<table><caption>**Table 4.** Latin square for Experiment 2 following a mixed design with exemplars presented as between-subjects while the similar and dissimilar conditions were presented in a within-subject design.</caption><thead><tr><th rowspan="2"></th><th colspan="3">LIST A</th><th colspan="3">LIST B</th></tr><tr><th>Block 1</th><th>Block 2</th><th>Block 3</th><th>Block 4</th><th>Block 5</th><th>Block 6</th></tr></thead><tbody><tr><td>Concept 1</td><td>1A-1B-1C</td><td>2A-2B-2C</td><td>3A-3B-3C</td><td>1A-2C-3B</td><td>1B-2B-3C</td><td>1C-2A-3A</td></tr><tr><td>Concept 2</td><td>1B-2A-3B</td><td>1C-2A-3C</td><td>1A-2B-3A</td><td>1A-1B-1C</td><td>2A-2B-2C</td><td>3A-3B-3C</td></tr><tr><td>...</td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

For each concept, a pseudo-word was generated using Wuggy (Keuleers & Brysbaert, 2010) to serve as a referent label (e.g., Remation, Unglith). The pseudo-words were generated based on the English language and were matched for lexicality and number of letters. For each concept, a test exemplar that corresponded to a novel scenario composed of a single exemplar was constructed (e.g., When he entered the living room, he saw a picture on a mantelpiece and was astonished that his neighbour had apparently met Bill Gates). Finally, nonsensical sentences were constructed to serve as filler in an attention check during the reading phase. The fillers were constructed following the same rules as for the actual exemplars but contained unrelated words to create a sense of semantic ambiguity (e.g., Getting upstairs, she could not remember whether she had poisoned the front door). The experiment contained a proportion of 18% fillers which were also presented with a pseudo-word generated using Wuggy that served as a referent.

## Procedure

During the reading phase, participants were presented with a pseudo-word (e.g., REMATION) and 3 exemplars -either similar or dissimilar- that appeared one by one on the screen. Participants were never provided with a definition of the concepts but were asked to pay attention to the pseudo-word and the corresponding exemplars that were meant to illustrate their meaning. Each exemplar was presented alongside its pseudo-word referent. To make sure that they were paying attention, nonsensical sentences were randomly scattered
during the reading phase. After reading each exemplar, participants had to choose the “next”
button to go to the next trial or the “nonsensical” button to indicate that they had spotted a
nonsensical sentence. The blocks were divided in series of 7 concepts. The presentation of
exemplars and their corresponding pseudo-word were all randomised within each series.
Following each series of 7 concepts, participants entered a testing phase.

During the testing phases, participants were presented with a novel test exemplar that was constructed as part of a 4th scenario for each concept. The test exemplars were presented alongside their corresponding pseudo-word. The task of the participants was to judge on a scale from 0 to 100 whether this novel exemplar was a good example to describe the pseudo-word.

The study was self-paced and took about 30 minutes to complete.

## RESULTS and DISCUSSION

The similarity judgments of novel exemplars to those stored during the reading phase
were compared by means of a paired samples t-test. The analysis revealed that the test
exemplars were judged as better examples of the concepts when participants were exposed to
dissimilar exemplars during the reading phase (M<sub>Dissimilar</sub> = 70.28; SD = 7.54) compared to
when they were exposed to similar exemplars (M<sub>Similar</sub> = 63.04; SD = 9.06) - t(71) = 5.66,
p<0.001; Mean Difference = 7.20; 95CI [4.65, 9.74]; Cohen's d = 0.71; see Figure 4).
<figure><img src="image_25.png" /><figcaption>**Figure 4.** Mean similarity judgments (on a scale from 0 to 100) as a function of reading phase exposure (similar vs. dissimilar exemplars; p<0.001)</figcaption></figure>

We conducted a Bayesian paired-samples t-test using JASP (2020). For the informed prior we used Oosterwijk's recommendation (t-distribution with location 0.350, scale 0.102, and 3 degrees of freedom), because it is considered a good prior for small-to-medium effect sizes (see Gronau & Wagenmakers, 2019). This analysis showed a Bayes Factor of 51900 (median = 0.53; 95CI [0.33-0.82]; see Figure 5). This means the results are about 51900 times more likely under the alternative hypothesis compared to the null.
<figure><img src="image_26.png" /><figcaption>**Figure 5.** Prior and Posterior distribution for the paired samples t-test comparing the similar and dissimilar conditions.</figcaption></figure>

The aim of Experiment 2 was to test for the generalisation of meaning on the basis of exposure to similar or contextually diverse exemplars. According to the account of similarity-based generalisation, participants were expected to better generalise the meaning of concepts when exposed to similar exemplars. In contrast, the account of contextual diversity assumes a facilitation of generalisation to novel exemplars when participants are exposed to contextually diverse exemplars.

Results have shown that exposure to contextually diverse exemplars enhanced performance in the testing phase. Participants had less difficulty identifying the novel test exemplars presented as a 4th scenario when stored exemplars were learned through exposure to diverse exemplars. This result could be explained by the fact that contextual diversity during reading allows for the representation of words to become more context independent (e.g., Bolger et al., 2008; Pagan & Nation, 2019).
These results are in accordance with previous studies on the importance of diversity for generalisation. Posner and Keele (1968) have demonstrated that the ability to classify new instances of learned concepts correctly increased with the variability of stored patterns. More broadly, the hypothesis of generalisation from stored exemplars to novel instances assumes a mechanism of abstraction of idiosyncratic surface features while common core features are better identified in diverse contexts (Belenky & Schalk, 2014; Day & Goldstone, 2012; Gentner, 1983; Gick & Holyoak, 1983). This mechanism of abstraction assumes an attention parameter with more attention allocated to well attended dimensions compared to unattended ones. This assumption is at the core of GCM model of Nosofsky (1986, 1992, 2014; Vanpaemel, 2009). The model posits a set of selective-attention weights that modify the dimensions of the psychological space in which the exemplars are embedded (Carroll & Wish, 1974; Nosofsky, 2014). These results are in accordance with accounts of exemplar-based abstraction which posit a process of extracting features across multiple instances of a concept so that idiosyncratic features provide representation that are context independent (see Yee, 2019 for a review).

The literature on abstract concepts is rather young and most of it has been dedicated to their mechanisms of representation and grounding (see Borghi, 2017; Pecher, 2018 for reviews). As a result, little is known about how abstract concepts are generalised to all instances of a particular concept. Gentner and Asmuth (2017) have suggested a process of abstraction they refer to as Career of Metaphor that describes how abstract meaning can be generalised through figurative language. For example, they consider that the abstract relational meaning of the concept <anchor> can be abstracted through figurative expressions such as <religion is an anchor>. Repeated exposure to such figurative instances would highlight the common features between the concepts <anchor> and <religion> by abstracting
the meaning of the metaphor in favour of an underlying common relational structure which
can be extended to other situations that share similar structures.

This process of abstraction through metaphors seems compatible with an assumption
of exemplar-based generalisation particularly as it assumes exposure to multiple instances of a
concepts in diverse contexts. However, studies have shown that learning abstract concepts
through metaphors could hinder the ability to grasp the full richness of abstract concepts and
in turn the ability to generalise (see for e.g., Braithwaite and Goldstone, 2015; Pecher, Boot &
Van Dantzig, 2011). In addition, this assumption of learning relational similarities between
concepts though metaphors is limited by a lack of one-to-one mapping between abstract
concepts and metaphors (Pecher, 2018). The assumption of generalising abstract concepts
from exposure to multiple exemplars in diverse contexts seems to offer a better account. This
is particularly true when considering Paivio's Dual Coding Theory (DCT, 1968, 1971)
according to which abstract concepts are more loosely related to a multitude of contexts
compared to concrete concepts. Taken together, contextual diversity and the DCT would
suggest that the generalisation of abstract concepts could benefit from exposure of exemplars
in diverse contexts.

GENERAL DISCUSSION

The ability to extract meaningful representations from a few exemplars and generalise
to novel situations has been the object of much work in the concept learning literature focused
on abstraction mechanisms. This process of abstraction is a cognitive feat without which it
would not be possible to make sense of the world. Many accounts have been proposed to
explain the mechanisms underlying such an ability. The literature has long been dominated by
the debate opposing prototype and exemplar models of generalisation (Murphy, 2013). The
prototype account assumes a mechanism of generalisation based on the similarity of novel
instances to the most prototypical exemplar stored in memory (Rosch, 1975; Rosch & Mervis,
1975). This assumption does not provide a good explanation for the generalisation of abstract concepts due to their complexity and lack of organisation in clear-cut categories (Borghi, Barca, Binsofsky & Tummolini, 2018; Minda & Smith, 2001). As a result, this account has mostly been abandoned in favour of an exemplar view of concept learning (Feldman, 2003; Yee, 2019).

The exemplar view is best illustrated by the GCM model which posits that generalisation is a function of similarity between stored exemplars and novel instances (Medin & Schaffer, 1978; Nosofsky, 1986). This assumption suggests that the exemplar view provides a better explanation for mechanisms underlying the generalisation of abstract concepts as it allows for more flexibility in the description of how abstract concepts are categorised. The present studies have attempted to uncover such mechanisms by introducing a custom-made database of concepts called NewAbstractConcepts which refer to situations that are familiar to everybody but for which no word referent exist. The introduction of the database had many advantages. First, it offers a set of normed stimuli that would allow for the design of experiments to study mechanisms of concept learning, abstraction and generalisation for abstract concepts, which is a crucial aspect that has been missing from the abstract concept literature. Secondly, the introduction of stimuli that have no word referent avoids issues of background knowledge when testing adult participants in word learning experiments. Finally, crafting and testing such materials allowed for a better control of the exemplars that could be embedded in a psychological space that varied along two dimensions of similarity. Experiments 1a to 1c were designed to create such a psychological space following a technique of multidimensional scaling (MDS; Kruskal, 1964; Shepard, 1987). As a result, it was possible to represent the exemplars of the database in a psychological pace that varied along two dimensions on a similarity gradient from similar to dissimilar. Experiment 2 showed that dissimilar exemplars produced better performances of generalisation in the
processing of abstract concepts. The use of the psychological space and a Bayesian account of
the GCM model (see Vanpaemel, 2009; Lee & Wagenmakers, 2014) provides a powerful
framework the two questions that this project set out to answer: (1) What sort of features are
acquired during the learning of concepts through exposure to a set of exemplars? (2) How are
these features generalised to novel instances of an abstract concept?

The creation of this psychological space answers a first assumption of the GCM model
as regards to the dimensions of stored exemplars. In the present case, the similar and
dissimilar dimensions were obtained from a series of similarity rating tasks and the exemplars
of the NewAbstractConcepts concepts were leaned and stored during the reading phase of
experiment 2. A second assumption of the GCM model relates to selective attention weights
allocated to chosen dimensions. According to the results of Experiment 2 which showed
better performances for generalisation to novel instances, when stored exemplars were
dissimilar to each other, the attention parameter allocated more weight to the dissimilar
dimension compared to the similar dimension. This might seem counter-intuitive to allocate
more attention to the dissimilar dimension when from a theoretical viewpoint, results from
Experiment 2 suggest an abstraction of idiosyncratic surface features compared to common
core features. However, this parameter of selective-attention strategy is implemented to
model the influence of each dimension on classification predictions (Nosofky, 2014).
Experiment 2 suggests that contextual diversity is a better predictor of people's ability to
recognise the novel exemplars as a good example of a previously stored concept and as such
should receive a higher attention weight. Taken together, the psychological space obtained
from Experiment 1a to 1c and the attention parameters from Experiment 2 provides the
necessary background information to build a generalisation model for abstract concepts using
the GCM model in a Bayesian framework which allows for flexible parameter estimation (see
Figure 6).
<figure><img src="image_27.png" /><figcaption>Figure 6. Graphical model implementation of the GCM</figcaption></figure>

Let $x_i = (x_{i1}, x_{i2})$ denote the coordinate locations of stimulus $x_i$ in a 2-D psychological space.

The first dimension $x_{i1}$ has attention weight $w$ with $0 \le w_d \le 1$ and the second dimension $x_{i2}$ has attention $(1 - w)$. Given the coordinate of the stimulus, the distance between stimuli $x_i$ and $x_j$ is computed according to the Minkowski metric:

$$d(x_i, x_j) = [w |x_{i1} - x_{j1}|^r + (1 - w) |x_{i2} - x_{j2}|^r]^{1/r}$$

With $r$ is a Euclidean metric ($r = 2$) used for stimuli varying on integral dimensions (see Shepard, 1991 for a review).

Similarity between the $i^{th}$ stored exemplar and $j^{th}$ test exemplar is modelled as a decaying function of the distance between the stimuli:

$$s(x_i, x_j) = \exp\{-cd(x_i, x_j)\}$$

where $c$ is a generalisation parameter such as $c \sim Uniform(0,5)$

The overall probability that a test exemplar will be recognised as a good example of the NewAbstractConcepts is given by:

$$r_i = \frac{bs_{iA}}{(bs_{iA} + (1 - b)s_{iB}} \quad \text{with } b = 0.5$$

The observed decision is given by

$$y_i \sim \text{Binomial}(r_i, t)$$

with $t$, the total number of trial presentations.

The benefit of a Bayesian implementation of the GCM model lies in its ability to represent dependencies between mechanisms involved in the overall process of exemplar generalisation to novel instances. The stored exemplars are embedded in a dimensional space that represent the 2 salient dimensions of similarity or contextual diversity. The model also allows to allocate selective-attention weights to these dimensions with the idea a parameter
that can “stretch” attended dimensions and “shrink” unattended ones (Nosofsky, 2014).

Finally, the overall probability of generalisation meaning that people would be able to correctly recognise new instances of a concept is dependent on this psychological space, the attention and generalisation parameters which reflect prior knowledge established on the basis of the results obtained from Experiment 2 according to which abstract concepts such as those defined in the NewAbstractConcepts database are better generalised to new instances when learned through exposure to contextually diverse exemplars.

## CONCLUSION and FUTURE WORK

The present project provides a stepping stone for the study of generalisation mechanisms in the processing of abstract concepts. When considering the variability and complexity of abstract concepts, it is fair to assume that many other studies will need to be implemented in order to get a fuller picture of these mechanisms especially as regards to particular types of abstract concepts illustrated by the dimensions proposed by Borghi and colleagues (2018) about the variety of abstract concepts. It can be expected that the present model would require to implement more complex parameters especially as regards to the attention parameter. For emotion concepts for instance, generalisation could not only rely on the similar vs. dissimilar dimensions but emotional valence could also play a role. In using novel abstract concepts, the issue of the complexity of abstract concepts was side-stepped in order to propose a general-purpose model that could be used as a template for richer assumptions of generalisation of abstract concepts. The availability of the NewAbstractConcepts database will also provide an opportunity to implement other assumptions of the GCM model that were not specifically tested in the present project but also to use other models as a framework referent. With this project, we hoped to provide a satisfactory articulation of the literature on abstract concepts representation and generalisation mechanisms in concept learning which has mainly been missing in the literature.
# Chapter 7. General discussion and perspectives

Research on abstract conceptual knowledge is rooted in a long tradition which translates into a diversity of approaches, models and theories that aim to answer the fundamental questions we set out to explore in the present thesis such as (1) How is the meaning of abstract concepts represented? (2) How does it relate to other concepts whether similar or associated? (3) How does meaning relate to conceptual structure? (4) How is meaning generalised to all instances of the concept? The following chapter offers an overview of the results that were obtained to answer these questions. Such questions are a testament to the different levels of exploration that we set out to investigate in our study of the abstract concept phenomenon. More broadly, the long tradition of studying the semantic processing of concepts has often been compartmentalised into separate levels from semantics dealing with conceptual entities at the word-level to pragmatics dealing with complex utterances and statements (Cruse, 2010; Aitchison, 2012; Yule, 2020). As a result, the present chapter introduces a theoretical model of the semantic organisation and generalisation mechanisms of abstract concept processing. We consider this model to be an attempt to bridge the gap between different levels of investigation of psycholinguistic phenomena.

## Summary of results

The first two chapters of this thesis introduced normative databases that bridged the gap in the French literature as regards to the availability of normed stimuli used to study abstract concepts processing at the word level. As such, their main incentive was methodological and practical. The first database (Chapter 2) measured semantic similarity between pairs of French abstract concepts while the second database (Chapter 3) measured associative strength generated in a word association task. A second motivation for the creation of the two databases was to address previous findings that have shown that concepts are organized according to semantic similarity when concreteness increases and according to verbal
association when abstractness increases (see Crutch & Jackson, 2011). To address this
observation, both databases included a variable of graded levels of concreteness. The
computed descriptive statistics revealed stronger association strength for concrete concepts
compared to abstract concepts, which confirmed different organisation patterns where
concrete concepts are more tightly connected to a few contexts while abstract concepts are
more loosely connected to a larger number of varied contexts (Chapter 3). In addition, word
associations have shown a high correlation between cue and response concreteness which
shed more light on the organisation of the mental lexicon as a function of concreteness that
suggests abstract concepts are closely represented in the mental lexicon.

We then used the large amount of data generated by the word association task to build a
graphical representation of the mental lexicon and apply methods of cognitive network
science (Chapter 4). This was introduced as the first instance of building a semantic network
based on French word association data and the first to explore structural differences between
concrete and abstract concepts in the mental lexicon for any language. Graph analyses have
shown that the network exhibits a high clustering coefficient, sparse density, and a small
average shortest path length for both the concrete and abstract networks which is consistent
with a small-world structure therefore confirming seminal findings of Steyvers and
Tenenbaum (2005). Comparisons between local node statistics and global structural topology
have shown that abstract and concrete concepts present different overall patterns of structural
organisation with concrete concepts presenting an organisation in densely connected
communities compared to abstract concepts. These patterns confirmed previously acquired
knowledge about the differences between abstract and concrete concepts on a larger scale.

Previous studies have used simulations of spreading activation to derive highly predictive
judgments of similarity (see for example Kenett, Levi, Anaki & Faust, 2017; De Deyne et al.,
2018). As such, when considering the word level of semantic processing, both semantic
similarity and verbal association can refer to similarity-based processes. Taken together, this
suggests that the mental lexicon is organised according to patterns of similarity whether
measured in terms of semantic similarity between pairs of words in a database or as node
coordinates extracted by an algorithm of spreading activation in a semantic network built
from word association data. In Chapter 4, simulations of spreading activation have shown that
abstract and concrete concepts present different thresholds of activation with abstract concepts
being less influential in the network and therefore exhibiting lower activation levels compared
to concrete concepts. This pattern confirmed findings obtained from graph analyses according
to which abstract concepts are organised in a more diffuse pattern in the mental lexicon. This
also confirms previous findings according to which abstract concepts are more loosely
connected to a diversity of contexts (Paivio et al., 1968).

Many authors have suggested that, compared to concrete concepts, abstract concepts
appear to be semantically impoverished, with their representation requiring associations with
other concepts (Barsalou et al., 2008; Borghi, Scorolli, Caligiore, Baldassare & Tummolini,
2013; Borghi, Barca, Binkofski, Castelfranchi, Pezzulo & Tummolini, 2019, see also Recchia
& Jones, 2012). In Chapter 5, we explored the richness of abstract concepts and the nature of
their features. In a preliminary norming task, we constructed a set of pictures to illustrate a list
of lexical abstract concepts. The pictures were either concrete pictorial depictions of the
chosen lexical concepts or abstract pictures devoid of tangible features. These materials were
then used to form picture-word pairs where each lexical concept was paired with a picture
either concrete or abstract. The main hypothesis posited that participants would use a strategy
when presented with both picture-types and process the meaning of abstract concepts based
only on clear depictions rather than intangible pictures. On the contrary, results have shown
that participants continued to process concepts from abstract pictures devoid of tangible
features even when presented with more concrete ones. This suggested abstract pictures can
convey the same meaning as lexical abstract concepts which we took as evidence of the
richness of abstract concepts as they activate features beyond their linguistic or situational
components. These results were complementary to those of Bolognesi and Vernillo (2019)
who proposed a process of abstraction based on an inferential mechanism that moves from
concretely depicted entities to more abstract ones. These intangible features could be
construed as evidence of abstracted representations from varied exemplars of concepts
becoming generalised statistical traces in long-term memory (Barsalou, 2003).

This process of generalisation refers to an ability to extract meaningful representations
from exemplars and transfer to novel situations which reflects a higher-level mechanism with
pragmatic implications. In Chapter 6, we explored generalisation mechanisms in the
processing of abstract concepts by introducing a novel database of new abstract concepts
which were used to test whether abstract concepts are generalised based on patterns of
similarity or contextual diversity. A first series of norming studies aimed to build the database
composed of novel abstract concepts described by exemplars that were paired into similar or
dissimilar combinations. The abstract concepts were then presented to participants in a
similarity judgment task (Chapter 6, Experiment 2). Experiment 2 showed that dissimilar
exemplars produced better performances of generalisation compared to similar exemplars.
Exposure to contextually diverse exemplars enhanced participants abilities to generalised
learned abstract concepts to novel instances of said concepts. These results were construed as
evidence of the role of contextually diverse patterns for the processing of abstract concepts at
a higher level of complexity.

Taken together, results from all empirical chapters reveal a reversed pattern when moving
up in levels from the semantics of the word-level databases which revealed a pattern of
similarity to complex abstract exemplars which are generalised on the basis of contextual
diversity. The complexity of psycholinguistics phenomena means that scientific explorations
of the mental lexicon are often compartmentalised with studies focusing on a single identified
level of processing. The present thesis is evidence of this complexity with several studies each
focused on one single level. Further exploration will need to account for the link between
levels of processing. The present chapter summarises the findings across our empirical data
into a theoretical model as an attempt to render a fuller picture of the semantic organisation
and generalisation of abstract concepts.

A theoretical model and some implications for abstract concept
processing

A central controversy in cognitive science concerns the roles of similarity versus
contextual diversity. To gain some insights on this issue, we propose a theoretical model that
draws on the results from the present thesis. The model posits that similarity-versus diversity-
based processes can be characterized as extremes in a multilevel space with similarity-based
organisation at the word level and contextual diversity at the pragmatic level. The transition
from similarity-based to diversity-based processing is conceptualized as a transition through
levels.
<figure><img src="image_28.png" /><figcaption>**Figure 1.** Schematic representation of our theoretical model. Level I represents abstract concepts and features at the word levels. Concepts and features are activated following a threshold of activation. Thicker traits represent more influential nodes which possess a higher activation potential compared to thinner traits. Level II represents exemplars in memory as instances of concepts. Level III represents the generalised concepts based on the processing of contextually diverse exemplars which allow for the recognition of novel instances.</figcaption></figure>

The theoretical model presented in Figure 1 suggests a hierarchical structure based on an integration across levels of processing from features to generalised concepts. This hierarchical structure posits a reverse pattern of processing with the lowest level relying on similarity while the highest level describes a pattern of contextual diversity. At the word level, features are activated when they reach the required threshold. This notion of activation threshold comes from the modelling of a semantic network of the mental lexicon (Chapter 4). Results
from Chapter 4 revealed that some concepts are more influential than others due to their structural position in the network meaning that they facilitate the spread of activation within the network. Levels of activation are also determined by the weights attributed to links between nodes. In a network built from word association data, weights reflect the strength of word associations. Other aspects such as the number of inward or outward connections also impact the influence of a node and consequently the spread of activation. The comparison of node influence in the network suggested that abstract concepts have a lower threshold of activation compared to concrete concepts meaning that more features need to be activated in order to reach the required threshold. This notion of activation threshold is illustrated in Figure 1 by the thickness of traits that link the first and second levels of the model with thicker traits for more influential concepts. One aspect that could not be shown on the Figure is the nature of the features that activate a concept which could be intangible as well (Chapter 5). This has been confirmed by previous feature naming tasks according to which participants may provide only a linguistic approximation of conceptual content of abstract concepts which may themselves be decomposed into abstract features (McRae, Cree, Seidenberg, & McNorgan, 2005; McRae, de Sa, & Seidenberg, 1997; Sánchez-Casas, Ferré, García-Albea & Guasch, 2006; Vigliocco, Vinson, Lewis & Garrett, 2004; Vinson & Vigliocco, 2008). This has been previously regarded as a limitation of feature naming tasks but in light of the results obtained from Chapter 5, this could also be construed as evidence of the intangible nature of certain features that compose abstract concepts (see McRae et al., 2005 for a discussion of these limitations).

To the best of our knowledge, the semantic network we build in Chapter 4 was the first attempt in the French language and the first to investigate the structural properties of abstract and concrete concepts in a network. This attempt provided important insights as to the structural differences between abstract and concrete concepts. In a future project, we aim
to test the replicability of these findings in a larger English database by using the Small World
of Words data collected by De Deyne et al. (2019).

While the passage from level I to level II in the model was determined by similarity-
based processes, the passage between level II and level III is determined by diversity-based
processes which reflects a reversed pattern from lower to higher-level processing. The overall
incentive of studying conceptual knowledge is aimed to explore the ability to extract
meaningful representations from encountering a few instances of a concept. In Chapter 6, we
explored this ability for abstract concepts. When studying the acquisition of concepts,
researchers encounter some methodological difficulties such as the necessity to control
people's background knowledge. In order to avoid such difficulties, we introduced a database
of novel abstract concepts described by several exemplars that were either similar or
dissimilar to each other. Abstract concepts present such complexity and diversity that they
cannot be precisely arranged in categorises with clear-cut boundaries. As a result, we
suggested that the processing of abstract concepts would be better described using an
exemplar account of generalisation (see Feldman, 2003). The newly introduced database was
first used to create a psychological space based on two within exemplars dimensions which
were similarity and dissimilarity. The materials were then used in a word-learning experiment
where participants were presented with a pseudo-word to label the novel abstract concept
along with a set of exemplars that were either similar or dissimilar. In the test phase, they
were presented with a novel instance of each abstract concept and were asked whether this
could be construed as a new example of the learned concept. Participants performed better at
recognising the novel instances as an example when the abstract concepts were learned
through dissimilar set compared to similar sets of exemplars. We took these results as
evidence that abstract concepts are generalised following a mechanism based on contextual
diversity. Taken together with the previous level, this suggests that the more abstracted
conceptual knowledge becomes and the more its processing relies on a reversed pattern that
moves from a similarity-based mechanism for lower-levels of processing to a diversity-based
mechanism for higher-levels of processing.

When considering a broader cognitive framework, this reversed pattern is not specific
to the processing of semantic abstract concepts. The CAL model (Category Abstraction
Learning) developed by Schlegelmilch and von Helversen (2021) is a model that combines
similarity-based lower-level processes with dissimilarity-based higher abstraction processes to
explain many phenomena of category and reinforcement learning. Their model was aimed to
answer a major limitation present in most models of category learning which are unable to
explain the underlying generalisation mechanisms from features to exemplars to generalised
concepts. The model builds on the principle of stimulus generalisation (Shepard, 1987)
according to which the presentation of a stimulus activates stored exemplars in memory with
a level of activation that decays following the psychological distance between a novel
stimulus and exemplars stored in memory.

The parallel between the reversed pattern inferred from the results of our model to the
one described in the broader cognitive framework proposed by Schlegelmilch and von
Helversen (2021) highlights the importance of explaining language mechanisms within a
larger cognitive framework. This observation is in line with our attempt in Chapter 6 to
describe the mechanism for the generalisation of abstract concepts using the GCM model.
This pertains to the maturity of the cognitive science in general and its ability to grapple with
the challenge of defining unified models and theories without losing sight of specificities
that are intrinsic to a specific level of explanation.

The present observations have been centered on psycholinguistic phenomena at the
behavioural level of explanation. Our results present some implications for neural and
computational levels.
Implications for neural evidence

Network semantic analyses have shown a structural difference in the activation
patterns for abstract and concrete concepts (Chapter 4). Previous studies have confirmed the
impact of this concreteness gradient in neurological data. The literature on the neurological
profile of semantic processing has established that language is represented in a distributed
neural network (Patterson et al., 2007; Binder & Desai, 2011; Han et al., 2013; Martin, 2016;
Huth et al., 2016). The literature however has not reached a consensus on the nature of the
semantic neural network and proposes two distribution patterns which are debated namely:
the distributed-only models and the hub-and-spoke hypothesis (Patterson et al., 2007, 2015;
Mahon & Caramazza, 2009; Lambon Ralph et al., 2010; Martin et al., 2014; Martin, 2016).
According to distributed-only theories, conceptual knowledge is represented in a modality-
specific pattern which put an emphasis on the role of sensorimotor modalities such as shapes,
colours, sounds and actions which coincide with a grounded view of cognition (Martin, 2007;
Barsalou, 1999; 2008; Binder et al., 2009). The hub-and-spoke hypothesis posits the existence
of amodal hubs of processing in addition to modality-specific regions (Lambon Ralph, 2014;
Lambon Ralph & Patterson, 2008; Lambon Ralph, Sage, Jones, & Mayberry, 2010; Patterson,
Nestor, & Rogers, 2007; Rogers et al., 2004). In the hub-and-spoke model, some regions
called spokes process the modality-specific features of concepts which are connected through
cortical routes to hub regions. These hubs code for similarity-based processes that abstract
specific features in favour of similar core components of concepts (Patterson & Lambon
Ralph, 2016). In light of results from Chapter 6, this role of the hub regions coincides with the
description of the exemplar theory which posits a mechanism responsible for the abstraction
of idiosyncratic features in the generalisation of abstract concepts.
Neuropsychological data such as profiles of semantic dementia (SD) provide support for the hub-and-spoke hypothesis and identify the ATL (anterior temporal lobe) as a semantic hub region (see for example Patterson et al., 2007). Semantic dementia is a neurodegenerative disorder characterised by a selective loss of conceptual knowledge regardless of their modality and can be associated with bilateral lesions to the ATL (Hodges et al., 1992; Patterson et al., 2007; Binney et al., 2010; Gainotti, 2012; Hoffman et al., 2014; Lambon Ralph, 2014; Rice et al., 2015; Zhao et al., 2017). As a result, SD patients exhibit a poor comprehension of conceptual items presented in every modality (Bozeat et al., 2000, Coccia et al., 2004, Luzzi et al., 2007). Consequently, this pattern of cross-modality deficit has been construed as evidence of the amodal nature of the ATL and its role as a convergence zone which form amodal conceptual representations (see Figure 2; Patterson et al., 2007, Rogers et al., 2004; Rogers & McClelland, 2004).

<figure><img src="image_29.png" /><figcaption>**Figure 2.** Representation of the hub-and-spoke model with spoke regions which code for modality-specific components of concepts while the ATL is a semantic hub which aggregates across modalities and form amodal conceptual representations (see text for more details). From Lambon et al. (2017).</figcaption></figure>
Despite such evidence, the specific role of the ATL remains unclear. For instance, Loiselle et al. (2012) have explored the comprehension of concrete and abstract concepts in patients with a selective ATL resection. Typically, the concreteness effect refers to the robust behavioural phenomena found in neurologically intact participants which describes a facilitated processing of concrete concepts compared to abstract concepts in terms of speed and accuracy in varied tasks such as memory retrieval and lexical decision (see Paivio, 1991 for a review). In contrast, patients who exhibit patterns of semantic dementia have been known to present a reversed concreteness effect with a facilitated processing in favour of abstract concepts compared to concrete concepts (Breedin et al., 1994; Cipolotti & Warrington, 1995; Macoir, 2009; Papagno, Capasso, & Miceli, 2009; Warrington, 1975; Yi, Moore, & Grossman, 2007). It remains unclear whether this greater impairment for concrete concepts originates from damage to the ATL in SD patients or from degraded visuo-perceptual features from damaged inferior temporal lobe in SD patients (Desgranges et al., 2007; Nestor, Fryer, & Hodges, 2006). This latter hypothesis emerged from the role of the inferior temporal lobe in the processing of visual objects that often present lesions in SD patients. To test these two hypotheses, Loiselle et al. (2012) compared the processing of abstract and concrete concepts using a semantic judgment task in patients who underwent a selective ATL surgical resection and patients with selective medial temporal lobe damage sparing the ATL. Results have shown that both groups exhibited impaired processing for both concept types compared to the control group. Comprehension of concrete concepts were significantly more impaired for patients with selective ATL resection while both concept types were equally impaired in other patients. Loiselle et al. (2012) interpreted these results in relation with the dichotomy between abstract and concrete concepts with concrete concepts that are more dependent on similarity-based patterns while abstract concepts are inscribed in more diffuse associative patterns. This concurs with Paivio's assumption (Paivio, 1991)
according to which concrete concepts show stronger connections to a smaller number of contexts while abstract concepts are more loosely connected to a larger number of contexts. In the case of a lesion to the ATL, this loose pattern becomes an advantage as more diffuse statistical memory traces are maintained for abstract concepts while stronger and more localised connections are lost as a whole in the case of concrete concepts. These results confirm our conclusions on a neurological level of explanation which showed that abstract concepts present weaker associative connections (Chapter 3), are organised in a diffuse structural pattern in a semantic network which resulted in lower activation levels (Chapter 4), and can be processed and decomposed based on intangible features that leave memory traces which can be activated even when the concept is not clearly depicted but evoked based on pictures devoid of tangible features (Chapter 5).

Other studies have shown the existence of an abstract-to-concrete gradient in the ATL with concrete concepts represented in the medial-ventral ATL while abstract concepts are represented in the dorsal-lateral ATL (Lambon Ralph et al., 2017). More recently, Vignali, Turini, Collignon, Crepaldi and Bottini (2020) have explored the time course of the abstract and concrete concepts convergence in the ATL. They have shown the existence of a dorsal-to-medial and abstract-to-concrete gradient in the ATL which emerges in late stages of processing. According to this time course, abstract and concrete concepts are initially encoded in posterior temporal regions before converging in the ATL. These results provide additional evidence of the fact that semantic information for concrete and abstract concepts travel along different cortical routes with concrete concepts that originate from the ventral stream before converging to the ventral-medial ATL while abstract concepts originate from the posterior STG (superior temporal gyrus) and the orbito-frontal cortex before converging to the dorsal-lateral ATL.
Taken together, evidence from SD patients and the graded convergence in the ATL seem to suggest that the ATL could act as a lever between newly processed concepts grounded in modal and linguistic features to generalised concepts in the form of amodal conceptual representations through a mechanism responsible for the abstraction of idiosyncratic features (Patterson & Lambon Ralph, 2016). However, several questions on the role of the ATL in the processing of abstract concepts need to be answered. The processing advantage of abstract concepts in the ATL would suggest that being composed of abstract features themselves, abstract concepts should benefit from a faster mechanism of generalisation. Time course analyses of concrete and abstract concepts have not produced faster convergence for the abstract concepts compared to the concrete concepts however (Loiselle et al., 2012). In addition, the hub-and-spoke mechanisms are said to be similarity-based whereas we have seen in Chapter 6 that concepts were more easily generalised on the basis of dissimilar exemplars. A way to elucidate these seemingly contradictory assumptions would be to test for the processing of abstract concepts by replicating the experiments from Chapter 5 and 6 using fMRI techniques. In Chapter 5, we have seen that abstract concepts could be activated by intangible pictures. An fMRI implementation of this experiment would provide some evidence as to the nature of these features based on brain activation during the processing of the pictures and the retrieval of the corresponding abstract concepts. Results from chapter 6 have shown that abstract concepts are better generalised from dissimilar exemplars. We propose an implementation of the newAbstractConcepts database in an experiment that would test for the effect of similar and dissimilar exemplars on the activation of the ATL using fMRI techniques to explore whether dissimilar exemplars produce higher levels of activation of the ATL given that they should facilitate generalisation.

Our study and findings of such psycholinguistic phenomena were meant to advance our knowledge of language processing in the human cognitive system and provide insight as
to the challenge posed by abstract concepts. Due to the interdisciplinary nature of the
cognitive sciences, these findings can be interpreted on a larger scale beyond the frontiers of
psycholinguistics or even cognitive psychology. In the next section, we deal with the
implications of the present findings for natural language processing which falls under the
umbrella of the cognitive sciences due to its ties to the field of computer science.

Implications for natural language processing

Natural language processing (NLP) is a field of Artificial Intelligence (AI) motivated
by the comprehension of human language by machines which uses computational techniques
to analyse, represent and reproduce human language. From the advent of the Internet and the
Social Web, a vast amount of unstructured linguistic content has been generated so much so
that it becomes impossible to manipulate using traditional techniques. This vast amount of
information has also provided us with an opportunity. Never before have we had access to
such a wealth of data which provides insights into the ecological use of language by humans.
For instance, the use of large linguistic corpora obtained from the mining of social media
platforms has allowed for the development of word frequency databases such as Wordlex
(Gimenes & New, 2016) which provides a more accurate and up-to-date picture of the
common use of language compared to previous databases which were computed from books.

The unstructured nature of linguistic information generated by humans for humans
presents a major challenge for machines as it is not directly processable to them. That is why,
NLP uses algorithms able to mine, aggregate and parse large text corpora but which are
unable to interpret their meaning (Allen, 1987; Dyer, 1994; Cambria & White, 2014).
According to Dyer (1994), these limitations are due to the lack of high-level symbolic
capabilities of NLP algorithms. Such capabilities would require the acquisition of lexical
semantic representation, grounding language in perceptual and motor experiences and the
ability to abstract and generalise conceptual knowledge. This lack of capabilities keeps NLP
models from achieving human-level language understanding despite major advances since the
advent of the field in the 1950s.

<figure><img src="image_30.png" /><figcaption>**Figure 3.** Advances in NLP research cross 3 major era following the syntactics, semantics and pragmatics curves (From Cambria & White, 2014).</figcaption></figure>

In recent years, more efforts of collaboration between cognitive and computational
fields have been put forward in order to achieve psychologically plausible NLP models.
Cambria and White (2014) envisioned the advance of NLP research according to three major
eras represented as overlapping curves (see Figure 3). The leap between the syntactic and
semantic curves represent the current interest for the use of cognitively plausible models at
the word level which have been used as a paradigm shift from models centered on words as
single linguistic units to the notion of concepts as meaningful representation grounded in
actions, events and situations (Cambria & Hussain, 2012). One example of this collaborative effort is the development of semantic networks which were first motivated by the need to represent semantic concepts in a computer memory slot (Quillian, 1969; Chapter 1). On the other side of this collaboration, psycholinguistics researchers borrowed powerful computational tools for the representation of the semantic information on a large scale (see Chapter 4).

The semantics curve includes the representation of common-sense knowledge built on intuitive theories about physics (forces, masses, gravity...) and psychology (desires, beliefs, intents...; see for example Gerstenberg & Tenenbaum, 2017). This interest for the representation of common sense is inscribed within a larger goal to build machines that learn and think like humans do (Lake, Ullman, Tenenbaum, & Gershman, 2017). The field of AI as a whole has become aware of the invaluable blueprint that the human mind can provide in its ability to learn quickly and flexibly new concepts in relation to previously acquired concepts (Lupyan & Bergen, 2016; Lupyan & Clark, 2015). This ability when implemented in intelligent systems using deep learning techniques can provide a powerful framework for tasks of scene understanding based on object recognition and the comprehension of intent in acting agents (Karpathy & Fei-Fei, 2015; Vinyals et al., 2014; K. Xu et al., 2015). Current algorithms have made tremendous progress in object recognition but still struggle with scene understanding which would allow for a leap between semantics and pragmatics curves (Figure 3). This leap would require the implementation of causality, intuitive physics and intuitive psychology in computational models (Lake et al. 2017). In chapter 6, we developed a database of novel abstract concepts to study mechanisms of generalisation to novel instances. This human capacity requires the ability to recognise a pattern between memorised exemplars and novel instances of a concept. Future work in cognitive sciences will have to provide answers as to the role such intuitive theories about psychology and physics come into play in the
representation, acquisition and generalisation of concepts in the mind which would benefit for
both cognitive and computational sciences. Cognitive systems provide blueprints for the
development of intelligent systems while computational implementation represent an
additional incentive to apply a sense of mathematical formalism to verbal theories and test
them in a computational framework.

Conclusions and future work

The present thesis was designed to study the representation of abstract concepts
according to several levels of processing. We first explored the word level of processing and
proposed two databases of semantically similar and verbally associated word pairs. Our
findings have put an emphasis on the importance to consider concreteness as a graded
variable and revealed the role of concreteness as a gradient of organisation in the mental
lexicon. The data from the associative database was then used to build a multidimensional
semantic network which allowed us to explore the structural organisation of the mental
lexicon and its impact on the processing of concepts. These databases and network used only
the verbal modality for the representation of concepts. The next study used both the verbal
and visual modality of abstract concepts to explore the featural nature of abstract concepts and
found that abstract concepts can be activated by pictures devoid of tangible features. We
interpreted this activation as evidence of statistical memory traces which can be activated
even in the absence of the clear depiction of a concept. We construed these findings as a part
of mechanisms of abstraction and generalisation mechanisms. We used this study as a
transition to the study of generalisation in the processing of abstract concept within the
framework of the exemplar theory and found that abstract concepts are more easily abstracted
from their idiosyncratic features and generalised to novel instances on the basis of contextual
diversity. This accumulation of evidence revealed a reversed pattern of organisation from
similarity-based processes at the word-level to diversity-based processes at the level of
generalised concepts. This reversed pattern was summarised in a theoretical model which
represented the three levels of processing from the word-level to pragmatics considerations.
Finally, we explored the implications for neural and computational evidence.

The present thesis represents a snapshot of our reflection on the processing of abstract
concepts at the time of writing which can be construed as a launchpad for future projects. For
instance, the techniques of semantic network analysis are common to both the exploration of
behavioural data and neurological data. This means that it would be possible to create a
database of verbal association using a neurological association task and create a neural
network of the same data to explore the parallels between the structural and neural
organisation of the mental lexicon. More importantly, the reversed pattern illustrated in the
theoretical model in particular provided an incentive to incorporate all levels of processing in
a same experiment. This experiment would use the framework already developed in Chapter 5
and the newAbstractConcepts database. This follow-up study would attempt to replicate the
previous findings according to which abstract concepts are better generalised on the basis of
contextual diversity in a first phase and in a second phase, the pseudowords used to label
these novel concepts would be used in a semantic priming experiment to explore the role of
generalisation mechanisms at the word-level also. Finally, we hope that the availability of the
databases and network we have proposed will serve for future researchers and students
interested in the study of the French mental lexicon for abstract concepts.
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