to the free energy principle biological agents resist a tendency to disorder in their interactions with a dynamically changing environment by keeping themselves in sensory and physiological states that are expected given their embodiment and the niche they inhabit (Friston in Nat Rev Neurosci 11(2): 127-138, 2010. doi:10.1038/nrn2787). Why would a biological agent that aims at minimising uncertainty in its encounters with the world ever be motivated to seek out novelty? Novelty for such an agent would arrive in the form of sensory and physiological states that are unexpected. Such an agent ought therefore to avoid novel and surprising interactions with the world one might think. Yet humans and many other animals find play and other forms of novelty-seeking and exploration hugely rewarding. How can this be understood in frameworks for studying the mind that emphasise prediction error minimisation? This problem has been taken up in recent research concerned with epistemic action—actions an agent engages in to reduce uncertainty. However that work leaves two questions unanswered, which it is the aim of our paper to address. First, no account has been given yet of why it should feel good to the agent to engage the world play-fully and with curiosity. Second an appeal is made to precision-estimation to explain epistemic action, yet it remains unclear how precision-weighting works in action more generally, or active inference. We argue that an answer to both questions may lie in the bodily states of an agent that track the rate at which free energy is being reduced. The recent literature on the predictive brain has connected the valence of emotional experiences to the rate of change in the reduction of prediction error (Joffily and Coricelli in PLoS Comput Biol 9(6):e1003094, 2013. doi: 10.1371/journal.pcbi.1003094; Van de Cruys, in Metzinger and Wiese (eds) Philosophy and predictive processing, vol 24, MIND Group, Frankfurt am Main, 2017. doi: 10.15502/9783958573253). In this literature valenced emotional experiences are hypothesised to be identical with changes in the rate at which prediction error is reduced. Experiences are negatively valenced when overall prediction error increases and are positively valenced when the sum of prediction errors decrease. We offer an ecological-enactive interpretation of the concept of valence and its connection to rate of change of prediction error. We show how rate of change should be understood in terms of embodied states of affordance-related action readiness. We then go on to apply this ecological-enactive account of error dynamics to provide an answer to the first question we have raised: It may explain why it should feel good to an agent to be curious and playful. Our ecological-enactive account also allows us to show how error dynamics may provide an answer to the second question we have raised regarding how precision-weighting works in active inference. An agent that is sensitive to rates of error reduction can tune precision on the fly. We show how this ability to tune precision on the go can allow agents to develop skills for adapting better and better to the unexpected, and search out opportunities for resolving uncertainty and progressing in its learning.

The explore-exploit dilemma occurs anytime we must choose between exploring unknown options for information and exploiting known resources for reward. Previous work suggests that people use two different strategies to solve the explore-exploit dilemma: directed exploration, driven by information seeking, and random exploration, driven by decision noise. Here, we show that these two strategies rely on different neural systems. Using transcranial magnetic stimulation to inhibit the right frontopolar cortex, we were able to selectively inhibit directed exploration while leaving random exploration intact. This suggests a causal role for right frontopolar cortex in directed, but not random, exploration and that directed and random exploration rely on (at least partially) dissociable neural systems.

PurposeIntelligent manufacturing has attracted extensive attention from national strategy, academic research and enterprises' practices. The purpose of this study is to investigate the influence of intelligent manufacturing on performance in manufacturing firms. Moreover, how intelligent manufacturing technology affects enterprise performance, this study provided a practice that can be replicated by other businesses.Design/methodology/approachThis study uses text mining to collect the intelligence level of Chinese listed companies. It uses quantitative analysis to test the proposed model based on samples of 2,091 manufacturers.FindingsIntelligent manufacturing has positive effect on short-term performance and long-term performance. Intelligent manufacturing can empower firms with ambidextrous capabilities, including exploit capability and explore capability. Exploit capability has positive effects on short-term performance and long-term performance. Explore capability has negative effects on short-term performance, but has positive effects on long-term performance.Originality/valueOn the theoretical side, it enriches the research framework between intelligent manufacturing and enterprise performance. This study explains the preconditions and results of ambidextrous capabilities. Moreover, based on the practice-based view (PBV), this study proposes that technologies can be used as strategies, filling a gap in the existing research on strategic management. On the practical side, how to quantify the intelligent manufacturing level of enterprises provides a certain reference. Also, this study provides an easy to imitate practice that can serve as a model for under-performing enterprises.

Sex-based modulation of cognitive processes could set the stage for individual differences in vulnerability to neuropsychiatric disorders. While value-based decision making processes in particular have been proposed to be influenced by sex differences, the overall correct performance in decision making tasks often show variable or minimal differences across sexes. Computational tools allow us to uncover latent variables that define different decision making approaches, even in animals with similar correct performance. Here, we quantify sex differences in mice in the latent variables underlying behavior in a classic value-based decision making task: a restless two-armed bandit. While male and female mice had similar accuracy, they achieved this performance via different patterns of exploration. Male mice tended to make more exploratory choices overall, largely because they appeared to get ‘stuck’ in exploration once they had started. Female mice tended to explore less but learned more quickly during exploration. Together, these results suggest that sex exerts stronger influences on decision making during periods of learning and exploration than during stable choices. Exploration during decision making is altered in people diagnosed with addictions, depression, and neurodevelopmental disabilities, pinpointing the neural mechanisms of exploration as a highly translational avenue for conferring sex-modulated vulnerability to neuropsychiatric diagnoses. When faced with a decision to make, humans and other animals reflect on past experiences of similar situations to choose the best option. However, in an uncertain situation, this decision process requires balancing two competing priorities: exploiting options that are expected to be rewarding (exploitation), and exploring alternatives that could be more valuable (exploration). Decision making and exploration are disrupted in many mental disorders, some of which can differ in either presentation or risk of development across women and men. This raises the question of whether sex differences in exploration and exploitation could contribute to the vulnerability to these conditions. To shed light on this question, Chen et al. studied exploration in male and female mice as they played a video game. The mice had the option to touch one of two locations on a screen for a chance to win a small reward. The likelihood of success was different between the two options, and so the mice were incentivized to determine which was the more rewarding button. While the mice were similarly successful in finding rewards regardless of sex, on average male mice were more likely to keep exploring between the options while female mice more quickly gained confidence in an option. These differences were stronger during uncertain periods of learning and exploration than when making choices in a well-known situation, indicating that periods of uncertainty are when the influence of sex on cognition are most visible. However, not every female or male mouse was the same – there was as much variability within a sex as was seen between sexes. These results indicate that sex mechanisms, along with many other influences cause individual differences in the cognitive processes important for decision making. The approach used by Chen et al. could help to study individual differences in cognition in other species, and shed light on how individual differences in decision-making processes could contribute to risk and resilience to mental disorders.

Open-world games, characterized by their expansive and interactive environments, may offer unique cognitive escapism opportunities, potentially leading to relaxation and enhanced well-being. These games, such as \"The Legend of Zelda: Breath of the Wild\" and \"The Legend of Zelda: Tears of the Kingdom,\" allow players to experience a sense of freedom and autonomy, which can reduce stress and improve mental health. While previous research has examined the general impact of video games on mental well-being, specific studies on the effects of open-world games among postgraduate students are limited. This study aims to investigate the relationships between cognitive escapism provided by open-world games and their effects on relaxation and well-being. The goal was to understand how the immersive nature of these games contributes to stress reduction and overall mental health improvement among postgraduate students. A mixed methods approach was used, which involved in-depth exploratory qualitative interviews and a survey of 609 players of popular open-world games. Quantitative data were collected using standardized questionnaires to measure open-world games' affordance of cognitive escapism, relaxation, and well-being. Qualitative data were obtained through 32 in-depth interviews that explored players' experiences and perceptions of cognitive escapism, relaxation, and mental well-being. Qualitative data (n=32; n=15, 47% female; n=16, 50% male; n=1, 3% preferred not to disclose gender; mean age 23.19, SD 2.19 y) revealed that cognitive escapism through immersive game worlds allowed players to temporarily disconnect from real-world stressors, resulting in enhanced mood and psychological well-being. Players indicated that the nonlinear gameplay and freedom to explore interactive environments provided a sense of relaxation and mental rejuvenation. Quantitative analysis (N=609) showed a substantial mediating role of relaxation in the relationship between cognitive escapism offered by open-world games and well-being. Specifically, cognitive escapism had a significant positive effect on players' relaxation (β=.15; SE 0.04; P<.001; 95% CI 0.0695-0.2331), which in turn had a significant and positive effect on players' well-being scores (β=.12; SE 0.04; P=.002; 95% CI 0.0445-0.2032). The study demonstrates that open-world games offer substantial benefits for cognitive escapism, significantly improving relaxation and well-being among postgraduate students. The immersive and autonomous nature of these games is crucial in reducing stress and enhancing mental health. Future research may investigate the long-term effects of regular engagement with open-world games and explore their potential therapeutic applications for managing stress and anxiety.