Big Question 5

The inferential cognitive geometry of language and action planning: Common computations?

The efficiency and flexibility with which humans generate meaning during language comprehension (or production) is remarkable. How does our brain do it? To move beyond the many extant attempts to address this big quest, BQ5 will treat linguistic inference as an instance of an advanced generative planning solution to the multi-step, sequential choice problems that we also face in other cognitive domains (e.g. chess, foraging and spatial navigation). Thus BQ5 anticipates to make unique progress in unravelling the mechanisms of fast, flexible and generative linguistic inference by leveraging recent major advances in our understanding of the representations and computations necessary for sequential model-based action planning. This approach will also lead us to revise current dual-system dogma’s in non-linguistic domains, that have commonly over-focused on the contrast between a cognitive (flexible, but slow) and a habitual (fast, but inflexible) system: The current quest will encourage the integration of so-called ‘cognitive habits’ and their associated cognitive map-related neural mechanisms into theoretical models of both linguistic and non-linguistic inference.

We will leverage current rapid conceptual and methodological progress in our understanding of other cognitive systems, such as the ‘cognitive mapping’ mechanisms for action planning (Behrens et al., 2018; Bellmund et al., 2018), as well as predictive inference in perception (Martin, 2016; Martin, 2020), to advance our understanding of how we generate meaning in the state space of language. In non-linguistic problems, the goal state is a function of the reward that is to be maximized. In the linguistic problem that we consider here, the goal state is the compositional meaning that needs to be generated during comprehension and production. Leveraging the recently developed approaches to understand perceptual inference and action planning, we will contribute unique advances in our understanding of the neural code and computations that underlie the unbounded combinatoriality of language, i.e., the ease with which we can generate meaning.

People involved

Steering group

CCN 2019 • Confirmed Speakers • 2019 Conference on Cognitive Computational  Neuroscience | 13-16 September 2019 | Berlin, Germany

Prof. dr. Roshan Cools
PI / Coordinator BQ5
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Dr. Xiaochen Zheng
Coordinating Postdoc BQ5
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Dr. Andrea Martin
PI / Coordinator BQ5
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Team members

Dr. Stefan Frank
Coordinator BQ1
Tenure track researcher
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Prof. dr. Peter Hagoort
Programme Director
PI / Coordinator BQ2
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Dr. Ashley Lewis
Coordinating Postdoc BQ2
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Hanneke den Ouden — Motivational & Cognitive Control

Dr. Hanneke den Ouden
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Dr. Ioanna Zioga
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Rene Terporten — Motivational & Cognitive Control

Dr. Rene Terporten
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Dr. Bob van Tiel
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Prof. dr. Ivan Toni
PI / Coordinator BQ3
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Roel Willems – ELIT

Dr. Roel Willems
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Dr. Silvy Collin
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PhD Candidates


Elena Mainetto
PhD Candidate
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Dr. Mark Blokpoel
Dr. Monique Flecken
Dr. Naomi de Haas
Dr. Saskia Haegens
Dr. Iris van Rooij
Dr. Yingying Tan


Dr. Branka Milivojevic – Postdoc
Dr. Mona Garvert

Research Highlights (2020)


Isolating representations of word meaning

Team members: Elena Mainetto, Xiaochen Zheng, Hanneke den Ouden, Mona Garvert, Naomi de Haas, Andrea Martin, and Roshan Cools (in collaboration with Stefan Frank and Denny Merkx from BQ1)

In this pilot experiment, we test the hypothesis that humans represent word meaning in a manner that is dependent on sentence context and independent of word form. We conducted a pilot study (n=32) and will follow it up with a large sample replication (n = 118). Subjects learned to associate symbols with homonyms’ meanings that can be derived from a meaning-constraining sentence. Next, subjects were tested in a set of tasks, including a repetition priming task where they performed orientation discrimination judgments on a sequence of the trained symbols. Linear mixed effect modeling of RTs revealed a trend in the expected direction of the key meaning repetition factor (estimate= -2.032, std=1.052, p=0.0552) (see Figure 1). Thus reaction times are faster when the same meaning of a homonym word is consecutively elicited by a symbol, compared with when different meanings of the same homonym word are consecutively elicited. We will replicate this effect with a powered study.

Figure 1. Linear mixed effect model coefficients of the meaning repetition factor for each subject. A coefficient with value of 0 indicates no effect; a negative meaning coefficient indicates faster RT for subsequent symbols with the same meaning compared to different meanings.

This study is performed by an interdisciplinary team comprising three areas of specialization: language, relational mapping and model-based planning. The team aims at advancing research on whether generative meaning inference relies on computations analogous to those implied in generative action planning known to operate on map-like representations that are composed of behaviourally relevant distances. The current project is challenging due to language differences and common conceptual misalignment between neurolinguists and decision/memory neuroscientists.

Through active, resilient and well-coordinated team science, we achieved an integrative novel design, unique ideas and preliminary advance in understanding that were not otherwise possible.