How hard is cognitive science?

Rich, Patricia; Haan, Ronald de; Wareham, Todd; Rooij, Iris van

doi:10.31234/osf.io/k79nv

Cited by 32 publications

(20 citation statements)

References 27 publications

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“…We have mentioned the Interaction Engine Hypothesis, Coupled Oscillators models, and dyadic models of self‐regulation and adaptation, which are promising but currently underspecified to capture the key patterns we see in the meta‐analytic data. By providing computational implementations of these and other models, future research can be more explicit about the assumptions of the theories, as well as compare the predictions of different models and test them on the data available (Guest & Martin, 2021; Navarro, 2021; Rich et al, 2021). Furthermore, the development of better mechanistic models can help the field better capture the meaningful aspects of cross‐linguistic, sociodemographic, ethnic, and cultural diversity.…”

Section: Discussionmentioning

confidence: 99%

A systematic review and Bayesian meta‐analysis of the development of turn taking in adult–child vocal interactions

et al. 2022

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Fluent conversation requires temporal organization between conversational exchanges. By performing a systematic review and Bayesian multi‐level meta‐analysis, we map the trajectory of infants’ turn‐taking abilities over the course of early development (0 to 70 months). We synthesize the evidence from 26 studies (78 estimates from 429 unique infants, of which at least 152 are female) reporting response latencies in infant–adult dyadic interactions. The data were collected between 1975 and 2019, exclusively in North America and Europe. Infants took on average circa 1 s to respond, and the evidence of changes in response over time was inconclusive. Infants’ response latencies are related to those of their adult conversational partners: an increase of 1 s in adult response latency (e.g., 400 to 1400 ms) would be related to an increase of over 1 s in infant response latency (from 600 to 1857 ms). These results highlight the dynamic reciprocity involved in the temporal organization of turn‐taking. Based on these results, we provide recommendations for future avenues of enquiry: studies should analyze how turn‐by‐turn exchanges develop on a longitudinal timescale, with rich assessment of infants’ linguistic and social development.

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Section: Discussionmentioning

confidence: 99%

A systematic review and Bayesian meta‐analysis of the development of turn taking in adult–child vocal interactions

et al. 2022

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“…Thus, while Markov assumptions may be at play, algorithms designed merely to address a stationary Markov decision process will catastrophically fail in the more general settings considered here. Nonetheless, without further assumptions, the problem would be intractable (or even uncomputable) [4]. Thus, we further assume that the external world W is changing somewhat predictably over time.…”

Section: A Sketch Of Our Frameworkmentioning

confidence: 99%

“…The success of deep networks is a result of their ability to efficiently learn what counts as 'local' [159]. 4 In prospective learning, in contrast to retrospective learning, what counts as local is also a function of potential future environments. Thus, the key difference between retrospective and prospective representation learning is that the internal representation for prospective learning must trade-off between being effective for the current scenario, and being effective for potential future scenarios.…”

Section: Putting It All Togethermentioning

confidence: 99%

“…Constraints (including biases and priors) facilitates finding general solutions that will apply to future problems and thus makes learning possible. While the general problem is intractable (or even uncomputable) [4], built-in priors, inductive biases, and other constraints to regularize the search space [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…Because intelligences do not know a priori which action-outcome contingencies are currently available, or how previous contingencies have changed, they should explore the world to gather additional information [6]. 4. Causal estimation enables learning the structure of relations that guide choosing actions for specific outcomes, even when the specific action-outcome contingencies have never been observed before.…”

Section: Introductionmentioning

confidence: 99%

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Prospective Learning: Back to the Future

Vogelstein¹,

Verstynen²,

Kording³

et al. 2022

Preprint

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Research on both natural intelligence (NI) and artificial intelligence (AI) generally assumes that the future resembles the past: intelligent agents or systems (what we call 'intelligence') observe and act on the world, then use this experience to act on future experiences of the same kind. We call this 'retrospective learning'. For example, an intelligence may see a set of pictures of objects, along with their names, and learn to name them. A retrospective learning intelligence would merely be able to name more pictures of the same objects. We argue that this is not what true intelligence is about. In many real world problems, both NIs and AIs will have to learn for an uncertain future. Both must update their internal models to be useful for future tasks, such as naming fundamentally new objects and using these objects effectively in a new context or to achieve previously unencountered goals. This ability to learn for the future we call 'prospective learning'. We articulate four relevant factors that jointly define prospective learning. Continual learning enables intelligences to remember those aspects of the past which it believes will be most useful in the future. Prospective constraints (including biases and priors) facilitate the intelligence finding general solutions that will be applicable to future problems. Curiosity motivates taking actions that inform future decision making, including in previously unmet situations. Causal estimation enables learning the structure of relations that guide choosing actions for specific outcomes, even when the specific action-outcome contingencies have never been observed before. We argue that a paradigm shift from retrospective to prospective learning will enable the communities that study intelligence to unite and overcome existing bottlenecks to more effectively explain, augment, and engineer intelligences."No man ever steps in the same river twice. For it's not the same river and he's not the same man." -Heraclitus

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On Logical Inference over Brains, Behaviour, and Artificial Neural Networks

Guest

Martin

2023

Comput Brain Behav

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In the cognitive, computational, and neuro-sciences, practitioners often reason about what computational models represent or learn, as well as what algorithm is instantiated. The putative goal of such reasoning is to generalize claims about the model in question, to claims about the mind and brain, and the neurocognitive capacities of those systems. Such inference is often based on a model’s performance on a task, and whether that performance approximates human behavior or brain activity. Here we demonstrate how such argumentation problematizes the relationship between models and their targets; we place emphasis on artificial neural networks (ANNs), though any theory-brain relationship that falls into the same schema of reasoning is at risk. In this paper, we model inferences from ANNs to brains and back within a formal framework — metatheoretical calculus — in order to initiate a dialogue on both how models are broadly understood and used, and on how to best formally characterize them and their functions. To these ends, we express claims from the published record about models’ successes and failures in first-order logic. Our proposed formalization describes the decision-making processes enacted by scientists to adjudicate over theories. We demonstrate that formalizing the argumentation in the literature can uncover potential deep issues about how theory is related to phenomena. We discuss what this means broadly for research in cognitive science, neuroscience, and psychology; what it means for models when they lose the ability to mediate between theory and data in a meaningful way; and what this means for the metatheoretical calculus our fields deploy when performing high-level scientific inference.

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How hard is cognitive science?

Cited by 32 publications

References 27 publications

A systematic review and Bayesian meta‐analysis of the development of turn taking in adult–child vocal interactions

A systematic review and Bayesian meta‐analysis of the development of turn taking in adult–child vocal interactions

Prospective Learning: Back to the Future

On Logical Inference over Brains, Behaviour, and Artificial Neural Networks

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