Varieties of imagination in psychology:
What is the “imagination” for?
- Functional perspective: imagination is a tool for mental simulation, it allows us to try
things out in our minds before acting, such as playing out a conversation
- Evolutionary view: it is adaptive, imagination helps us with planning, empathy
(imagining others’ perspectives) and even survival (anticipating danger)
- Galton explored mental imagery in scientists vs. artists. He saw imagination as
something that varied between individuals and professions, implying different functions
What are varieties of “imagination” in psychology?
- Visual imagery: “seeing” things in your mind’s eye (Galton’s focus)
- Auditory imagery: hearing sounds, music, or an internal voice (Nedergaard and Lupyan
explored)
- Motor imagery: mentally simulating movement
- Verbal/linguistic imagery: internal dialogue, self-talk
- Autobiographical memory: reconstructing past experiences
- Future thinking: mentally rehearsing or planning future events
- Fantasy/imaginative play: creative invention of novel words or scenarios
- Galton found variation in visual imagery – some people see vivid mental imagery; others
barely see any at all. Nedergaard and Lupyan showed that not everyone has an inner
voice, some people think silently without verbal imagery
What is the underlying format of inner mental life? Words or pictures?
- Some people rely more on images (visual thinkers)
- Others rely more on words (verbal thinkers)
- Many people use a blend, switching between formats depending on the task
- Galton: investigated whether people “see” mental images
- Found variability: some people (often artists) had vivid visual mental images, others
(scientists) had very little
- Nedergaard and Lupyan: argued that inner speech isn’t universal
- Some people experience thoughts as silent mental speech, others do not and that is
normal variation
- Suggests no single correct format for inner mental life
- Takeaway: inner experience is multimodal and varies across individuals
Structures of Memory
What’s going on physically?
- Memory formation isn’t just abstract, its tied to neural activity, synaptic strength, and
circuit level changes
- During sleep, especially slow wave sleep, the brain reactivates recent experiences,
involves coordinated waves: slow oscillations (cortical), sleep spindles (thalamocortical),
sharp wave ripples (hippocampus)
-
These are like the hardware level signals that coordinate memory transfer from short
term to long term storage
Think of it as a data transfer protocol between brain regions
Where do our memories live?
- Memories are first stored in the hippocampus, which acts like a fast-learning buffer
- Over time, especially during sleep, they are replayed and integrated into long term
cortical networks
- After this transfer, memories become less hippocampus-dependent
How are memories formed? Complementary learning systems
- Comes from McClelland, McNaughton, and O’Reilly and Klinzing builds on it
- Hippocampus: fast learner, great for episodic, flexible memory (but not stable over
time)
- Neocortex: slow learner, good for integrating patterns over time (more semantic,
generalized)
- Sleep is when the two systems interact, hippocampus replays memories and the cortex
gradually integrate them
- Analogy: hippocampus=fast sketch, cortex=painting over time
Is the memory trace gone after consolidation?
- No, hippocampus doesn’t necessarily “forget” after consolidation
- Evidence of residual hippocampal involvement even after systems consolidation
- Some memories (especially context rich or emotional ones) remain accessible via the
hippocampus, even if they are stored cortically
- Challenges the idea that the two systems are divorced after consolidation, more of a
shifting role
What do we remember?
Memory via the computational level
What is the brain for in terms of memory?
- Memory isn’t just passive storage, it is an adaptive system
- Nairne (2010): memory is shaped by evolution to enhance survival, its designed to retain
fitness-relevant information
- Bainbridge (2019): memorability is stable, predictable property of certain stimuli, brain
prioritized what’s inherently likely to be useful or informative
- Memory= optimized information retention system, not perfect recorder
What makes something memorable?
- From Bainbridge (2019)
- Some images are consistently remembered across people, this is called intrinsic
memorability
- Memorability is not about attention, distinctiveness, or personal relevance
-
It is a stimulus level property, certain features (people, faces, emotional content and
scenes with narrative structure) tend to stick
From Nairne (2010):
Memory favours adaptive relevance:
Things related to survival (food, predators)
Things with social value (faces, gossip)
Contexts that simulate ancestral problems (planning in a survival scenario improves
recall)
Computation in recognition vs. recall
- Recall relies more on associative networks, rebuilding a memory trace from partial input
- Recognition is more like pattern matching or signal detection
- Recognition: does this feel familiar?
- Recall: can I construct this from memory?
What makes something memorable?
- Memorability= a mix of:
- Intrinsic stimulus properties: faces, objects, emotional content, narrative structure,
predictable across people
- Adaptive relevance: survival value, social info, fitness related cues, evolutionary basis for
what gets prioritized
- In computation terms: The brain is wired to optimize memory for useful, informative, or
evolutionarily relevant input, using predictive rules that apply across individuals
When do we forget?
Memory via algorithmic level
What is working memory?
- Mental workspace that lets you hold and manipulate info in real time (keeping a phone
number in mind while dialing)
- Cowan’s key points:
- The magic number is 4, not 7 (as Miller claimed)
- Working memory capacity is about 4 chunks not items
- Its capacity limited, focus driven and depends on attention
- It’s not a separate storage system, it’s just the activated part of long-term memory, plus
whatever is in the focus of attention
Algorithmically:
- Working memory does not equal short term memory container
- It’s more like:
- 1. Activate relevant long-term info
- 2. Focus attention on 4 elements
- 3. Use them for reasoning, problem solving, decision making
-
WM is the mental stage where the show happens, not the storage room, it’s the
spotlight
When do we forget?
Davis and Zhong, 2017:
- Forgetting happens constantly, even while we’re encoding and consolidating
- It’s not just about decay over time, it can happen soon after learning or much later,
depending on conditions (interference, retrieval failure)
- Forgetting can occur even after consolidation, as part of an active, regulating process
- Forgetting isn’t a bug, it’s a feature, its adaptive
Why do we forget?
Multiple levels of explanation:
Algorithmic:
- Decay: info fades with time unless rehearsal
- Interference: new or old info disrupts memory
- Retrieval failure: it’s there but inaccessible
- Limits of working memory: can’t juggle too many items at once
Biological:
- Forgetting is actively regulated via molecular mechanisms such as:
- Protein degradation
- Synaptic remodeling
- Inhibition of memory traces
- For example:
- The brain may remove or suppress synapses that support old or less relevant memories
- There’s a trade-off: forgetting creates space and helps prevent clutter, filtering
relevance
Making plans and decisions
What is simulation?
- Simulation=mentally constructing possible future events based on fragments of past
experiences
- From Schacter:
- Brain uses episodic memory (specific past events) to imagine future scenarios
- This process is adaptive: helps us plan, predict outcomes, avoid danger
- Episodic simulation involves recombining stored details (people, places, emotions) into
novel configurations
- Memory does not equal just recall, it’s a toolbox for constructing hypothetical
experiences
- Simulation is a core function of episodic memory, not just remembering but reimagining
What are errors of simulations?
- Because simulation is constructive, it is vulnerable to distortions
-
Type of simulation errors:
False memories: imagined events can feel real, especially if constructed from real
fragments
Overconfidence: we trust vivid simulations, even if they’re inaccurate
Neglect of uncertainty: simulations can feel more certain than they actually are
Biases in reconstruction: we reuse emotionally charged or recently accessed elements,
which may skew realism
Schacter calls these costs of a flexible memory system, the same system that help us
imagine the future can also lead us astray, like photoshop
How does knowing the limits of simulation help us understand ourselves better?
- Big insight: realizing that future thoughts are constructed, not predicted, can change
how we:
- Plan: by adding skepticism to overly optimistic of anxious forecasts
- Judge others: by understanding our memory/simulation processes may not reflect
objective reality
- Reflect on the past: knowing that reconstruction is not always accurate
- Relate to ourselves: recognizing patterns in how we simulate can reveal values, fears,
hopes
- Self-awareness grows when we realize our inner simulations are filtered through
memory, emotion and attention, not just raw facts
Beliefs and Biases
Can we even apply a cognitive approach to decision making?
- Mental energy is finite and decision making is a resource intensive cognitive task
- The brain has limited attentional and executive resources
- Decisions are computations: evaluate options, compare values, inhibit impulsive actions,
hold goals in mind
- Those rely on working memory, executive control, and attention – all limited systems
- Tierney’s main idea: as those systems get taxed, performance deteriorates, you rely
more on defaults, heuristics, or impulses
Attempt 1: Is transformation gradual vs. a stepwise function?
This is how a cognitive depletion unfolds over time
- Tierney describes decision fatigue as something that builds gradually, not suddenly,
you’re fine for a while but the cumulative effect makes you more impulsive, irritable,
and default prone
- However, behavioural shifts (giving up or making worse decisions) can appear stepwise,
as if a switch was flipped. That’s the phenomenology of a gradual transformation that
feels abrupt
- In algorithmic terms:
- Under the hood: gradual deterioration
- On the surface: threshold behaviours
-
Like phone battery
Attempt 2: Are beliefs represented continuously vs. discretely?
- The way beliefs are stored can be fuzzy (graded confidence) but decisions force discrete
outcomes
- During fatigue, Tierney shows people lean into heuristics or defaults, which suggests a
discretization of thinking, less nuanced deliberation, more binary choices
- Beliefs may start as continuous evaluations but under fatigue you get more discrete
processing (yes or no)
Attempt 3: What’s a unit of an emotion?
- Decision fatigue has emotional consequences
- Neural level: bursts of activity in emotional circuits (amygdala)
- Algorithmic: a shift in motivation state (avoid vs. approach)
- Subjective: a distinct feeling + appraisal + physiological state
- A cognitive emotional episode that biases decision weights (ex: risk aversion due to
fatigue induced frustration)
What do we think about?
Do we choose to think?
- A lot of our thoughts arise spontaneously, without any intention
- Bear et al. (2020):
- Participants were randomly probed and asked: did you choose to think about what you
were just thinking about?
- Answer: roughly half the time, no
- Our minds are constantly producing spontaneous thoughts, memories, simulations,
worries, that just happen
What does it mean to not think?
- There are moments when we report being in a blank state
- But the brain is likely still processing, just not in a way that’s accessible to introspection
- So not thinking might mean:
- No verbal thought
- No conscious access to the process
- Or being in a flow or automatic state, where thinking feels seamless and silent
- Morris connects this to inattentional blindness (gorilla), we can miss major events and
major thoughts because attention isn’t on them
What do we think about?
Often think about:
- Ourselves: self-related rumination, goals, emotions
- The future: plans, worries, simulations
- Social others: what they are doing/thinking about us
-
Stuff we saw/heard recently: residual sensory info
Thinking is biased by:
Availability: what is easy to retrieve
Relevance to goals
Emotion (anxious=negative loops, happy=future oriented)
Spontaneous thoughts are systematic, certain types of content (goals, recent
experiences) are more likely to arise
Cognitive processes in language
What are the units?
- The basic units of language are not words or strings, but hierarchical structures –
syntactic trees, phrases, and recursive patterns
- Language is not linear, it’s compositional
- Nelson et al.:
- Show that the brain processes phrase structures in real time, not just word by word
- Their neurophysiological recordings show distinct brain signals for open nodes (starting
a phrase) and close nodes (completing one)
- Language units= syntactic structures, not just surface-level strings
How do symbols and rules give rise to language?
Everaert at al.:
- Argue for a generative grammar view:
- We don’t memorize sentences; we build them for rules and symbols
- These rules are domain specific to language and may be innate
Nelson at al.:
- Provide neurophysiological evidence for real-time syntactic rule application
- Brain patterns track phrase building operations, suggesting we actually construct
hierarchical meaning on the fly
- Takeaway: language emerges from symbolic elements + computational rules =
structured infinite expressibility
How is language the interface between perception and thought?
- Language sits at the crossroads:
- Perception gives us raw input: sounds, signs, gestures
- Thought is abstract, complex, and high-dimensional
- Language bridges the two:
- It maps percepts to concepts using structured, rule-governed systems
- It lets us externalize thought and internalize perception
- Language connects mental representations to external symbols
- It’s a representational system that lets thought interact with the world
- Brain processes show how perception activates abstract structure building, guiding
meaning extraction
Cognitive processes in development
Why should we care what’s built in?
- Because built in learning shapes learning:
- They act as scaffolding for later knowledge, if certain systems are present early, they
guide what and how we learn
- Helps us answer: what does a child come into the world ready to understand?
- If we know what’s built in, we can tailor education, interventions, and theories of
cognition more effectively
- Built-in mechanisms = the cognitive hardware that software (experience) run on
- Infants as young as 5 months old show surprise when simple arithmetic outcomes are
violated
- Suggests some core numerical expectations may be innate, not learned
- Some preschoolers spontaneously focus on spatial aspects of tasks without being
prompted
- That spontaneous spatial focus is predictive of later math ability – implying it might be a
built-in bias that supports learning
Why can it look like there’s nothing built-in?
- Because innate abilities don’t always show themselves overtly, they can be:
- Context dependent: some tasks don’t activate them
- Masked by noise: attention, motivation, task design
- Subtle or unconscious: spatial biases that kids don’t verbalize
- Built-in systems might only show up as biases or preferences, not as full-blown skills
- Many kids don’t show spontaneous spatial focus, but that doesn’t mean the capacity
isn’t there. It might just not have been triggered
- Without clever experimental setups, we might not detect infant’s number sense
because they can’t count or explain
So, what’s built in?
- Numerical cognition: infants track changes in small quantities
- Spatial attention: some kids naturally attend to space and use it to solve problems
- Object tracking: infants track continuous objects even when they’re occluded
- Causal inference: infants expect contact to produce motion, etc.
- These are called core knowledge systems, innate-ish modules that guide how infants
interpret the world
Cognitive processes in social interactions
How do we connect with other people?
- Leslie proposed that we connect through a dedicated cognitive module called the theory
of mind mechanism
- ToMM allows us to:
- Attribute mental states (beliefs, desires, intentions) to others
-
Make predictions about behaviour based on those states
It is domain specific, evolutionarily tuned for social understanding
Helps us understand not just what someone does, but why they do it
We don’t just simulate or mirror, we represent agents as having minds
Why do we mimic other people? (Computational: what problem is it trying to solve?)
- Mimicry solves the problem of prediction and alignment
- Mimicry helps:
- Build social rapport and group cohesion
- Simulate other’s actions and emotions so we can predict behaviour
- Possibly even help us learn about the world via others (social learning)
- ToMM + mimicry gives us the full social toolbox
- Mimicry is a means but not the full mechanism, it is helpful but not sufficient for true
mental state attribution
Are we just mirroring people, or is there something deeper? (Algorithmic: what do we
represent?)
- We don’t just copy or mirror people
- We represent:
- Agents as having goals, beliefs, intentions
- Nested representations (she thinks that I believe)
- Causal reasoning about mental states  behaviours
- ToBy:
- Deals with observable behaviour (scripts, routines)
- Pairs with ToMM, which interprets underlying mental states
- At the algorithmic level we represent:
- Not just movements (mirror neuron style)
- But abstract, intentional models of others’ minds
Imperfect Minds
Where does theory of mind go wrong?
It normally helps us understand other’s beliefs and intentions but when it goes wrong it can fuel
paranoia, delusions, and social withdrawal
- People with delusional ideation may:
- Over attribute mental states: hyperactive ToM
- Misinterpret intentions as malevolent
- Link unrelated events into a coherent but incorrect narrative (aberrant memory=glue)
- So, ToM isn’t absent, its distorted, often too active, building connections and intentions
where none exist
Depression angle:
- In depression, ToM may be biased
- Leads to social withdrawal and ruminative looping on perceived negative evaluations
- Does not vanish, it becomes noisy, biased, or over-extended, warping social perception
What is a cognitive approach to mental dysfunction?
- The cognitive approach sees dysfunction not just as bad feelings, but as malfunctioning
information processing
- Depression = biased attention, biased memory, impaired inhibition
- Cognitive control can’t suppress negative thoughts, so people ruminate
- This isn’t just a mood; it’s thought architecture breaking down
- Delusions = overactive pattern detection + impaired error monitoring
- Memory becomes sticky with emotionally salient but false or distorted content
- Belief updating is rigid, cannot revise even when evidence says you’re wrong
- Mental illness = the system still works, just in the wrong way
What is a cognitive approach to pain/suffering?
- Cognitive approach to suffering focuses on how we represent pain, interpret it and
relate to it
- Depression isn’t just sadness, it’s recursive, sticky thought loops
- Delusional suffering is narrative-based, pain becomes embedded in explanations that
feel true but aren’t grounded
- Suffering isn’t just sensation, it’s interpretation, rumination, misrepresentation
- This gives hope: change the way cognition is structured  reduce suffering