Using games to understand the mind.

Board, card or video games have been played by virtually every individual in the world. Games are popular because they are intuitive and fun. These distinctive qualities of games also make them ideal for studying the mind. By being intuitive, games provide a unique vantage point for understanding the inductive biases that support behaviour in more complex, ecological settings than traditional laboratory experiments. By being fun, games allow researchers to study new questions in cognition such as the meaning of 'play' and intrinsic motivation, while also supporting more extensive and diverse data collection by attracting many more participants. We describe the advantages and drawbacks of using games relative to standard laboratory-based experiments and lay out a set of recommendations on how to gain the most from using games to study cognition. We hope this Perspective will lead to a wider use of games as experimental paradigms, elevating the ecological validity, scale and robustness of research on the mind.

The ventral striatum dissociates information expectation, reward anticipation, and reward receipt.

Do dopaminergic reward structures represent the expected utility of information similarly to a reward? Optimal experimental design models from Bayesian decision theory and statistics have proposed a theoretical framework for quantifying the expected value of information that might result from a query. In particular, this formulation quantifies the value of information before the answer to that query is known, in situations where payoffs are unknown and the goal is purely epistemic: That is, to increase knowledge about the state of the world. Whether and how such a theoretical quantity is represented in the brain is unknown. Here we use an event-related functional MRI (fMRI) task design to disentangle information expectation, information revelation and categorization outcome anticipation, and response-contingent reward processing in a visual probabilistic categorization task. We identify a neural signature corresponding to the expectation of information, involving the left lateral ventral striatum. Moreover, we show a temporal dissociation in the activation of different reward-related regions, including the nucleus accumbens, medial prefrontal cortex, and orbitofrontal cortex, during information expectation versus reward-related processing.

Stepwise versus globally optimal search in children and adults.

How do children and adults search for information when stepwise-optimal strategies fail to identify the most efficient query? The value of questions is often measured in terms of stepwise information gain (expected reduction of entropy on the next time step) or other stepwise-optimal methods. However, such myopic models are not guaranteed to identify the most efficient sequence of questions, that is, the shortest path to the solution. In two experiments we contrast stepwise methods with globally optimal strategies and study how younger children (around age 8, N = 52), older children (around age 10, N = 99), and adults (N = 101) search in a 20-questions game where planning ahead is required to identify the most efficient first question. Children searched as efficiently as adults, but also as myopically. Both children and adults tended to rely on heuristic stepwise-optimal strategies, focusing primarily on questions' implications for the next time step, rather than planning ahead.

Asking the right questions about the psychology of human inquiry: Nine open challenges.

The ability to act on the world with the goal of gaining information is core to human adaptability and intelligence. Perhaps the most successful and influential account of such abilities is the Optimal Experiment Design (OED) hypothesis, which argues that humans intuitively perform experiments on the world similar to the way an effective scientist plans an experiment. The widespread application of this theory within many areas of psychology calls for a critical evaluation of the theory's core claims. Despite many successes, we argue that the OED hypothesis remains lacking as a theory of human inquiry and that research in the area often fails to confront some of the most interesting and important questions. In this critical review, we raise and discuss nine open questions about the psychology of human inquiry.

Generalized Information Theory Meets Human Cognition: Introducing a Unified Framework to Model Uncertainty and Information Search.

Searching for information is critical in many situations. In medicine, for instance, careful choice of a diagnostic test can help narrow down the range of plausible diseases that the patient might have. In a probabilistic framework, test selection is often modeled by assuming that people's goal is to reduce uncertainty about possible states of the world. In cognitive science, psychology, and medical decision making, Shannon entropy is the most prominent and most widely used model to formalize probabilistic uncertainty and the reduction thereof. However, a variety of alternative entropy metrics (Hartley, Quadratic, Tsallis, Rényi, and more) are popular in the social and the natural sciences, computer science, and philosophy of science. Particular entropy measures have been predominant in particular research areas, and it is often an open issue whether these divergences emerge from different theoretical and practical goals or are merely due to historical accident. Cutting across disciplinary boundaries, we show that several entropy and entropy reduction measures arise as special cases in a unified formalism, the Sharma-Mittal framework. Using mathematical results, computer simulations, and analyses of published behavioral data, we discuss four key questions: How do various entropy models relate to each other? What insights can be obtained by considering diverse entropy models within a unified framework? What is the psychological plausibility of different entropy models? What new questions and insights for research on human information acquisition follow? Our work provides several new pathways for theoretical and empirical research, reconciling apparently conflicting approaches and empirical findings within a comprehensive and unified information-theoretic formalism.

Generalization guides human exploration in vast decision spaces.

From foraging for food to learning complex games, many aspects of human behaviour can be framed as a search problem with a vast space of possible actions. Under finite search horizons, optimal solutions are generally unobtainable. Yet, how do humans navigate vast problem spaces, which require intelligent exploration of unobserved actions? Using various bandit tasks with up to 121 arms, we study how humans search for rewards under limited search horizons, in which the spatial correlation of rewards (in both generated and natural environments) provides traction for generalization. Across various different probabilistic and heuristic models, we find evidence that Gaussian process function learning-combined with an optimistic upper confidence bound sampling strategy-provides a robust account of how people use generalization to guide search. Our modelling results and parameter estimates are recoverable and can be used to simulate human-like performance, providing insights about human behaviour in complex environments.

Naïve and Robust: Class-Conditional Independence in Human Classification Learning.

Humans excel in categorization. Yet from a computational standpoint, learning a novel probabilistic classification task involves severe computational challenges. The present paper investigates one way to address these challenges: assuming class-conditional independence of features. This feature independence assumption simplifies the inference problem, allows for informed inferences about novel feature combinations, and performs robustly across different statistical environments. We designed a new Bayesian classification learning model (the dependence-independence structure and category learning model, DISC-LM) that incorporates varying degrees of prior belief in class-conditional independence, learns whether or not independence holds, and adapts its behavior accordingly. Theoretical results from two simulation studies demonstrate that classification behavior can appear to start simple, yet adapt effectively to unexpected task structures. Two experiments-designed using optimal experimental design principles-were conducted with human learners. Classification decisions of the majority of participants were best accounted for by a version of the model with very high initial prior belief in class-conditional independence, before adapting to the true environmental structure. Class-conditional independence may be a strong and useful default assumption in category learning tasks.

Asking better questions: How presentation formats influence information search.

While the influence of presentation formats have been widely studied in Bayesian reasoning tasks, we present the first systematic investigation of how presentation formats influence information search decisions. Four experiments were conducted across different probabilistic environments, where subjects (N = 2,858) chose between 2 possible search queries, each with binary probabilistic outcomes, with the goal of maximizing classification accuracy. We studied 14 different numerical and visual formats for presenting information about the search environment, constructed across 6 design features that have been prominently related to improvements in Bayesian reasoning accuracy (natural frequencies, posteriors, complement, spatial extent, countability, and part-to-whole information). The posterior variants of the icon array and bar graph formats led to the highest proportion of correct responses, and were substantially better than the standard probability format. Results suggest that presenting information in terms of posterior probabilities and visualizing natural frequencies using spatial extent (a perceptual feature) were especially helpful in guiding search decisions, although environments with a mixture of probabilistic and certain outcomes were challenging across all formats. Subjects who made more accurate probability judgments did not perform better on the search task, suggesting that simple decision heuristics may be used to make search decisions without explicitly applying Bayesian inference to compute probabilities. We propose a new take-the-difference (TTD) heuristic that identifies the accuracy-maximizing query without explicit computation of posterior probabilities. (PsycINFO Database Record

Children's sequential information search is sensitive to environmental probabilities.

We investigated 4th-grade children's search strategies on sequential search tasks in which the goal is to identify an unknown target object by asking yes-no questions about its features. We used exhaustive search to identify the most efficient question strategies and evaluated the usefulness of children's questions accordingly. Results show that children have good intuitions regarding questions' usefulness and search adaptively, relative to the statistical structure of the task environment. Search was especially efficient in a task environment that was representative of real-world experiences. This suggests that children may use their knowledge of real-world environmental statistics to guide their search behavior. We also compared different related search tasks. We found positive transfer effects from first doing a number search task on a later person search task.

How embodied is perceptual decision making? Evidence for separate processing of perceptual and motor decisions.

The extent to which different cognitive processes are "embodied" is widely debated. Previous studies have implicated sensorimotor regions such as lateral intraparietal (LIP) area in perceptual decision making. This has led to the view that perceptual decisions are embodied in the same sensorimotor networks that guide body movements. We use event-related fMRI and effective connectivity analysis to investigate whether the human sensorimotor system implements perceptual decisions. We show that when eye and hand motor preparation is disentangled from perceptual decisions, sensorimotor areas are not involved in accumulating sensory evidence toward a perceptual decision. Instead, inferior frontal cortex increases its effective connectivity with sensory regions representing the evidence, is modulated by the amount of evidence, and shows greater task-positive BOLD responses during the perceptual decision stage. Once eye movement planning can begin, however, an intraparietal sulcus (IPS) area, putative LIP, participates in motor decisions. Moreover, sensory evidence levels modulate decision and motor preparation stages differently in different IPS regions, suggesting functional heterogeneity of the IPS. This suggests that different systems implement perceptual versus motor decisions, using different neural signatures.

Synthesis-guided structure revision of the sarcodonin, sarcoviolin, and hydnellin natural product family.

A sweeping structural revision of the sarcodonin natural product family (published structures 1a-13a) is proposed after extensive studies aimed at their chemical synthesis. Key features of revised structure 1b include replacement of the N,N-dioxide moiety with an oxime, ring-opening of the central diketopiperazine, and transposition of the terphenyl wing from the 1ß-2ß position of 1a to the 2ß-3ß position of 1b. This structure revision arose from the serendipitous synthesis of a benzodioxane aminal (44) whose structure was unambiguously determined by X-ray crystallography and whose spectral properties bore considerable resemblance to the published data for the sarcodonins. A versatile new method for O-arylation of hydroxamic acids is also reported herein, as well as a manganese(III)-mediated α-oxidation of hydroxamic acids to aminals.

Experience matters: information acquisition optimizes probability gain.

Deciding which piece of information to acquire or attend to is fundamental to perception, categorization, medical diagnosis, and scientific inference. Four statistical theories of the value of information-information gain, Kullback-Liebler distance, probability gain (error minimization), and impact-are equally consistent with extant data on human information acquisition. Three experiments, designed via computer optimization to be maximally informative, tested which of these theories best describes human information search. Experiment 1, which used natural sampling and experience-based learning to convey environmental probabilities, found that probability gain explained subjects' information search better than the other statistical theories or the probability-of-certainty heuristic. Experiments 1 and 2 found that subjects behaved differently when the standard method of verbally presented summary statistics (rather than experience-based learning) was used to convey environmental probabilities. Experiment 3 found that subjects' preference for probability gain is robust, suggesting that the other models contribute little to subjects' search behavior.

Multiple parietal reach regions in humans: cortical representations for visual and proprioceptive feedback during on-line reaching.

Reaching toward a visual target involves at least two sources of information. One is the visual feedback from the hand as it approaches the target. Another is proprioception from the moving limb, which informs the brain of the location of the hand relative to the target even when the hand is not visible. Where these two sources of information are represented in the human brain is unknown. In the present study, we investigated the cortical representations for reaching with or without visual feedback from the moving hand, using functional magnetic resonance imaging. To identify reach-dominant areas, we compared reaching with saccades. Our results show that a reach-dominant region in the anterior precuneus (aPCu), extending into medial intraparietal sulcus, is equally active in visual and nonvisual reaching. A second region, at the superior end of the parieto-occipital sulcus (sPOS), is more active for visual than for nonvisual reaching. These results suggest that aPCu is a sensorimotor area whose sensory input is primarily proprioceptive, while sPOS is a visuomotor area that receives visual feedback during reaching. In addition to the precuneus, medial, anterior intraparietal, and superior parietal cortex were also activated during both visual and nonvisual reaching, with more anterior areas responding to hand movements only and more posterior areas responding to both hand and eye movements. Our results suggest that cortical networks for reaching are differentially activated depending on the sensory conditions during reaching. This indicates the involvement of multiple parietal reach regions in humans, rather than a single homogenous parietal reach region.

Human cortical representations for reaching: mirror neurons for execution, observation, and imagery.

We used functional magnetic resonance imaging (fMRI) to map the cortical representations of executed reaching, observed reaching, and imagined reaching in humans. Whereas previous studies have mostly examined hand actions related to grasping, hand-object interactions, or local finger movements, here we were interested in reaching only (i.e. the transport phase of the hand to a particular location in space), without grasping. We hypothesized that mirror neuron areas specific to reaching-related representations would be active in all three conditions. An overlap between executed, observed, and imagined reaching activations was found in dorsal premotor cortex as well as in the superior parietal lobe and the intraparietal sulcus, in accord with our hypothesis. Activations for observed reaching were more dorsal than activations typically reported in the literature for observation of hand-object interactions (grasping). Our results suggest that the mirror neuron system is specific to the type of hand action performed, and that these fronto-parietal activations are a putative human homologue of the neural circuits underlying reaching in macaques. The parietal activations reported here for executed, imagined, and observed reaching are also consistent with previous functional imaging studies on planned reaching and delayed pointing movements, and extend the proposed localization of human reach-related brain areas to observation as well as imagery of reaching.

A probabilistic model of eye movements in concept formation.

It has been unclear whether optimal experimental design accounts of data selection may offer insight into evidence acquisition tasks in which the learner's beliefs change greatly during the course of learning. Data from Rehder and Hoffman's eye movement version of Shepard, Horland and Jenkins' classic concept learning task provide an opportunity to address these issues. We introduce a principled probabilistic concept-learning model that describes the development of subjects' beliefs on that task. We use that learning model, together with a sampling function inspired by theory of optimal experimental design, to predict subjects' eye movements on the active learning version of that task. Results show that the same rational sampling function can predict eye movements early in learning, when uncertainty is high, as well as late in learning when the learner is certain of the true category.

Finding useful questions: on Bayesian diagnosticity, probability, impact, and information gain.

Several norms for how people should assess a question's usefulness have been proposed, notably Bayesian diagnosticity, information gain (mutual information), Kullback-Liebler distance, probability gain (error minimization), and impact (absolute change). Several probabilistic models of previous experiments on categorization, covariation assessment, medical diagnosis, and the selection task are shown to not discriminate among these norms as descriptive models of human intuitions and behavior. Computational optimization found situations in which information gain, probability gain, and impact strongly contradict Bayesian diagnosticity. In these situations, diagnosticity's claims are normatively inferior. Results of a new experiment strongly contradict the predictions of Bayesian diagnosticity. Normative theoretical concerns also argue against use of diagnosticity. It is concluded that Bayesian diagnosticity is normatively flawed and empirically unjustified.

What a speaker's choice of frame reveals: reference points, frame selection, and framing effects.

Framing effects are well established: Listeners' preferences depend on how outcomes are described to them, or framed. Less well understood is what determines how speakers choose frames. Two experiments revealed that reference points systematically influenced speakers' choices between logically equivalent frames. For example, speakers tended to describe a 4-ounce cup filled to the 2-ounce line as half full if it was previously empty but described it as half empty if it was previously full. Similar results were found when speakers could describe the outcome of a medical treatment in terms of either mortality or survival (e.g., 25% die vs. 75% survive). Two additional experiments showed that listeners made accurate inferences about speakers' reference points on the basis of the selected frame (e.g., if a speaker described a cup as half empty, listeners inferred that the cup used to be full). Taken together, the data suggest that frames reliably convey implicit information in addition to their explicit content, which helps explain why framing effects are so robust.