Bias to (and away from) the Extreme: Comparing Two Models of Categorical Perception Effects

Best, R. M., & Goldstone, R. L. (2019). Bias to (and away from) the Extreme: Comparing Two Models of Categorical Perception Effects.  Journal of Experimental Psychology: Learning, Memory, and Cognition, 7, 1166-1176.

Categorical Perception (CP) effects manifest as faster or more accurate discrimination between objects that come from different categories compared to objects that come from the same category, controlling for the physical differences between the objects. The most popular explanations of CP effects have relied on perceptual warping causing stimuli near a category boundary to appear more similar to stimuli within their own category and/ or less similar to stimuli from other categories. Hanley and Roberson (2011), on the basis of a pattern not previously noticed in CP experiments, proposed an explanation of CP effects that relies not on perceptual warping, but instead on inconsistent usage of category labels. Experiments 1 and 2 in this paper show a pattern opposite the one Hanley and Roberson pointed out. Experiment 3, using the same stimuli but with different choice statistics (i.e., different probabilities of each face being the target), obtains the same pattern as the one Hanley and Roberson showed. Simulations show that both category label and perceptual models are able to reproduce the patterns of results from both experiments, provided they include information about the choice statistics. This suggests two conclusions. First, the results described by Hanley and Roberson should not be taken as evidence in favor of a category label model. Second, given that participants did not receive feedback on their choices, there must be some mechanism by which participants monitor their own choices and adapt to the choice statistics present in the experiment.

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Becoming cognitive science

Goldstone, R. L. (2019).  Becoming cognitive science.  Topics in Cognitive Science, 1-12.

Cognitive science continues to make a compelling case for having a coherent, unique, and fundamental¬†subject of inquiry: What is the nature of minds, where do they come from, and how do¬†they work? Central to this inquiry is the notion of agents that have goals, one of which is their¬†own persistence, who use dynamically constructed knowledge to act in the world to achieve those¬†goals. An agentive perspective explains why a special class of systems have a cluster of co-occurring¬†capacities that enable them to exhibit adaptive behavior in a complex environment: perception,¬†attention, memory, representation, planning, and communication. As an intellectual endeavor,¬†cognitive science may not have achieved a hard core of uncontested assumptions that Lakatos¬†(1978) identifies as emblematic of a successful research program, but there are alternative conceptions¬†according to which cognitive science has been successful. First, challenges of the early, core¬†tenet of ‚ÄúMind as Computation‚ÄĚ have helped put cognitive science on a stronger foundation‚ÄĒone¬†that incorporates relations between minds and their environments. Second, even if a full cross-disciplinary¬†theoretic consensus is elusive, cognitive science can inspire distant, deep, and transformative¬†connections between pairs of fields. To be intellectually vital, cognitive science need not¬†resemble a traditional discipline with its associated insularity and unchallenged assumptions.¬†Instead, there is strength and resilience in the diverse perspectives and methods that cognitive¬†science assembles together. This interdisciplinary enterprise is fragile and perhaps inherently¬†unstable, as the looming absorption of cognitive science into psychology shows. Still, for many¬†researchers, the excitement and benefits of triangulating on the nature of minds by integrating¬†diverse cases cannot be secured by a stable discipline with an uncontested core of assumptions.

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Building Inner Tools

Goldstone, R. L. (2019).  Building inner tools.  In M. Colombo, E. Irvine, M. Stapleton (Eds.)Andy Clark and his Critics. Oxford, England: Oxford University Press (pp. 113-127).

Humans show a striking penchant for creating tools to benefit our own thought¬†processes. Andy Clark (2003, 2008) has convincingly argued that the tools that we¬†as humans recruit become integrated parts of an extended cognitive system that¬†includes us as just one component. By extending cognition beyond our brains,¬†Clark presents an ‚Äúembiggened‚ÄĚ perspective on what it means to be a cognizer¬†and a person more generally. This perspectival shift runs counter to some recent¬†forms of argumentation that in effect work to minimize personhood. For example,¬†arguments for lack of personal culpability can take the form of ‚ÄúIt wasn‚Äôt my fault.¬†It was the fault of my ___ ‚ÄĚ to be filled in, perhaps, by ‚Äúupbringing,‚ÄĚ ‚Äúgenes,‚Ä̬†‚Äúneurochemistry,‚ÄĚ ‚Äúdiet,‚ÄĚ or ‚Äúimproperly functioning amygdala.‚ÄĚ Instead, Clark (see¬†also Dennett 1989) offers the opposite line of argumentation, according to which¬†we consist not only of our amygdalae and hippocampi but also potentially our¬†glasses, notebooks, friends, supporting technologies, and culture.

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Beyond the lab: Using big data to discover principles of cognition

Lupyan, G., & Goldstone, R. L. (2019). Introduction to special issue. Beyond the lab: Using big data to discover principles of cognition.  Behavior Research Methods, 51, 1473-1476.

Like many other scientific disciplines, psychological science¬†has felt the impact of the big-data revolution. This impact¬†arises from the meeting of three forces: data availability, data¬†heterogeneity, and data analyzability. In terms of data¬†availability, consider that for decades, researchers relied on¬†the Brown Corpus of about one million words (Kuńćera &¬†Francis, 1969). Modern resources, in contrast, are larger by¬†six orders of magnitude (e.g., Google‚Äôs 1T corpus) and are¬†available in a growing number of languages. About 240 billion¬†photos have been uploaded to Facebook,1¬† and Instagram¬†receives over 100 million new photos each day.2¬† The largescale¬†digitization of these data has made it possible in principle¬†to analyze and aggregate these resources on a previously¬†unimagined scale. Heterogeneity¬† refers to the availability of¬†different types¬† of data. For example, recent progress in automatic¬†image recognition is owed not just to improvements in¬†algorithms and hardware, but arguably more to the ability to¬†merge large collections of images with linguistic labels (produced¬†by crowdsourced human taggers) that serve as training¬†data to the algorithms. Making use of heterogeneous data¬†sources often depends on their standardization. For example,¬†the ability to combine demographic and grammatical data¬†about thousands of languages led to the finding that languages¬†spoken by more people have simpler morphologies (Lupyan¬†& Dale, 2010 ). The ability to combine these data types would¬†have been substantially more difficult without the existence of¬†standardized language and country codes that could be used to¬†merge the different data sources. Finally, analyzability¬† must be¬†ensured, for without appropriate tools to process and analyze¬†different types of data, the ‚Äú data‚Ä̬† are merely bytes.

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See all of the papers appearing in the Big Data Special Issue of Behavior Research Methods

The Evolutionary Dynamics of Cooperation in Collective Search

Tump, A. N., Wu, C. M., Bouhlel, I., & Goldstone, R. L. (2019).The Evolutionary Dynamics of Cooperation in Collective Search.  Proceedings of the 41st Annual Conference of the Cognitive Science Society. (pp. 883-889). Montreal, Canada: Cognitive Science Society.

How does cooperation arise in an evolutionary context? We approach¬†this problem using a collective search paradigm where¬†interactions are dynamic and there is competition for rewards.¬†Using evolutionary simulations, we find that the unconditional¬†sharing of information can be an evolutionary advantageous¬†strategy without the need for conditional strategies or explicit¬†reciprocation. Shared information acts as a recruitment signal¬†and facilitates the formation of a self-organized group.¬†Thus, the improved search efficiency of the collective bestows¬†byproduct benefits onto the original sharer. A key mechanism¬†is a visibility radius, where individuals have unconditional¬†access to information about neighbors within a limited¬†distance. Our results show that for a variety of initial¬†conditions‚ÄĒincluding populations initially devoid of prosocial¬†individuals‚ÄĒand across both static and dynamic fitness landscapes,¬†we find strong selection pressure to evolve unconditional¬†sharing.

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Patterns of coordination in simultaneously and sequentially improvising jazz musicians

Setzler, M., & Goldstone, R. L. (2019).  Patterns of coordination in simultaneously and sequentially improvising jazz musicians.  Proceedings of the 41st Annual Conference of the Cognitive Science Society. (pp. 1035-1040). Montreal, Canada: Cognitive Science Society.

In Joint Action (JA) tasks, individuals must coordinate their¬†actions so as to achieve some desirable outcome at the grouplevel.¬†Group function is an emergent outcome of ongoing,¬†mutually constraining interactions between agents. Here we¬†investigate JA in dyads of improvising jazz pianists. Participants‚Äô¬†musical output is recorded in one of two conditions:¬†a real condition, in which two pianists improvise together as¬†they typically would, and a virtual condition, in which a single¬†pianist improvises along with a ‚Äúghost partner‚ÄĚ ‚Äď a recording¬†of another pianist taken from a previous real trial. The conditions¬†are identical except for that in real trials subjects are¬†mutually coupled to one another, whereas there is only unidirectional¬†influence in virtual trials (i.e. recording to musician).¬†We quantify ways in which the rhythmic structures spontaneously¬†produced in these improvisations is shaped by mutual¬†coupling of co-performers. Musical signatures of underlying¬†coordination patterns are also shown to parallel the subjective¬†experience of improvisers, who preferred playing in trials with¬†bidirectional influence despite not explicitly knowing which¬†condition they had played in. These results illuminate how¬†mutual coupling shapes emergent, group-level structure in the¬†creative, open-ended and fundamentally collaborative domain¬†of expert musical improvisation.

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Complex exploration dynamics from simple heuristics in a collective learning environment

Sloman, S. J., Goldstone, R. L., & Gonzalez, C. (2019). Complex exploration dynamics from simple heuristics in a collective learning environment.  Proceedings of the 41st Annual Conference of the Cognitive Science Society. (pp. 2818-2824). Montreal, Canada: Cognitive Science Society.

Effective problem solving requires both exploration and exploitation.¬†We analyze data from a group problem-solving task¬†to gain insight into how people use information from past experiences¬†and from others to achieve explore-exploit trade-offs in¬†complex environments. The behavior we observe is consistent¬†with the use of simple, reinforcement-based heuristics. Participants¬†increase exploration immediately after experiencing a¬†low payoff, and decrease exploration immediately after experiencing¬†a high or improved payoff. We suggest that whether¬†an outcome is perceived as ‚Äúhigh‚ÄĚ or ‚Äúlow‚ÄĚ is a dynamic function¬†of the outcome information available to participants. The¬†degree to which the distribution of observed information reflects¬†the true range of possible outcomes plays an important¬†role in determining whether or not this heuristic is adaptive in¬†a given environment.

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Self-Organized Division of Cognitive Labor

Andrade-Lotero, E., & Goldstone, R. L. (2019).  Self-Organized Division of Cognitive Labor.  Proceedings of the 41st Annual Conference of the Cognitive Science Society. (pp. 91-97). Montreal, Canada: Cognitive Science Society.

The division of labor phenomenon has been observed with respect to both manual and cognitive labor, but there is no clear understanding of the intra- and inter-individual mechanisms that allow for its emergence, especially when there are multiple divisions possible and communication is limited. Situations fitting this description include individuals in a group splitting a geographical region for resource harvesting without explicit negotiation, or a couple tacitly negotiating the hour of the day for each to shower so that there is sufficient hot water. We studied this phenomenon by means of an iterative two-person game where multiple divisions are possible, but no explicit communication is allowed. Our results suggest that there are a limited number of biases toward divisions of labor, which serve as attractors in the dynamics of dyadic coordination. However, unlike Schelling’s focal points, these biases do not attract players’ attention at the onset of the interaction, but are only revealed and consolidated by the in-game dynamics of dyadic interaction.

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A Computational Model of Scientific Discovery in a Very Simple World, Aiming at Psychological Realism

Lara-Dammer, F., Hofstadter, D. R., & Goldstone, R. L. (2019). A Computational Model of Scientific Discovery in a Very Simple World, Aiming at Psychological Realism.  Journal of Experimental & Theoretical Artificial Intelligence, 1-22. 10.1080/0952813X.2019.1592234

We propose a computational model of human scientific discovery and perception of the world. As a prerequisite for such a model, we simulate dynamic microworlds in which physical events take place, as well as an observer that visually perceives and makes interpretations of events in the microworld. Moreover, we give the observer the ability to actively conduct experiments in order to gain evidence about natural regularities in the world. We have broken up the description of our project into two pieces. The first piece deals with the interpreter constructing relatively simple visual descriptions of objects and collisions within a context. The second phase deals with the interpreter positing relationships among the entities, winding up with elaborated construals and conjectures of mathematical laws governing the world. This paper focuses only on the second phase. As is the case with most human scientific observation, observations are subject to interpretation, and the discoveries are influenced by these interpretations.

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When does interleaving practice improve learning?

Carvalho, P. F., & Goldstone, R. L. (2019). When does interleaving practice improve learning? In J. Dunlosky & K. A. Rawson (Eds.) The Cambridge Handbook of Cognition and Education.  Cambridge, England: Cambridge University Press. (pp. 411-436).

As you flip through the pages of this handbook you will notice that the content does not seem to be randomly organized. The content of the handbook is sequenced in a particular way: foundations before general strategies, background before applications, etc. The editors envisaged a sequence of topics, the authors of each topic envisaged a sequence of information in each chapter, and so on. We selected a particular sequence because we considered it to be effective. Deciding how to sequence information takes place all the time in educational contexts, from educators deciding how to organize their syllabus to educational technology designers deciding how to organize a piece of educational software, from handbook editors and writers deciding how to organize their materials, to students making decisions as to how to organize their study. One might imagine that as long as all students study the same materials, regardless of the sequence in which they study it, they will all learn the same information. This could not be further from the truth. In this chapter, we will review evidence of how and why the sequence of study changes what is learned. In doing so, we will try to uncover the powerful ways in which sequence can improve or deter learning.

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The emergence of social norms and conventions

Hawkins, R. X. D., Goodman, N. D., & Goldstone, R. L. (2019). The emergence of social norms and conventions.  Trends in Cognitive Science.

The utility of our actions frequently depends upon the beliefs and behavior of other agents. Thankfully, through experience, we learn norms and conventions that provide stable expectations for navigating our social world. Here, we review several distinct influences on their content and distribution. At the level of individuals locally interacting in dyads, success depends on rapidly adapting pre-existing norms to the local context. Hence, norms are shaped by complex cognitive processes involved in learning and social reasoning. At the population level, norms are influenced by intergenerational transmission and the structure of the social network. As human social connectivity continues to increase, understanding and predicting how these levels and time scales interact to produce new norms will be crucial for improving communities. 

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