Simplicity: a unifying principle in cognitive science?

Simplifying and Facilitating Comprehension: The “as if” Heuristic and Its Implications for Psychological Science

Simplicity is a fundamental tenet of cognition intended to cope with a complex and intricate world. Based on the writings of the German philosopher Hans Vaihinger, this article introduces a wide-ranging simplification scheme denoted the “as if” heuristic. Following this heuristic, much of our productive and constructive thoughts about the world, specifically in science, are based on idealized fictitious assumptions. Although descriptions of the world as portrayed by psychological models and theories may contain fictitious elements (antithetical or at least indifferent to the search for truth), they afford a simplification tool that facilitates our comprehension of a complex and obscured world. Numerous examples from the psychological literature in which the “as if” heuristic is apparent are presented. Specifically, we analyze the implications of exploiting the heuristic for the development of psychological constructs, theory building, and the foundations of psychological measurement. While highlighting the gains acquired from the use of the “as if” heuristic, we also discuss its possible pitfalls if not properly used.

Special Number or a Mere Numerical Array? Effect of Repdigits on Judgments and Choices

Previous studies have shown that people find special meaning in numerical arrays. In this article, we have focused on the features of numerical arrays, repdigits (e.g., "777"), and examined the effect of repdigits on judgments and choices. We formulated the following hypotheses: (1) when people want to assign special meanings to numbers [in the case of purchase or choice of alternatives that contain numbers (e.g., serial numbers)], repdigits will be chosen since people tend to prefer numbers that contain repdigits, and (2) when people think about probabilistic or statistical events involving numerical arrays, they will regard repdigits as a mere set of numerical arrays, and preference for them will disappear. Through five behavioral experiments, we examined these two hypotheses and the results generally supported them. We also discussed the features and psychological processes of repdigits in judgments and choices.

The perceptual relevance of balance, evenness, and entropy in musical rhythms

There is an uncountable number of different ways of characterizing almost any given real-world stimulus. This necessitates finding stimulus features that are perceptually relevant - that is, they have distinct and independent effects on the perception and cognition of the stimulus. Here, we provide a theoretical framework for empirically testing the perceptual relevance of stimulus features through their association with recognition, memory bias, and æsthetic evaluation. We deploy this framework in the auditory domain to explore the perceptual relevance of three recently developed mathematical characterizations of periodic temporal patterns: balance, evenness, and interonset interval entropy. By modelling recognition responses and liking ratings from 177 participants listening to a total of 1252 different musical rhythms, we obtain very strong evidence that all three features have distinct effects on the memory for, and the liking of, musical rhythms. Interonset interval entropy is a measure of the unpredictability of a rhythm derived from the distribution of its durations. Balance and evenness are both obtained from the discrete Fourier transform (DFT) of periodic patterns represented as points on the unit circle, and we introduce a teleological explanation for their perceptual relevance: the DFT coefficients representing balance and evenness are relatively robust to small random temporal perturbations and hence are coherent in noisy environments. This theory suggests further research to explore the meaning and relevance of robust coefficients such as these to the perception of patterns that are periodic in time and, possibly, space.

Simplicity and informativeness in semantic category systems

Recent research has shown that semantic category systems, such as color and kinship terms, find an optimal balance between simplicity and informativeness. We argue that this situation arises through pressure for simplicity from learning and pressure for informativeness from communicative interaction, two distinct pressures that often (but not always) pull in opposite directions. Another account argues that learning might also act as a pressure for informativeness, that learners might be biased toward inferring informative systems. This results in two competing hypotheses about the human inductive bias. We formalize these competing hypotheses in a Bayesian iterated learning model in order to simulate what kinds of languages are expected to emerge under each. We then test this model experimentally to investigate whether learners' biases, isolated from any communicative task, are better characterized as favoring simplicity or informativeness. We find strong evidence to support the simplicity account. Furthermore, we show how the application of a simplicity principle in learning can give the impression of a bias for informativeness, even when no such bias is present. Our findings suggest that semantic categories are learned through domain-general principles, negating the need to posit a domain-specific mechanism.

Explanatory preferences for complexity matching

People are adept at generating and evaluating explanations for events around them. But what makes for a satisfying explanation? While some scholars argue that individuals find simple explanations to be more satisfying (Lombrozo, 2007), others argue that complex explanations are preferred (Zemla, et al. 2017). Uniting these perspectives, we posit that people believe a satisfying explanation should be as complex as the event being explained-what we term the complexity matching hypothesis. Thus, individuals will prefer simple explanations for simple events, and complex explanations for complex events. Four studies provide robust evidence for the complexity-matching hypothesis. In studies 1-3, participants read scenarios and then predicted the complexity of a satisfying explanation (Study 1), generated an explanation themselves (Study 2), and evaluated explanations (Study 3). Lastly, in Study 4, we explored a different manipulation of complexity to demonstrate robustness across paradigms. We end with a discussion of mechanisms that might underlie this preference-matching phenomenon.

The QBIT Theory of Consciousness

The QBIT theory is an attempt toward solving the problem of consciousness based on empirical evidence provided by various scientific disciplines including quantum mechanics, biology, information theory, and thermodynamics. This theory formulates the problem of consciousness in the following four questions, and provides preliminary answers for each question: Question 1: What is the nature of qualia?

ANSWER

A quale is a superdense pack of quantum information encoded in maximally entangled pure states. Question 2: How are qualia generated?

ANSWER

When a pack of quantum information is compressed beyond a certain threshold, a quale is generated. Question 3: Why are qualia subjective?

ANSWER

A quale is subjective because a pack of information encoded in maximally entangled pure states are essentially private and unshareable. Question 4: Why does a quale have a particular meaning?

ANSWER

A pack of information within a cognitive system gradually obtains a particular meaning as it undergoes a progressive process of interpretation performed by an internal model installed in the system. This paper introduces the QBIT theory of consciousness, and explains its basic assumptions and conjectures.

How Culture and Biology Interact to Shape Language and the Language Faculty

Recent work suggests that linguistic structure develops through cultural evolution, as a consequence of the repeated cycle of learning and use by which languages persist. This work has important implications for our understanding of the evolution of the cognitive basis for language; in particular, human language and the cognitive capacities underpinning it are likely to have been shaped by co-evolutionary processes, where the cultural evolution of linguistic systems is shaped by and in turn shapes the biological evolution of the capacities underpinning language learning. I review several models of this co-evolutionary process, which suggest that the precise relationship between evolved biases in individuals and the structure of linguistic systems depends on the extent to which cultural evolution masks or unmasks individual-level cognitive biases from selection. I finish by discussing how these co-evolutionary models might be extended to cases where the biases involved in learning are themselves shaped by experience, as is the case for language.

When Can Making a Drawing Hinder Problem Solving? Effect of the Drawing Strategy on Linear Overgeneralizations and Problem Solving

The strategy of making a drawing has been claimed to facilitate mathematical problem solving. However, De Bock et al. (2003) surprisingly found that drawing negatively affected performance in solving non-linear geometry problems, in which the area or volume of similar figures or solids had to be determined by a given scaling factor. The authors suggested that making a drawing increased the number of overgeneralizations and negatively affected students' performance. Our study involves a partial replication and also an important validation and extension of this study by addressing two factors: low-quality drawing strategy and poor visual monitoring, both of which might explain the negative effect of drawing. First, we expected that improving the quality of the drawing strategy by prompting students to highlight important information in their drawings would diminish the negative effect of the drawing strategy. Second, we expected that fostering visual monitoring while drawing, by offering problems with small scaling factors, would diminish the negative effect of the drawing strategy. We conducted a randomized controlled trial with 180 students (ninth- to eleventh-graders) to investigate the effects of drawing and visual monitoring on solving non-linear geometry problems. Our results replicated the previous finding that drawing negatively affects performance. We confirmed that linear overgeneralizations are a prevalent reason for this finding. Elaborating on previous findings revealed that the quality of the drawing strategy but not visual monitoring was responsible for the effect of the drawing strategy on linear overgeneralizations. Furthermore, an exploratory analysis of students' awareness of linear overgeneralizations indicated that improving the quality of drawing strategy and enhancing visual monitoring did not lead to a greater awareness of the mistakes learners made because of linear overgeneralizations. We conclude that the way the drawing strategy is used determines whether it is useful or damaging, and more efforts are essential to enable students to apply it appropriately.

Face Representations via Tensorfaces of Various Complexities

Neurons selective for faces exist in humans and monkeys. However, characteristics of face cell receptive fields are poorly understood. In this theoretical study, we explore the effects of complexity, defined as algorithmic information (Kolmogorov complexity) and logical depth, on possible ways that face cells may be organized. We use tensor decompositions to decompose faces into a set of components, called tensorfaces, and their associated weights, which can be interpreted as model face cells and their firing rates. These tensorfaces form a high-dimensional representation space in which each tensorface forms an axis of the space. A distinctive feature of the decomposition algorithm is the ability to specify tensorface complexity. We found that low-complexity tensorfaces have blob-like appearances crudely approximating faces, while high-complexity tensorfaces appear clearly face-like. Low-complexity tensorfaces require a larger population to reach a criterion face reconstruction error than medium- or high-complexity tensorfaces, and thus are inefficient by that criterion. Low-complexity tensorfaces, however, generalize better when representing statistically novel faces, which are faces falling beyond the distribution of face description parameters found in the tensorface training set. The degree to which face representations are parts based or global forms a continuum as a function of tensorface complexity, with low and medium tensorfaces being more parts based. Given the computational load imposed in creating high-complexity face cells (in the form of algorithmic information and logical depth) and in the absence of a compelling advantage to using high-complexity cells, we suggest face representations consist of a mixture of low- and medium-complexity face cells.

Predictability and Uncertainty in the Pleasure of Music: A Reward for Learning?

Music ranks among the greatest human pleasures. It consistently engages the reward system, and converging evidence implies it exploits predictions to do so. Both prediction confirmations and errors are essential for understanding one's environment, and music offers many of each as it manipulates interacting patterns across multiple timescales. Learning models suggest that a balance of these outcomes (i.e., intermediate complexity) optimizes the reduction of uncertainty to rewarding and pleasurable effect. Yet evidence of a similar pattern in music is mixed, hampered by arbitrary measures of complexity. In the present studies, we applied a well-validated information-theoretic model of auditory expectation to systematically measure two key aspects of musical complexity: predictability (operationalized as information content [IC]), and uncertainty (entropy). In Study 1, we evaluated how these properties affect musical preferences in 43 male and female participants; in Study 2, we replicated Study 1 in an independent sample of 27 people and assessed the contribution of veridical predictability by presenting the same stimuli seven times. Both studies revealed significant quadratic effects of IC and entropy on liking that outperformed linear effects, indicating reliable preferences for music of intermediate complexity. An interaction between IC and entropy further suggested preferences for more predictability during more uncertain contexts, which would facilitate uncertainty reduction. Repeating stimuli decreased liking ratings but did not disrupt the preference for intermediate complexity. Together, these findings support long-hypothesized optimal zones of predictability and uncertainty in musical pleasure with formal modeling, relating the pleasure of music listening to the intrinsic reward of learning.SIGNIFICANCE STATEMENT Abstract pleasures, such as music, claim much of our time, energy, and money despite lacking any clear adaptive benefits like food or shelter. Yet as music manipulates patterns of melody, rhythm, and more, it proficiently exploits our expectations. Given the importance of anticipating and adapting to our ever-changing environments, making and evaluating uncertain predictions can have strong emotional effects. Accordingly, we present evidence that listeners consistently prefer music of intermediate predictive complexity, and that preferences shift toward expected musical outcomes in more uncertain contexts. These results are consistent with theories that emphasize the intrinsic reward of learning, both by updating inaccurate predictions and validating accurate ones, which is optimal in environments that present manageable predictive challenges (i.e., reducible uncertainty).

Functional connectivity in human auditory networks and the origins of variation in the transmission of musical systems

Music producers, whether original composers or performers, vary in their ability to acquire and faithfully transmit music. This form of variation may serve as a mechanism for the emergence of new traits in musical systems. In this study, we aim to investigate whether individual differences in the social learning and transmission of music relate to intrinsic neural dynamics of auditory processing systems. We combined auditory and resting-state functional magnetic resonance imaging (fMRI) with an interactive laboratory model of cultural transmission, the signaling game, in an experiment with a large cohort of participants (N=51). We found that the degree of interhemispheric rs-FC within fronto-temporal auditory networks predicts—weeks after scanning—learning, transmission, and structural modification of an artificial tone system. Our study introduces neuroimaging in cultural transmission research and points to specific neural auditory processing mechanisms that constrain and drive variation in the cultural transmission and regularization of musical systems.

A review of computational models of basic rule learning: The neural-symbolic debate and beyond

We present a critical review of computational models of generalization of simple grammar-like rules, such as ABA and ABB. In particular, we focus on models attempting to account for the empirical results of Marcus et al. (Science, 283(5398), 77-80 1999). In that study, evidence is reported of generalization behavior by 7-month-old infants, using an Artificial Language Learning paradigm. The authors fail to replicate this behavior in neural network simulations, and claim that this failure reveals inherent limitations of a whole class of neural networks: those that do not incorporate symbolic operations. A great number of computational models were proposed in follow-up studies, fuelling a heated debate about what is required for a model to generalize. Twenty years later, this debate is still not settled. In this paper, we review a large number of the proposed models. We present a critical analysis of those models, in terms of how they contribute to answer the most relevant questions raised by the experiment. After identifying which aspects require further research, we propose a list of desiderata for advancing our understanding on generalization.

Above and beyond the concrete: The diverse representational substrates of the predictive brain

In recent years, scientists have increasingly taken to investigate the predictive nature of cognition. We argue that prediction relies on abstraction, and thus theories of predictive cognition need an explicit theory of abstract representation. We propose such a theory of the abstract representational capacities that allow humans to transcend the "here-and-now." Consistent with the predictive cognition literature, we suggest that the representational substrates of the mind are built as a hierarchy, ranging from the concrete to the abstract; however, we argue that there are qualitative differences between elements along this hierarchy, generating meaningful, often unacknowledged, diversity. Echoing views from philosophy, we suggest that the representational hierarchy can be parsed into: modality-specific representations, instantiated on perceptual similarity; multimodal representations, instantiated primarily on the discovery of spatiotemporal contiguity; and categorical representations, instantiated primarily on social interaction. These elements serve as the building blocks of complex structures discussed in cognitive psychology (e.g., episodes, scripts) and are the inputs for mental representations that behave like functions, typically discussed in linguistics (i.e., predicators). We support our argument for representational diversity by explaining how the elements in our ontology are all required to account for humans' predictive cognition (e.g., in subserving logic-based prediction; in optimizing the trade-off between accurate and detailed predictions) and by examining how the neuroscientific evidence coheres with our account. In doing so, we provide a testable model of the neural bases of conceptual cognition and highlight several important implications to research on self-projection, reinforcement learning, and predictive-processing models of psychopathology.

How Efficiency Shapes Human Language

Cognitive science applies diverse tools and perspectives to study human language. Recently, an exciting body of work has examined linguistic phenomena through the lens of efficiency in usage: what otherwise puzzling features of language find explanation in formal accounts of how language might be optimized for communication and learning? Here, we review studies that deploy formal tools from probability and information theory to understand how and why language works the way that it does, focusing on phenomena ranging from the lexicon through syntax. These studies show how a pervasive pressure for efficiency guides the forms of natural language and indicate that a rich future for language research lies in connecting linguistics to cognitive psychology and mathematical theories of communication and inference.

Sources of suboptimality in a minimalistic explore-exploit task

People often choose between sticking with an available good option (exploitation) and trying out a new option that is uncertain but potentially more rewarding (exploration)^1,2. Laboratory studies on explore-exploit decisions often contain real-world complexities such as non-stationary environments, stochasticity under exploitation and unknown reward distributions^3-7. However, such factors might limit the researcher's ability to understand the essence of people's explore-exploit decisions. For this reason, we introduce a minimalistic task in which the optimal policy is to start off exploring and to switch to exploitation at most once in each sequence of decisions. The behaviour of 49 laboratory and 143 online participants deviated both qualitatively and quantitatively from the optimal policy, even when allowing for bias and decision noise. Instead, people seem to follow a suboptimal rule in which they switch from exploration to exploitation when the highest reward so far exceeds a certain threshold. Moreover, we show that this threshold decreases approximately linearly with the proportion of the sequence that remains, suggesting a temporal ratio law. Finally, we find evidence for 'sequence-level' variability that is shared across all decisions in the same sequence. Our results emphasize the importance of examining sequence-level strategies and their variability when studying sequential decision-making.

Unifying Physics and Psychophysics on the Basis of Symmetry, Least-Action ≈ Simplicity Principle, and Conservation Laws ≈ Veridicality

Psychophysics is the branch of experimental psychology that deals with the study of sensation and perception. A consensus has grown up among experts in psychophysics in the last hundred years that the human being’s percepts are inferences, which are based on a minimum, or simplicity, principle that is applied to the currently available sensory data. These educated guesses play the critical role in establishing veridical perceptual representations of the three-dimensional environment, where by “veridical” we mean that the percept agrees with what is “out there.” These veridical representations cannot be achieved without making use of symmetries, much like those known in physics, where they are essential for characterizing our physical world and deriving the conservation laws. But, unlike in physics, the important role that symmetry plays in psychophysics has been demonstrated and explained only within the last 10 years. Symmetries represent regularities in our physical world. These symmetries also serve as the source of the redundancies that are inherent in 3D objects and make vision possible. The main goal of this article is to show that the similarity between the mathematical formalisms used in physics and in psychophysics is not coincidental and that exploring this similarity can benefit the sciences of perception and cognition. This article includes a brief tutorial about symmetry groups and their relationship to transformation groups as well as to their invariants. It was included to make this material available to readers who are not familiar with these topics.

From random to regular: neural constraints on the emergence of isochronous rhythm during cultural transmission

A core design feature of human communication systems and expressive behaviours is their temporal organization. The cultural evolutionary origins of this feature remain unclear. Here, we test the hypothesis that regularities in the temporal organization of signalling sequences arise in the course of cultural transmission as adaptations to aspects of cortical function. We conducted two experiments on the transmission of rhythms associated with affective meanings, focusing on one of the most widespread forms of regularity in language and music: isochronicity. In the first experiment, we investigated how isochronous rhythmic regularities emerge and change in multigenerational signalling games, where the receiver (learner) in a game becomes the sender (transmitter) in the next game. We show that signalling sequences tend to become rhythmically more isochronous as they are transmitted across generations. In the second experiment, we combined electroencephalography (EEG) and two-player signalling games over 2 successive days. We show that rhythmic regularization of sequences can be predicted based on the latencies of the mismatch negativity response in a temporal oddball paradigm. These results suggest that forms of isochronicity in communication systems originate in neural constraints on information processing, which may be expressed and amplified in the course of cultural transmission.

Coevolution of Lexical Meaning and Pragmatic Use

According to standard linguistic theory, the meaning of an utterance is the product of conventional semantic meaning and general pragmatic rules on language use. We investigate how such a division of labor between semantics and pragmatics could evolve under general processes of selection and learning. We present a game‐theoretic model of the competition between types of language users, each endowed with certain lexical representations and a particular pragmatic disposition to act on them. Our model traces two evolutionary forces and their interaction: (i) pressure toward communicative efficiency and (ii) transmission perturbations during the acquisition of linguistic knowledge. We illustrate the model based on a case study on scalar implicatures, which suggests that the relationship between underspecified semantics and pragmatic inference is one of coevolution.

The Stochastic Complexity of Spin Models: Are Pairwise Models Really Simple?

Models can be simple for different reasons: because they yield a simple and computationally efficient interpretation of a generic dataset (e.g., in terms of pairwise dependencies)—as in statistical learning—or because they capture the laws of a specific phenomenon—as e.g., in physics—leading to non-trivial falsifiable predictions. In information theory, the simplicity of a model is quantified by the stochastic complexity, which measures the number of bits needed to encode its parameters. In order to understand how simple models look like, we study the stochastic complexity of spin models with interactions of arbitrary order. We show that bijections within the space of possible interactions preserve the stochastic complexity, which allows to partition the space of all models into equivalence classes. We thus found that the simplicity of a model is not determined by the order of the interactions, but rather by their mutual arrangements. Models where statistical dependencies are localized on non-overlapping groups of few variables are simple, affording predictions on independencies that are easy to falsify. On the contrary, fully connected pairwise models, which are often used in statistical learning, appear to be highly complex, because of their extended set of interactions, and they are hard to falsify.

Compression in cultural evolution: Homogeneity and structure in the emergence and evolution of a large-scale online collaborative art project

Cultural evolutionary theory provides a framework for explaining change in population-level distributions. A consistent finding in the literature is that multiple transmission episodes shape a distribution of cultural traits to become more compressible, i.e., a set of derived traits are more compressed than their ancestral forms. Importantly, this amplification of compressible patterns can become manifest in two ways, either via the homogenisation of variation or through the organisation of variation into structured and specialised patterns. Using a novel, large-scale dataset from Reddit Place, an online collaborative art project, we investigate the emergence and evolution of compressible patterns on a 1000x1000 pixel canvas. Here, all Reddit users could select a coloured pixel, place it on the canvas, and then wait for a fixed period before placing another pixel. By analysing all 16.5 million pixel placements by over 1 million individuals, we found that compression follows a quadratic trajectory through time. From a non-structured state, where individual artworks exist relatively independently from one another, Place gradually transitions to a structured state where pixel placements form specialised, interdependent patterns.

Social norm complexity and past reputations in the evolution of cooperation

Indirect reciprocity is the most elaborate and cognitively demanding of all known cooperation mechanisms, and is the most specifically human because it involves reputation and status. By helping someone, individuals may increase their reputation, which may change the predisposition of others to help them in future. The revision of an individual's reputation depends on the social norms that establish what characterizes a good or bad action and thus provide a basis for morality. Norms based on indirect reciprocity are often sufficiently complex that an individual's ability to follow subjective rules becomes important, even in models that disregard the past reputations of individuals, and reduce reputations to either 'good' or 'bad' and actions to binary decisions. Here we include past reputations in such a model and identify the key pattern in the associated norms that promotes cooperation. Of the norms that comply with this pattern, the one that leads to maximal cooperation (greater than 90 per cent) with minimum complexity does not discriminate on the basis of past reputation; the relative performance of this norm is particularly evident when we consider a 'complexity cost' in the decision process. This combination of high cooperation and low complexity suggests that simple moral principles can elicit cooperation even in complex environments.

Simple and Complex Working Memory Tasks Allow Similar Benefits of Information Compression

Complex working memory span tasks were designed to engage multiple aspects of working memory and impose interleaved processing demands that limit the use of mnemonic strategies, such as chunking. Consequently, the average span is usually lower (4 ± 1 items) than in simple span tasks (7 ± 2 items). One possible reason for the higher span of simple span tasks is that participants can take advantage of the spare time to chunk multiple items together to form fewer independent units, approximating 4 ± 1 chunks. It follows that the respective spans of these two types of tasks could be equal (at around 4 ± 1) if stimulus lists exclusively used nonchunkable stimulus items. To manipulate the chunkability of the stimulus lists, our method involved a measure of their compressibility, i.e., the extent to which a pattern exists that can be detected and used as a basis of chunk formation. We predicted an interaction between the types of tasks and chunkability/compressibility, supporting a single higher span for the condition in which a simple span task was combined with chunkable items. The three other conditions were predicted to prevent chunking processes, either because the interleaved processing task did not allow any chunking process to occur or because the noncompressible material inherently limited the chunkability of information. The prediction that chunking is important solely in simple spans was not confirmed: Effects of information compression contributed to performance levels to a similar extent in both tasks according to a theoretically-based metric. This result suggests that i) complex span tasks might overestimate storage capacity in general, and ii) the difference between simple and complex span performance levels must rest in some mechanism other than prevention of a chunking strategy by the interleaved processing task in complex span tasks.

Statistical learning and probabilistic prediction in music cognition: mechanisms of stylistic enculturation

Music perception depends on internal psychological models derived through exposure to a musical culture. It is hypothesized that this musical enculturation depends on two cognitive processes: (1) statistical learning, in which listeners acquire internal cognitive models of statistical regularities present in the music to which they are exposed; and (2) probabilistic prediction based on these learned models that enables listeners to organize and process their mental representations of music. To corroborate these hypotheses, I review research that uses a computational model of probabilistic prediction based on statistical learning (the information dynamics of music (IDyOM) model) to simulate data from empirical studies of human listeners. The results show that a broad range of psychological processes involved in music perception-expectation, emotion, memory, similarity, segmentation, and meter-can be understood in terms of a single, underlying process of probabilistic prediction using learned statistical models. Furthermore, IDyOM simulations of listeners from different musical cultures demonstrate that statistical learning can plausibly predict causal effects of differential cultural exposure to musical styles, providing a quantitative model of cultural distance. Understanding the neural basis of musical enculturation will benefit from close coordination between empirical neuroimaging and computational modeling of underlying mechanisms, as outlined here.

The Effort Paradox: Effort Is Both Costly and Valued

According to prominent models in cognitive psychology, neuroscience, and economics, effort (be it physical or mental) is costly: when given a choice, humans and non-human animals alike tend to avoid effort. Here, we suggest that the opposite is also true and review extensive evidence that effort can also add value. Not only can the same outcomes be more rewarding if we apply more (not less) effort, sometimes we select options precisely because they require effort. Given the increasing recognition of effort's role in motivation, cognitive control, and value-based decision-making, considering this neglected side of effort will not only improve formal computational models, but also provide clues about how to promote sustained mental effort across time.

Compression in Working Memory and Its Relationship With Fluid Intelligence

Working memory has been shown to be strongly related to fluid intelligence; however, our goal is to shed further light on the process of information compression in working memory as a determining factor of fluid intelligence. Our main hypothesis was that compression in working memory is an excellent indicator for studying the relationship between working-memory capacity and fluid intelligence because both depend on the optimization of storage capacity. Compressibility of memoranda was estimated using an algorithmic complexity metric. The results showed that compressibility can be used to predict working-memory performance and that fluid intelligence is well predicted by the ability to compress information. We conclude that the ability to compress information in working memory is the reason why both manipulation and retention of information are linked to intelligence. This result offers a new concept of intelligence based on the idea that compression and intelligence are equivalent problems.

How to Create Shared Symbols

Human cognition and behavior are dominated by symbol use. This paper examines the social learning strategies that give rise to symbolic communication. Experiment 1 contrasts an individual-level account, based on observational learning and cognitive bias, with an inter-individual account, based on social coordinative learning. Participants played a referential communication game in which they tried to communicate a range of recurring meanings to a partner by drawing, but without using their conventional language. Individual-level learning, via observation and cognitive bias, was sufficient to produce signs that became increasingly effective, efficient, and shared over games. However, breaking a referential precedent eliminated these benefits. The most effective, most efficient, and most shared signs arose when participants could directly interact with their partner, indicating that social coordinative learning is important to the creation of shared symbols. Experiment 2 investigated the contribution of two distinct aspects of social interaction: behavior alignment and concurrent partner feedback. Each played a complementary role in the creation of shared symbols: Behavior alignment primarily drove communication effectiveness, and partner feedback primarily drove the efficiency of the evolved signs. In conclusion, inter-individual social coordinative learning is important to the evolution of effective, efficient, and shared symbols.

Young people’s conceptions of political information

Purpose

The purpose of this paper is to explore young people’s conceptions of political information. The study sought to identify what political information sources young people encounter, how they construe these sources and the messages they communicate, and how the information experiences of young people may be better understood to inform information literacy interventions to support the development of political agency.

Design/methodology/approach

Using personal construct theory as a conceptual framework, repertory grid (RG) interviews were used to explore the different ways in which 23 young people aged 14-15 from a town in Northern England conceive of political information and how they evaluate its quality and authority.

Findings

The study identified the sources of information young people engage with for finding and receiving what they understand as political information. The results from the RG interviews indicated that young people use a wide range of sources of political information to become informed about politics and the world around them. These sources of information include family, friends, teachers, television news, newspapers, radio shows, comedy shows, social media and community meetings. Participants were aware that they passively encounter information sources as well as actively engage in debate and discussion with other sources. Some participants had difficulty critically evaluating the political information sources they encounter. The nature of young people’s experiences of political information varied greatly. The degree of complexity in the experiences of political information varied not only between participants but was also dependent on their particular relationship with the information sources under scrutiny.

Research limitations/implications

The paper has implications for personal construct analysis as a research approach broadly, from the point of view of its use within library and information science research. It is the first study to apply the personal construct approach to the study of young people’s political information use and to consider implications for information literacy support that would have been difficult to access using other approaches.

Practical implications

The paper provides insight into an understudied area; that of young people’s conceptions of political information. This insight may be used to inform the improvement of political information provision and information literacy support for young people.

Social implications

A deeper understanding of the different ways in which young people identify, engage with and use information for political purposes may contribute to a clearer understanding of young people’s information needs, ideally leading to improved political education and a strengthened democratic process.

Originality/value

The paper explores a relatively under-researched area of library and information science research, and does so using a relatively under-used method in the domain. Insights into the perceived characteristics of different sources of political information are novel and contribute to the development of information behaviour and information literacy fields in terms of information for empowerment and democracy.

Learning abstract visual concepts via probabilistic program induction in a Language of Thought

The ability to learn abstract concepts is a powerful component of human cognition. It has been argued that variable binding is the key element enabling this ability, but the computational aspects of variable binding remain poorly understood. Here, we address this shortcoming by formalizing the Hierarchical Language of Thought (HLOT) model of rule learning. Given a set of data items, the model uses Bayesian inference to infer a probability distribution over stochastic programs that implement variable binding. Because the model makes use of symbolic variables as well as Bayesian inference and programs with stochastic primitives, it combines many of the advantages of both symbolic and statistical approaches to cognitive modeling. To evaluate the model, we conducted an experiment in which human subjects viewed training items and then judged which test items belong to the same concept as the training items. We found that the HLOT model provides a close match to human generalization patterns, significantly outperforming two variants of the Generalized Context Model, one variant based on string similarity and the other based on visual similarity using features from a deep convolutional neural network. Additional results suggest that variable binding happens automatically, implying that binding operations do not add complexity to peoples' hypothesized rules. Overall, this work demonstrates that a cognitive model combining symbolic variables with Bayesian inference and stochastic program primitives provides a new perspective for understanding people's patterns of generalization.

Changing What You See by Changing What You Know: The Role of Attention

Attending is a cognitive process that incorporates a person's knowledge, goals, and expectations. What we perceive when we attend to one thing is different from what we perceive when we attend to something else. Yet, it is often argued that attentional effects do not count as evidence that perception is influenced by cognition. I investigate two arguments often given to justify excluding attention. The first is arguing that attention is a post-perceptual process reflecting selection between fully constructed perceptual representations. The second is arguing that attention as a pre-perceptual process that simply changes the input to encapsulated perceptual systems. Both of these arguments are highly problematic. Although some attentional effects can indeed be construed as post-perceptual, others operate by changing perceptual content across the entire visual hierarchy. Although there is a natural analogy between spatial attention and a change of input, the analogy falls apart when we consider other forms of attention. After dispelling these arguments, I make a case for thinking of attention not as a confound, but as one of the mechanisms by which cognitive states affect perception by going through cases in which the same or similar visual inputs are perceived differently depending on the observer's cognitive state, and instances where cuing an observer using language affects what one sees. Lastly, I provide two compelling counter-examples to the critique that although cognitive influences on perception can be demonstrated in the laboratory, it is impossible to really experience them for oneself in a phenomenologically compelling way. Taken together, the current evidence strongly supports the thesis that what we know routinely influences what we see, that the same sensory input can be perceived differently depending on the current cognitive state of the viewer, and that phenomenologically salient demonstrations are possible if certain conditions are met.

Human behavioral complexity peaks at age 25

Random Item Generation tasks (RIG) are commonly used to assess high cognitive abilities such as inhibition or sustained attention. They also draw upon our approximate sense of complexity. A detrimental effect of aging on pseudo-random productions has been demonstrated for some tasks, but little is as yet known about the developmental curve of cognitive complexity over the lifespan. We investigate the complexity trajectory across the lifespan of human responses to five common RIG tasks, using a large sample (n = 3429). Our main finding is that the developmental curve of the estimated algorithmic complexity of responses is similar to what may be expected of a measure of higher cognitive abilities, with a performance peak around 25 and a decline starting around 60, suggesting that RIG tasks yield good estimates of such cognitive abilities. Our study illustrates that very short strings of, i.e., 10 items, are sufficient to have their complexity reliably estimated and to allow the documentation of an age-dependent decline in the approximate sense of complexity.

It has been unclear how this ability evolves over a person’s lifetime and it had not been possible to be assessed with previous classical tools for statistical randomness. To better understand how age impacts behavior, we have assessed more than 3,400 people aged 4 to 91 years old. Each participant performed a series of online tasks that assessed their ability to behave randomly. The five tasks included listing the hypothetical results of a series of 12 coin flips so that they would “look random to somebody else,” guessing which card would appear when selected from a randomly shuffled deck, and listing the hypothetical results of 10 rolls of a die. We analyzed the participants’ choices according to their algorithmic randomness, which is based on the idea that patterns that are more random are harder to encode in a short computer program. After controlling for characteristics such as gender, language, and education. We have found that age was the only factor that affected the ability to behave randomly. This ability peaked at age 25, on average, and declined from then on. We also demonstrate that a relatively short list of choices, say 10 hypothetical coin flips, can be used to reliably gauge randomness of human behavior. A similar approach could be then used to study potential connections between the ability to behave randomly, cognitive decline, neurodegenerative diseases and abilities such as human creativity.

The computational nature of memory modification

Retrieving a memory can modify its influence on subsequent behavior. We develop a computational theory of memory modification, according to which modification of a memory trace occurs through classical associative learning, but which memory trace is eligible for modification depends on a structure learning mechanism that discovers the units of association by segmenting the stream of experience into statistically distinct clusters (latent causes). New memories are formed when the structure learning mechanism infers that a new latent cause underlies current sensory observations. By the same token, old memories are modified when old and new sensory observations are inferred to have been generated by the same latent cause. We derive this framework from probabilistic principles, and present a computational implementation. Simulations demonstrate that our model can reproduce the major experimental findings from studies of memory modification in the Pavlovian conditioning literature.

Culture and biology in the origins of linguistic structure

Language is systematically structured at all levels of description, arguably setting it apart from all other instances of communication in nature. In this article, I survey work over the last 20 years that emphasises the contributions of individual learning, cultural transmission, and biological evolution to explaining the structural design features of language. These 3 complex adaptive systems exist in a network of interactions: individual learning biases shape the dynamics of cultural evolution; universal features of linguistic structure arise from this cultural process and form the ultimate linguistic phenotype; the nature of this phenotype affects the fitness landscape for the biological evolution of the language faculty; and in turn this determines individuals' learning bias. Using a combination of computational simulation, laboratory experiments, and comparison with real-world cases of language emergence, I show that linguistic structure emerges as a natural outcome of cultural evolution once certain minimal biological requirements are in place.

Symptoms and the body: Taking the inferential leap

The relationship between the conscious experience of physical symptoms and indicators of objective physiological dysfunction is highly variable and depends on characteristics of the person, the context and their interaction. This relationship often breaks down entirely in the case of "medically unexplained" or functional somatic symptoms, violating the basic assumption in medicine that physical symptoms have physiological causes. In this paper, we describe the prevailing theoretical approach to this problem and review the evidence pertaining to it. We then use the framework of predictive coding to propose a new and more comprehensive model of the body-symptom relationship that integrates existing concepts within a unifying framework that addresses many of the shortcomings of current theory. We describe the conditions under which a close correspondence between the experience of symptoms and objective physiology might be expected, and when they are likely to diverge. We conclude by exploring some theoretical and clinical implications of this new account.

Cultural Transmission and Evolution of Melodic Structures in Multi-generational Signaling Games

It has been proposed that languages evolve by adapting to the perceptual and cognitive constraints of the human brain, developing, in the course of cultural transmission, structural regularities that maximize or optimize learnability and ease of processing. To what extent would perceptual and cognitive constraints similarly affect the evolution of musical systems? We conducted an experiment on the cultural evolution of artificial melodic systems, using multi-generational signaling games as a laboratory model of cultural transmission. Signaling systems, using five-tone sequences as signals, and basic and compound emotions as meanings, were transmitted from senders to receivers along diffusion chains in which the receiver in each game became the sender in the next game. During transmission, structural regularities accumulated in the signaling systems, following principles of proximity, symmetry, and good continuation. Although the compositionality of signaling systems did not increase significantly across generations, we did observe a significant increase in similarity among signals from the same set. We suggest that our experiment tapped into the cognitive and perceptual constraints operative in the cultural evolution of musical systems, which may differ from the mechanisms at play in language evolution and change.

Algorithmic complexity for psychology: a user-friendly implementation of the coding theorem method

Kolmogorov-Chaitin complexity has long been believed to be impossible to approximate when it comes to short sequences (e.g. of length 5-50). However, with the newly developed coding theorem method the complexity of strings of length 2-11 can now be numerically estimated. We present the theoretical basis of algorithmic complexity for short strings (ACSS) and describe an R-package providing functions based on ACSS that will cover psychologists' needs and improve upon previous methods in three ways: (1) ACSS is now available not only for binary strings, but for strings based on up to 9 different symbols, (2) ACSS no longer requires time-consuming computing, and (3) a new approach based on ACSS gives access to an estimation of the complexity of strings of any length. Finally, three illustrative examples show how these tools can be applied to psychology.

The Simplicity Principle in Human Concept Learning

How do we learn concepts and categories from examples? Part of the answer might be that we induce the simplest category consistent with a given set of example objects. This seemingly obvious idea, akin to simplicity principles in many fields, plays surprisingly little role in contemporary theories of concept learning, which are mostly based on the storage of exemplars, and avoid summarization or overt abstraction of any kind. This article reviews some evidence that complexity minimization does indeed play a central role in human concept learning. The chief finding is that subjects' ability to learn concepts depends heavily on the concepts' intrinsic complexity; more complex concepts are more difficult to learn. This pervasive effect suggests, contrary to exemplar theories, that concept learning critically involves the extraction of a simplified or abstracted generalization from examples.

Wordform Similarity Increases With Semantic Similarity: An Analysis of 100 Languages

Although the mapping between form and meaning is often regarded as arbitrary, there are in fact well-known constraints on words which are the result of functional pressures associated with language use and its acquisition. In particular, languages have been shown to encode meaning distinctions in their sound properties, which may be important for language learning. Here, we investigate the relationship between semantic distance and phonological distance in the large-scale structure of the lexicon. We show evidence in 100 languages from a diverse array of language families that more semantically similar word pairs are also more phonologically similar. This suggests that there is an important statistical trend for lexicons to have semantically similar words be phonologically similar as well, possibly for functional reasons associated with language learning.

Brain potentials predict learning, transmission and modification of an artificial symbolic system

It has recently been argued that symbolic systems evolve while they are being transmitted across generations of learners, gradually adapting to the relevant brain structures and processes. In the context of this hypothesis, little is known on whether individual differences in neural processing capacity account for aspects of 'variation' observed in symbolic behavior and symbolic systems. We addressed this issue in the domain of auditory processing. We conducted a combined behavioral and EEG study on 2 successive days. On day 1, participants listened to standard and deviant five-tone sequences: as in previous oddball studies, an mismatch negativity (MMN) was elicited by deviant tones. On day 2, participants learned an artificial signaling system from a trained confederate of the experimenters in a coordination game in which five-tone sequences were associated to affective meanings (emotion-laden pictures of human faces). In a subsequent game with identical structure, participants transmitted and occasionally changed the signaling system learned during the first game. The MMN latency from day 1 predicted learning, transmission and structural modification of signaling systems on day 2. Our study introduces neurophysiological methods into research on cultural transmission and evolution, and relates aspects of variation in symbolic systems to individual differences in neural information processing.

The simplicity principle in perception and cognition

The simplicity principle, traditionally referred to as Occam's razor, is the idea that simpler explanations of observations should be preferred to more complex ones. In recent decades the principle has been clarified via the incorporation of modern notions of computation and probability, allowing a more precise understanding of how exactly complexity minimization facilitates inference. The simplicity principle has found many applications in modern cognitive science, in contexts as diverse as perception, categorization, reasoning, and neuroscience. In all these areas, the common idea is that the mind seeks the simplest available interpretation of observations- or, more precisely, that it balances a bias toward simplicity with a somewhat opposed constraint to choose models consistent with perceptual or cognitive observations. This brief tutorial surveys some of the uses of the simplicity principle across cognitive science, emphasizing how complexity minimization in a number of forms has been incorporated into probabilistic models of inference. WIREs Cogn Sci 2016, 7:330-340. doi: 10.1002/wcs.1406 For further resources related to this article, please visit the WIREs website.