26 found
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  1.  24
    Optimal foraging in semantic memory.Thomas T. Hills, Michael N. Jones & Peter M. Todd - 2012 - Psychological Review 119 (2):431-440.
  2. Précis of simple heuristics that make us Smart.Peter M. Todd & Gerd Gigerenzer - 2000 - Behavioral and Brain Sciences 23 (5):727-741.
    How can anyone be rational in a world where knowledge is limited, time is pressing, and deep thought is often an unattainable luxury? Traditional models of unbounded rationality and optimization in cognitive science, economics, and animal behavior have tended to view decision-makers as possessing supernatural powers of reason, limitless knowledge, and endless time. But understanding decisions in the real world requires a more psychologically plausible notion of bounded rationality. In Simple heuristics that make us smart (Gigerenzer et al. 1999), we (...)
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  3.  70
    Environments That Make Us Smart Ecological Rationality.Peter M. Todd & Gerd Gigerenzer - 2007 - Current Directions in Psychological Science 16 (3):167-171.
    Traditional views of rationality posit general-purpose decision mechanisms based on logic or optimization. The study of ecological rationality focuses on uncovering the “adaptive toolbox” of domain-specific simple heuristics that real, computationally bounded minds employ, and explaining how these heuristics produce accurate decisions by exploiting the structures of information in the environments in which they are applied. Knowing when and how people use particular heuristics can facilitate the shaping of environments to engender better decisions.
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  4.  57
    Building the Theory of Ecological Rationality.Peter M. Todd & Henry Brighton - 2016 - Minds and Machines 26 (1-2):9-30.
    While theories of rationality and decision making typically adopt either a single-powertool perspective or a bag-of-tricks mentality, the research program of ecological rationality bridges these with a theoretically-driven account of when different heuristic decision mechanisms will work well. Here we described two ways to study how heuristics match their ecological setting: The bottom-up approach starts with psychologically plausible building blocks that are combined to create simple heuristics that fit specific environments. The top-down approach starts from the statistical problem facing the (...)
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  5.  47
    Hidden processes in structural representations: A reply to Abbott, Austerweil, and Griffiths (2015).Michael N. Jones, Thomas T. Hills & Peter M. Todd - 2015 - Psychological Review 122 (3):570-574.
  6.  80
    Foraging in Semantic Fields: How We Search Through Memory.Thomas T. Hills, Peter M. Todd & Michael N. Jones - 2015 - Topics in Cognitive Science 7 (3):513-534.
    When searching for concepts in memory—as in the verbal fluency task of naming all the animals one can think of—people appear to explore internal mental representations in much the same way that animals forage in physical space: searching locally within patches of information before transitioning globally between patches. However, the definition of the patches being searched in mental space is not well specified. Do we search by activating explicit predefined categories and recall items from within that category, or do we (...)
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  7.  60
    Learning and connectionist representations.David E. Rumelhart & Peter M. Todd - 1993 - In David E. Meyer & Sylvan Kornblum (eds.), Attention and Performance XIV: Synergies in Experimental Psychology, Artificial Intelligence, and Cognitive Neuroscience. MIT Press. pp. 3--30.
  8. More is not always better: The benefits of cognitive limits.Ralph Hertwig & Peter M. Todd - 2003 - Thinking: Psychological Perspectives on Reasoning, Judgment and Decision Making.
  9.  83
    Mate choice turns cognitive.Geoffrey F. Miller & Peter M. Todd - 1998 - Trends in Cognitive Sciences 2 (5):190-198.
  10.  84
    Fishing for the Right Words: Decision Rules for Human Foraging Behavior in Internal Search Tasks.Andreas Wilke, John M. C. Hutchinson, Peter M. Todd & Uwe Czienskowski - 2009 - Cognitive Science 33 (3):497-529.
    Animals depleting one patch of resources must decide when to leave and switch to a fresh patch. Foraging theory has predicted various decision mechanisms; which is best depends on environmental variation in patch quality. Previously we tested whether these mechanisms underlie human decision making when foraging for external resources; here we test whether humans behave similarly in a cognitive task seeking internally generated solutions. Subjects searched for meaningful words made from random letter sequences, and as their success rate declined, they (...)
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  11. Situating rationality: Ecologically rational decision making with simple heuristics.Henry Brighton & Peter M. Todd - 2008 - In Murat Aydede & P. Robbins (eds.), The Cambridge Handbook of Situated Cognition. Cambridge: Cambridge University Press. pp. 322--346.
     
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  12.  59
    Shepard's mirrors or Simon 's scissors?Peter M. Todd & Gerd Gigerenzer - 2001 - Behavioral and Brain Sciences 24 (4):704-705.
    Shepard promotes the important view that evolution constructs cognitive mechanisms that work with internalized aspects of the structure of their environment. But what can this internalization mean? We contrast three views: Shepard's mirrors reflecting the world, Brunswik's lens inferring the world, and Simon 's scissors exploiting the world. We argue that Simon 's scissors metaphor is more appropriate for higher-order cognitive mechanisms and ask how far it can also be applied to perceptual tasks. [Barlow; Kubovy & Epstein; Shepard].
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  13.  21
    Simple Threshold Rules Solve Explore/Exploit Trade‐offs in a Resource Accumulation Search Task.Ke Sang, Peter M. Todd, Robert L. Goldstone & Thomas T. Hills - 2020 - Cognitive Science 44 (2):e12817.
    How, and how well, do people switch between exploration and exploitation to search for and accumulate resources? We study the decision processes underlying such exploration/exploitation trade‐offs using a novel card selection task that captures the common situation of searching among multiple resources (e.g., jobs) that can be exploited without depleting. With experience, participants learn to switch appropriately between exploration and exploitation and approach optimal performance. We model participants' behavior on this task with random, threshold, and sampling strategies, and find that (...)
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  14. The Evolutionary Psychology of Extraterrestrial Intelligence: Are There Universal Adaptations in Search, Aversion, and Signaling?Peter M. Todd & Geoffrey F. Miller - 2018 - Biological Theory 13 (2):131-141.
    To understand the possible forms of extraterrestrial intelligence, we need not only astrobiology theories about how life evolves given habitable planets, but also evolutionary psychology theories about how intelligence emerges given life. Wherever intelligent organisms evolve, they are likely to face similar behavioral challenges in their physical and social worlds. The cognitive mechanisms that arise to meet these challenges may then be copied, repurposed, and shaped by further evolutionary selection to deal with more abstract, higher-level cognitive tasks such as conceptual (...)
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  15.  65
    Simple inference heuristics versus complex decision machines.Peter M. Todd - 1999 - Minds and Machines 9 (4):461-477.
  16.  18
    An Evolutionary Analysis of Learned Attention.Richard A. Hullinger, John K. Kruschke & Peter M. Todd - 2015 - Cognitive Science 39 (6):1172-1215.
    Humans and many other species selectively attend to stimuli or stimulus dimensions—but why should an animal constrain information input in this way? To investigate the adaptive functions of attention, we used a genetic algorithm to evolve simple connectionist networks that had to make categorization decisions in a variety of environmental structures. The results of these simulations show that while learned attention is not universally adaptive, its benefit is not restricted to the reduction of input complexity in order to keep it (...)
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  17.  53
    Interfacing Mind and Environment: The Central Role of Search in Cognition.Wai-Tat Fu, Thomas Hills & Peter M. Todd - 2015 - Topics in Cognitive Science 7 (3):384-390.
    Search can be found in almost every cognitive activity, ranging across vision, memory retrieval, problem solving, decision making, foraging, and social interaction. Because of its ubiquity, research on search has a tendency to fragment into multiple areas of cognitive science. The proposed topic aims at providing integrative discussion of the central role of search from multiple perspectives. We focus on controlled search processes, which require a goal, uncertainty about the nature, location, or acquisition method of the objects to be searched (...)
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  18.  27
    How can we open up the adaptive toolbox?Peter M. Todd & Gerd Gigerenzer - 2000 - Behavioral and Brain Sciences 23 (5):89-100.
  19. The Evolution of Cooperative Strategies for Asymmetric Social Interactions.Jörg Rieskamp & Peter M. Todd - 2006 - Theory and Decision 60 (1):69-111.
    How can cooperation be achieved between self-interested individuals in commonly-occurring asymmetric interactions where agents have different positions? Should agents use the same strategies that are appropriate for symmetric social situations? We explore these questions through the asymmetric interaction captured in the indefinitely repeated investment game (IG). In every period of this game, the first player decides how much of an endowment he wants to invest, then this amount is tripled and passed to the second player, who finally decides how much (...)
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  20. Priming and conservation between spatial and cognitive search.T. Hills, Peter M. Todd & Robert L. Goldstone - 2007 - In McNamara D. S. & Trafton J. G. (eds.), Proceedings of the 29th Annual Cognitive Science Society. Cognitive Science Society. pp. 359--364.
  21. Adding the missing link back into mate choice research.Rui Mata, Andreas Wilke & Peter M. Todd - 2005 - Behavioral and Brain Sciences 28 (2):289-289.
    Evolutionary psychologists should go beyond research on individual differences in attitudes and focus more on detailed models of psychological mechanisms. We argue for complementing attitude research with agent-based computational modeling of mate choice. Agent-based models require detailed specification of individual choice mechanisms that can be evaluated in terms of both their psychological plausibility and the population-level outcomes they produce.
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  22.  30
    Let evolution take care of its own.Geoffrey F. Miller & Peter M. Todd - 1991 - Behavioral and Brain Sciences 14 (1):101-102.
  23.  22
    Habit formation generates secondary modules that emulate the efficiency of evolved behavior.Samuel A. Nordli & Peter M. Todd - 2017 - Behavioral and Brain Sciences 40.
    We discuss the evolutionary implications of connections drawn between the authors' learned “secondary modules” and the habit-formation system that appears to be ubiquitous among vertebrates. Prior to any subsequent coevolution with social learning, we suggest that aspects of general intelligence likely arose in tandem with mechanisms of adaptive motor control that rely on basal ganglia circuitry.
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  24.  11
    Emergent computation: Self-organizing, collective, and cooperative phenomena in natural and artificial computing networks.Peter M. Todd - 1993 - Artificial Intelligence 60 (1):171-183.
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  25.  50
    Pulling the trigger on the living kind module.Peter M. Todd & Alejandro López - 1998 - Behavioral and Brain Sciences 21 (4):592-592.
    Atran conjectures that a triggering algorithm for a living- kind module could involve inputs from other modules that detect animacy and intentionality. Here we further speculate about how algorithms for detecting specific intentions could be used to trigger between- or within-species categorization. Such categorization may be adaptively important in Eldredge's energy and information realms.
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  26.  90
    Made to measure: Ecological rationality in structured environments. [REVIEW]Seth Bullock & Peter M. Todd - 1999 - Minds and Machines 9 (4):497-541.
    A working assumption that processes of natural and cultural evolution have tailored the mind to fit the demands and structure of its environment begs the question: how are we to characterize the structure of cognitive environments? Decision problems faced by real organisms are not like simple multiple-choice examination papers. For example, some individual problems may occur much more frequently than others, whilst some may carry much more weight than others. Such considerations are not taken into account when (i) the performance (...)
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