<figure><img src="image_1.png" /></figure>

HAL
open science

Association: one term, five concepts

Thomas F Chartier, Joël Fagot, Arnaud Rey

► To cite this version:

Thomas F Chartier, Joël Fagot, Arnaud Rey. Association: one term, five concepts. Neuroscience and Biobehavioral Reviews, 2025, 181, pp.106525. 10.1016/j.neubiorev.2025.106525. hal-04865055v2

HAL Id: hal-04865055

https://hal.science/hal-04865055v2

Submitted on 24 Sep 2025 (v2), last revised 18 Dec 2025 (v3)

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.
1 Association: one term, five concepts

2

3 Thomas F. Chartier¹,*, Joël Fagot¹,², Arnaud Rey¹

4 ¹Centre de Recherche en Psychologie et Neurosciences, CNRS & Aix-Marseille

5 Université, UMR 7290, Bâtiment 9 Case D, 3 place Victor Hugo, 13331 Marseille Cedex

6 3 - France

7 ² Station de Primatologie, CNRS-Celphedia, UPS 846, 13790 Rousset-sur-Arc, France

8 * Corresponding author : cft.nbr@mailo.com

9

10

11 Abstract

12

13 'Association' is certainly one of the most prevalent terms in the study of mind, brain and

14 behavior. It applies to actions, stimuli, neurons, brain areas, ideas, words, etc. However

15 we argue that this term can convey five main meanings, expressing five distinct notions

16 of link, whose coexistence and conflation result in conceptual blurring. This largely

17 underappreciated polysemy problem prevents associative accounts of biological

18 cognitive systems from being properly articulated, understood and criticized. Based on a

19 review traversing psychological, neuronal and behavioral sciences, we delineate and

20 characterize these five categories of usage, to show how such classification improves

21 transparency across fields and casts new light on various debates. The scope is

22 extended to include artificial systems. The review is meant to promote interdisciplinary

23 collaboration and dialogue around the central notion of association.

24

25 Highlights

26 • 'Association' is a highly ambiguous term in studies of mind, brain and behavior

27 • Five complementary, non-overlapping meanings of 'association' are characterized

28 • Such categorization allows redescription and clarification of a vast literature

29 • This striking case is emblematic of general semantic issues in interdisciplinarity

30

31

32
33

# 1. An apparently simple notion

Association is a famous concept in behavioral neurosciences, which denotes typically a link between neuronal, mental, perceptual or behavioral entities regarded as elementary, often implying the idea that high-level phenomena can be decomposed into lower-level, simple associations.

This concept is central in various theories. For instance, association norms, which quantify the degree of mental relatedness between words, are a foundational tool for language and memory research [1,2]. In cognitive evolution, the idea of a potential human singularity related to the expansion of cortical associative areas has been put forward repeatedly [3,4]. In computational neurosciences, associations between basic computing units are at the core of Artificial Neural Networks (ANNs), which gave rise to today's Deep Learning and its vast applications. Constant debates on the contributions of associations to cognitive operations, such as planning, decision-making, imagination or speech [5-11], further stimulate the overwhelming use of the term 'association' (6.5 Million PubMed entries) in various fields.

But do all these scientists agree on what an 'association' is? Do they mean the same kind of link? Does the concept keep the same content in research topics as diverse as creativity [11], lexical acquisition [12], insect neurobiology [13], statistical learning [14] conditioned behavior [15], attractor networks [16], computer memory [17] or consumer psychology [18]?

Comparing usages of that term reveals how difficult it is to determine universal features. For example, a 'stimulus-reward association' [19] can denote a relation between purely external events, whereas a 'cortical association' [20] links internal, neuronal entities; an 'association of thoughts' does not refer to neuronal but to mental entities, and while it can be reversible ('Newton' can make you think of 'apple', and reciprocally), an 'input-output association' of an ANN such as Deep Learning image classifier [21] is typically irreversible (the network is not designed, given a class, to output an exemplar image).

The term 'association' thus appears highly polysemic, considerably variable according to the nature of associated items and their link. A statement such as 'A and B are associated', without further context, will be interpreted differently depending on a person's background. Hence paradoxically, making general claims about associations is difficult despite the concept's prevalence. Perhaps due to its apparent simplicity, the term 'association' has eluded theoretical unification efforts, and we are not aware of a consensual definition available today throughout behavioral neurosciences.

67

68

69
## 2. Scope and objectives

71 The present article argues that this definitional, hence conceptual, problem is vastly
72 underestimated by the community and impedes discussions on the real merits and limits
73 of associative accounts. Consensus, we believe, is absent for a principled reason: it is
74 impossible, because the term 'association' actually bears multiple, separate meanings.
75 Based on a broad literature review, traversing neuronal, psychological and behavioral
76 sciences, we propose to acknowledge that usages of this term fall under five main
77 categories, for which we propose names, delineate characteristics, and show
78 complementarity. Benefits of this conceptual distinction are illustrated. As a cautionary
79 word, we do not claim that all notions of associations mentioned below are ultimately
80 correct descriptors of cognitive systems. What the article discusses is not *how the brain*
81 *works*, but *how scientists speak about* how the brain works, thereby hoping to provide
82 sound communication basis for further debates.

83

## 3. On five notions of association

85

### 3.1. Acknowledging polysemy

87 To illustrate polysemy around the term 'association', let us think as a beginner student,
88 trying to figure out what the concept is by browsing Trends in Cognitive Sciences issues
89 published since last year.

90 In an article exploring the role of lateral hypothalamus [22], they first read how rats learn
91 about a '*cue-food association*' in their environment: 'association' here denotes a spatio-
92 temporal, i.e. statistical, dependence between two events or objects that are external to
93 the organism (e.g. tone and piece of food).

94 An article dealing with behavioral habits [23] calls in '*stimulus-response associations*' (S-R), pertaining to a stimulus-driven behavior control system as opposed to a goal-directed one: 'association' here denotes not an external co-occurrence, but a causal link
95 between an external signal and a behavioral manifestation of the organism.

98 In a further article concerning memory updating [24], none of the two associates are
99 external to the (human) organism, they are instead memories of events, so internal
100 mental objects: 'association' here denotes the capacity of one memory to reactivate, or
101 evoke, another one.

102 An article about human thalamocortical circuits [25] mentions the association between
103 mediodorsal thalamus and prefrontal cortex: 'association' here denotes again a capacity
104 for activation or excitation, but between cerebral regions, which are neuronal entities, not
105 mental ones.
106 After one last article on episodic memory [26], the student is nearing a state of total confusion, as they read this time about 'association areas' being cortical regions 107 supporting uni- or plurimodal sensory integration: 'association' here denotes previously 108 separate information coming together. 109

110 As these biological examples make clear, the most diligent student cannot hope to grasp 111 the notion of 'association', not unanimously recognized even by scientists. The five 112 cases above, we argue, represent five main categories of usage for the word 113 'association', that cohabit in brain and behavior research and, we will show, apply 114 equally to artificial systems. These main meanings are routinely conflated because they 115 share the superficial 'link idea' of an association – from the latin 'ad-' (to, toward) and 116 'sociare' (to join). As such, they are subcategories of a more general 'link' category. Yet, 117 these five subcategories have different properties, concern different natures of 118 associates and express different notions of link which. Considering minimal 2-item 119 associations, these notions may be summarized as follows (see also Figure 1):

120 • external **contingency** (A and B co-occur),

121 • behavioral **conversion** (A is converted into B),

122 • mental **evocability** (A can evoke B),

123 • neuronal **excitability** (A can excite B),

124 • information **convergence** (A and B come together).

125 If one considers a biological or artificial cognitive system (hereafter the 'System'), the 126 first notion concerns what the System may learn from in its (sensory or data) 127 environment; the second concerns a reaction of the System to a specific element of its 128 environment, regardless of internal implementation; the third, fourth and fifth are 129 processes internal to the System. For a biological organism, this view is akin to De 130 Houwer & Hughes' three levels of analysis [27] – descriptive (contingency), functional 131 (conversion), mechanistic (evocability, excitability, convergence). However, these 132 notions of association apply not just to organisms, but also to parts of them, such as 133 nervous systems, isolated neural circuits (e.g. hippocampal networks [28] or *Aplysia's* 134 buccal ganglia electrophysiology preparation [15]), and likewise to parts of, or whole 135 artificial systems. 136
ASSOCIATIONS FIVE CONCEPTS 5/22

<table><caption>**Figure 1:** Features defining the five main categories of association. Examples from the literature are cited in the Main Text. A simple diagnostic tree is depicted that can assist the reader in assigning an expression containing 'association' to its corresponding category: first identify the nature of the A and B associates, then the nature of the System against which the association is defined, and then ask successively these three questions Q1, Q2 and Q3.</caption><thead><tr><th rowspan="2"></th><th rowspan="2">Categories of A-B associations</th><th rowspan="2">Nature of A and B associates</th><th colspan="2">Examples mentioned in Main text</th></tr><tr><th>usages</th><th>fields or topics</th></tr></thead><tbody><tr><td rowspan="2">EXTERNAL</td><td>Q1: are A and B events exterior to the System?<br>yes<br><br>no</td><td>correlation<br>category #1</td><td>events described by an observer</td><td>'Stimulus-Reward association' or 'action-outcome association' [20,23], 'sequential association' [30], 'causal association' [31], 'partial/frequent association' [32], 'one-trial association' [33], 'repeated association' [35], 'temporal association' [35], 'paradigmatic/syntagmatic association' [74]</td><td>animal behavior, data mining, statistical learning, linguistics,</td></tr><tr><td>Q2: is A an input and B an output of the System?<br>yes<br><br>no</td><td>conversion<br>category #2</td><td>A: stimulus, signal, input, perturbation<br>B: reaction, state, behavior</td><td>'input-output association' [22], 'stimulus-response association' (S-R) [24], 'cue-action association' [38], 'habit association' [39], 'pattern associator' [44], 'associative map' [50], 'associative memory [17,29,116,117], 'content-addressable memory' [118,119], 'autoassociation' [42,43]</td><td>ANNs, animal behavior, neuropsychology, behavioral science, computational neuroscience, physical computer memory</td></tr><tr><td rowspan="2">INTERNAL</td><td>Q3: are A and B functional units whose activation can spread?<br>yes<br><br>no</td><td>evocability<br>category #3</td><td>mental objects (ideas, concepts, words)</td><td>'association norm' [2,3], 'Implicit Association Test' [51], 'free association' [52], 'word-object association' [13], 'form-meaning association' [53], 'semantic association' [73], 'associative memory recall' [75-77, 104], 'contextual association network' [111-114], 'Paired Associates' [115]</td><td>psycholinguistics, social cognition, psychoanalysis, surrealism, developmental psychology, semant-tics, neuropsychology, systems neuroscience, human memory</td></tr><tr><td>excitability<br>category #4</td><td>neuronal objects (single or groups of neurons, areas)</td><td>'cortical association' [21], 'thalamo-cortical association' [26], 'synaptic association' [55,56], 'association fiber/pathway' [57,108-110]</td><td>neurophysiology, neuroanatomy, brain connectivity, molecular and cellular neurobiology, systems neuroscience</td></tr><tr><td></td><td>A and B come together<br>yes<br><br>no</td><td>convergence<br>category #5</td><td>pieces of information</td><td>'association area' [4,5,27,14], 'association neuron' [59], 'association cell/layer' [40], 'auditory-visual association' [60], 'association between inputs' [29], 'association center' [61], 'association nuclei' [106-107]</td><td>neuroanatomy, neurophysiology, ANNs, episodic memory,</td></tr></tbody></table>
150 3.2. Characterizing the five notions

151

152 3.2.1. Contingency (association of events)

153 A first category of usage for the term ‘association’ is to denote a statistical link, or

154 contingency, between what we call external ‘events’: for a biological System, we mean
155 any physico-chemical processes (e.g. light, vibration, odor plume) happening outside the
156 System and capable of providing sensory information; for an artificial System, we mean
157 any piece of data (e.g. words, vectors, image features) amenable to processing.

158 Such A-B dependence may vary in degree, from strict (always together) to likely co-
159 occurrence (e.g. every second time); in order (fixed in ‘sequential’ [29] or ‘causal’ [30])

160 [REF] associations, variable in ‘partial’ or ‘frequent’ [31] associations); in temporal and
161 spatial scale (milliseconds to days, millimeters to kilometers); in number of repetitions

162 (from ‘one-trial association’, as in some conditioning protocols [32], to ‘repeated association’ and recurring ‘temporal association’, as in some statistical learning

164 protocols [33,34]). But all these dependences can be assigned a mathematical statistical
165 function: correlation, covariation, copula [35], maximal information coefficient [36], etc.

166

167 3.2.2. Conversion (association of input and output)

168 In this second category of usages, the System is considered to associate an external
169 ‘event’ A (defined as above) to a behavioral manifestation B (state, response, action,

170 etc.), i.e. to realize a **conversion** of a signal A into a reaction B, in an irreversible A-to-B
171 order. A typical biological example is the ‘*Stimulus-Response association*’ [23] of

172 organisms, also called ‘*Cue-Action association*’ [37] or ‘*habit association*’ [38]; in artificial
173 systems one often encounters ‘*input-output associations*’ [21], performed typically by

174 ANNs with unidirectional information propagation through two (perceptron [39]), three, or
175 many (Deep Learning) layers of artificial units. Such computational models go indistinctly

176 by the names ‘multilayer perceptron’ [40], ‘hetero-associative networks’ [41,42], or
177 ‘(feedforward) pattern associator’ [43], defined as ‘networks which learn to associate’

178 [44] or to ‘achieve a correspondence’ [45] between input and output patterns. Overall,
179 conversions are consistent with a black-box description, because the term ‘association’
180 refers primarily to *what* is being associated (input and output, stimulus and action), no to
181 *how* this is realized.

182 When the System can link several such pairs input-output pairs, one may describe its
183 transfer function, or ‘mapping’ [46], or ‘look-up table’ [47], i.e. a mathematical function

184 that brings into correspondence a set of inputs with a set of outputs. This vision, aligned
185 with information processing theory, has influenced cognitive psychology, for example

186 Shiffrin & Atkinson's model of long-term memory [48], schematized by a flow-chart with
187 ‘Stimulus input’ and ‘Response output’, or Carpenter & Grossberg's Adaptive
188 Resonance Theory [49], involving an ‘associative map’ between perceptual and motor codes.
189

190
191 3.2.3. Evocability (association of mental objects)
192 This third category pertains to mental mechanisms internal to the System. Though the
193 nature of associated mental items varies (words, concepts, memory of events, of
194 pictures, etc.), the nature of the link is the same, an **evocability**: two thoughts, two
195 words A and B, are ‘associated’ if A has a capacity for evoking B (‘making think of’ B).
196 When reciprocal, such mental association might be called a **co-evocability**. Note that
197 reciprocity does not preclude asymmetry: ‘apple’ without context may evoke ‘Newton’ in
198 your mind, but more rarely than the reverse.

199 ‘Association norms’ in psycholinguistics [1], which quantify evocation probabilities
200 between words in human minds, averaged over groups of participants, are a prime
201 example of such usage. So are ‘Implicit Association Tests’ [50] made famous by studies
202 on stereotypes, or the ‘free associations’ of psychoanalysis [51] and surrealism, which
203 involve evocation capacities among words or concepts. [13]‘Word-object associations’ in
204 developmental psychology [12], and more broadly ‘form-meaning associations’ in lexical
205 semantics [52], refer to conventionalized mental systems whereby a given linguistic form
206 (speech sound, manual sign, visual/tactile symbol) can evoke a given meaning or mental
207 representation. All such associations are typically evidenced with direct (verbal report,
208 introspection) as well as indirect behavioral methods (e.g. priming [53]).

209
210 3.2.4. Excitability (association of sets of neurons)

211 The term ‘association’ can also express a functional connection between neurons or
212 sets of neurons. It denotes not so much a physical link, but rather a capacity of set A to
213 trigger electrical activity in, or excite, set B – this is A-to-B **excitability**<fnref n="1" />. Reciprocal
214 axonal projections can implement mutual interaction, or **co-excitability**, as in the
215 mediodorsal thalamus - prefrontal cortex example above [25]. Expressions such as
216 ‘synaptic association’ between individual neurons [54,55], or ‘cortical association’ [20]
217 and ‘association fiber’ [56] between cerebral regions (see Box 1), are frequently found in
218 studies of neural circuits or neuroanatomy.

219 This usage of ‘association’ has an obvious similarity with **evocability**, since the A-B
220 interaction scheme is analog in both and involves a spreading activation. Yet, mental
221 objects can be defined without hypothesis regarding their biological substrate,
222 furthermore they are accessible mainly through behavioral methods; in contrast, sets of

<footnote n="1">¹ exogenous, as opposed to endogenous excitability, i.e. self-excitability [121,122]</footnote>
223 neurons are material biological objects<fnref n="2" />, amenable to physico-chemical measures such
224 as electrophysiology, calcium imaging or fMRI. Until a better understanding of how
225 neuronal activity translates into mental process (see Outstanding questions), we prefer
226 to cautiously consider this as but a formal analogy.

227 In ANNs, it is debatable whether computing units, also called ‘artificial neurons’ and
228 which are likewise characterized by activity levels and spreading activation capacities,
229 should be construed as mental or neuronal entities. In fact, the old
230 symbolic/connectionist opposition is itself undermined by the existence of connectionist
231 models employing symbolic units [57], so this choice may be examined on case-by-case
232 basis.

233
234 3.2.5. Convergence (association of signals)
235 In this last category, it is not neuronal or mental objects that are said to be ‘associated’,
236 but signals, pieces of information, which flow unidirectionally inside the System and
237 meet at a certain point or area. This notion of **convergence** does not refer to the typical
238 underlying network structure (two directed edges connected to a node) but to the
239 information thus joined – so to signals, not wires. Specifically, it refers to the linkage
240 between two previously independent pieces of information.

241 Typical examples are ‘association neurons’, as in crows’ nidopallium caudolaterale
242 region [58], or ‘association cells’, as in Rosenblatt’s perceptron [39], which are individual
243 neurons receiving multiple afferent inputs and thereby serving signal convergence. The
244 notion also applies to entire regions, e.g. in primate brains the posterior superior
245 temporal sulcus / middle temporal gyrus region, said to serve ‘the formation of
246 associations between auditory and visual features that represent the same object’ [59],
247 or the hippocampal CA3 network [55], which ‘allows arbitrary associations between
248 inputs originating from very different parts of the cerebral cortex’ [28], or in insect brains
249 the antennal lobe, the ‘primary olfactory association center’ [60], and the Mushroom
250 Bodies, an ‘association area’ [13].

251

252

253

254

255

<footnote n="2">² Neuronal assemblies can be defined by functional criteria, such as assembly-wide synchronizable activity, or ‘ignition’ [123].</footnote>
<figure><img src="image_2.png" /><figcaption>**Figure 2: Complementary, non-overlapping kinds of associations.** Example situation where five different kinds of associations can be recognized, depending on one's focus. The person is exposed to a **contingency** between sound and picture of a cat; the expected response is to speak out loud the name of the animal ('cat'), so the person realizes a stimulus-response **conversion**; this behavior involves the **convergence** of auditory and visual input features of the two stimuli, in the posterior temporal lobe area of the brain; furthermore, the behavior likely involves an **evocability** of a 'cat' concept by a mental representation of the cat picture or sound. Finally, innumerous **excitabilities** (not depicted here) between individual neurons, sets of neurons, brain regions, are at play and participate in biological implementation of the behavior.</figcaption></figure>

### 3.3. Complementarity and non-exclusivity of the categories

The five notions just outlined, though non-overlapping, need not exclude each other's presence. Instead, it is sometimes possible to describe two or more types of associations in a given situation. To illustrate this crucial point (see Figure 2), let us consider a human participant exposed to matched auditory and visual features of animals (e.g. the sound 'meow' of a cat and a cat picture), and expected to speak their

256

257

258

259

260

261

262

263

264

265

266

267

268

269

270

271

272

273

274

275
276 name aloud (e.g. saying 'cat'), as in [59]. Here one may be interested in different kinds
277 of associations, which nonetheless all participate in understanding the situation – this is
278 why it is generally difficult to speak of a 'simple association' or 'simple associative task'
279 ([61–63]; see [64]). Notably, one may recognize here:

280 an external **contingency** (sound-picture association)

281 a **conversion** (sound or picture stimulus - name response association)

282 an **evocability**, probably even co-evocability, between mental representations of
283 the sound and the picture (sound-picture association)

284 a **convergence** of auditory and visual information, e.g. in the posterior temporal
285 lobe (audio-visual association)

286 and, of course, all kinds of **excitabilities** involved in the nervous system
287 (neuronal association – not depicted here)

288 Hence, in the same situation, two researchers with a different focus in mind may refer to
289 different things as 'an association', which is where scientific dialogue can really fall
290 down.

291

292 3.4. Tangential cases

293 'Association' is a frequent synonym for 'correlation' in scientific data. Yet, even when
294 applied to behavioral or neurophysiological data, the term may express no more than a
295 statistical notion, unspecific to either mind, brain or behavior. For instance, the fact that
296 generalized 3-Hz spike-wave complexes are ‘associated with’ absence epilepsy [66]
297 expresses the clinical observation of statistical dependence between EEG pattern and
298 patient's behavior, so this associative link is a statistical notion, not a biological one. This
299 is true more generally in Brain-Wide Association Studies, which correlate brain
300 structure/function with 'cognition or psychiatric symptomatology' [65]. Arguably, the
301 associations involved here could still fit in the **contingency** category, but only provided
302 the System considered is the scientists themselves observing data, not the study's
303 participants.

304 A second type of difficulties comes from defining frontiers between biological organism
305 and environment, so events and behaviors. Precision concerning A, B and System might
306 suffice here to apply the classification.

307 For external 'events' requiring an animal's behavior, one can still apply the criterion of
308 'happening outside' the System. A fruit's odor stimulus may require approach and
309 sniffing, yet the odor plume (A) exists independently of the animal, and so does its
310 **contingency** with light scattered (B) by the fruit. For events involving internal,
311 mechanical modification of the body (tactile stimuli), it will perhaps be more satisfactory,
312 and rigorous, to define the System as the organism's nervous system, to which these
313 stimulations are external.
For behaviors, let us consider a rat in a Skinner box, pressing a lever (action A) and receiving food (outcome B). If paw movement is included in the definition of A, then A is not external to the organism, so only for an observer would A-B qualify as **contingency**.

For the rat, the rigorous **contingency** is between the external, mechanical lever actuation A (caused by the rat) and food delivery B. Surely, outcome B influences the rat's future actions, so some internal A-B association is established: this can involve signal **convergence** between motor command and food sensory feedback, as is described in amygdala [67–69] and primary motor cortex [70]. Finally, A-B is not a **conversion** for the rat, because it is the external world (here, box mechanisms), not the rat, that causally links lever actuation to food delivery. If instead one were interested in the box as the System, then the lever actuation (input) - food delivery (output) association would exactly be a **conversion**. To sum it up: for an animal, a 'Cue-Action association' is a **conversion**, a (carefully defined) 'Action-Outcome association' is a **contingency**.

It is evident that the five categories described remain imperfect (difficult fits just mentioned) and quite general, since sub-categories could be distinguished (e.g. causal vs. non-causal associations, among contingencies). Nonetheless, concentrating on this minimal, 5-category classification already allows sufficient disambiguations and clarifications which encourages its use.

333

# 4. Benefits of the new framework

334

335

## 4.1. Improving concept transparency across authors and disciplines

336

The proposed classification can serve as term standardization around associations across behavioral neurosciences. Beginning with scientific communications that include term definitions and crucial in this regard. Take one psychology article [71] on semantic similarity and word co-occurrence. Both concepts could be called associations by certain readers (e.g. 'semantic associations' [72], or 'word associations' among which 'syntagmatic' and 'paradigmatic' ones [73]). Here the authors call only the first one an association, and further define it as 'the mental activation of one term when another term is presented' ([71] p.1). This could be improved by directly calling these notions **evocability** and **contingency**, respectively, making it even easier for a reader to relate the article's conclusions to, say, 'associative memory recall' in neuropsychological tests (WMS [74], CANTAB [75], FNAME [76]) on the one hand, and 'association rules' in data mining [29] on the other hand.

337

338

339

340

341

342

343

344

345

346

347

348

349

350

Moreover, this classification can help clarify the vast synonymy around the concepts of association, notably with terms such as 'mapping', 'integration', 'binding',
351 'correspondence', 'pairing', etc. Some synonyms are rather straightforward: for instance,
352 'input-output mapping' in a study of sensorimotor performance in primates [77] is
353 synonymous for 'input-output association' in computational models [21], so **conversion**;
354 others are more obscure, for instance a psychologist may be surprised that 'Content-
355 Addressable Memory' is synonymous for 'associative memory' in computer sciences,
356 though it means something distinct from mental evocability (see Box 2). What is more,
357 polysemy exists also among those terms, e.g. 'mapping' means **conversion** in 'input-
358 output mapping' [77], but **evocability** in 'form-meaning mapping' [52]. A reference
359 framework, such as the classification we propose, is thus needed to achieve
360 transparency.

361

362 4.2. Disentangling associations in the human brain

363 There are different kinds of links expressed by the term 'association' in the context of
364 human brain anatomy, function and pathology.

365 Association *areas* are anatomically defined as parts of the cortex other than primary
366 motor and primary sensory [78]. Lesions in three major associative regions (frontal,
367 limbic, parieto-temporo-occipital) reveal their role in complex cognitive functions (e.g.
368 planning, emotional regulation, agnosias, respectively [78]) which are broadly
369 recognized as involving the combination or integration of information from multiple
370 sources. These functions are also supported by anatomical descriptions of converging
371 afferents in unimodal cortices (or 'parasensory areas') and multimodal cortices; for
372 instance, the temporal lobe receives visual and auditory input [59]. So 'association' here
373 means **convergence** [78,79].

374 Association *nuclei* are a named given to some thalamic nuclei (e.g. pulvinar) because
375 they project specifically to association cortices [80,81]. So 'association' here indirectly
376 refers to a **convergence**, though not happening in these neuronal structures; otherwise,
377 thalamo-cortical projections, like any projection, constitute examples of **excitability** –
378 and sometimes **co-excitability** as in the reciprocal projection between mediodorsal
379 thalamus and prefrontal cortex [25].

380 Association *pathways*, or associational *fibers*, are one of three major types of white
381 matter tracts in the brain, together with projection and commissural fibers. They are
382 within-hemisphere connections allowing bidirectional information flow, i.e. **co-**
383 **excitability**, between cortical areas [82–84]. Some are local ('U-shaped' or 'arcuate'
384 fibers) and connect adjacent gyri within the same lobe, others are long-range ('fasciculi')
385 and connect distant regions potentially in distinct lobes, e.g. the arcuate fasciculus that
386 connects Broca's and Wernicke's areas among others [84].

387 The contextual associations *network* is functionally identified as a distributed network of
388 cortical areas activated when perceiving an object that is rich in mental evocations
389 [85,86]. This associations network has overlaps with association areas such as the
390 anterior cingular and parahippocampal cortex, and closely relates to the brain's Default
391 Network thought to be active during passive as well as advanced thinking situations
392 [87,88]. So 'association' here implies **evocability**, which can be evidenced by correlating
393 participants' verbal reports of subjective experience during imaging experiments.

394 Therefore, signal **convergence** in association cortices, as well as cortico-cortical **co-**
395 **excitability** carried out by association pathways, are probably two mechanisms involved
396 in mental **evocabilities** in humans.

397

398 4.3. Misunderstandings around Associative Memory

399 Distinct notions of association are fundamentally implied by the term 'associative
400 memory': **evocability** in psychology, **conversion** in computer sciences.

401 Associative memory, seen by psychologists, is the human capacity to store and retrieve
402 relationships between *a priori* unrelated mental entities A and B, linked through
403 experience [78]. In your mind, a person's name (A) may evoke the mental image of a
404 given street (B) once you know this person lives there, otherwise both mental entities
405 are not related. A representative dysfunction is associative agnosia [78], the incapacity
406 to relate a percept, e.g. visual, to its meaning. Associative memory thus means
407 **evocability**, typically measured by cued recall in so-called Paired Associate learning
408 paradigms [89]: after lists of pairs of items have been learned (word-word, picture-
409 picture, name-face, etc.), one item of a pair (the cue) is provided and recall of the other

410 is assessed.

<figure><img src="image_3.png" /></figure>

417 **Figure 3: Pattern completion can be mistaken for evocability.** An auto-associative
418 function (X→X) between character strings, can yield a property of pattern completion:
419 degraded inputs still retrieve the original. When a particular input, such as here the
420 character string [ROMEO......], deceptively resembles a 'concept', then pattern
421 completion can be mistakenly interpreted as evocability, though no mental constructs
422 exists for that input within the System.

423
424 Associative memory, in computer systems and (neuro)computational models, generally
425 entails 'pattern completion' [16,28,90,91]: the capacity of an information storage system
426 to output a whole piece of information X, given a partial or degraded version X' as input
427 (input X' → output X). Here is how both notions of associative memory appear
428 deceptively similar (see Figure 2). Suppose a computerized associative memory system
429 has stored the eleven-character string [ROMEOJULIET]. Consider two degraded input
430 strings [R..E.J.L..T] and [ROMEO......], both with six characters missing. In both cases,
431 successful pattern completion yields back the full string: [ROMEOJULIET], but in the
432 latter case one gets the impression of 'mental recall', with the 'concept' of ROMEO
433 capable of 'evoking' that of JULIET. However, ROMEO...... is no more meaningful to the
434 system than R..E.J.L..T and neither are stored as individualized entities. Besides, this
435 way of retrieving stored data based on its content explains why in computer sciences
436 'associative memory' also goes by the name 'Content-Addressable Memory' (CAM
437 [92,93], as opposed to Random-Access Memory or RAM).

438 Furthermore, pattern completion here is a chance by-product of establishing auto-
439 associative systems trained to map items X onto themselves (input X → output X). Such
440 'auto-associations' [41,42], as mathematical identity functions, are particular cases of
441 **conversion**. A classical example, inspired by biophysics' spin-glass models, is the
442 Hopfield network [94]: in a dynamic system of recurrently connected artificial neurons, a
443 set of patterns are stored as stable attractor states. The system, when placed in a state
444 X' not too distant from attractor X, will go back to state X: this realizes pattern
445 completion, here a property of robustness to noise.

446
447 4.4. Reformulating controversies
448
449 Several properties of associations are subject to disagreement among scientists. Let us
450 briefly consider directionality, with a comparative psychology problem named
451 'Associative Symmetry' [95,96], involving the comparison of forward and backward
452 associations – a topic also active in human memory research [97–99]. Easily obtaining
453 evidence for the symmetry property in humans, but not other species, has raised
454 speculations concerning humans’ potential cognitive singularity, in relation with language
455 capabilities and the interchangeability property between symbols and referents; an
456 extensive debate continues since the 1980s [100]. But initially, the question is whether
457 exposure to a unidirectional stimulus association (A-then-B) can result in a bidirectional
458 mental association between stimulus representations (A-to-B and B-to-A) – so in the
459 present terminology: whether a sequential **contingency** can establish **co-evocability**.
460 And because most experimental protocols rely on Stimulus-Response measures (e.g.
461 having seen picture A, selecting picture B rather than X), a third notion of association
462 comes into play here, **conversion**. Failing to properly distinguish the three notions has
463 led to misinterpretations of empirical data.
463 Conceptual approximations have consisted in 1) conflating sequentiality between the
464 contingency and the S-R **conversion**, 2) conflating directionality between the S-R
465 **conversion** and the **evocability**. First, a review of human data [101] has shown that in
466 most studies, stimuli A and B did partially or fully overlap in time; hence though the
467 conversion was sequential, the contingency was rather simultaneous, which could
468 manifestly yield a co-evocability without needing to infer the reverse mental association
469 B-to-A – the putative decisive phenomenon. This and other flaws have cast doubt on the
470 reliability of the effect in humans [101]. Second, authors of human studies have typically
471 considered that because after A-then-B exposure, non-humans do not produce the
472 conversion indicative of the reverse order (having seen picture B, failing to select picture
473 A), they do not have the reverse B-to-A evocability. This constitutes the classical logical
474 error called ‘affirming the consequent fallacy’ [102,103]. So this alleged cognitive gap
475 remains to be firmly established, and in fact several studies, using modified protocols,
476 have provided indications of associative symmetry in pigeons, baboons and sea lions
477 [104–106]. The present classification thus allows to devise more adequate empirical
478 testing and revisit this controversy.
479

479

480 4.5. Bridging neuroscience, psychology and AI: one example

481 Large Language Models (LLMs), on which renowned AI chatbots are based, have been
482 under the spotlight in recent years. But how do their operations compare with brains and
483 minds?

484 Identifying what types of associations are at play in LLMs provides one entry point for
485 knowing which neuroscience and psychology findings can illuminate which aspects of
486 LLMs, and vice versa. Overall, LLMs learn from **contingencies** in data (typically word
487 co-occurrence) and realize **conversions** between input text or 'prompt' [107], and output
488 text meant to be user-desired responses. Their internal mechanisms involve
489 **convergences** (e.g. Querie-Key convergence, inside Transformers' 'multi-head
490 attention' modules [108]) and, at the basic level of their artificial neurons, **excitabilities
491 which spread activation feedforward. So, for instance, prompt-output conversions relate
492 to the vast knowledge on Stimulus-Response in animal learning research [30].

493 In spite of LLM's impressive applications (e.g. problem solving [109]), a basic failure
494 named 'Reversal Curse' was recently documented in the LLMs GPT-3 to -4: a model
495 trained on "A is B" types of sentences will often fail to generalize to the reverse order "B
496 is A" [110]. For instance, when asked "Who is Tom Cruise's mother?", GPT-4 did
497 correctly answer "Mary Lee Pfeiffer", but failed when asked the reverse "Who is Mary
498 Lee Pfeiffer's son?". In the model, 'Tom Cruise' (A) thus seems to evoke 'Mary Lee
499 Pfeiffer' (B) but not reciprocally, otherwise at least some elements relating to A should
500 appear in responses to "B is A" prompts, which was not observed here. This raises
501 further questions. Do LLMs truly form concepts such as 'Tom Cruise' (and under what
502 exact form), or do concepts need to be built-in? Do these concepts qualify as mental?
503 Why do they show only one-way **evocability**? From this perspective, both Reversal
504 Curse and the Associative Symmetry problem just mentioned can obviously be likened
505 (again, synonymy), hence psychology literature on the latter can illuminate nascent
506 investigations of the former [111]. Furthermore, understanding of LLMs architecture
507 could provide insights into neuronal implementations of mental objects and evocabilities.

508

509

5. Concluding remarks

510 This review shows that highly diverse interpretations of the central concept of
511 'association' in behavioral neurosciences obscure the comparison of associative
512 accounts across disciplines. By delineating and naming five distinct meanings for that
513 term, we have proposed a way to disambiguate its usages. The article is meant as an
514 aid to scientific dialogue and an invitation to discuss this deep polysemy problem within
515 the community. An obvious recommendation is that researchers should always define
516 what they mean by 'association' and abide by that definition carefully.

517 More broadly, this article touches upon transdisciplinary communication issues. Twenty-
518 first century science with its fast pace leaves little time for dialogue and term
519 standardization across disciplines, resulting in semantic (hence conceptual) divergences
520 – as showcased here in the emblematic case of ‘association’. Such unnecessary
521 barriers to a global understanding have been invoked as part of the ‘replication crisis’
522 [112,113]. Fortunately, various recent publication efforts aim at improving terminology:
523 compilation of disparate opinions around one concept (e.g. ‘cognition’ [114]); proposed
524 definition on a focal concept by one or few authors (e.g. ‘working memory’ [115],
525 ‘cognition’ [116]); consensus definition of an entire field’s terminology by a forum of
526 experts (e.g. action-control [117]), sometimes even employing a systematic consensus
527 procedure (e.g. semantics [52]). This last form is perhaps the closest to the Science
528 Forum theory advocated by Adriaan de Groot in the 1980s [118]. A major, recent science
529 funding initiative [119], dedicated to fostering theory de-compartmentalization and
530 agreement on the focal concept of ‘consciousness’, examplifies which role funders can
531 play here.

532

533
534 Acknowledgements

535 We are thankful to Shreejata Gupta, Pierre Perruchet, Michael Zock and Stéphane

536 Rousset for insightful discussions. We also thank Alexandra Elbakyan, as without her

537 resource this wide reviewing effort would have been simply impossible.

538 Funding was provided to T.C. and A.R. by ANR ‘HEBBIAN’ AAPG2023 project from the

539 Agence Nationale de la Recherche.

540

541 Declaration of interest

542 We have no conflict of interest to report.

543

544 Bibliography

545 1. Nelson, D.L. et al. (2004) The University of South Florida free association, rhyme, and word fragment norms. *Behavior Research Methods, Instruments, & Computers* 36, 402–407

546
547
548 2. Cabana, Á. et al. (2023) The “Small World of Words” free association norms for Rioplatense Spanish. *Behav Res* 56, 968–985

550 3. Galakhova, A.A. et al. (2022) Evolution of cortical neurons supporting human cognition. *Trends in Cognitive Sciences* 26, 909–922

552 4. Buckner, R.L. and Krienen, F.M. (2013) The evolution of distributed association networks in the human brain. *Trends in Cognitive Sciences* 17, 648–665

554 5. Dickinson, A. (2012) Associative learning and animal cognition. *Phil. Trans. R. Soc. B* 367, 2733–2742

556 6. Mackintosh, N.J. (1997) Has the Wheel Turned Full Circle? Fifty Years of Learning Theory, 1946–1996. *The Quarterly Journal of Experimental Psychology Section A* 50, 879–898

559 7. Gallistel, C.R. and Gibbon, J. (2002) *The Symbolic Foundations of Conditioned Behavior*, ((0 edn) ), Psychology Press

561 8. Neisser, U. (1976) *Cognition and reality*, W. H. Freeman

562 9. Shettleworth, S.J. (2009) *Cognition, Evolution, and Behavior*, Oxford University PressNew York, NY

564 10. Heyes, C. (2012) Simple minds: a qualified defence of associative learning. *Phil. Trans. R. Soc. B* 367, 2695–2703

566 11. Beaty, R.E. et al. (2021) Forward flow and creative thought: Assessing associative cognition and its role in divergent thinking. *Thinking Skills and Creativity* 41, 100859

568 12. Bernhardt, B.M. et al. (2007) Early word-object associations and later language development. *First Language* 27, 315–328

570 13. Wilson, R.I. et al. (2004) Transformation of Olfactory Representations in the *Drosophila Antennal Lobe*. *Science* 303, 366–370

572 14. Rose, M. et al. (2011) Functional Dissociation of Hippocampal Mechanism during Implicit Learning Based on the Domain of Associations. *J. Neurosci.* 31, 13739–13745

574 15. Nargeot, R. and Simmers, J. (2011) Neural mechanisms of operant conditioning and learning-induced behavioral plasticity in Aplysia. *Cell. Mol. Life Sci.* 68, 803–816
576 16. Griniasty, M. et al. (1993) Conversion of Temporal Correlations Between Stimuli to
577 Spatial Correlations Between Attractors. *Neural Computation* 5, 1–17

578 17. Moopenn, A. et al. (1987) Electronic Implementation of Associative Memory

579 Based on Neural Network Models. *IEEE Trans. Syst., Man, Cybern.* 17, 325–331

580 18. Sweldens, S. et al. (2010) Evaluative Conditioning Procedures and the Resilience
581 of Conditioned Brand Attitudes. *J Consum Res* 37, 473–489

582 19. Klein-Flügge, M.C. et al. (2013) Segregated Encoding of Reward-Identity and
583 Stimulus-Reward Associations in Human Orbitofrontal Cortex. *J. Neurosci.* 33, 3202–
584 3211

585 20. Larkum, M. (2013) A cellular mechanism for cortical associations: an organizing
586 principle for the cerebral cortex. *Trends in Neurosciences* 36, 141–151

587 21. Li, C. et al. (2020) Cross-Site Scripting Guardian: A Static XSS Detector Based on
588 Data Stream Input-Output Association Mining. *Applied Sciences* 10, 4740

589 22. Sharpe, M.J. (2024) The cognitive (lateral) hypothalamus. *Trends in Cognitive
590 Sciences* 28, 18–29

591 23. Buabang, E.K. et al. (2025) Leveraging cognitive neuroscience for making and
592 breaking real-world habits. *Trends in Cognitive Sciences* 29, 41–59

593 24. Wahlheim, C.N. and Zacks, J.M. (2024) Memory updating and the structure of
594 event representations. *Trends in Cognitive Sciences* DOI: 10.1016/j.tics.2024.11.008

595 25. Scott, D.N. et al. (2024) Thalamocortical architectures for flexible cognition and
596 efficient learning. *Trends in Cognitive Sciences* 28, 739–756

597 26. Kerrén, C. et al. (2025) Exploring the role of dimensionality transformation in
598 episodic memory. *Trends in Cognitive Sciences* DOI: 10.1016/j.tics.2025.01.007

599 27. De Houwer, J. and Hughes, S. (2020) *The psychology of learning: An introduction
600 from a functional-cognitive perspective.*, The MIT Press

601 28. Rolls, E.T. (2013) The mechanisms for pattern completion and pattern separation
602 in the hippocampus. *Front. Syst. Neurosci.* 7

603 29. Harms, S.K. and Deogun, J.S. (2004) Sequential Association Rule Mining with
604 Time Lags. *Journal of Intelligent Information Systems* 22, 7–22

605 30. Shanks, D.R. (2010) Learning: From Association to Cognition. *Annu. Rev.
606 Psychol.* 61, 273–301

607 31. Jin, Z. et al. (2012) Discovery of Causal Rules Using Partial Association. in *2012
608 IEEE 12th International Conference on Data Mining*, Brussels, Belgium, pp. 309–318

609 32. Wood, E.R. et al. (2004) One-Trial Odor-Reward Association: A Form of Event
610 Memory Not Dependent on Hippocampal Function. *Behavioral Neuroscience* 118,
611 526–539

612 33. Perruchet, P. et al. (2006) Dissociating the effects of automatic activation and
613 explicit expectancy on reaction times in a simple associative learning task. *Journal of
614 Experimental Psychology: Learning, Memory, and Cognition* 32, 955–965

615 34. Schapiro, A.C. et al. (2013) Neural representations of events arise from temporal
616 community structure. *Nat Neurosci* 16, 486–492

617 35. Tjøstheim, D. et al. (2022) Statistical Dependence: Beyond Pearson's p. *Statist. Sci.* 37

619 36. Reshef, D.N. et al. (2011) Detecting Novel Associations in Large Data Sets.
620 *Science* 334, 1518–1524

621 37. Dagenais, E. et al. (2016) Role of executive functions in prospective memory in
622 multiple sclerosis: Impact of the strength of cue-action association. *Journal of Clinical
623 and Experimental Neuropsychology* 38, 127–140
624 38. Gardner, B. et al. (2021) Breaking habits or breaking habitual behaviours? Old habits as a neglected factor in weight loss maintenance. *Appetite* 162, 105183

625 39. Rosenblatt, F. (1958) The perceptron: A probabilistic model for information storage and organization in the brain. *Psychological Review* 65, 386–408

626 40. Popescu, M.C. et al. (2009) Multilayer perceptron and neural networks, 8(7), 579-588. (2009). *WSEAS Transactions on Circuits and Systems* 8, 579–588

627 41. Pulvermüller, F. et al. (2021) Biological constraints on neural network models of cognitive function. *Nat Rev Neurosci* 22, 488–502

628 42. Knoblauch, A. and Palm, G. (2020) Iterative Retrieval and Block Coding in Autoassociative and Heteroassociative Memory. *Neural Computation* 32, 205–260

629 43. Thomas, M.S. and McClelland, J.L. (2023) Connectionist models of cognition. In *The Cambridge handbook of computational psychology* (Cambridge University Press.), pp. 29–79

630 44. Gasser, M. (1990) Connectionism and Universals of Second Language Acquisition. *Stud Sec Lang Acq* 12, 179–199

631 45. Le Cun, Y. (1986) Learning Process in an Asymmetric Threshold Network. In *Disordered Systems and Biological Organization* (Bienenstock, E. et al., eds), pp.

632 46. 233–240, Springer Berlin Heidelberg

633 47. Palm, G. (1980) On associative memory. *Biol. Cybernetics* 36, 19–31

634 48. Clark, A. and Karmiloff-Smith, A. (1993) The cognizer's innards: A philosophical and psychological perspective on the development of thought. *Mind & Language* 8

635 49. Shiffrin, R.M. and Atkinson, R.C. (1969) Storage and retrieval processes in long-term memory. *Psychological Review* 76, 179–193

636 50. Greenwald, A.G. and Banaji, M.R. (1995) Implicit social cognition: Attitudes, self-esteem, and stereotypes. *Psychological Review* 102, 4–27

637 51. Rabeyron, T. and Massicotte, C. (2020) Entropy, Free Energy, and Symbolization: Free Association at the Intersection of Psychoanalysis and Neuroscience. *Front. Psychol.* 11, 366

638 52. Reilly, J. et al. (2025) What we mean when we say semantic: Toward a multidisciplinary semantic glossary. *Psychon Bull Rev* 32, 243–280

639 53. Henson, R.N. et al. (2014) Stimulus-response bindings in priming. *Trends in Cognitive Sciences* 18, 376–384

640 54. Milner, T.A. and Bacon, C.E. (1989) GABAergic neurons in the rat hippocampal formation: ultrastructure and synaptic relationships with catecholaminergic terminals. *Journal of Neuroscience* 9, 3410–3427

641 55. Levy, W.B. (1996) A sequence predicting CA3 is a flexible associator that learns and uses context to solve hippocampal-like tasks. *Hippocampus* 6, 579–590

642 56. Philippi, C.L. et al. (2009) Damage to Association Fiber Tracts Impairs Recognition of the Facial Expression of Emotion. *J. Neurosci.* 29, 15089–15099

643 57. Hummel, J.E. and Holyoak, K.J. (2003) A symbolic-connectionist theory of relational inference and generalization. *Psychological Review* 110, 220–264

644 58. Kirschhock, M.E. and Nieder, A. (2023) Association neurons in the crow telencephalon link visual signs to numerical values. *Proc. Natl. Acad. Sci. U.S.A.* 120, e2313923120

645 59. Beauchamp, M.S. et al. (2004) Integration of Auditory and Visual Information about Objects in Superior Temporal Sulcus. *Neuron* 41, 809–823

646 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70. 71.
ASSOCIATIONS FIVE CONCEPTS 20/22

672 60. Ramaekers, A. et al. (2005) Glomerular Maps without Cellular Redundancy at 673 Successive Levels of the Drosophila Larval Olfactory Circuit. *Current Biology* 15, 982–992

675 61. Sloutsky, V.M. and Turner, B.M. (2023) Cognition: The power of simple 676 associative learning. *Current Biology* 33, R223–R225

677 62. Stark, C.E.L. and Squire, L.R. (2001) Simple and Associative Recognition 678 Memory in the Hippocampal Region. *Learn. Mem.* 8, 190–197

679 63. Shastri, L. and Ajjanagadde, V. (1993) From simple associations to systematic 680 reasoning: A connectionist representation of rules, variables and dynamic bindings 681 using temporal synchrony. *Behav Brain Sci* 16, 417–451

682 64. Hazeltine, E. and Schumacher, E.H. (2016) Understanding Central Processes. In 683 *Psychology of Learning and Motivation* 64, pp. 195–245, Elsevier

684 65. Marek, S. et al. (2022) Reproducible brain-wide association studies require 685 thousands of individuals. *Nature* 603, 654–660

686 66. Noachtar, S. and Rémi, J. (2009) The role of EEG in epilepsy: A critical review. 687 *Epilepsy & Behavior* 15, 22–33

688 67. Servonnet, A. et al. (2020) Optogenetic Activation of the Basolateral Amygdala 689 Promotes Both Appetitive Conditioning and the Instrumental Pursuit of Reward Cues. 690 *J. Neurosci.* 40, 1732–1743

691 68. Wolff, S.B.E. et al. (2014) Amygdala interneuron subtypes control fear learning 692 through disinhibition. *Nature* 509, 453–458

693 69. Rogan, M.T. et al. (1997) Fear conditioning induces associative long-term 694 potentiation in the amygdala. *Nature* 390, 604–607

695 70. Lee, C. et al. (2022) Cell-type-specific responses to associative learning in the 696 primary motor cortex. *eLife* 11, e72549

697 71. Lemaire, B. and Denhière, G. (2006) Effects of High-Order Co-occurrences on 698 Word Semantic Similarity. *cpl* DOI: 10.4000/cpl.471

699 72. Xu, N. et al. (2020) Reasoning with Multimodal Sarcastic Tweets via Modeling 700 Cross-Modality Contrast and Semantic Association. in *Proceedings of the 58th Annual 701 Meeting of the Association for Computational Linguistics*, Online, pp. 3777–3786

702 73. De Deyne, S. and Storms, G. (2008) Word associations: Network and semantic 703 properties. *Behav Res* 40, 213–231

704 74. Valentine, T. et al. (2020) Wechsler Adult Intelligence Scale-IV (WAIS-IV). In *The 705 Wiley Encyclopedia of Personality and Individual Differences* ((1st edn) ) (Carducci, B. 706 J. et al., eds), pp. 457–463, Wiley

707 75. Langley, C. et al. (2023) Cambridge Neuropsychological Test Automated Battery 708 (CANTAB). In *The SAGE Handbook of Clinical Neuropsychology: Clinical 709 Neuropsychological Assessment and Diagnosis*, pp. 435, Sage

710 76. Rentz, D.M. et al. (2011) Face-name associative memory performance is related 711 to amyloid burden in normal elderly. *Neuropsychologia* 49, 2776–2783

712 77. Yamamoto, K. et al. (2006) Rapid and Long-Lasting Plasticity of Input-Output 713 Mapping. *Journal of Neurophysiology* 96, 2797–2801

714 78. Kreutzer, J.S. et al., eds. (2011) *Encyclopedia of Clinical Neuropsychology*, 715 Springer New York

716 79. Pandya, D.N. and Yeterian, E.H. (1985) Architecture and Connections of Cortical 717 Association Areas. In *Association and Auditory Cortices* 4 (Peters, A. and Jones, E. 718 G., eds), pp. 3–61, Springer US
ASSOCIATIONS FIVE CONCEPTS 21/22

719 80. Armstrong, E. (1981) A quantitative comparison of the Hominoid Thalamus. IV.

720 Posterior association nuclei—the pulvinar and lateral posterior nucleus. *American J*

721 *Phys Anthropol* 55, 369–383

722 81. Vertes, R.P. et al. (2015) Thalamus. In *The Rat Nervous System*, pp. 335–390,

723 Elsevier

724 82. Felleman, D.J. and Van Essen, D.C. (1991) Distributed hierarchical processing in

725 the primate cerebral cortex. *Cereb Cortex* 1, 1–47

726 83. Goldman-Rakic, P.S. (1988) Topography of Cognition: Parallel Distributed

727 Networks in Primate Association Cortex. *Annu. Rev. Neurosci.* 11, 137–156

728 84. Garagnani, M. et al. (2008) A neuroanatomically grounded Hebbian-learning

729 model of attention–language interactions in the human brain. *Eur J of Neuroscience*

730 27, 492–513

731 85. Chaumon, M. et al. (2014) Visual Predictions in the Orbitofrontal Cortex Rely on

732 Associative Content. *Cerebral Cortex* 24, 2899–2907

733 86. Bar, M. et al. (2007) The units of thought. *Hippocampus* 17, 420–428

734 87. Buckner, R.L. and DiNicola, L.M. (2019) The brain’s default network: updated

735 anatomy, physiology and evolving insights. *Nat Rev Neurosci* 20, 593–608

736 88. Baror, S. et al. (2022) How associative thinking influences scene perception.

737 *Consciousness and Cognition* 103, 103377

738 89. Feldman, S.M. and Underwood, B.J. (1957) Stimulus recall following paired-

739 associate learning. *Journal of Experimental Psychology* 53, 11–15

740 90. Kohonen, T. et al. (1977) A principle of neural associative memory. *Neuroscience*

741 2, 1065–1076

742 91. Wynn, J.S. et al. (2020) Eye movements support behavioral pattern completion.

743 *Proc. Natl. Acad. Sci. U.S.A.* 117, 6246–6254

744 92. Fatehi, M.T. (1975) A study of a holographic analog associative memory with

745 application to pattern recognition. PhD thesis. The Ohio State University

746 93. Tong, L. et al. (2021) 2D materials–based homogeneous transistor-memory

747 architecture for neuromorphic hardware. *Science* 373, 1353–1358

748 94. Hopfield, J.J. and Tank, D.W. (1986) Computing with Neural Circuits: A Model.

749 *Science* 233, 625–633

750 95. Sidman, M. et al. (1982) A search for symmetry in the conditional discriminations

751 of rhesus monkeys, baboons, and children. *J Exp Anal Behav* 37, 23–44

752 96. Hogan, D.E. and Zentall, T.R. (1977) Backward Associations in the Pigeon. *The*

753 *American Journal of Psychology* 90, 3

754 97. Bireta, T.J. et al. (2010) Backward recall and benchmark effects of working

755 memory. *Memory & Cognition* 38, 279–291

756 98. St Clair-Thompson, H.L. and Allen, R.J. (2013) Are forward and backward recall

757 the same? A dual-task study of digit recall. *Mem Cogn* 41, 519–532

758 99. Guitard, D. et al. (2020) Forward and backward recall: Different visuospatial

759 processes when you know what’s coming. *Mem Cogn* 48, 111–126

760 100. Lionello-DeNolf, K.M. (2021) An update on the search for symmetry in

761 nonhumans. *J Exp Anal Behav* 115, 309–325

762 101. Chartier, T.F. and Fagot, J. (2022) Associative symmetry: a divide between

763 humans and nonhumans? *Trends in Cognitive Sciences* 26, 286–289

764 102. Wagner-Egger, P. (2007) Conditional reasoning and the Wason selection task:

765 Biconditional interpretation instead of reasoning bias. *Thinking & Reasoning* 13, 484–

766 505
ASSOCIATIONS FIVE CONCEPTS 22/22

767 103. Oaksford, M. and Chater, N. (2003) Conditional Probability and the Cognitive Science of Conditional Reasoning. *Mind & Language* 18, 359–379
768
769 104. Frank, A.J. and Wasserman, E.A. (2005) Associative symmetry in the pigeon after successive matching-to-sample training. *J Exp Anal Behav* 84, 147–165
770
771 105. Chartier, T.F. and Fagot, J. (2022) Simultaneous learning of directional and non-directional stimulus relations in baboons (Papio papio). *Learn Behav* DOI: 10.3758/s13420-022-00522-8
772
773
774 106. Schusterman, R.J. and Kastak, D. (1993) A California Sea Lion (Zalophus Californianus) is Capable of Forming Equivalence Relations. *Psychol Rec* 43, 823–839
775
776
777 107. Hu, Y. et al. (2024) Improving large language models for clinical named entity recognition via prompt engineering. *Journal of the American Medical Informatics Association* 31, 1812–1820
778
779
780 108. Vaswani, A. et al. Attention is All you Need. In *Advances in neural information processing systems 30: 31st Annual Conference on Neural Information Processing Systems (NIPS 2017): Long Beach, California, USA, 4-9 December 2017*, Curran Associates, Inc
781
782
783
784 109. Besta, M. et al. (2024) Graph of Thoughts: Solving Elaborate Problems with Large Language Models. *AAAI* 38, 17682–17690
785
786 110. Berglund, L. et al. (2023) The Reversal Curse: LLMs trained on “A is B” fail to learn “B is A”arXiv
787
788 111. Yu, L. et al. (2023) MetaMath: Bootstrap Your Own Mathematical Questions for Large Language ModelsarXiv
789
790 112. Eronen, M.I. and Bringmann, L.F. (2021) The Theory Crisis in Psychology: How to Move Forward. *Perspect Psychol Sci* 16, 779–788
791
792 113. Oberauer, K. and Lewandowsky, S. (2019) Addressing the theory crisis in psychology. *Psychon Bull Rev* 26, 1596–1618
793
794 114. Bayne, T. et al. (2019) What is cognition? *Current Biology* 29, R608–R615
795 115. Cowan, N. (2017) The many faces of working memory and short-term storage. *Psychon Bull Rev* 24, 1158–1170
796
797 116. De Houwer, J. et al. (2017) What is cognition? A functional-cognitive perspective. In *Process-based CBT*, pp. 119–136, New Harbinger Publications.
798
799 117. Frings, C. et al. (2024) Consensus definitions of perception-action-integration in action control. *Commun Psychol* 2, 7
800
801 118. Groot, A.D. de (1984) The theory of science forum: Subject and purport. *Methodology and Science* 17, 230–259
802
803 119. “Accelerating Research on Consciousness” funding initiative - https://www.templetonworldcharity.org/our-priorities/discovery/accelerating-research-consciousness
804
805
806